Thanks to all the developers for your contribution to the project!
October 18, 2016
We are pleased to announce Bioconductor 3.4, consisting of 1294
software packages, 309 experiment data packages, and 933
up-to-date annotation packages.
There are 100 new software packages, and many updates and improvements
to existing packages; Bioconductor 3.4 is compatible with R 3.3,
and is supported on Linux, 32- and 64-bit Windows, and Mac OS X. This
release will include an updated Bioconductor Amazon Machine Image
and Docker containers.
Visit http://bioconductor.org for details and downloads.
* Getting Started with Bioconductor 3.4
* New Software Packages
* NEWS from new and existing packages
* Deprecated and Defunct Packages
Getting Started with Bioconductor 3.4
To update to or install Bioconductor 3.4:
1. Install R 3.3 (>= 3.3.1 recommended). Bioconductor 3.4 has been
designed expressly for this version of R.
2. Follow the instructions at http://bioconductor.org/install/
New Software Packages
There are 100 new software packages in this release of Bioconductor.
alpine - Fragment sequence bias modeling and correction for RNA-seq
transcript abundance estimation.
AMOUNTAIN- A pure data-driven gene network, weighted gene co-expression
network (WGCN) could be constructed only from expression profile.
Different layers in such networks may represent different time points,
multiple conditions or various species. AMOUNTAIN aims to search active
modules in multi-layer WGCN using a continuous optimization approach.
anamiR - 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.
Anaquin - 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.
annotatr - 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.
ASAFE - 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.
ASpli - Integrative pipeline for the analyisis of alternative splicing
BaalChIP - 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.
BayesKnockdown - 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.
bigmelon - Methods for working with Illumina arrays using gdsfmt.
bioCancer - bioCancer is a Shiny App to visualize and analyse
interactively Multi-Assays of Cancer Genomic Data.
BiocWorkflowTools - Provides functions to ease the transition between
Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow.
CancerInSilico - 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.
CancerSubtypes - 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.
ccmap - Finds drugs and drug combinations that are predicted to reverse
or mimic gene expression signatures. These drugs might reverse diseases
or mimic healthy lifestyles.
CCPROMISE - 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.
CellMapper - 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.
chromstaR - This package implements functions for combinatorial and
differential analysis of ChIP-seq data. It includes uni- and
multivariate peak-calling, export to genome browser viewable files, and
functions for enrichment analyses.
clusterExperiment - This package provides functions for running and
comparing many different clusterings of single-cell sequencing data.
covEB - Using bayesian methods to estimate correlation matrices assuming
that they can be written and estimated as block diagonal matrices. These
block diagonal matrices are determined using shrinkage parameters that
values below this parameter to zero.
covRNA - This package provides the analysis methods fourthcorner and RLQ
analysis for large-scale transcriptomic data.
crisprseekplus - Bioinformatics platform containing interface to work
with offTargetAnalysis and compare2Sequences in the CRISPRseek package,
crossmeta] - Implements cross-platform and cross-species meta-analyses
of Affymentrix, Illumina, and Agilent microarray data. This package
automates common tasks such as downloading, normalizing, and annotating
raw GEO data. A user interface makes it easy to select control and
treatment samples for each contrast and study. This input is used for
subsequent surrogate variable analysis (models unaccounted sources of
variation) and differential expression analysis. Final meta-analysis of
differential expression values can include genes measured in only a
subset of studies.
ctsGE - 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.
CVE - 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).
CytoML - This package is designed to use GatingML2.0 as the standard
format to exchange the gated data with other software platform.
DeepBlueR - Accessing the DeepBlue Epigenetics Data Server through R.
DEsubs - 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.
Director - Director is an R package designed to streamline the
visualization of molecular effects in regulatory cascades. It utilizes
library D3 to provide a fast and easy, browser-enabled solution to
discovering potentially interesting downstream effects of regulatory
and/or co-expressed molecules. The diagrams are robust, interactive, and
packaged as highly-portable HTML files that eliminate the need for
third-party software to view. This enables a straightforward approach
for scientists to interpret the data produced, and bioinformatics
developers an alternative means to present relevant data.
dSimer - dSimer is an R package which provides computation of nine
methods for measuring disease-disease similarity, including a standard
cosine similarity measure and eight function-based methods. The disease
similarity matrix obtained from these nine methods can be visualized
through heatmap and network. Biological data widely used in
disease-disease associations study are also provided by dSimer.
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.
esetVis - Utility functions for visualization of expressionSet (or
SummarizedExperiment) Bioconductor object, including spectral map, tsne
and linear discriminant analysis. Static plot via the ggplot2 package or
interactive via the ggvis or rbokeh packages are available.
ExperimentHub - This package provides a client for the Bioconductor
ExperimentHub web resource. ExperimentHub provides a central location
where curated data from experiments, publications or training courses
can be accessed. Each resource has associated metadata, tags and date of
modification. The client creates and manages a local cache of files
retrieved enabling quick and reproducible access.
ExperimentHubData - Functions to add metadata to ExperimentHub db and
resource files to AWS S3 buckets.
fCCAC - 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).
fgsea - The package implements an algorithm for fast gene set enrichment
analysis. Using the fast algorithm allows to make more permutations and
get more fine grained p-values, which allows to use accurate stantard
approaches to multiple hypothesis correction.
FitHiC - 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.
flowPloidy - 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.
FunChIP - Preprocessing and smoothing of ChIP-Seq peaks and efficient
implementation of the k-mean alignment algorithm to classify them.
GAprediction - [GAprediction] predicts gestational age using Illumina
HumanMethylation450 CpG data.
gCrisprTools - 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.
GEM - Tools for analyzing EWAS, methQTL and GxE genome widely.
geneAttribution - 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.
GeneGeneInteR - 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.
geneplast - Geneplast is designed for evolutionary and plasticity
analysis based on orthologous groups distribution in a given species
tree. It uses Shannon information theory and orthologs abundance to
estimate the Evolutionary Plasticity Index. Additionally, it implements
the Bridge algorithm to determine the evolutionary root of a given gene
based on its orthologs distribution.
geneXtendeR - 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.
GOpro - 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/,
GRmetrics- Functions for calculating and visualizing growth-rate
inhibition (GR) metrics.
HelloRanges - 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.
ImpulseDE - 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.
IPO - 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.
KEGGlincs - See what is going on 'under the hood' of KEGG pathways by
explicitly re-creating the pathway maps from information obtained from
LINC - 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.
LOBSTAHS - 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.
M3Drop - 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.
MADSEQ - The MADSEQ package provides a group of hierarchical Bayeisan
models for the detection of mosaic aneuploidy, the inference of the type
of aneuploidy and also for the quantification of the fraction of
aneuploid cells in the sample.
maftools - Analyze and visualize Mutation Annotation Format (MAF) files
from large scale sequencing studies. This package provides various
functions to perform most commonly used analyses in cancer genomics and
to create feature rich customizable visualzations with minimal effort.
MAST - Methods and models for handling zero-inflated single cell assay data.
matter - 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.
meshes - 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.
MetaboSignal - 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.
MetCirc - MetCirc comprises a workflow to interactively explore
metabolomics data: create MSP, bin m/z values, calculate similarity
between precursors and visualise similarities.
methylKit - 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.
MGFR - The package is designed to detect marker genes from RNA-seq data.
MODA - 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.
MoonlightR - 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
msPurity - Assess the contribution of the targeted precursor in
fragmentation acquired or anticipated isolation windows using a metric
called "precursor purity". Also provides simple processing steps
(averaging, filtering, blank subtraction, etc) for DI-MS data. Works for
both LC-MS(/MS) and DI-MS(/MS) data.
MultiAssayExperiment - Develop an integrative environment where multiple
assays are managed and preprocessed for genomic data analysis.
MutationalPatterns - An extensive toolset for the characterization and
visualization of a wide range of mutational patterns in base
netprioR - A model for semi-supervised prioritisation of genes
integrating network data, phenotypes and additional prior knowledge
about TP and TN gene labels from the literature or experts.
normr - Robust normalization and difference calling procedures for
ChIP-seq and alike data. Read counts are modeled jointly as a binomial
mixture model with a user-specified number of components. A fitted
background estimate accounts for the effect of enrichment in certain
regions and, therefore, represents an appropriate null hypothesis. This
robust background is used to identify significantly enriched or depleted
PathoStat - 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.
PharmacoGx - Contains a set of functions to perform large-scale analysis
of pharmacogenomic data.
philr - 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
Pi - 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, and evidence of independent genetic association with gene
expression in disease-relevant tissues, cell types and states. 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
Pigengene - Pigengene package provides an efficient way to infer
biological signatures from gene expression profiles. The signatures are
independent from the underlying platform, e.g., the input can be
microarray or RNA Seq data. It can even infer the signatures using data
from one platform, and evaluate them on the other. Pigengene identifies
the modules (clusters) of highly coexpressed genes using coexpression
network analysis, summarizes the biological information of each module
in an eigengene, learns a Bayesian network that models the probabilistic
dependencies between modules, and builds a decision tree based on the
expression of eigengenes.
proFIA - 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.
psichomics - Automatically retrieve data from RNA-Seq sources such as
The Cancer Genome Atlas or load your own files and process the data.
This tool allows you to analyse and visualise alternative splicing.
qsea - 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.
RCAS - 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.
rDGIdb - 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/).
readat - This package contains functionality to import, transform and
annotate data from ADAT files generated by the SomaLogic SOMAscan platform.
recount - 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
regsplice - Statistical methods for detection of differential exon usage
in RNA-seq and exon microarray data sets, using L1 regularization
(lasso) to improve power.
sights - SIGHTS is a suite of normalization methods, statistical tests,
and diagnostic graphical tools for high throughput screening (HTS)
assays. HTS assays use microtitre plates to screen large libraries of
compounds for their biological, chemical, or biochemical activity.
signeR - 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
SIMLR - 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.
SNPediaR - SNPediaR provides some tools for downloading and parsing data
from the SNPedia web site <http://www.snpedia.com>. 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.
SPLINTER - 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.
SRGnet - We developed SRMnet to analyze synergistic regulatory
mechanisms in transcriptome profiles that act to enhance the overall
cell response to combination of mutations, drugs or environmental
exposure. This package can be used to identify regulatory modules
downstream of synergistic response genes, prioritize synergistic
regulatory genes that may be potential intervention targets, and
contextualize gene perturbation experiments.
StarBioTrek - This tool StarBioTrek presents some methodologies to
measure pathway activity and cross-talk among pathways integrating also
the information of network data.
statTarget - An easy to use tool provide 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.
SVAPLSseq - 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.
switchde - Inference and detection of switch-like differential
expression across single-cell RNA-seq trajectories.
synergyfinder - 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.
TVTB - 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).
uSORT - 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.
yamss - 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.
YAPSA - 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.
yarn - 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.
NEWS from new and existing packages
There is too much NEWS to include here, see the full release
Deprecated and Defunct Packages
1 software package (betr) was marked as deprecated, to be removed in the
17 previously deprecated software packages were removed from this release.
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