Showing 4 of total 4 results (show query)
adrientaudiere
MiscMetabar:Miscellaneous Functions for Metabarcoding Analysis
Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. 'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' R packages. It helps to build reproducible and robust bioinformatics pipelines in R. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) <doi:10.21105/joss.06038>.
Maintained by Adrien Taudière. Last updated 11 days ago.
sequencingmicrobiomemetagenomicsclusteringclassificationvisualizationampliconamplicon-sequencingbiodiversity-informaticsecologyilluminametabarcodingngs-analysis
17 stars 6.44 score 23 scripts
bioc
DEWSeq:Differential Expressed Windows Based on Negative Binomial Distribution
DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data.
Maintained by bioinformatics team Hentze. Last updated 5 months ago.
sequencinggeneregulationfunctionalgenomicsdifferentialexpressionbioinformaticseclipngs-analysis
5 stars 5.30 score 4 scripts
tomkellygenetics
graphsim:Simulate Expression Data from 'igraph' Networks
Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.
Maintained by S. Thomas Kelly. Last updated 3 years ago.
benchmarkinggene-expressiongene-regulatory-networksgeneticsgenomic-data-analysisgenomicsgraph-algorithmsigraph-networksjossngs-analysissimulated-datasimulation-modeling
24 stars 5.08 score 2 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 9 days ago.
chipseqsequencingchiponchipdataimportrnaseqanalysischip-seqenriched-regionsgenome-analysismspcnext-generation-sequencingngs-analysisoverlapping-peakspeakpeaks
20 stars 4.26 score 5 scripts