R-universe search: dnaseq

bioc

maftools:Summarize, Analyze and Visualize MAF Files

Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.

Maintained by Anand Mayakonda. Last updated 5 months ago.

datarepresentation dnaseq visualization drivermutation variantannotation featureextraction classification somaticmutation sequencing functionalgenomics survival bioinformatics cancer-genome-atlas cancer-genomics genomics maf-files tcga curl bzip2 xz-utils zlib

11.0 match 459 stars 14.63 score 948 scripts 18 dependents

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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.

visualization copynumbervariation sequencing coverage dnaseq chipseq methylseq dataimport onechannel bioconductor bioinformatics data-visualization genome genomics-visualization plotting-in-r

11.0 match 306 stars 11.22 score 656 scripts 4 dependents

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ALDEx2:Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account

A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report predicted p-values and posterior Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate the effect of scale on the results and report on the scale-dependent robustness of results.

Maintained by Greg Gloor. Last updated 5 months ago.

differentialexpression rnaseq transcriptomics geneexpression dnaseq chipseq bayesian sequencing software microbiome metagenomics immunooncology scale simulation posterior p-value

11.0 match 28 stars 10.70 score 424 scripts 3 dependents

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QDNAseq:Quantitative DNA Sequencing for Chromosomal Aberrations

Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively.

Maintained by Daoud Sie. Last updated 5 months ago.

copynumbervariation dnaseq genetics genomeannotation preprocessing qualitycontrol sequencing

11.0 match 49 stars 10.10 score 177 scripts 4 dependents

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GenVisR:Genomic Visualizations in R

Produce highly customizable publication quality graphics for genomic data primarily at the cohort level.

Maintained by Zachary Skidmore. Last updated 5 months ago.

infrastructure datarepresentation classification dnaseq

11.0 match 215 stars 9.87 score 76 scripts

cnuge

debar:A Post-Clustering Denoiser for COI-5P Barcode Data

The 'debar' sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package is designed to detect and correct insertion and deletion errors within sequencer outputs. This is accomplished through comparison of input sequences against a profile hidden Markov model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted for through the introduction of a placeholder character. Since the PHMM is a probabilistic representation of the COI barcode, corrections are not always perfect. For this reason 'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder characters (default is 7 base pairs in either direction, this feature can be disabled). Testing has shown that this censorship results in the correct sequence length being restored, and erroneous base pairs being masked the vast majority of the time (>95%).

Maintained by Cameron M. Nugent. Last updated 1 years ago.

bioinformatics denoising dna-barcoding dna-sequencing hidden-markov-model machine-learning

26.1 match 1 stars 4.00 score 8 scripts

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regioneR:Association analysis of genomic regions based on permutation tests

regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features.

Maintained by Bernat Gel. Last updated 5 months ago.

genetics chipseq dnaseq methylseq copynumbervariation

11.0 match 9.00 score 2.7k scripts 21 dependents

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TitanCNA:Subclonal copy number and LOH prediction from whole genome sequencing of tumours

Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data.

Maintained by Gavin Ha. Last updated 5 months ago.

sequencing wholegenome dnaseq exomeseq statisticalmethod copynumbervariation hiddenmarkovmodel genetics genomicvariation immunooncology 10x-genomics copy-number-variation genome-sequencing hmm tumor-heterogeneity

11.0 match 96 stars 8.47 score 68 scripts

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SPsimSeq:Semi-parametric simulation tool for bulk and single-cell RNA sequencing data

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

Maintained by Joris Meys. Last updated 5 months ago.

geneexpression rnaseq singlecell sequencing dnaseq

11.0 match 10 stars 7.14 score 29 scripts 1 dependents

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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 2 months ago.

sequencing dnaseq atacseq generegulation qualitycontrol coverage nucleosomepositioning immunooncology

11.0 match 7.12 score 146 scripts 1 dependents

bioc

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.

copynumbervariation dnaseq coverage wholegenome visualization sequencing

11.0 match 15 stars 7.03 score 18 scripts

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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.

microbiome software sequencing dnaseq atacseq coverage network

11.0 match 21 stars 6.64 score 23 scripts

bioc

YAPSA:Yet Another Package for Signature Analysis

This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata.

Maintained by Zuguang Gu. Last updated 5 months ago.

sequencing dnaseq somaticmutation visualization clustering genomicvariation statisticalmethod biologicalquestion

11.0 match 6.41 score 57 scripts

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spatialHeatmap:spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions

The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org.

Maintained by Jianhai Zhang. Last updated 4 months ago.

spatial visualization microarray sequencing geneexpression datarepresentation network clustering graphandnetwork cellbasedassays atacseq dnaseq tissuemicroarray singlecell cellbiology genetarget

11.0 match 5 stars 6.26 score 12 scripts

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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.

visualization copynumbervariation coverage onechannel dataimport sequencing dnaseq bioconductor bioconductor-package bioinformatics copy-number-variation genomics genomics-visualization

11.0 match 6 stars 6.24 score 16 scripts 2 dependents

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dearseq:Differential Expression Analysis for RNA-seq data through a robust variance component test

Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.

Maintained by Boris P. Hejblum. Last updated 5 months ago.

biomedicalinformatics cellbiology differentialexpression dnaseq geneexpression genetics genesetenrichment immunooncology kegg regression rnaseq sequencing systemsbiology timecourse transcription transcriptomics

11.0 match 8 stars 6.20 score 11 scripts 1 dependents

bioc

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.

immunooncology sequencing dnaseq qualitycontrol alignment preprocessing coverage atacseq dnaseseq atac-seq bioconductor pipeline cpp openjdk

11.0 match 23 stars 6.11 score 3 scripts

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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.

singlecell normalization copynumbervariation sequencing wholegenome coverage alignment qualitycontrol dataimport dnaseq

11.0 match 5.92 score 84 scripts

bioc

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.

software genetics geneticvariability genomicvariation datarepresentation gui dnaseq wholegenome visualization multiplecomparison dataimport variantannotation sequencing coverage alignment sequencematching

11.0 match 2 stars 5.76 score 16 scripts

bioc

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.

software sequencing dnaseq singlecell coverage

11.0 match 8 stars 5.64 score 11 scripts

bioc

mnem:Mixture Nested Effects Models

Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.

Maintained by Martin Pirkl. Last updated 4 months ago.

pathways systemsbiology networkinference network rnaseq pooledscreens singlecell crispr atacseq dnaseq geneexpression cpp

11.0 match 4 stars 5.64 score 15 scripts 4 dependents

bioc

QSutils:Quasispecies Diversity

Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data.

Maintained by Mercedes Guerrero-Murillo. Last updated 5 months ago.

software genetics dnaseq geneticvariability sequencing alignment sequencematching dataimport

11.0 match 5.56 score 8 scripts 1 dependents

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CNVfilteR:Identifies false positives of CNV calling tools by using SNV calls

CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines.

Maintained by Jose Marcos Moreno-Cabrera. Last updated 5 months ago.

copynumbervariation sequencing dnaseq visualization dataimport

11.0 match 5 stars 5.18 score 1 scripts

bioc

semisup:Semi-Supervised Mixture Model

Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.

Maintained by Armin Rauschenberger. Last updated 5 months ago.

snp genomicvariation somaticmutation genetics classification clustering dnaseq microarray multiplecomparison

11.0 match 1 stars 5.08 score 4 scripts

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tweeDEseq:RNA-seq data analysis using the Poisson-Tweedie family of distributions

Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

immunooncology statisticalmethod differentialexpression sequencing rnaseq dnaseq

11.0 match 4.91 score 45 scripts 1 dependents

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RcwlPipelines:Bioinformatics pipelines based on Rcwl

A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language.

Maintained by Qiang Hu. Last updated 5 months ago.

software workflowstep alignment preprocessing qualitycontrol dnaseq rnaseq dataimport immunooncology

11.0 match 4.89 score 26 scripts 1 dependents

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decompTumor2Sig:Decomposition of individual tumors into mutational signatures by signature refitting

Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor.

Maintained by Rosario M. Piro. Last updated 5 months ago.

software snp sequencing dnaseq genomicvariation somaticmutation biomedicalinformatics genetics biologicalquestion statisticalmethod

11.0 match 1 stars 4.78 score 10 scripts 1 dependents

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HGC:A fast hierarchical graph-based clustering method

HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6.

Maintained by XGlab. Last updated 5 months ago.

singlecell software clustering rnaseq graphandnetwork dnaseq cpp

11.0 match 4.70 score 25 scripts

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nempi:Inferring unobserved perturbations from gene expression data

Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.

Maintained by Martin Pirkl. Last updated 5 months ago.

software geneexpression differentialexpression differentialmethylation genesignaling pathways network classification neuralnetwork networkinference atacseq dnaseq rnaseq pooledscreens crispr singlecell systemsbiology

11.0 match 2 stars 4.60 score 2 scripts

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GeneBreak:Gene Break Detection

Recurrent breakpoint gene detection on copy number aberration profiles.

Maintained by Evert van den Broek. Last updated 5 months ago.

acgh copynumbervariation dnaseq genetics sequencing wholegenome visualization

11.0 match 2 stars 4.60 score 6 scripts

bioc

seqArchR:Identify Different Architectures of Sequence Elements

seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos.

Maintained by Sarvesh Nikumbh. Last updated 5 months ago.

motifdiscovery generegulation mathematicalbiology systemsbiology transcriptomics genetics clustering dimensionreduction featureextraction dnaseq nmf nonnegative-matrix-factorization promoter-sequence-architectures scikit-learn sequence-analysis sequence-architectures unsupervised-machine-learning

11.0 match 1 stars 4.48 score 9 scripts 1 dependents

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coMET:coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns

Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as lon:g as the data can be translated to genomic level and for any species.

Maintained by Tiphaine Martin. Last updated 15 hours ago.

software differentialmethylation visualization sequencing genetics functionalgenomics microarray methylationarray methylseq chipseq dnaseq riboseq rnaseq exomeseq dnamethylation genomewideassociation motifannotation

11.0 match 4.41 score 17 scripts

bioc

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.

copynumbervariation sequencing coverage normalization wholegenome dnaseq genomicvariation fortran

11.0 match 4.30 score 6 scripts

bioc

Clomial:Infers clonal composition of a tumor

Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor.

Maintained by Habil Zare. Last updated 5 months ago.

genetics geneticvariability sequencing clustering multiplecomparison bayesian dnaseq exomeseq targetedresequencing immunooncology

11.0 match 4.30 score 3 scripts

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SigsPack:Mutational Signature Estimation for Single Samples

Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues.

Maintained by Franziska Schumann. Last updated 5 months ago.

somaticmutation snp variantannotation biomedicalinformatics dnaseq

11.0 match 2 stars 4.30 score 4 scripts

bioc

regioneReloaded:RegioneReloaded: Multiple Association for Genomic Region Sets

RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs.

Maintained by Roberto Malinverni. Last updated 5 months ago.

genetics chipseq dnaseq methylseq copynumbervariation clustering multiplecomparison

11.0 match 5 stars 4.30 score 2 scripts

bioc

ATACseqTFEA:Transcription Factor Enrichment Analysis for ATAC-seq

Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions.

Maintained by Jianhong Ou. Last updated 2 months ago.

sequencing dnaseq atacseq mnaseseq generegulation

11.0 match 1 stars 4.18 score 4 scripts

bioc

NADfinder:Call wide peaks for sequencing data

Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation.

Maintained by Jianhong Ou. Last updated 2 months ago.

sequencing dnaseq generegulation peakdetection

11.0 match 4.18 score 1 scripts

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seq.hotSPOT:Targeted sequencing panel design based on mutation hotspots

seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues.

Maintained by Sydney Grant. Last updated 5 months ago.

software technology sequencing dnaseq wholegenome

11.0 match 4.00 score 3 scripts

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compSPOT:compSPOT: Tool for identifying and comparing significantly mutated genomic hotspots

Clonal cell groups share common mutations within cancer, precancer, and even clinically normal appearing tissues. The frequency and location of these mutations may predict prognosis and cancer risk. It has also been well established that certain genomic regions have increased sensitivity to acquiring mutations. Mutation-sensitive genomic regions may therefore serve as markers for predicting cancer risk. This package contains multiple functions to establish significantly mutated hotspots, compare hotspot mutation burden between samples, and perform exploratory data analysis of the correlation between hotspot mutation burden and personal risk factors for cancer, such as age, gender, and history of carcinogen exposure. This package allows users to identify robust genomic markers to help establish cancer risk.

Maintained by Sydney Grant. Last updated 5 months ago.

software technology sequencing dnaseq wholegenome classification singlecell survival multiplecomparison

11.0 match 4.00 score 3 scripts

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omicplotR:Visual Exploration of Omic Datasets Using a Shiny App

A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata.

Maintained by Daniel Giguere. Last updated 5 months ago.

software differentialexpression geneexpression gui rnaseq dnaseq metagenomics transcriptomics bayesian microbiome visualization sequencing immunooncology

11.0 match 4.00 score 5 scripts

bioc

SARC:Statistical Analysis of Regions with CNVs

Imports a cov/coverage file (normalised read coverages from BAM files) and a cnv file (list of CNVs - similiar to a BED file) from WES/ WGS CNV (copy number variation) detection pipelines and utilises several metrics to weigh the likelihood of a sample containing a detected CNV being a true CNV or a false positive. Highly useful for diagnostic testing to filter out false positives to provide clinicians with fewer variants to interpret. SARC uniquely only used cov and csv (similiar to BED file) files which are the common CNV pipeline calling filetypes, and can be used as to supplement the Interactive Genome Browser (IGV) to generate many figures automatedly, which can be especially helpful in large cohorts with 100s-1000s of patients.

Maintained by Krutik Patel. Last updated 5 months ago.

software copynumbervariation visualization dnaseq sequencing

11.0 match 4.00 score 2 scripts

ranbi1990

ssizeRNA:Sample Size Calculation for RNA-Seq Experimental Design

We propose a procedure for sample size calculation while controlling false discovery rate for RNA-seq experimental design. Our procedure depends on the Voom method proposed for RNA-seq data analysis by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size calculation method proposed for microarray experiments by Liu and Hwang (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions that calculates appropriate sample sizes for two-sample t-test for RNA-seq experiments with fixed or varied set of parameters. The outputs also contain a plot of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. To install this package, please use 'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. For R version 3.5 or greater, please use 'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.

Maintained by Ran Bi. Last updated 6 years ago.

geneexpression differentialexpression experimentaldesign sequencing rnaseq dnaseq microarray

11.0 match 1 stars 3.53 score 28 scripts 1 dependents

bioc

CNViz:Copy Number Visualization

CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data.

Maintained by Rebecca Greenblatt. Last updated 5 months ago.

visualization copynumbervariation sequencing dnaseq

11.0 match 3.30 score 1 scripts

bioc

uSORT:uSORT: A self-refining ordering pipeline for gene selection

This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization.

Maintained by Hao Chen. Last updated 5 months ago.

immunooncology rnaseq gui cellbiology dnaseq

11.0 match 3.30 score