Showing 164 of total 164 results (show query)
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GenomicRanges:Representation and manipulation of genomic intervals
The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure.
Maintained by Hervé Pagès. Last updated 4 months ago.
geneticsinfrastructuredatarepresentationsequencingannotationgenomeannotationcoveragebioconductor-packagecore-package
44 stars 17.68 score 13k scripts 1.3k dependentsbioc
SummarizedExperiment:A container (S4 class) for matrix-like assays
The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.
Maintained by Hervé Pagès. Last updated 5 months ago.
geneticsinfrastructuresequencingannotationcoveragegenomeannotationbioconductor-packagecore-package
34 stars 16.84 score 8.6k scripts 1.2k dependentsbioc
Rsamtools:Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import
This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files.
Maintained by Bioconductor Package Maintainer. Last updated 4 months ago.
dataimportsequencingcoveragealignmentqualitycontrolbioconductor-packagecore-packagecurlbzip2xz-utilszlibcpp
28 stars 15.34 score 3.2k scripts 569 dependents
r-lib
covr:Test Coverage for Packages
Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' <https://about.codecov.io> or 'Coveralls' <https://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.
Maintained by Jim Hester. Last updated 2 months ago.
codecovcoveragecoverage-reporttravis-ci
337 stars 15.25 score 2.3k scripts 9 dependentsbioc
GenomicAlignments:Representation and manipulation of short genomic alignments
Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.
Maintained by Hervé Pagès. Last updated 5 months ago.
infrastructuredataimportgeneticssequencingrnaseqsnpcoveragealignmentimmunooncologybioconductor-packagecore-package
10 stars 15.21 score 3.1k scripts 528 dependentsbioc
ensembldb:Utilities to create and use Ensembl-based annotation databases
The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates.
Maintained by Johannes Rainer. Last updated 5 months ago.
geneticsannotationdatasequencingcoverageannotationbioconductorbioconductor-packagesensembl
35 stars 14.08 score 892 scripts 108 dependentsbioc
edgeR:Empirical Analysis of Digital Gene Expression Data in R
Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.
Maintained by Yunshun Chen. Last updated 18 days ago.
alternativesplicingbatcheffectbayesianbiomedicalinformaticscellbiologychipseqclusteringcoveragedifferentialexpressiondifferentialmethylationdifferentialsplicingdnamethylationepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysproteomicsqualitycontrolregressionrnaseqsagesequencingsinglecellsystemsbiologytimecoursetranscriptiontranscriptomicsopenblas
13.40 score 17k scripts 255 dependentsbioc
HDF5Array:HDF5 datasets as array-like objects in R
The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects.
Maintained by Hervé Pagès. Last updated 10 days ago.
infrastructuredatarepresentationdataimportsequencingrnaseqcoverageannotationgenomeannotationsinglecellimmunooncologybioconductor-packagecore-packageu24ca289073
12 stars 13.20 score 844 scripts 126 dependentsbioc
plyranges:A fluent interface for manipulating GenomicRanges
A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased.
Maintained by Michael Love. Last updated 10 days ago.
infrastructuredatarepresentationworkflowstepcoveragebioconductordata-analysisdplyrgenomic-rangesgenomicstidy-data
144 stars 12.66 score 1.9k scripts 20 dependentsbioc
systemPipeR:systemPipeR: Workflow Environment for Data Analysis and Report Generation
systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates.
Maintained by Thomas Girke. Last updated 5 months ago.
geneticsinfrastructuredataimportsequencingrnaseqriboseqchipseqmethylseqsnpgeneexpressioncoveragegenesetenrichmentalignmentqualitycontrolimmunooncologyreportwritingworkflowstepworkflowmanagement
53 stars 11.52 score 344 scripts 3 dependentsbioc
karyoploteR:Plot customizable linear genomes displaying arbitrary data
karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones.
Maintained by Bernat Gel. Last updated 5 months ago.
visualizationcopynumbervariationsequencingcoveragednaseqchipseqmethylseqdataimportonechannelbioconductorbioinformaticsdata-visualizationgenomegenomics-visualizationplotting-in-r
307 stars 11.25 score 656 scripts 4 dependentsbioc
scater:Single-Cell Analysis Toolkit for Gene Expression Data in R
A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.
Maintained by Alan OCallaghan. Last updated 22 days ago.
immunooncologysinglecellrnaseqqualitycontrolpreprocessingnormalizationvisualizationdimensionreductiontranscriptomicsgeneexpressionsequencingsoftwaredataimportdatarepresentationinfrastructurecoverage
11.07 score 12k scripts 43 dependentsbioc
EnrichedHeatmap:Making Enriched Heatmaps
Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources.
Maintained by Zuguang Gu. Last updated 5 months ago.
softwarevisualizationsequencinggenomeannotationcoveragecpp
190 stars 10.87 score 330 scripts 1 dependentsbioc
ORFik:Open Reading Frames in Genomics
R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more.
Maintained by Haakon Tjeldnes. Last updated 1 months ago.
immunooncologysoftwaresequencingriboseqrnaseqfunctionalgenomicscoveragealignmentdataimportcpp
33 stars 10.56 score 115 scripts 2 dependentsbioc
derfinder:Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach
This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks.
Maintained by Leonardo Collado-Torres. Last updated 4 months ago.
differentialexpressionsequencingrnaseqchipseqdifferentialpeakcallingsoftwareimmunooncologycoverageannotation-agnosticbioconductorderfinder
42 stars 10.03 score 78 scripts 6 dependentsbioc
DropletUtils:Utilities for Handling Single-Cell Droplet Data
Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.
Maintained by Jonathan Griffiths. Last updated 4 months ago.
immunooncologysinglecellsequencingrnaseqgeneexpressiontranscriptomicsdataimportcoveragezlibcpp
10.01 score 2.7k scripts 9 dependentsbioc
rGREAT:GREAT Analysis - Functional Enrichment on Genomic Regions
GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions.
Maintained by Zuguang Gu. Last updated 16 days ago.
genesetenrichmentgopathwayssoftwaresequencingwholegenomegenomeannotationcoveragecpp
86 stars 9.96 score 320 scripts 1 dependentsbioc
PureCN:Copy number calling and SNV classification using targeted short read sequencing
This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.
Maintained by Markus Riester. Last updated 1 days ago.
copynumbervariationsoftwaresequencingvariantannotationvariantdetectioncoverageimmunooncologybioconductor-packagecell-free-dnacopy-numberlohtumor-heterogeneitytumor-mutational-burdentumor-purity
132 stars 9.88 score 40 scriptsbioc
recount:Explore and download data from the recount project
Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.
Maintained by Leonardo Collado-Torres. Last updated 4 months ago.
coveragedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaredataimportimmunooncologyannotation-agnosticbioconductorcountderfinderdeseq2exongenehumanilluminajunctionrecount
41 stars 9.57 score 498 scripts 3 dependentsbioc
bambu:Context-Aware Transcript Quantification from Long Read RNA-Seq data
bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.
Maintained by Ying Chen. Last updated 2 months ago.
alignmentcoveragedifferentialexpressionfeatureextractiongeneexpressiongenomeannotationgenomeassemblyimmunooncologylongreadmultiplecomparisonnormalizationrnaseqregressionsequencingsoftwaretranscriptiontranscriptomicsbambubioconductorlong-readsnanoporenanopore-sequencingrna-seqrna-seq-analysistranscript-quantificationtranscript-reconstructioncpp
203 stars 9.04 score 91 scripts 1 dependentsbioc
scone:Single Cell Overview of Normalized Expression data
SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.
Maintained by Davide Risso. Last updated 1 months ago.
immunooncologynormalizationpreprocessingqualitycontrolgeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecellcoverage
53 stars 9.00 score 104 scriptsbioc
bamsignals:Extract read count signals from bam files
This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.
Maintained by Johannes Helmuth. Last updated 5 months ago.
dataimportsequencingcoveragealignmentcurlbzip2xz-utilszlibcpp
15 stars 8.95 score 31 scripts 11 dependentsbioc
GenomicScores:Infrastructure to work with genomewide position-specific scores
Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor.
Maintained by Robert Castelo. Last updated 2 months ago.
infrastructuregeneticsannotationsequencingcoverageannotationhubsoftware
8 stars 8.71 score 83 scripts 6 dependentsbioc
QuasR:Quantify and Annotate Short Reads in R
This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files.
Maintained by Michael Stadler. Last updated 1 months ago.
geneticspreprocessingsequencingchipseqrnaseqmethylseqcoveragealignmentqualitycontrolimmunooncologycurlbzip2xz-utilszlibcpp
6 stars 8.63 score 79 scripts 1 dependentsbioc
FRASER:Find RAre Splicing Events in RNA-Seq Data
Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.
Maintained by Christian Mertes. Last updated 5 months ago.
rnaseqalternativesplicingsequencingsoftwaregeneticscoverageaberrant-splicingdiagnosticsoutlier-detectionrare-diseaserna-seqsplicingopenblascpp
41 stars 8.50 score 155 scriptsbioc
csaw:ChIP-Seq Analysis with Windows
Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control.
Maintained by Aaron Lun. Last updated 2 months ago.
multiplecomparisonchipseqnormalizationsequencingcoveragegeneticsannotationdifferentialpeakcallingcurlbzip2xz-utilszlibcpp
8.32 score 498 scripts 7 dependentsbioc
velociraptor:Toolkit for Single-Cell Velocity
This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class.
Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.
singlecellgeneexpressionsequencingcoveragerna-velocity
55 stars 8.06 score 52 scriptsbioc
recount3:Explore and download data from the recount3 project
The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files.
Maintained by Leonardo Collado-Torres. Last updated 4 months ago.
coveragedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaredataimportannotation-agnosticbioconductorcountderfinderexongenehumanilluminajunctionmouserecountrecount3
33 stars 8.03 score 216 scriptsbioc
baySeq:Empirical Bayesian analysis of patterns of differential expression in count data
This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.
Maintained by Samuel Granjeaud. Last updated 5 months ago.
sequencingdifferentialexpressionmultiplecomparisonsagebayesiancoverage
7.75 score 79 scripts 3 dependentsbioc
MIRA:Methylation-Based Inference of Regulatory Activity
DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.
Maintained by John Lawson. Last updated 5 months ago.
immunooncologydnamethylationgeneregulationgenomeannotationsystemsbiologyfunctionalgenomicschipseqmethylseqsequencingepigeneticscoverage
12 stars 7.56 score 7 scripts 1 dependentsbioc
methrix:Fast and efficient summarization of generic bedGraph files from Bisufite sequencing
Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions.
Maintained by Anand Mayakonda. Last updated 5 months ago.
dnamethylationsequencingcoveragebedgraphbioinformaticsdna-methylation
32 stars 7.53 score 39 scripts 1 dependentsbioc
CAGEfightR:Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor
CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization.
Maintained by Malte Thodberg. Last updated 5 months ago.
softwaretranscriptioncoveragegeneexpressiongeneregulationpeakdetectiondataimportdatarepresentationtranscriptomicssequencingannotationgenomebrowsersnormalizationpreprocessingvisualization
8 stars 7.46 score 67 scripts 1 dependentsbioc
HilbertCurve:Making 2D Hilbert Curve
Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve.
Maintained by Zuguang Gu. Last updated 5 months ago.
softwarevisualizationsequencingcoveragegenomeannotationcpp
42 stars 7.45 score 48 scriptsbioc
GenomicDistributions:GenomicDistributions: fast analysis of genomic intervals with Bioconductor
If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another.
Maintained by Kristyna Kupkova. Last updated 5 months ago.
softwaregenomeannotationgenomeassemblydatarepresentationsequencingcoveragefunctionalgenomicsvisualization
26 stars 7.44 score 25 scriptsbioc
igvShiny:igvShiny: a wrapper of Integrative Genomics Viewer (IGV - an interactive tool for visualization and exploration integrated genomic data)
This package is a wrapper of Integrative Genomics Viewer (IGV). It comprises an htmlwidget version of IGV. It can be used as a module in Shiny apps.
Maintained by Arkadiusz Gladki. Last updated 5 months ago.
softwareshinyappssequencingcoverage
37 stars 7.40 score 120 scriptsbioc
regionReport:Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results
Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results.
Maintained by Leonardo Collado-Torres. Last updated 2 months ago.
differentialexpressionsequencingrnaseqsoftwarevisualizationtranscriptioncoveragereportwritingdifferentialmethylationdifferentialpeakcallingimmunooncologyqualitycontrolbioconductorderfinderdeseq2edgerregionreportrmarkdown
9 stars 7.22 score 46 scriptsbioc
signatureSearch:Environment for Gene Expression Searching Combined with Functional Enrichment Analysis
This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods.
Maintained by Brendan Gongol. Last updated 5 months ago.
softwaregeneexpressiongokeggnetworkenrichmentsequencingcoveragedifferentialexpressioncpp
17 stars 7.18 score 74 scripts 1 dependentsbioc
ATACseqQC:ATAC-seq Quality Control
ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.
Maintained by Jianhong Ou. Last updated 3 months ago.
sequencingdnaseqatacseqgeneregulationqualitycontrolcoveragenucleosomepositioningimmunooncology
7.12 score 146 scripts 1 dependentsbioc
ACE:Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing
Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers.
Maintained by Jos B Poell. Last updated 5 months ago.
copynumbervariationdnaseqcoveragewholegenomevisualizationsequencing
15 stars 7.03 score 18 scriptsbioc
animalcules:Interactive microbiome analysis toolkit
animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.
Maintained by Jessica McClintock. Last updated 5 months ago.
microbiomemetagenomicscoveragevisualization
55 stars 6.95 score 23 scriptsbioc
diffcoexp:Differential Co-expression Analysis
A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance.
Maintained by Wenbin Wei. Last updated 5 months ago.
geneexpressiondifferentialexpressiontranscriptionmicroarrayonechanneltwochannelrnaseqsequencingcoverageimmunooncology
15 stars 6.89 score 37 scriptsbioc
GenomicFiles:Distributed computing by file or by range
This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
geneticsinfrastructuredataimportsequencingcoverage
6.86 score 89 scripts 16 dependentsbioc
megadepth:megadepth: BigWig and BAM related utilities
This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3.
Maintained by David Zhang. Last updated 4 months ago.
softwarecoveragedataimporttranscriptomicsrnaseqpreprocessingbambigwigdasptermegadepthrecount2recount3
12 stars 6.69 score 7 scripts 3 dependentsbioc
extraChIPs:Additional functions for working with ChIP-Seq data
This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.
Maintained by Stevie Pederson. Last updated 29 days ago.
7 stars 6.67 score 25 scriptsbioc
miaSim:Microbiome Data Simulation
Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.
Maintained by Yagmur Simsek. Last updated 5 months ago.
microbiomesoftwaresequencingdnaseqatacseqcoveragenetwork
21 stars 6.64 score 23 scriptsbioc
SingleMoleculeFootprinting:Analysis tools for Single Molecule Footprinting (SMF) data
SingleMoleculeFootprinting provides functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location.
Maintained by Guido Barzaghi. Last updated 4 days ago.
dnamethylationcoveragenucleosomepositioningdatarepresentationepigeneticsmethylseqqualitycontrolsequencing
2 stars 6.46 score 27 scriptsbioc
SpliceWiz:interactive analysis and visualization of alternative splicing in R
The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.
Maintained by Alex Chit Hei Wong. Last updated 17 days ago.
softwaretranscriptomicsrnaseqalternativesplicingcoveragedifferentialsplicingdifferentialexpressionguisequencingcppopenmp
16 stars 6.41 score 8 scriptsbioc
chipseq:chipseq: A package for analyzing chipseq data
Tools for helping process short read data for chipseq experiments.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
chipseqsequencingcoveragequalitycontroldataimport
6.35 score 91 scripts 4 dependentsbioc
RCAS:RNA Centric Annotation System
RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery.
Maintained by Bora Uyar. Last updated 5 months ago.
softwaregenetargetmotifannotationmotifdiscoverygotranscriptomicsgenomeannotationgenesetenrichmentcoverage
6.32 score 29 scripts 1 dependentsbioc
CopyNumberPlots:Create Copy-Number Plots using karyoploteR functionality
CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data.
Maintained by Bernat Gel. Last updated 5 months ago.
visualizationcopynumbervariationcoverageonechanneldataimportsequencingdnaseqbioconductorbioconductor-packagebioinformaticscopy-number-variationgenomicsgenomics-visualization
6 stars 6.24 score 16 scripts 2 dependentsbioc
scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.
Maintained by Andreas C. Kapourani. Last updated 5 months ago.
immunooncologydnamethylationdifferentialmethylationdifferentialexpressiongeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionbayesiansequencingcoveragesinglecellbayesian-inferencegeneralised-linear-modelsheterogeneityhierarchical-modelsmethylation-analysissingle-cellcpp
20 stars 6.23 score 42 scriptsbioc
atena:Analysis of Transposable Elements
Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner.
Maintained by Robert Castelo. Last updated 2 months ago.
transcriptiontranscriptomicsrnaseqsequencingpreprocessingsoftwaregeneexpressioncoveragedifferentialexpressionfunctionalgenomics
10 stars 6.18 score 1 scriptsbioc
esATAC:An Easy-to-use Systematic pipeline for ATACseq data analysis
This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly.
Maintained by Zheng Wei. Last updated 5 months ago.
immunooncologysequencingdnaseqqualitycontrolalignmentpreprocessingcoverageatacseqdnaseseqatac-seqbioconductorpipelinecppopenjdk
23 stars 6.11 score 3 scriptsbioc
ramwas:Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms
A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) <doi:10.1093/bioinformatics/bty069>.
Maintained by Andrey A Shabalin. Last updated 5 months ago.
dnamethylationsequencingqualitycontrolcoveragepreprocessingnormalizationbatcheffectprincipalcomponentdifferentialmethylationvisualization
10 stars 6.08 score 85 scriptsbioc
raer:RNA editing tools in R
Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources.
Maintained by Kent Riemondy. Last updated 5 months ago.
multiplecomparisonrnaseqsinglecellsequencingcoverageepitranscriptomicsfeatureextractionannotationalignmentbioconductor-packagerna-seq-analysissingle-cell-analysissingle-cell-rna-seqcurlbzip2xz-utilszlib
8 stars 5.98 score 6 scriptsbioc
wiggleplotr:Make read coverage plots from BigWig files
Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage.
Maintained by Kaur Alasoo. Last updated 5 months ago.
immunooncologycoveragernaseqchipseqsequencingvisualizationgeneexpressiontranscriptionalternativesplicing
5.97 score 26 scripts 3 dependentsbioc
SCOPE:A normalization and copy number estimation method for single-cell DNA sequencing
Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background.
Maintained by Rujin Wang. Last updated 5 months ago.
singlecellnormalizationcopynumbervariationsequencingwholegenomecoveragealignmentqualitycontroldataimportdnaseq
5.92 score 84 scriptsbioc
tidyCoverage:Extract and aggregate genomic coverage over features of interest
`tidyCoverage` framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods. It facilitates the extraction, aggregation and visualization of genomic coverage over individual or thousands of genomic loci, relying on `CoverageExperiment` and `AggregatedCoverage` classes. This accelerates the integration of genomic track data in genomic analysis workflows.
Maintained by Jacques Serizay. Last updated 5 months ago.
21 stars 5.80 score 6 scriptsbioc
BindingSiteFinder:Binding site defintion based on iCLIP data
Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.
Maintained by Mirko Brüggemann. Last updated 10 days ago.
sequencinggeneexpressiongeneregulationfunctionalgenomicscoveragedataimportbinding-site-classificationbinding-sitesbioconductor-packageicliprna-binding-proteins
6 stars 5.80 score 3 scriptsbioc
TVTB:TVTB: The VCF Tool Box
The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE).
Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.
softwaregeneticsgeneticvariabilitygenomicvariationdatarepresentationguidnaseqwholegenomevisualizationmultiplecomparisondataimportvariantannotationsequencingcoveragealignmentsequencematching
2 stars 5.76 score 16 scriptsbioc
BPRMeth:Model higher-order methylation profiles
The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles.
Maintained by Chantriolnt-Andreas Kapourani. Last updated 5 months ago.
immunooncologydnamethylationgeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionrnaseqbayesiankeggsequencingcoveragesinglecellopenblascpp
5.75 score 94 scripts 1 dependentsbioc
DAMEfinder:Finds DAMEs - Differential Allelicly MEthylated regions
'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots.
Maintained by Stephany Orjuela. Last updated 5 months ago.
dnamethylationdifferentialmethylationcoverage
10 stars 5.70 score 9 scriptsbioc
lute:Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments
Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms.
Maintained by Sean K Maden. Last updated 5 months ago.
rnaseqsequencingsinglecellcoveragetranscriptomicsnormalization
3 stars 5.65 score 3 scriptsbioc
VplotR:Set of tools to make V-plots and compute footprint profiles
The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest.
Maintained by Jacques Serizay. Last updated 5 months ago.
nucleosomepositioningcoveragesequencingbiologicalquestionatacseqalignment
10 stars 5.64 score 11 scriptsbioc
breakpointR:Find breakpoints in Strand-seq data
This package implements functions for finding breakpoints, plotting and export of Strand-seq data.
Maintained by David Porubsky. Last updated 5 months ago.
softwaresequencingdnaseqsinglecellcoverage
8 stars 5.64 score 11 scriptsbioc
GenomicPlot:Plot profiles of next generation sequencing data in genomic features
Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. 'GenomicPlot' facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well.
Maintained by Shuye Pu. Last updated 2 months ago.
alternativesplicingchipseqcoveragegeneexpressionrnaseqsequencingsoftwaretranscriptionvisualizationannotation
3 stars 5.62 score 4 scriptsbioc
diffHic:Differential Analysis of Hi-C Data
Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.
Maintained by Aaron Lun. Last updated 3 months ago.
multiplecomparisonpreprocessingsequencingcoveragealignmentnormalizationclusteringhiccurlbzip2xz-utilszlibcpp
5.58 score 38 scriptsbioc
PICB:piRNA Cluster Builder
piRNAs (short for PIWI-interacting RNAs) and their PIWI protein partners play a key role in fertility and maintaining genome integrity by restricting mobile genetic elements (transposons) in germ cells. piRNAs originate from genomic regions known as piRNA clusters. The piRNA Cluster Builder (PICB) is a versatile toolkit designed to identify genomic regions with a high density of piRNAs. It constructs piRNA clusters through a stepwise integration of unique and multimapping piRNAs and offers wide-ranging parameter settings, supported by an optimization function that allows users to test different parameter combinations to tailor the analysis to their specific piRNA system. The output includes extensive metadata columns, enabling researchers to rank clusters and extract cluster characteristics.
Maintained by Franziska Ahrend. Last updated 2 months ago.
geneticsgenomeannotationsequencingfunctionalpredictioncoveragetranscriptomics
5 stars 5.57 scorebioc
UMI4Cats:UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data
UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay.
Maintained by Mireia Ramos-Rodriguez. Last updated 5 months ago.
qualitycontrolpreprocessingalignmentnormalizationvisualizationsequencingcoveragechromatinchromatin-interactiongenomicsumi4c
5 stars 5.57 score 7 scripts
krisrs1128
multimedia:Multimodal Mediation Analysis
Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.
Maintained by Kris Sankaran. Last updated 1 months ago.
coveragemicrobiomeregressionsequencingsoftwarestatisticalmethodstructuralequationmodelscausal-inferencedata-integrationmediation-analysis
1 stars 5.49 score 13 scriptsbioc
rprimer:Design Degenerate Oligos from a Multiple DNA Sequence Alignment
Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette.
Maintained by Sofia Persson. Last updated 5 months ago.
alignmentddpcrcoveragemultiplesequencealignmentsequencematchingqpcr
4 stars 5.49 score 13 scriptsbioc
metagene2:A package to produce metagene plots
This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages.
Maintained by Eric Fournier. Last updated 5 months ago.
chipseqgeneticsmultiplecomparisoncoveragealignmentsequencing
4 stars 5.45 score 8 scriptsbioc
beer:Bayesian Enrichment Estimation in R
BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.
Maintained by Athena Chen. Last updated 5 months ago.
softwarestatisticalmethodbayesiansequencingcoveragejagscpp
10 stars 5.38 score 12 scriptsbioc
chevreulProcess:Tools for managing SingleCellExperiment objects as projects
Tools analyzing SingleCellExperiment objects as projects. for input into the Chevreul app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.
Maintained by Kevin Stachelek. Last updated 2 months ago.
coveragernaseqsequencingvisualizationgeneexpressiontranscriptionsinglecelltranscriptomicsnormalizationpreprocessingqualitycontroldimensionreductiondataimport
5.38 score 2 scripts 2 dependentsbioc
sevenC:Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs
Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes.
Maintained by Jonas Ibn-Salem. Last updated 5 months ago.
dna3dstructurechipchipcoveragedataimportepigeneticsfunctionalgenomicsclassificationregressionchipseqhicannotation3d-genomechip-seqchromatin-interactionhi-cpredictionsequence-motiftranscription-factors
12 stars 5.38 score 3 scriptsbioc
NewWave:Negative binomial model for scRNA-seq
A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.
Maintained by Federico Agostinis. Last updated 5 months ago.
softwaregeneexpressiontranscriptomicssinglecellbatcheffectsequencingcoverageregressionbatch-effectsdimensionality-reductionnegative-binomialscrna-seq
4 stars 5.33 score 27 scriptsbioc
nucleR:Nucleosome positioning package for R
Nucleosome positioning for Tiling Arrays and NGS experiments.
Maintained by Alba Sala. Last updated 5 months ago.
nucleosomepositioningcoveragechipseqmicroarraysequencinggeneticsqualitycontroldataimport
5.32 score 21 scriptsbioc
DeMixT:Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms
DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.
Maintained by Ruonan Li. Last updated 5 months ago.
softwarestatisticalmethodclassificationgeneexpressionsequencingmicroarraytissuemicroarraycoveragecppopenmp
5.27 score 25 scriptsbioc
PhIPData:Container for PhIP-Seq Experiments
PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
Maintained by Athena Chen. Last updated 5 months ago.
infrastructuredatarepresentationsequencingcoverage
6 stars 5.26 score 6 scripts 1 dependentsbioc
consensusSeekeR:Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges
This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. In genomic analysis where feature identification generates a position value surrounded by a genomic range, such as ChIP-Seq peaks and nucleosome positions, the replication of an experiment may result in slight differences between predicted values. This package enables the conciliation of the results into consensus regions.
Maintained by Astrid Deschênes. Last updated 5 months ago.
biologicalquestionchipseqgeneticsmultiplecomparisontranscriptionpeakdetectionsequencingcoveragechip-seq-analysisgenomic-data-analysisnucleosome-positioning
1 stars 5.26 score 5 scripts 1 dependentsbioc
epistack:Heatmaps of Stack Profiles from Epigenetic Signals
The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provided as a `GRanges` (easily obtained from `gtf` or `bed` files). 3) a score to sort the features, such as peak height or gene expression value.
Maintained by DEVAILLY Guillaume. Last updated 5 months ago.
rnaseqpreprocessingchipseqgeneexpressioncoveragebioinformatics
6 stars 5.26 score 5 scriptsbioc
qsvaR:Generate Quality Surrogate Variable Analysis for Degradation Correction
The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation.
Maintained by Hedia Tnani. Last updated 4 months ago.
softwareworkflowstepnormalizationbiologicalquestiondifferentialexpressionsequencingcoveragebioconductorbraindegradationhumanqsva
5.26 score 4 scriptsbioc
periodicDNA:Set of tools to identify periodic occurrences of k-mers in DNA sequences
This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website.
Maintained by Jacques Serizay. Last updated 5 months ago.
sequencematchingmotifdiscoverymotifannotationsequencingcoveragealignmentdataimport
6 stars 5.26 score 5 scriptsbioc
CNVPanelizer:Reliable CNV detection in targeted sequencing applications
A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level.
Maintained by Thomas Wolf. Last updated 5 months ago.
classificationsequencingnormalizationcopynumbervariationcoverage
5.23 score 12 scriptsbioc
ASpli:Analysis of Alternative Splicing Using RNA-Seq
Integrative pipeline for the analysis of alternative splicing using RNAseq.
Maintained by Ariel Chernomoretz. Last updated 5 months ago.
immunooncologygeneexpressiontranscriptionalternativesplicingcoveragedifferentialexpressiondifferentialsplicingtimecoursernaseqgenomeannotationsequencingalignment
5.21 score 45 scripts 1 dependentsbioc
runibic:runibic: row-based biclustering algorithm for analysis of gene expression data in R
This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data.
Maintained by Patryk Orzechowski. Last updated 5 months ago.
microarrayclusteringgeneexpressionsequencingcoveragecppopenmp
4 stars 5.20 score 7 scriptsbioc
scGPS:A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)
The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.
Maintained by Quan Nguyen. Last updated 5 months ago.
singlecellclusteringdataimportsequencingcoverageopenblascpp
4 stars 5.20 score 7 scriptsbioc
snapcount:R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts
snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in).
Maintained by Rone Charles. Last updated 5 months ago.
coveragegeneexpressionrnaseqsequencingsoftwaredataimport
3 stars 5.19 score 13 scriptsbioc
MEDIPS:DNA IP-seq data analysis
MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis.
Maintained by Lukas Chavez. Last updated 5 months ago.
dnamethylationcpgislanddifferentialexpressionsequencingchipseqpreprocessingqualitycontrolvisualizationmicroarraygeneticscoveragegenomeannotationcopynumbervariationsequencematching
5.17 score 74 scriptsbioc
topdownr:Investigation of Fragmentation Conditions in Top-Down Proteomics
The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage.
Maintained by Sebastian Gibb. Last updated 5 months ago.
immunooncologyinfrastructureproteomicsmassspectrometrycoveragemass-spectrometrytopdown
1 stars 5.08 scorebioc
DegNorm:DegNorm: degradation normalization for RNA-seq data
This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy.
Maintained by Ji-Ping Wang. Last updated 5 months ago.
rnaseqnormalizationgeneexpressionalignmentcoveragedifferentialexpressionbatcheffectsoftwaresequencingimmunooncologyqualitycontroldataimportopenblascppopenmp
1 stars 5.08 score 3 scriptsbioc
chevreulShiny:Tools for managing SingleCellExperiment objects as projects
Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.
Maintained by Kevin Stachelek. Last updated 26 days ago.
coveragernaseqsequencingvisualizationgeneexpressiontranscriptionsinglecelltranscriptomicsnormalizationpreprocessingqualitycontroldimensionreductiondataimport
5.08 scorebioc
gDNAx:Diagnostics for assessing genomic DNA contamination in RNA-seq data
Provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin.
Maintained by Robert Castelo. Last updated 2 months ago.
transcriptiontranscriptomicsrnaseqsequencingpreprocessingsoftwaregeneexpressioncoveragedifferentialexpressionfunctionalgenomicssplicedalignmentalignment
1 stars 5.08 score 3 scriptsbioc
chevreulPlot:Plots used in the chevreulPlot package
Tools for plotting SingleCellExperiment objects in the chevreulPlot package. Includes functions for analysis and visualization of single-cell data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.
Maintained by Kevin Stachelek. Last updated 30 days ago.
coveragernaseqsequencingvisualizationgeneexpressiontranscriptionsinglecelltranscriptomicsnormalizationpreprocessingqualitycontroldimensionreductiondataimport
5.08 score 2 scriptsbioc
TrIdent:TrIdent - Transduction Identification
The `TrIdent` R package automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events. Transductomics is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction.
Maintained by Jessie Maier. Last updated 26 days ago.
coveragemetagenomicspatternlogicclassificationsequencingbacteriophagehorizontal-gene-transferpattern-matchingphagesequencing-coveragetransductiontransductomicsvirus-like-particle
2 stars 5.04 score 7 scriptsbioc
recoup:An R package for the creation of complex genomic profile plots
recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively.
Maintained by Panagiotis Moulos. Last updated 5 months ago.
immunooncologysoftwaregeneexpressionpreprocessingqualitycontrolrnaseqchipseqsequencingcoverageatacseqchiponchipalignmentdataimport
1 stars 5.02 score 2 scriptsbioc
broadSeq:broadSeq : for streamlined exploration of RNA-seq data
This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.
Maintained by Rishi Das Roy. Last updated 5 months ago.
geneexpressiondifferentialexpressionrnaseqtranscriptomicssequencingcoveragegenesetenrichmentgo
4 stars 5.00 score 7 scriptsbioc
easylift:An R package to perform genomic liftover
The easylift package provides a convenient tool for genomic liftover operations between different genome assemblies. It seamlessly works with Bioconductor's GRanges objects and chain files from the UCSC Genome Browser, allowing for straightforward handling of genomic ranges across various genome versions. One noteworthy feature of easylift is its integration with the BiocFileCache package. This integration automates the management and caching of chain files necessary for liftover operations. Users no longer need to manually specify chain file paths in their function calls, reducing the complexity of the liftover process.
Maintained by Abdullah Al Nahid. Last updated 5 months ago.
softwareworkflowstepsequencingcoveragegenomeassemblydataimport
5 stars 5.00 score 7 scriptsbioc
GreyListChIP:Grey Lists -- Mask Artefact Regions Based on ChIP Inputs
Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis.
Maintained by Matt Eldridge. Last updated 5 months ago.
chipseqalignmentpreprocessingdifferentialpeakcallingsequencinggenomeannotationcoverage
4.93 score 10 scripts 4 dependentsbioc
CNVrd2:CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data.
CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions.
Maintained by Hoang Tan Nguyen. Last updated 5 months ago.
copynumbervariationsnpsequencingsoftwarecoveragelinkagedisequilibriumclustering.jagscpp
3 stars 4.92 scorebioc
Melissa:Bayesian clustering and imputationa of single cell methylomes
Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach.
Maintained by C. A. Kapourani. Last updated 5 months ago.
immunooncologydnamethylationgeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionrnaseqbayesiankeggsequencingcoveragesinglecell
4.90 score 7 scriptsbioc
ribosomeProfilingQC:Ribosome Profiling Quality Control
Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis.
Maintained by Jianhong Ou. Last updated 2 months ago.
riboseqsequencinggeneregulationqualitycontrolvisualizationcoverage
4.88 score 17 scriptsbioc
NBAMSeq:Negative Binomial Additive Model for RNA-Seq Data
High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.
Maintained by Xu Ren. Last updated 5 months ago.
rnaseqdifferentialexpressiongeneexpressionsequencingcoveragedifferential-expressiongene-expressiongeneralized-additive-modelsgeneralized-linear-modelsnegative-binomial-regressionsplines
2 stars 4.78 score 2 scriptsbioc
strandCheckR:Calculate strandness information of a bam file
This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering.
Maintained by Thu-Hien To. Last updated 5 months ago.
rnaseqalignmentqualitycontrolcoverageimmunooncology
4.78 score 7 scriptsbioc
methylPipe:Base resolution DNA methylation data analysis
Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data.
Maintained by Mattia Furlan. Last updated 5 months ago.
methylseqdnamethylationcoveragesequencing
4.73 score 1 scripts 1 dependentsbioc
ELViS:An R Package for Estimating Copy Number Levels of Viral Genome Segments Using Base-Resolution Read Depth Profile
Base-resolution copy number analysis of viral genome. Utilizes base-resolution read depth data over viral genome to find copy number segments with two-dimensional segmentation approach. Provides publish-ready figures, including histograms of read depths, coverage line plots over viral genome annotated with copy number change events and viral genes, and heatmaps showing multiple types of data with integrative clustering of samples.
Maintained by Jin-Young Lee. Last updated 25 days ago.
copynumbervariationcoveragegenomicvariationbiomedicalinformaticssequencingnormalizationvisualizationclustering
4.70 score 7 scripts
xinghuq
DA:Discriminant Analysis for Evolutionary Inference
Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.
Maintained by Xinghu Qin. Last updated 4 years ago.
biomedicalinformaticschipseqclusteringcoveragednamethylationdifferentialexpressiondifferentialmethylationsoftwaredifferentialsplicingepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysqualitycontrolrnaseqregressionsagesequencingsystemsbiologytimecoursetranscriptiontranscriptomicsdapcdiscriminant-analysisecologicalkernelkernel-localkernel-principle-componentspopulation-structure-inferenceprincipal-components
1 stars 4.70 score 1 scriptsbioc
openPrimeR:Multiplex PCR Primer Design and Analysis
An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package.
Maintained by Matthias Döring. Last updated 9 days ago.
softwaretechnologycoveragemultiplecomparison
4.64 score 22 scriptsbioc
svaRetro:Retrotransposed transcript detection from structural variants
svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts.
Maintained by Ruining Dong. Last updated 5 months ago.
dataimportsequencingannotationgeneticsvariantannotationcoveragevariantdetection
4.60 score 4 scriptsbioc
ChIPComp:Quantitative comparison of multiple ChIP-seq datasets
ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control.
Maintained by Li Chen. Last updated 5 months ago.
chipseqsequencingtranscriptiongeneticscoveragemultiplecomparisondataimport
4.49 score 51 scriptsbioc
uncoverappLib:Interactive graphical application for clinical assessment of sequence coverage at the base-pair level
a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name.
Maintained by Emanuela Iovino. Last updated 5 months ago.
softwarevisualizationannotationcoverage
3 stars 4.48 score 4 scriptsbioc
Macarron:Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets
Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition.
Maintained by Sagun Maharjan. Last updated 5 months ago.
sequencingmetabolomicscoveragefunctionalpredictionclustering
4.41 score 13 scriptsbioc
phenomis:Postprocessing and univariate analysis of omics data
The 'phenomis' package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the 'ropls' and 'biosigner' packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics).
Maintained by Etienne A. Thevenot. Last updated 5 months ago.
batcheffectclusteringcoveragekeggmassspectrometrymetabolomicsnormalizationproteomicsqualitycontrolsequencingstatisticalmethodtranscriptomics
4.40 score 6 scriptsbioc
ChIPanalyser:ChIPanalyser: Predicting Transcription Factor Binding Sites
ChIPanalyser is a package to predict and understand TF binding by utilizing a statistical thermodynamic model. The model incorporates 4 main factors thought to drive TF binding: Chromatin State, Binding energy, Number of bound molecules and a scaling factor modulating TF binding affinity. Taken together, ChIPanalyser produces ChIP-like profiles that closely mimic the patterns seens in real ChIP-seq data.
Maintained by Patrick C.N. Martin. Last updated 5 months ago.
softwarebiologicalquestionworkflowsteptranscriptionsequencingchiponchipcoveragealignmentchipseqsequencematchingdataimportpeakdetection
4.38 score 12 scriptsbioc
Spaniel:Spatial Transcriptomics Analysis
Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue.
Maintained by Rachel Queen. Last updated 5 months ago.
singlecellrnaseqqualitycontrolpreprocessingnormalizationvisualizationtranscriptomicsgeneexpressionsequencingsoftwaredataimportdatarepresentationinfrastructurecoverageclustering
4.34 score 22 scriptsbioc
compEpiTools:Tools for computational epigenomics
Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples.
Maintained by Mattia Furlan. Last updated 5 months ago.
geneexpressionsequencingvisualizationgenomeannotationcoverage
4.30 score 6 scriptsbioc
ChIPexoQual:ChIPexoQual
Package with a quality control pipeline for ChIP-exo/nexus data.
Maintained by Rene Welch. Last updated 5 months ago.
chipseqsequencingtranscriptionvisualizationqualitycontrolcoveragealignment
1 stars 4.30 score 5 scriptsbioc
RJMCMCNucleosomes:Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq)
This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.
Maintained by Astrid Deschênes. Last updated 5 months ago.
biologicalquestionchipseqnucleosomepositioningsoftwarestatisticalmethodbayesiansequencingcoveragebayesian-t-mixturebioconductorc-plus-plusgenome-wide-profilingmultinomial-dirichlet-priornucleosome-positioningnucleosomesreversible-jump-mcmcgslcpp
4.30 score 1 scriptsbioc
CNAnorm:A normalization method for Copy Number Aberration in cancer samples
Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found.
Maintained by Stefano Berri. Last updated 5 months ago.
copynumbervariationsequencingcoveragenormalizationwholegenomednaseqgenomicvariationfortran
4.30 score 6 scriptsbioc
gmoviz:Seamless visualization of complex genomic variations in GMOs and edited cell lines
Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale.
Maintained by Kathleen Zeglinski. Last updated 5 months ago.
visualizationsequencinggeneticvariabilitygenomicvariationcoverage
4.30 score 9 scriptsbioc
RiboDiPA:Differential pattern analysis for Ribo-seq data
This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.
Maintained by Ji-Ping Wang. Last updated 4 months ago.
riboseqgeneexpressiongeneregulationdifferentialexpressionsequencingcoveragealignmentrnaseqimmunooncologyqualitycontroldataimportsoftwarenormalizationcpp
4.30 score 1 scriptsbioc
Ularcirc:Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis)
Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions.
Maintained by David Humphreys. Last updated 5 months ago.
datarepresentationvisualizationgeneticssequencingannotationcoveragealternativesplicingdifferentialsplicing
4.30 score 4 scriptsbioc
RiboProfiling:Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation
Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage.
Maintained by A. Popa. Last updated 5 months ago.
riboseqsequencingcoveragealignmentqualitycontrolsoftwareprincipalcomponent
4.30 score 10 scriptsbioc
loci2path:Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs
loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB.
Maintained by Tianlei Xu. Last updated 5 months ago.
functionalgenomicsgeneticsgenesetenrichmentsoftwaregeneexpressionsequencingcoveragebiocarta
1 stars 4.30 score 2 scriptsbioc
CeTF:Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis
This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data).
Maintained by Carlos Alberto Oliveira de Biagi Junior. Last updated 5 months ago.
sequencingrnaseqmicroarraygeneexpressiontranscriptionnormalizationdifferentialexpressionsinglecellnetworkregressionchipseqimmunooncologycoveragecpp
4.30 score 9 scriptsbioc
SMITE:Significance-based Modules Integrating the Transcriptome and Epigenome
This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules.
Maintained by Neil Ari Wijetunga. Last updated 5 months ago.
immunooncologydifferentialmethylationdifferentialexpressionsystemsbiologynetworkenrichmentgenomeannotationnetworksequencingrnaseqcoverage
1 stars 4.26 score 13 scriptsbioc
HelloRanges:Introduce *Ranges to bedtools users
Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks.
Maintained by Michael Lawrence. Last updated 5 months ago.
sequencingannotationcoveragegenomeannotationdataimportsequencematchingvariantannotation
4.19 score 26 scripts 1 dependentsbioc
betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data
A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.
Maintained by Koyel Majumdar. Last updated 3 months ago.
dnamethylationdifferentialmethylationimmunooncologybiomedicalinformaticsmethylationarraysoftwaremultiplecomparisonsequencingspatialcoveragegenetargethiddenmarkovmodelmicroarray
4.18 scorebioc
dinoR:Differential NOMe-seq analysis
dinoR tests for significant differences in NOMe-seq footprints between two conditions, using genomic regions of interest (ROI) centered around a landmark, for example a transcription factor (TF) motif. This package takes NOMe-seq data (GCH methylation/protection) in the form of a Ranged Summarized Experiment as input. dinoR can be used to group sequencing fragments into 3 or 5 categories representing characteristic footprints (TF bound, nculeosome bound, open chromatin), plot the percentage of fragments in each category in a heatmap, or averaged across different ROI groups, for example, containing a common TF motif. It is designed to compare footprints between two sample groups, using edgeR's quasi-likelihood methods on the total fragment counts per ROI, sample, and footprint category.
Maintained by Michaela Schwaiger. Last updated 5 months ago.
nucleosomepositioningepigeneticsmethylseqdifferentialmethylationcoveragetranscriptionsequencingsoftware
4.18 score 7 scriptsbioc
MICSQTL:MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci)
Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.
Maintained by Qian Li. Last updated 5 months ago.
geneexpressiongeneticsproteomicsrnaseqsequencingsinglecellsoftwarevisualizationcellbasedassayscoverage
4.18 score 3 scriptsbioc
IntEREst:Intron-Exon Retention Estimator
This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files).
Maintained by Ali Oghabian. Last updated 1 days ago.
softwarealternativesplicingcoveragedifferentialsplicingsequencingrnaseqalignmentnormalizationdifferentialexpressionimmunooncology
4.16 score 12 scriptsbioc
BUMHMM:Computational pipeline for computing probability of modification from structure probing experiment data
This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.
Maintained by Alina Selega. Last updated 5 months ago.
immunooncologygeneticvariabilitytranscriptiongeneexpressiongeneregulationcoveragegeneticsstructuralpredictiontranscriptomicsbayesianclassificationfeatureextractionhiddenmarkovmodelregressionrnaseqsequencing
4.15 score 14 scriptsbioc
methimpute:Imputation-guided re-construction of complete methylomes from WGBS data
This package implements functions for calling methylation for all cytosines in the genome.
Maintained by Aaron Taudt. Last updated 5 months ago.
immunooncologysoftwarednamethylationepigeneticshiddenmarkovmodelsequencingcoveragecppopenmp
4.11 score 13 scriptsbioc
DMRcaller:Differentially Methylated Regions caller
Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects.
Maintained by Nicolae Radu Zabet. Last updated 5 months ago.
differentialmethylationdnamethylationsoftwaresequencingcoverage
4.08 score 8 scripts
nutriverse
squeacr:Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) Tools
In the recent past, measurement of coverage has been mainly through two-stage cluster sampled surveys either as part of a nutrition assessment or through a specific coverage survey known as Centric Systematic Area Sampling (CSAS). However, such methods are resource intensive and often only used for final programme evaluation meaning results arrive too late for programme adaptation. SQUEAC, which stands for Semi-Quantitative Evaluation of Access and Coverage, is a low resource method designed specifically to address this limitation and is used regularly for monitoring, planning and importantly, timely improvement to programme quality, both for agency and Ministry of Health (MoH) led programmes. This package provides functions for use in conducting a SQUEAC investigation.
Maintained by Ernest Guevarra. Last updated 3 months ago.
acute-malnutritioncmamcoveragesqueacsurveywasting
2 stars 4.08 score 6 scripts 1 dependentsbioc
profileplyr:Visualization and annotation of read signal over genomic ranges with profileplyr
Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal.
Maintained by Tom Carroll. Last updated 5 months ago.
chipseqdataimportsequencingchiponchipcoverage
4.03 score 54 scriptsbioc
fCCAC:functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets
Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.
Maintained by Pedro Madrigal. Last updated 5 months ago.
epigeneticstranscriptionsequencingcoveragechipseqfunctionalgenomicsrnaseqatacseqmnaseseq
4.00 score 1 scriptsbioc
GenomicOZone:Delineate outstanding genomic zones of differential gene activity
The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data.
Maintained by Hua Zhong. Last updated 5 months ago.
softwaregeneexpressiontranscriptiondifferentialexpressionfunctionalpredictiongeneregulationbiomedicalinformaticscellbiologyfunctionalgenomicsgeneticssystemsbiologytranscriptomicsclusteringregressionrnaseqannotationvisualizationsequencingcoveragedifferentialmethylationgenomicvariationstructuralvariationcopynumbervariation
4.00 scorebioc
seqCAT:High Throughput Sequencing Cell Authentication Toolkit
The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours.
Maintained by Erik Fasterius. Last updated 5 months ago.
coveragegenomicvariationsequencingvariantannotation
4.00 scorebioc
survtype:Subtype Identification with Survival Data
Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwarestatisticalmethodgeneexpressionsurvivalclusteringsequencingcoverage
4.00 score 3 scriptsbioc
TFHAZ:Transcription Factor High Accumulation Zones
It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters.
Maintained by Gaia Ceddia. Last updated 5 months ago.
softwarebiologicalquestiontranscriptionchipseqcoverage
4.00 score 2 scriptsbioc
CexoR:An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates
Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Then, irreproducible discovery rate for overlapping peak-pairs across biological replicates is computed.
Maintained by Pedro Madrigal. Last updated 5 months ago.
functionalgenomicssequencingcoveragechipseqpeakdetection
4.00 score 1 scriptsbioc
MBttest:Multiple Beta t-Tests
MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions.
Maintained by Yuan-De Tan. Last updated 5 months ago.
sequencingdifferentialexpressionmultiplecomparisonsagegeneexpressiontranscriptionalternativesplicingcoveragedifferentialsplicing
4.00 score 3 scriptsbioc
BubbleTree:BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data
CNV analysis in groups of tumor samples.
Maintained by Todd Creasy. Last updated 5 months ago.
copynumbervariationsoftwaresequencingcoverage
3.95 score 15 scriptsbioc
geneXtendeR:Optimized Functional Annotation Of ChIP-seq Data
geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.
Maintained by Bohdan Khomtchouk. Last updated 5 months ago.
chipseqgeneticsannotationgenomeannotationdifferentialpeakcallingcoveragepeakdetectionchiponchiphistonemodificationdataimportnaturallanguageprocessingvisualizationgosoftwarebioconductorbioinformaticscchip-seqcomputational-biologyepigeneticsfunctional-annotation
9 stars 3.95 score 5 scriptsbioc
DMCHMM:Differentially Methylated CpG using Hidden Markov Model
A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks.
Maintained by Farhad Shokoohi. Last updated 5 months ago.
differentialmethylationsequencinghiddenmarkovmodelcoverage
3.78 score 3 scriptsbioc
PIPETS:Poisson Identification of PEaks from Term-Seq data
PIPETS provides statistically robust analysis for 3'-seq/term-seq data. It utilizes a sliding window approach to apply a Poisson Distribution test to identify genomic positions with termination read coverage that is significantly higher than the surrounding signal. PIPETS then condenses proximal signal and produces strand specific results that contain all significant termination peaks.
Maintained by Quinlan Furumo. Last updated 5 months ago.
sequencingtranscriptiongeneregulationpeakdetectiongeneticstranscriptomicscoverage
3.78 score 2 scriptsbioc
borealis:Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution
Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses.
Maintained by Garrett Jenkinson. Last updated 5 months ago.
sequencingcoveragednamethylationdifferentialmethylation
3.73 score 27 scriptsbioc
srnadiff:Finding differentially expressed unannotated genomic regions from RNA-seq data
srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values.
Maintained by Zytnicki Matthias. Last updated 3 months ago.
immunooncologygeneexpressioncoveragesmallrnaepigeneticsstatisticalmethodpreprocessingdifferentialexpressioncpp
3.70 score 3 scriptsbioc
DMCFB:Differentially Methylated Cytosines via a Bayesian Functional Approach
DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.
Maintained by Farhad Shokoohi. Last updated 5 months ago.
differentialmethylationsequencingcoveragebayesianregression
3.60 score 3 scriptsbioc
BadRegionFinder:BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage
BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available.
Maintained by Sarah Sandmann. Last updated 2 months ago.
coveragesequencingalignmentwholegenomeclassification
3.60 score 1 scriptsbioc
MADSEQ:Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data
The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample.
Maintained by Yu Kong. Last updated 5 months ago.
genomicvariationsomaticmutationvariantdetectionbayesiancopynumbervariationsequencingcoveragejagscpp
4 stars 3.60 score 1 scriptsnutriverse
sleacr:Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Tools
In the recent past, measurement of coverage has been mainly through two-stage cluster sampled surveys either as part of a nutrition assessment or through a specific coverage survey known as Centric Systematic Area Sampling (CSAS). However, such methods are resource intensive and often only used for final programme evaluation meaning results arrive too late for programme adaptation. SLEAC, which stands for Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage, is a low resource method designed specifically to address this limitation and is used regularly for monitoring, planning and importantly, timely improvement to programme quality, both for agency and Ministry of Health (MoH) led programmes. SLEAC is designed to complement the Semi-quantitative Evaluation of Access and Coverage (SQUEAC) method. This package provides functions for use in conducting a SLEAC assessment.
Maintained by Ernest Guevarra. Last updated 2 months ago.
acute-malnutritioncmamcoveragenutritionsleacwasting
1 stars 3.48 score 5 scriptsbioc
BasicSTARRseq:Basic peak calling on STARR-seq data
Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17.
Maintained by Annika Buerger. Last updated 5 months ago.
peakdetectiongeneregulationfunctionalpredictionfunctionalgenomicscoverage
3.30 score 1 scriptsbioc
Scale4C:Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data
Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples.
Maintained by Carolin Walter. Last updated 5 months ago.
visualizationqualitycontroldataimportsequencingcoverage
3.30 score 1 scriptsbioc
traseR:GWAS trait-associated SNP enrichment analyses in genomic intervals
traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results.
Maintained by li chen. Last updated 5 months ago.
geneticssequencingcoveragealignmentqualitycontroldataimport
3.30 score 3 scriptsbioc
SICtools:Find SNV/Indel differences between two bam files with near relationship
This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region.
Maintained by Xiaobin Xing. Last updated 5 months ago.
alignmentsequencingcoveragesequencematchingqualitycontroldataimportsoftwaresnpvariantdetection
3.30 score 1 scriptsbioc
DEScan2:Differential Enrichment Scan 2
Integrated peak and differential caller, specifically designed for broad epigenomic signals.
Maintained by Dario Righelli. Last updated 5 months ago.
immunooncologypeakdetectionepigeneticssoftwaresequencingcoveragecpp
3.30 score 2 scriptsbioc
BBCAnalyzer:BBCAnalyzer: an R/Bioconductor package for visualizing base counts
BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots.
Maintained by Sarah Sandmann. Last updated 5 months ago.
sequencingalignmentcoveragegeneticvariabilitysnp
3.30 score 1 scriptsbioc
transcriptR:An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification
The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample.
Maintained by Armen R. Karapetyan. Last updated 5 months ago.
immunooncologytranscriptionsoftwaresequencingrnaseqcoverage
3.30 score 2 scriptsbioc
Basic4Cseq:Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data
Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile.
Maintained by Carolin Walter. Last updated 5 months ago.
immunooncologyvisualizationqualitycontrolsequencingcoveragealignmentrnaseqsequencematchingdataimport
3.30 score 5 scriptsbioc
RepViz:Replicate oriented Visualization of a genomic region
RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data.
Maintained by Thomas Faux, Asta Laiho. Last updated 5 months ago.
workflowstepvisualizationsequencingchipseqatacseqsoftwarecoveragegenomicvariation
3.30 score 1 scriptsbioc
MDTS:Detection of de novo deletion in targeted sequencing trios
A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value.
Maintained by Jack M.. Fu. Last updated 5 months ago.
statisticalmethodtechnologysequencingtargetedresequencingcoveragedataimport
2.78 score 1 scripts