This is a list of the last 100 packages added to Bioconductor and available in the development version of Bioconductor. The list is also available as an RSS Feed.

MGFM Marker Gene Finder in Microarray gene expression data

The package is designed to detect marker genes from Microarray gene expression data sets

simulatorZ Simulator for Collections of Independent Genomic Data Sets

simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSets and SummarizedExperiment objects.

MSnID Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications.

Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc.

GOexpress Visualise microarray and RNAseq data using gene ontology annotations

The package contains methods to visualise the expression levels of genes from a microarray or RNA-seq experiment and offers a clustering analysis to identify GO terms enriched in genes with expression levels best clustering predefined groups of samples. Annotations for the genes present in the expression dataset are obtained from Ensembl through the biomaRt package. The random forest framework is used to evaluate the ability of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. An ANOVA approach is also available as an alternative statistical framework.

EnrichmentBrowser Seamless navigation through combined results of set-based and network-based enrichment analysis

The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

systemPipeR systemPipeR: NGS workflow and report generation environment

R package for building end-to-end analysis pipelines with automated report generation for next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and many others. An important feature is support for running command-line software, such as NGS aligners, on both single machines or compute clusters. This includes both interactive job submissions or batch submissions to queuing systems of clusters.

seqplots SeqPlots - An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps.

SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations.

geecc Gene set Enrichment analysis Extended to Contingency Cubes

Use log-linear models to perform hypergeometric and chi-squared tests for gene set enrichments for two (based on contingency tables) or three categories (contingency cubes). Categories can be differentially expressed genes, GO terms, sequence length, GC content, chromosmal position, phylostrata, ....

GOsummaries Word cloud summaries of GO enrichment analysis

A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data.

TSCAN TSCAN: Tools for Single-Cell ANalysis

TSCAN enables users to easily construct and tune pseudotemporal cell ordering as well as analyzing differentially expressed genes. TSCAN comes with a user-friendly GUI written in shiny. More features will come in the future.

rain Rhythmicity Analysis Incorporating Non-parametric Methods

This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements.

FourCSeq Package analyse 4C sequencing data

FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/ to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated.

switchBox Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm.

The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle).

Rnits R Normalization and Inference of Time Series data

R/Bioconductor package for normalization, curve registration and inference in time course gene expression data

MethylMix MethylMix: Identifying methylation driven cancer genes.

MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression.

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.

MBASED Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection

The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE.

hiReadsProcessor Functions to process LM-PCR reads from 454/Illumina data.

hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification.

erccdashboard Assess Differential Gene Expression Experiments with ERCC Controls

Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures.

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.

quantro A test for when to use quantile normalization

A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups.

SemDist Information Accretion-based Function Predictor Evaluation

This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor.

RUVnormalize RUV for normalization of expression array data

RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis.

MPFE Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data.

Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate.

miRNAtap miRNAtap: microRNA Targets - Aggregated Predictions

The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation).

M3D Identifies differentially methylated regions across testing groups.

This package identifies statistically significantly differentially methylated regions of CpGs. It uses kernel methods (the Maximum Mean Discrepancy) to measure differences in methylation profiles, and relates these to inter-replicate changes, whilst accounting for variation in coverage profiles.

ClassifyR A framework for two-class classification problems, with applications to differential variability and differential distribution testing.

The software formalises a framework for classification in R. There are four stages. Data transformation, feature selection, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly does a couple of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, if they have better performing methods.

metagene A package to produce metagene plots

This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Pre-calculated features (such as transcription start sites of protein coding gene or enhancer) are available. Bam files are used to increase the resolution. Multiple combination of group of features and or group of bam files can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles.

metabomxtr A package to run mixture models for truncated metabolomics data with normal or lognormal distributions.

The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions.

interactiveDisplayBase Base package for enabling powerful shiny web displays of Bioconductor objects

The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects.

STAN STrand-specific ANnotation of genomic data

STAN (STrand-specic ANnotation of genomic data) implements bidirectional Hidden Markov Models (bdHMM), which are designed for studying directed genomic processes, such as gene transcription, DNA replication, recombination or DNA repair by integrating genomic data. bdHMMs model a sequence of successive observations (e.g. ChIP or RNA measurements along the genome) by a discrete number of 'directed genomic states', which e.g. reflect distinct genome-associated complexes. Unlike standard HMM approaches, bdHMMs allow the integration of strand-specific (e.g. RNA) and non strand-specific data (e.g. ChIP).

ballgown Flexible, isoform-level differential expression analysis

Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation.

missMethyl Analysis of methylation array data

Normalisation and testing for differential variability for data from Illumina's Infinium HumanMethylation450 array. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test.

OncoSimulR Simulation of cancer progresion with order restrictions

Functions for simulating and plotting cancer progression data, including drivers and passengers, and allowing for order restrictions. Simulations use continuous-time models (based on Bozic et al., 2010 and McFarland et al., 2013) and fitness functions account for possible restrictions in the order of accumulation of mutations.

netbiov A package for visualizing complex biological network

A package that provides an effective visualization of large biological networks

MultiMed Testing multiple biological mediators simultaneously

Implements permutation method with joint correction for testing multiple mediators

mQTL.NMR Metabolomic Quantitative Trait Locus Mapping for 1H NMR data

mQTL.NMR provides a complete mQTL analysis pipeline for 1H NMR data. Distinctive features include normalisation using most-used approaches, peak alignment using RSPA approach, dimensionality reduction using SRV and binning approaches, and mQTL analysis for animal and human cohorts.

MEIGOR MEIGO - MEtaheuristics for bIoinformatics Global Optimization

Global Optimization

GSReg Gene Set Regulation (GS-Reg)

A package for gene set analysis based on the variability of expressions. It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods.

shinyMethyl Interactive visualization for Illumina's 450k methylation arrays

Interactive tool for visualizing Illumina's 450k array data

Pviz Peptide Annotation and Data Visualization using Gviz

Pviz adapts the Gviz package for protein sequences and data.

pRolocGUI Interactive visualisation of spatial proteomics data

The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny.

Polyfit Add-on to DESeq to improve p-values and q-values

Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values.

pepStat Statistical analysis of peptide microarrays

Statistical analysis of peptide microarrays

oposSOM Comprehensive analysis of transciptome data

This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data.

flowDensity Sequential Flow Cytometry Data Gating

This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data.

DOQTL Genotyping and QTL Mapping in DO Mice

DOQTL is a quantitative trait locus (QTL) mapping pipeline designed for Diversity Outbred mice and other multi-parent outbred populations. The package reads in data from genotyping arrays and perform haplotype reconstruction using a hidden Markov model (HMM). The haplotype probabilities from the HMM are then used to perform linkage mapping. When founder sequences are available, DOQTL can use the haplotype reconstructions to impute the founder sequences onto DO genomes and perform association mapping.

riboSeqR Analysis of sequencing data from ribosome profiling experiments.

Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments.

Pbase Manipulating and exploring protein and proteomics data

A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment.

ssviz A small RNA-seq visualizer and analysis toolkit

Small RNA sequencing viewer

RGSEA Random Gene Set Enrichment Analysis

Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements.

DEGreport Report of DEG analysis

Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.

GSAR Gene Set Analysis in R

Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure.

RUVSeq Remove Unwanted Variation from RNA-Seq Data

This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

wavClusteR wavClusteR

Infer PAR-CLIP induced transitions and discriminate them from sequencing error, SNPs, contaminants and additional non-experimental causes, using a non-parametric mixture model. wavClusteR resolves cluster boundaries at high resolution and provides robust estimation of cluster statistics. In addition, the package allows to integrate RNA-Seq data to estimate FDR over the entire range of relative substitution frequencies. Furthermore, the package provides post-processing of results and functions to export results for UCSC genome browser visualization and motif search analysis. Key functions support parallel multicore computing. While wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other Next-Generation Sequencing data obtained from substitution inducing experimental procedures (e.g. BisSeq)

cosmiq cosmiq - COmbining Single Masses Into Quantities

cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step.

flowClean flowClean

A quality control tool for flow cytometry data based on compositional data analysis.

proteoQC An R package for proteomics data quality control

This package creates a HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data.

MethylAid Visual and interactive quality control of large Illumina 450k data sets

A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored.

DupChecker a package for checking high-throughput genomic data redundancy in meta-analysis

Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data.

MoPS MoPS - Model-based Periodicity Screening

Identification and characterization of periodic fluctuations in time-series data.

HDTD Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD)

Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables.

monocle Analysis tools for single-cell expression experiments.

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.

blima Package for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level.

Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing.

hiAnnotator Functions for annotating GRanges objects.

hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame.

S4Vectors S4 implementation of vectors and lists

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).

fastLiquidAssociation functions for genome-wide application of Liquid Association

This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses.

ChIPseeker ChIPseeker for ChIP peak Annotation, Comparison, and Visualization

This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes.

NetPathMiner NetPathMiner for Biological Network Construction, Path Mining and Visualization

NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation.

meshr Tools for conducting enrichment analysis of MeSH

A set of annotation maps describing the entire MeSH assembled using data from MeSH

MeSHDbi DBI to construct MeSH-related package from sqlite file.

The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (org.MeSH.XXX.db). loadMeSHDbiPkg import sqlite file and generate org.MeSH.XXX.db.

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.

gaucho Genetic Algorithms for Understanding Clonal Heterogeneity and Ordering

Use genetic algorithms to determine the relationship between clones in heterogenous populations such as cancer sequencing samples

RefNet A queryable collection of molecular interactions, from many sources

Molecular interactions with metadata, some archived, some dynamically obtained

compcodeR RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods

This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data and interfaces to several packages for performing the differential expression analysis.

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 prevalenece of clonal clusters in tumour whole genome sequencing data.

npGSEA Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA)

Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results.

Sushi Tools for visualizing genomics data

Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures

DMRforPairs DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles

DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance.

VariantFiltering Filtering of coding and non-coding genetic variants

Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minimum allele frequencies across human populations, splice site strength, conservation, etc.

CoverageView Coverage visualization package for R

This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome

metaMS MS-based metabolomics annotation pipeline

MS-based metabolomics data processing and compound annotation pipeline.

flowCL Semantic labelling of flow cytometric cell populations

Semantic labelling of flow cytometric cell populations.

BiocCheck Bioconductor-specific package checks

Bioconductor-specific package checks

Rariant Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies

The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples.

FRGEpistasis Epistasis Analysis for Quantitative Traits by Functional Regression Model

A Tool for Epistasis Analysis Based on Functional Regression Model

flowCyBar Analyze flow cytometric data using gate information

A package to analyze flow cytometric data using gate information to follow population/community dynamics

CompGO An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation

This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots.

ChIPQC Quality metrics for ChIPseq data

Quality metrics for ChIPseq data

ABSSeq ABSSeq: a new RNA-Seq analysis method based on absolute expression differences and generalized Poisson model

Inferring differential expression genes by absolute expression differences between two groups, utilizing generalized Poisson model to account for over-dispersion across samples and heterogeneity of differential expression across genes.

ASSIGN Adaptive Signature Selection and InteGratioN (ASSIGN)

ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literatures or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature.

nondetects Non-detects in qPCR data

Methods to model and impute non-detects in the results of qPCR experiments.

messina Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems.

Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.

viper Virtual Inference of Protein-activity by Enriched Regulon analysis

Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms

UNDO Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions

UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge.

sapFinder A package for variant peptides detection and visualization in shotgun proteomics.

sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics.

Rcpi Toolkit for Compound-Protein Interaction in Drug Discovery

The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery.

QDNAseq Quantitative DNA sequencing for chromosomal aberrations

Quantitative DNA sequencing for chromosomal aberrations.

rpx R Interface to the ProteomeXchange Repository

This package implements an interface to proteomics data submitted to the ProteomeXchange consortium.

MLSeq Machine learning interface for RNA-Seq data

This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART, to RNA-Seq data.

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