Showing 23 of total 23 results (show query)
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MassSpecWavelet:Peak Detection for Mass Spectrometry data using wavelet-based algorithms
Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See <doi:10.1093/bioinformatics/btl355} for further details.
Maintained by Sergio Oller Moreno. Last updated 3 months ago.
immunooncologymassspectrometryproteomicspeakdetection
9 stars 9.41 score 37 scripts 18 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
PeacoQC:Peak-based selection of high quality cytometry data
This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data.
Maintained by Annelies Emmaneel. Last updated 5 months ago.
flowcytometryqualitycontrolpreprocessingpeakdetection
16 stars 7.38 score 28 scripts 3 dependentsbioc
DiffBind:Differential Binding Analysis of ChIP-Seq Peak Data
Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.
Maintained by Rory Stark. Last updated 2 months ago.
sequencingchipseqatacseqdnaseseqmethylseqripseqdifferentialpeakcallingdifferentialmethylationgeneregulationhistonemodificationpeakdetectionbiomedicalinformaticscellbiologymultiplecomparisonnormalizationreportwritingepigeneticsfunctionalgenomicscurlbzip2xz-utilszlibcpp
7.13 score 512 scripts 2 dependentsbioc
peakPantheR:Peak Picking and Annotation of High Resolution Experiments
An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained.
Maintained by Arnaud Wolfer. Last updated 5 months ago.
massspectrometrymetabolomicspeakdetectionfeature-detectionmass-spectrometry
12 stars 6.65 score 23 scriptsbioc
Linnorm:Linear model and normality based normalization and transformation method (Linnorm)
Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.
Maintained by Shun Hang Yip. Last updated 5 months ago.
immunooncologysequencingchipseqrnaseqdifferentialexpressiongeneexpressiongeneticsnormalizationsoftwaretranscriptionbatcheffectpeakdetectionclusteringnetworksinglecellcpp
6.26 score 61 scripts 5 dependentsbioc
normr:Normalization and difference calling in ChIP-seq data
Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.
Maintained by Johannes Helmuth. Last updated 5 months ago.
bayesiandifferentialpeakcallingclassificationdataimportchipseqripseqfunctionalgenomicsgeneticsmultiplecomparisonnormalizationpeakdetectionpreprocessingalignmentcppopenmp
11 stars 5.93 score 13 scripts
bhklab
CREAM:Clustering of Genomic Regions Analysis Method
Provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.
Maintained by Benjamin Haibe-Kains. Last updated 4 years ago.
peakdetectionfunctionalpredictionbiomedicalinformaticsclusteringbiomedical-informaticsfunctional-predictionpeak-detection
12 stars 5.43 score 15 scriptsbioc
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
Damsel:Damsel: an end to end analysis of DamID
Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R.
Maintained by Caitlin Page. Last updated 5 months ago.
differentialmethylationpeakdetectiongenepredictiongenesetenrichment
5.20 score 20 scriptsbioc
ptairMS:Pre-processing PTR-TOF-MS Data
This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a singl<e ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and quantification, feature alignment, missing value imputation and feature annotation. Applications to exhaled air and cell culture in headspace are described in the vignettes and examples. This package was used for data analysis of Gassin Delyle study on adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS), and permit to identfy four potentiel biomarquers of the infection.
Maintained by camille Roquencourt. Last updated 5 months ago.
softwaremassspectrometrypreprocessingmetabolomicspeakdetectionalignmentcpp
7 stars 5.15 score 3 scriptsbioc
yamss:Tools for high-throughput metabolomics
Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. At the core of these methods is a peak detection phase that pools information across all samples simultaneously. This is in contrast to other methods that detect peaks in a sample-by-sample basis.
Maintained by Leslie Myint. Last updated 5 months ago.
massspectrometrymetabolomicspeakdetectionsoftware
3 stars 5.08 score 9 scriptsbioc
gcapc:GC Aware Peak Caller
Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation.
Maintained by Mingxiang Teng. Last updated 5 months ago.
sequencingchipseqbatcheffectpeakdetection
9 stars 4.95 score 7 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
spatzie:Identification of enriched motif pairs from chromatin interaction data
Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments.
Maintained by Jennifer Hammelman. Last updated 5 months ago.
dna3dstructuregeneregulationpeakdetectionepigeneticsfunctionalgenomicsclassificationhictranscription
4.30 score 5 scriptsbioc
idr2d:Irreproducible Discovery Rate for Genomic Interactions Data
A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks.
Maintained by Konstantin Krismer. Last updated 5 months ago.
dna3dstructuregeneregulationpeakdetectionepigeneticsfunctionalgenomicsclassificationhic
4.30 score 6 scriptsbioc
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 3 months ago.
sequencingdnaseqgeneregulationpeakdetection
4.18 score 1 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
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
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
TRESS:Toolbox for mRNA epigenetics sequencing analysis
This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detect transcriptome wide m6A methylation regions 2. detect transcriptome wide differential m6A methylation regions.
Maintained by Zhenxing Guo. Last updated 5 months ago.
epigeneticsrnaseqpeakdetectiondifferentialmethylation
3.48 score 5 scripts 1 dependentsbioc
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
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 scripts