On 10/18/2016 06:07 PM, Lorena Pantano wrote:
Hi all,

I just checked my package and I see some issues, maybe something i did wrong :

1- http://bioconductor.org/packages/release/bioc/html/isomiRs.html 
<http://bioconductor.org/packages/release/bioc/html/isomiRs.html> : it says 
this is the development page, I guessed that this will go, but then if I go to
<http://bioconductor.org/packages/devel/bioc/html/isomiRs.html:> the page 
doesn’t exists
3-finaly: https://github.com/Bioconductor-mirror/isomiRs 
<https://github.com/Bioconductor-mirror/isomiRs> is in a different commit than 
<https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/isomiRs/> and didn’t get 
bump to the new numbers for this release.

Maybe the first two points will go (I see that happens to other packages), but 
the last one worry me, can someone from the team help me to know how to get the 
svn and bioconductor-mirror sync again?

The first two issues should be addressed for all packages.

The third issue may still be a problem for some packages (but not isomiRs), but this will be addressed in the next 24-48 hours.


Thanks a lot!

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[1]
and Docker containers[2].

Visit http://bioconductor.org[3] for details and downloads.

[1]: http://bioconductor.org/help/bioconductor-cloud-ami/
[2]: http://bioconductor.org/help/docker/
[3]: http://bioconductor.org


* 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 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 
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, 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 
and a modified Sankey plugin of the JavaScript 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 

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 
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 treatments.

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 
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 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 
prioritised genes).

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 
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 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 
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 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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