On Oct 18, 2016, at 5:21 PM, Hervé Pagès <hpa...@fredhutch.org> wrote:
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
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
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 using
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
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
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
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
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, and
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 the R package htmltools
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
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
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
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 KGML
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
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
MetCirc - MetCirc comprises a workflow to interactively explore metabolomics
data: create MSP, bin m/z values, calculate similarity between precursors and
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
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 substitution data.
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 regions.
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
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 reference measure.
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
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 http://biorxiv.org/content/early/2016/08/08/068478.
regsplice - Statistical methods for detection of differential exon usage in
RNA-seq and exon microarray data sets, using L1 regularization (lasso) to
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 provided.
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
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 announcement at
Deprecated and Defunct Packages
1 software package (betr) was marked as deprecated, to be removed in the next
17 previously deprecated software packages were removed from this release.
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