Showing 10 of total 10 results (show query)
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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
11.0 match 1 stars 5.02 score 2 scriptsbioc
motifTestR:Perform key tests for binding motifs in sequence data
Taking a set of sequence motifs as PWMs, test a set of sequences for over-representation of these motifs, as well as any positional features within the set of motifs. Enrichment analysis can be undertaken using multiple statistical approaches. The package also contains core functions to prepare data for analysis, and to visualise results.
Maintained by Stevie Pederson. Last updated 8 days ago.
motifannotationchipseqchiponchipsequencematchingsoftware
11.0 match 1 stars 4.90 score 2 scriptsbioc
epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Maintained by Xiaosai Yao. Last updated 6 days ago.
generegulationnetworkgeneexpressiontranscriptionchiponchipdifferentialexpressiongenetargetnormalizationgraphandnetwork
11.0 match 4.90 score 10 scriptsbioc
epidecodeR:epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation
epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes.
Maintained by Kandarp Joshi. Last updated 5 months ago.
differentialexpressiongeneregulationhistonemodificationfunctionalpredictiontranscriptiongeneexpressionepitranscriptomicsepigeneticsfunctionalgenomicssystemsbiologytranscriptomicschiponchipdifferential-expressiongenomicsgenomics-visualization
11.0 match 5 stars 4.70 score 1 scriptsbioc
puma:Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0)
Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions.
Maintained by Xuejun Liu. Last updated 5 months ago.
microarrayonechannelpreprocessingdifferentialexpressionclusteringexonarraygeneexpressionmrnamicroarraychiponchipalternativesplicingdifferentialsplicingbayesiantwochanneldataimporthta2.0
11.0 match 4.53 score 17 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
11.0 match 4.38 score 12 scriptsbioc
rmspc:Multiple Sample Peak Calling
The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples.
Maintained by Meriem Bahda. Last updated 19 days ago.
chipseqsequencingchiponchipdataimportrnaseqanalysischip-seqenriched-regionsgenome-analysismspcnext-generation-sequencingngs-analysisoverlapping-peakspeakpeaks
11.0 match 20 stars 4.08 score 5 scriptsbioc
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
11.0 match 4.03 score 54 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
11.0 match 9 stars 3.95 score 5 scriptsbioc
qsea:IP-seq data analysis and vizualization
qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples.
Maintained by Matthias Lienhard. Last updated 5 months ago.
sequencingdnamethylationcpgislandchipseqpreprocessingnormalizationqualitycontrolvisualizationcopynumbervariationchiponchipdifferentialmethylation
11.0 match 3.30 score 7 scripts