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.

MCbiclust Massive correlating biclusters for gene expression data and associated methods

Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set.

POST Projection onto Orthogonal Space Testing for High Dimensional Data

Perform orthogonal projection of high dimensional data of a set, and statistical modeling of phenotye with projected vectors as predictor.

discordant The Discordant Method: A Novel Approach for Differential Correlation

Discordant is a method to determine differential correlation of molecular feature pairs from -omics data using mixture models. Algorithm is explained further in Siska et al.

karyoploteR Karyotype plots with 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.

splatter Simple Simulation of Single-cell RNA Sequencing Data

Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented.

goSTAG A tool to use GO Subtrees to Tag and Annotate Genes within a set

Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster.

ChIPexoQual ChIPexoQual

Package with a quality control pipeline for ChIP-exo/nexus data.

DMRScan Detection of Differentially Methylated Regions

This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG.

funtooNorm Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit

Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type.

mimager mimager: The Microarray Imager

Easily visualize and inspect microarrays for spatial artifacts.

coseq Co-Expression Analysis of Sequencing Data

Gaussian and Poisson mixture models are implemented to cluster biological entities (e.g., genes) from high-throughput sequencing data. Parameter estimation is performed using the EM algorithm and model selection criteria (to choose the number of clusters) are provided.

TCseq Time course sequencing data analysis

Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of temporal patterns of time course data.

RJMCMCNucleosomes Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq)

This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.

treeio Base Classes and Functions for Phylogenetic Tree Input and Output

Base classes and functions for parsing and exporting phylogenetic trees.

scone Single Cell Overview of Normalized Expression data

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

samExploreR samExploreR package: high-performance read summarisation to count vectors with avaliability of sequencing depth reduction simulation

This R package is designed for subsampling procedure to simulate sequencing experiments with reduced sequencing depth. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA, HiSeq, MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. Was developed with usage of Rsubread.

scDD Mixture modeling of single-cell RNA-seq data to indentify genes with differential distributions

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

ramwas Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

RaMWAS provides a complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other methylomic data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net.

clusterSeq Clustering of high-throughput sequencing data by identifying co-expression patterns

Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples.

sparseDOSSA Sparse Data Observations for Simulating Synthetic Abundance

The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis.

BUMHMM Computational pipeline for computing probability of modification from structure probing experiment data

This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.

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 using generalized linear mixture models using weighted GC strategy, then applied into peak significance estimation.

RnaSeqGeneEdgeRQL Gene-level RNA-seq differential expression and pathway analysis using Rsubread and the edgeR quasi-likelihood pipeline

A workflow package for RNA-Seq experiments

chimeraviz Visualization tools for gene fusions

chimeraviz manages data from fusion gene finders and provides useful visualization tools.

DelayedArray Delayed operations on array-like objects

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames.

IWTomics Interval-Wise Testing for Omics Data

Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset.

GRridge Better prediction by use of co-data: Adaptive group-regularized ridge regression

This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC.

MWASTools MWASTools: an integrated pipeline to perform metabolome-wide association studies

MWAS provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY.

phosphonormalizer Compensates for the bias introduced by median normalization in phosphoproteomics

It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization.

BPRMeth Model higher-order methylation profiles

BPRMeth package uses the Binomial Probit Regression likelihood to model methylation profiles and extract higher order features. These features quantitate precisely notions of shape of a methylation profile. Using these higher order features across promoter-proximal regions, we construct a powerful predictor of gene expression. Also, these features are used to cluster proximal-promoter regions using the EM algorithm.

yarn YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization

Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.

fCCAC functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets

An application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq).

CCPROMISE PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data

Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented.

proFIA Preprocessing of FIA-HRMS data

Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced.

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.

regsplice Regularization-Based Methods for Detection of Differential Exon Usage

Statistical methods for detection of differential exon usage in RNA-seq and exon microarray data sets, using L1 regularization (lasso) to improve power.

StarBioTrek StarBioTrek

This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data.

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).

M3Drop Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

This package fits a Michaelis-Menten model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells.

meshes MeSH Enrichment and Semantic analyses

MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species.

annotatr Annotation of Genomic Regions to Genomic Annotations

Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations.

crisprseekplus crisprseekplus

Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis.

HelloRanges Introduce *Ranges to bedtools users

Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks.

MutationalPatterns Studying patterns in base substitution catalogues

An extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution data.

anamiR An integrated analysis package of miRNA and mRNA expression data

This package is intended to identify potential interactions of miRNA-target gene interactions from miRNA and mRNA expression data. It contains functions for statistical test, databases of miRNA-target gene interaction and functional analysis.

psichomics Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

Package with a Shiny-based graphical interface for the integrated analysis of alternative splicing data from The Cancer Genome Atlas (TCGA). This tool interactively performs survival, principal components and differential splicing analyses with direct incorporation of clinical features (such as tumour stage or survival) associated with TCGA samples.

MoonlightR Identify oncogenes and tumor suppressor genes from omics data

Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.

matter A framework for rapid prototyping with binary data on disk

Memory-efficient reading, writing, and manipulation of structured binary data on disk as vectors, matrices, and arrays. This package is designed to be used as a back-end for Cardinal for working with high-resolution mass spectrometry imaging data.

PathoStat PathoStat Statistical Microbiome Analysis Package

The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis.

MAST Model-based Analysis of Single Cell Transcriptomics

Methods and models for handling zero-inflated single cell assay data.

flowPloidy Analyze flow cytometer data to determine sample ploidy

Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards.

KEGGlincs Visualize all edges within a KEGG pathway and overlay LINCS data [option]

See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files.

geneXtendeR Optimal Gene Extensions From Histone Modification ChIP-seq Data

geneXtendeR is designed to optimally annotate a histone modification ChIP-seq peak input file with functionally important genomic features (e.g., genes associated with peaks) based on optimization calculations. geneXtendeR optimally extends the boundaries of every gene in a genome by some genomic distance (in DNA base pairs) for the purpose of flexibly incorporating cis-regulatory elements (CREs), such as enhancers and promoters, as well as downstream elements that are important to the function of the gene relative to an epigenetic histone modification ChIP-seq dataset. geneXtender computes optimal gene extensions tailored to the broadness of the specific epigenetic mark (e.g., H3K9me1, H3K27me3), as determined by a user-supplied ChIP-seq peak input file. As such, geneXtender maximizes the signal-to-noise ratio of locating genes closest to and directly under peaks. By performing a computational expansion of this nature, ChIP-seq reads that would initially not map strictly to a specific gene can now be optimally mapped to the regulatory regions of the gene, thereby implicating the gene as a potential candidate, and thereby making the ChIP-seq experiment more successful. Such an approach becomes particularly important when working with epigenetic histone modifications that have inherently broad peaks.

CancerInSilico An R interface for computational modeling of tumor progression

The CancerInSilico package provides an R interface for running mathematical models of tumor progresson. This package has the underlying models implemented in C++ and the output and analysis features implemented in R.

statTarget Statistical Analysis of Metabolite Profile

An easy to use tool provides a graphical user interface for quality control based shift signal correction, integration of metabolomic data from multi-batch experiments, and the comprehensive statistic analysis in non-targeted or targeted metabolomics.

DEsubs DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments

DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases.

GOpro Find the most characteristic gene ontology terms for groups of human genes

Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng).

SPLINTER Splice Interpreter Of Transcripts

SPLINTER provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments.

SIMLR SIMLR: Single-cell Interpretation via Multi-kernel LeaRning

Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical to identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. SIMLR is capable of separating known subpopulations more accurately in single-cell data sets than do existing dimension reduction methods. Additionally, SIMLR demonstrates high sensitivity and accuracy on high-throughput peripheral blood mononuclear cells (PBMC) data sets generated by the GemCode single-cell technology from 10x Genomics.

FitHiC Confidence estimation for intra-chromosomal contact maps

Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.

Pi Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene, Pathway and Network Level

Priority index or Pi is developed as a genomic-led target prioritisation system, with the focus on leveraging human genetic data to prioritise potential drug targets at the gene, pathway and network level. The long term goal is to use such information to enhance early-stage target validation. Based on evidence of disease association from genome-wide association studies (GWAS), this prioritisation system is able to generate evidence to support identification of the specific modulated genes (seed genes) that are responsible for the genetic association signal by utilising knowledge of linkage disequilibrium (co-inherited genetic variants), distance of associated variants from the gene, evidence of independent genetic association with gene expression in disease-relevant tissues, cell types and states, and evidence of physical interactions between disease-associated genetic variants and gene promoters based on genome-wide capture HiC-generated promoter interactomes in primary blood cell types. Seed genes are scored in an integrative way, quantifying the genetic influence. Scored seed genes are subsequently used as baits to rank seed genes plus additional (non-seed) genes; this is achieved by iteratively exploring the global connectivity of a gene interaction network. Genes with the highest priority are further used to identify/prioritise pathways that are significantly enriched with highly prioritised genes. Prioritised genes are also used to identify a gene network interconnecting highly prioritised genes and a minimal number of less prioritised genes (which act as linkers bringing together highly prioritised genes).

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.

bigmelon Illumina methylation array analysis for large experiments

Methods for working with Illumina arrays using gdsfmt.

synergyfinder Calculate and Visualize Synergy Scores for Drug Combinations

Efficient implementations for all the popular synergy scoring models for drug combinations, including HSA, Loewe, Bliss and ZIP and visualization of the synergy scores as either a two-dimensional or a three-dimensional interaction surface over the dose matrix.

BaalChIP BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples.

readat Functionality to Read and Manipulate SomaLogic ADAT files

This package contains functionality to import, transform and annotate data from ADAT files generated by the SomaLogic SOMAscan platform.

BiocWorkflowTools Tools to aid the development of Bioconductor Workflow packages

Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow.

signeR Empirical Bayesian approach to mutational signature discovery

The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variaton (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.

LINC co-expression of lincRNAs and protein-coding genes

This package provides methods to compute co-expression networks of lincRNAs and protein-coding genes. Biological terms associated with the sets of protein-coding genes predict the biological contexts of lincRNAs according to the 'Guilty by Association' approach.

gCrisprTools Suite of Functions for Pooled Crispr Screen QC and Analysis

Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting.

MetaboSignal MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways

MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes.

philr Phylogenetic partitioning based ILR transform for metagenomics data

PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure.

geneAttribution Identification of candidate genes associated with genetic variation

Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format.

YAPSA Yet Another Package for Signature Analysis

This package provides functions and routines useful in the analysis of somatic signatures (cf. L. Alexandrov et al., Nature 2013). In particular, functions to perform a signature analysis with known signatures (LCD = linear combination decomposition) and a signature analysis on stratified mutational catalogue (SMC = stratify mutational catalogue) are provided.

eegc Engineering Evaluation by Gene Categorization (eegc)

This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells.

Anaquin Statistical analysis of sequins

The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls.

alpine alpine

Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation.

PharmacoGx Analysis of Large-Scale Pharmacogenomic Data

Contains a set of functions to perform large-scale analysis of pharmacogenomic data.

GAprediction Prediction of gestational age with Illumina HumanMethylation450 data

[GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data.

SVAPLSseq SVAPLSseq-An R package to adjust for the hidden factors of variability in differential gene expression studies based on RNAseq data

The package contains functions that are intended for the identification of differentially expressed genes between two groups of samples from RNAseq data after adjusting for various hidden biological and technical factors of variability.

CancerSubtypes Cancer subtypes identification, validation and visualization based on genomic data

CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based on the genomic datasets.

GRmetrics Calculate growth-rate inhibition (GR) metrics

Functions for calculating and visualizing growth-rate inhibition (GR) metrics.

switchde Switch-like differential expression across single-cell trajectories

Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories.

CellMapper Predict genes expressed selectively in specific cell types

Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before.

IPO Automated Optimization of XCMS Data Processing parameters

The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models.

CVE Cancer Variant Explorer

Shiny app for interactive variant prioritisation in precision cancer medicine. The input file for CVE is the output file of the recently released Oncotator Variant Annotation tool summarising variant-centric information from 14 different publicly available resources relevant for cancer researches. Interactive priortisation in CVE is based on known germline and cancer variants, DNA repair genes and functional prediction scores. An optional feature of CVE is the exploration of the tumour-specific pathway context that is facilitated using co-expression modules generated from publicly available transcriptome data. Finally druggability of prioritised variants is assessed using the Drug Gene Interaction Database (DGIdb).

BayesKnockdown BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data

A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data.

recount Explore and download data from the recount project

Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://biorxiv.org/content/early/2016/08/08/068478.

ASpli Analysis of alternative splicing using RNA-Seq

Integrative pipeline for the analyisis of alternative splicing using RNAseq.

ctsGE Clustering of Time Series Gene Expression data

Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles.

MODA MODA: MOdule Differential Analysis for weighted gene co-expression network

MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes.

SNPediaR Query data from SNPedia

SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way.

GeneGeneInteR Tools for Testing Gene-Gene Interaction at the Gene Level

The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results.

ImpulseDE Detection of DE genes in time series data using impulse models

ImpulseDE is suited to capture single impulse-like patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes whether across time points in a single experiment or between two time courses from two experiments. To optimize the running time, the code makes use of clustering steps and multi-threading.

RCAS RNA Centric Annotation System

RCAS is an automated system that provides dynamic genome annotations for custom input files that contain transcriptomic regions. Such transcriptomic regions could be, for instance, peak regions detected by CLIP-Seq analysis that detect protein-RNA interactions, RNA modifications (alias the epitranscriptome), CAGE-tag locations, or any other collection of target regions at the level of the transcriptome. RCAS is designed as a reporting tool for the functional analysis of RNA-binding sites detected by high-throughput experiments. It takes as input a BED format file containing the genomic coordinates of the RNA binding sites and a GTF file that contains the genomic annotation features usually provided by publicly available databases such as Ensembl and UCSC. RCAS performs overlap operations between the genomic coordinates of the RNA binding sites and the genomic annotation features and produces in-depth annotation summaries such as the distribution of binding sites with respect to gene features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions, and whole transcripts). Moreover, by detecting the collection of targeted transcripts, RCAS can carry out functional annotation tables for enriched gene sets (annotated by the Molecular Signatures Database) and GO terms. As one of the most important questions that arise during protein-RNA interaction analysis; RCAS has a module for detecting sequence motifs enriched in the targeted regions of the transcriptome. A full interactive report in HTML format can be generated that contains interactive figures and tables that are ready for publication purposes.

ASAFE Ancestry Specific Allele Frequency Estimation

Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs.

rDGIdb R Wrapper for DGIdb

The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (http://dgidb.genome.wustl.edu/).

methylKit DNA methylation analysis from high-throughput bisulfite sequencing results

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Perl is needed to read SAM files only.

DeepBlueR DeepBlueR

Accessing the DeepBlue Epigenetics Data Server through R.

LOBSTAHS Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences

LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment.

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