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
2-http://bioconductor.org/packages/devel/bioc/html/isomiRs.html: 
<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/ 
<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?

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
> 
> Bioconductors:
> 
> 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
> 
> Contents
> --------
> 
> * 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 
> RNAseq.
> 
> 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 
> GUIDEseqAnalysis.
> 
> 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/, 
> https://github.com/geneticsMiNIng).
> 
> 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 KGML 
> files.
> 
> 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 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 
> (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 
> 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
> 
>  https://bioconductor.org/news/bioc_3_4_release/
> 
> Deprecated and Defunct Packages
> ===============================
> 
> 1 software package (betr) was marked as deprecated, to be removed in the next 
> release.
> 
> 17 previously deprecated software packages were removed from this release.
> 
> _______________________________________________
> Bioc-devel@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/bioc-devel


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