Bioconductor Project Research Articles

CorMut: an R/Bioconductor package for computing correlated mutations based on selection pressure

Correlated mutations constitute a fundamental idea in evolutionary biology, and understanding correlated mutations will, in turn, facilitate an understanding of the genetic mechanisms governing evolution. CorMut is an R package designed to compute correlated mutations in the unit of codon or amino acid mutation. Three classical methods were incorporated, and the computation results can be represented as correlation mutation networks. CorMut also enables the comparison of correlated mutations between two different evolutionary conditions. CorMut is released under the GNU General Public License within bioconductor project, and freely available at http://bioconductor.org/packages/release/bioc/html/CorMut.html.

H5vc: scalable nucleotide tallies with HDF5

Summary: As applications of genome sequencing, including exomes and whole genomes, are expanding, there is a need for analysis tools that are scalable to large sets of samples and/or ultra-deep coverage. Many current tool chains are based on the widely used file formats BAM and VCF or VCF-derivatives. However, for some desirable analyses, data management with these formats creates substantial implementation overhead, and much time is spent parsing files and collating data. We observe that a tally data structure, i.e. the table of counts of nucleotides × samples × strands × genomic positions, provides a reasonable intermediate level of abstraction for many genomics analyses, including single nucleotide variant (SNV) and InDel calling, copy-number estimation and mutation spectrum analysis. Here we present h5vc, a data structure and associated software for managing tallies. The software contains functionality for creating tallies from BAM files, flexible and scalable data visualization, data quality assessment, computing statistics relevant to variant calling and other applications. Through the simplicity of its API, we envision making low-level analysis of large sets of genome sequencing data accessible to a wider range of researchers.Availability and implementation: The package h5vc for the statistical environment R is available through the Bioconductor project. The HDF5 system is used as the core of our implementation.Contact: pyl@embl.de or whuber@embl.deSupplementary information: Supplementary data are available at Bioinformatics online.

Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools.Results: Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.Availability: pRoloc is implemented in the R language and available under an open-source license from the Bioconductor project (http://www.bioconductor.org/). A vignette with a complete tutorial describing data import/export and analysis is included in the package. Test data is available in the companion package pRolocdata.Contact: lg390@cam.ac.uk

EdgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology.

The edgeR package, an R-based tool within the Bioconductor project, offers a flexible statistical framework for detection of changes in abundance based on counts. In this chapter, we illustrate the use of edgeR on a human embryonic stem cell dataset, in particular for RNA-seq and ChIP-seq data. We focus on a step-by-step statistical analysis of differential expression, going from raw data to a list of putative differentially expressed genes and give examples of integrative analysis using the ChIP-seq data. We emphasize data quality spot checks and the use of positive controls throughout the process and give practical recommendations for reproducible research.

SupraHex: An R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map

Biologists are increasingly confronted with the challenge of quickly understanding genome-wide biological data, which usually involve a large number of genomic coordinates (e.g. genes) but a much smaller number of samples. To meet the need for data of this shape, we present an open-source package called ‘supraHex’ for training, analysing and visualising omics data. This package devises a supra-hexagonal map to self-organise the input data, offers scalable functionalities for post-analysing the map, and more importantly, allows for overlaying additional data for multilayer omics data comparisons. Via applying to DNA replication timing data of mouse embryogenesis, we demonstrate that supraHex is capable of simultaneously carrying out gene clustering and sample correlation, providing intuitive visualisation at each step of the analysis. By overlaying CpG and expression data onto the trained replication-timing map, we also show that supraHex is able to intuitively capture an inherent relationship between late replication, low CpG density promoters and low expression levels. As part of the Bioconductor project, supraHex makes accessible to a wide community in a simple way, what would otherwise be a complex framework for the ultrafast understanding of any tabular omics data, both scientifically and artistically. This package can run on Windows, Mac and Linux, and is freely available together with many tutorials on featuring real examples at http://supfam.org/supraHex.

A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments

BackgroundHigh-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user.ResultsHere we show that expression profiles produced by extensively-replicated RNA-seq experiments lead to a rich diversity of count data distributions beyond the Poisson and the negative binomial, such as Poisson-Inverse Gaussian or Pólya-Aeppli, which can be captured by a more general family of count data distributions called the Poisson-Tweedie. The flexibility of the Poisson-Tweedie family enables a direct fitting of emerging features of large expression profiles, such as heavy-tails or zero-inflation, without the need to alter a single configuration parameter. We provide a software package for R called tweeDEseq implementing a new test for differential expression based on the Poisson-Tweedie family. Using simulations on synthetic and real RNA-seq data we show that tweeDEseq yields P-values that are equally or more accurate than competing methods under different configuration parameters. By surveying the tiny fraction of sex-specific gene expression changes in human lymphoblastoid cell lines, we also show that tweeDEseq accurately detects differentially expressed genes in a real large RNA-seq data set with improved performance and reproducibility over the previously compared methodologies. Finally, we compared the results with those obtained from microarrays in order to check for reproducibility.ConclusionsRNA-seq data with many replicates leads to a handful of count data distributions which can be accurately estimated with the statistical model illustrated in this paper. This method provides a better fit to the underlying biological variability; this may be critical when comparing groups of RNA-seq samples with markedly different count data distributions. The tweeDEseq package forms part of the Bioconductor project and it is available for download at http://www.bioconductor.org.

RDAVIDWebService: a versatile R interface to DAVID

The RDAVIDWebService package provides a class-based interface from R programs/scripts to fully access/control the database for annotation, visualization and integrated discovery, without the need for human interaction on its Web site (http://david.abcc.ncifcrf.gov). The library enhances the database for annotation, visualization and integrated discovery capabilities for Gene Ontology analysis by means of GOstats-based direct acyclic graph conversion methods, in addition to the usual many-genes-to-many-terms visualization. RDAVIDWebService is available as an R package from the Bioconductor project (www.bioconductor.org) and on the authors' Web site (www.bdmg.com.ar) under GPL-2 license, subjected to the terms of use of DAVID (http://david.abcc.ncifcrf.gov/content.jsp?file=WS.html). cfresno@bdmg.com.ar or efernandez@bdmg.com.ar.

GeneExpressionSignature: an R package for discovering functional connections using gene expression signatures

Comparisons of gene expression signatures provide a way to explore functional connections among biological events in global aspects of cell response. GeneExpressionSignature is an R package developed for the large-scale analysis of gene expression signatures. The package implements two rank-merging algorithms and two similarity-scoring algorithms. The functions of GeneExpressionSignature provide a flexible solution for gene expression signature-based studies and hold great potential in biomedical research applications, such as drug repurposing. GeneExpressionSignature is released under GPL v2 within the Bioconductor project and is freely available at http://www.bioconductor.org/packages/release/bioc/html/GeneExpressionSignature.html .

GSVA: gene set variation analysis for microarray and RNA-seq data.

BackgroundGene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.ResultsTo address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments.ConclusionsGSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

Gene expression signatures of angiocidin and darapladib treatment connect to therapy options in cervical cancer

To assign functional properties to gene expression profiles of cervical cancer stages and identify clinically relevant biomarker genes. Microarray samples of 24 normal and 102 cervical cancer biopsies from four publicly available studies were pooled and evaluated. High-quality microarrays were normalized using the CONOR package from the Bioconductor project. Gene expression profiling was performed using variance-component analysis for accessing most reliable probes, which were subsequently processed by gene set enrichment analysis. Of 22.277 probes that were subject to variance-component analysis, eleven probes had low heterogeneity, that is, a W/T ratio between 0.18 and 0.38. Seven of these probes are induced in all cervical cancer stages: they are GINS1, PAK2, DTL, AURKA, PRKDC, NEK2 and CEP55. The other four probes are induced in normal cervix: P11, EMP1, UPK1A and HSPC159. We performed GSEA of 9.873 probes exhibiting less variability, that is, having a W/T ratio of <0.75. Repeatedly, significant gene expression signatures were found that are related to treatment using angiocidin and darapladib. Additionally, expression signatures from immunological disease signatures were found, for example graft versus host disease and acute kidney rejection. Another finding comprises a gene expression signature in stage IB2 that refers to MT1-MMP-dependent migration and invasion. This gene signature is accompanied by gene expression signatures which refer to ECM receptor-mediated interactions. Analysis of cervical cancer patient gene expression data reveals a novel perspective on HPV-mediated transcription processes. This novel point of view contains a better understanding and even might provide improvements to cancer therapy.

FRMA ST: frozen robust multiarray analysis for Affymetrix Exon and Gene ST arrays

Frozen robust multiarray analysis (fRMA) is a single-array preprocessing algorithm that retains the advantages of multiarray algorithms and removes certain batch effects by downweighting probes that have high between-batch residual variance. Here, we extend the fRMA algorithm to two new microarray platforms--Affymetrix Human Exon and Gene 1.0 ST--by modifying the fRMA probe-level model and extending the frma package to work with oligo ExonFeatureSet and GeneFeatureSet objects. All packages are implemented in R. Source code and binaries are freely available through the Bioconductor project. Convenient links to all software and data packages can be found at http://mnmccall.com/software mccallm@gmail.com.

Impaired Manganese Metabolism Causes Mitotic Misregulation

Manganese is an essential trace element, whose intracellular levels need to be carefully regulated. Mn(2+) acts as a cofactor for many enzymes and excess of Mn(2+) is toxic. Alterations in Mn(2+) homeostasis affect metabolic functions and mutations in the human Mn(2+)/Ca(2+) transporter ATP2C1 have been linked to Hailey-Hailey disease. By deletion of the yeast orthologue PMR1 we have studied the impact of Mn(2+) on cell cycle progression and show that an excess of cytosolic Mn(2+) alters S-phase transit, induces transcriptional up-regulation of cell cycle regulators, bypasses the need for S-phase cell cycle checkpoints and predisposes to genomic instability. On the other hand, we find that depletion of the Golgi Mn(2+) pool requires a functional morphology checkpoint to avoid the formation of polyploid cells.

ClusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

Ggbio: an R package for extending the grammar of graphics for genomic data

We introduce ggbio, a new methodology to visualize and explore genomics annotations and high-throughput data. The plots provide detailed views of genomic regions, summary views of sequence alignments and splicing patterns, and genome-wide overviews with karyogram, circular and grand linear layouts. The methods leverage the statistical functionality available in R, the grammar of graphics and the data handling capabilities of the Bioconductor project. The plots are specified within a modular framework that enables users to construct plots in a systematic way, and are generated directly from Bioconductor data structures. The ggbio R package is available at http://www.bioconductor.org/packages/2.11/bioc/html/ggbio.html.

BeadArray Expression Analysis Using Bioconductor

Illumina whole-genome expression BeadArrays are a popular choice in gene profiling studies. Aside from the vendor-provided software tools for analyzing BeadArray expression data (GenomeStudio/BeadStudio), there exists a comprehensive set of open-source analysis tools in the Bioconductor project, many of which have been tailored to exploit the unique properties of this platform. In this article, we explore a number of these software packages and demonstrate how to perform a complete analysis of BeadArray data in various formats. The key steps of importing data, performing quality assessments, preprocessing, and annotation in the common setting of assessing differential expression in designed experiments will be covered.

MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation

MSnbase is an R/Bioconductor package for the analysis of quantitative proteomics experiments that use isobaric tagging. It provides an exploratory data analysis framework for reproducible research, allowing raw data import, quality control, visualization, data processing and quantitation. MSnbase allows direct integration of quantitative proteomics data with additional facilities for statistical analysis provided by the Bioconductor project. MSnbase is implemented in R (version ≥ 2.13.0) and available at the Bioconductor web site (http://www.bioconductor.org/). Vignettes outlining typical workflows, input/output capabilities and detailing underlying infrastructure are included in the package.

InSilicoDb: an R/Bioconductor package for accessing human Affymetrix expert-curated datasets from GEO

Microarray technology has become an integral part of biomedical research and increasing amounts of datasets become available through public repositories. However, re-use of these datasets is severely hindered by unstructured, missing or incorrect biological samples information; as well as the wide variety of preprocessing methods in use. The inSilicoDb R/Bioconductor package is a command-line front-end to the InSilico DB, a web-based database currently containing 86 104 expert-curated human Affymetrix expression profiles compiled from 1937 GEO repository series. The use of this package builds on the Bioconductor project's focus on reproducibility by enabling a clear workflow in which not only analysis, but also the retrieval of verified data is supported.

Thawing Frozen Robust Multi-array Analysis (fRMA)

BackgroundA novel method of microarray preprocessing - Frozen Robust Multi-array Analysis (fRMA) - has recently been developed. This algorithm allows the user to preprocess arrays individually while retaining the advantages of multi-array preprocessing methods. The frozen parameter estimates required by this algorithm are generated using a large database of publicly available arrays. Curation of such a database and creation of the frozen parameter estimates is time-consuming; therefore, fRMA has only been implemented on the most widely used Affymetrix platforms.ResultsWe present an R package, frmaTools, that allows the user to quickly create his or her own frozen parameter vectors. We describe how this package fits into a preprocessing workflow and explore the size of the training dataset needed to generate reliable frozen parameter estimates. This is followed by a discussion of specific situations in which one might wish to create one's own fRMA implementation. For a few specific scenarios, we demonstrate that fRMA performs well even when a large database of arrays in unavailable.ConclusionsBy allowing the user to easily create his or her own fRMA implementation, the frmaTools package greatly increases the applicability of the fRMA algorithm. The frmaTools package is freely available as part of the Bioconductor project.

Mannan oligosaccharide modulates gene expression profile in pigs experimentally infected with porcine reproductive and respiratory syndrome virus12

This study characterized gene expression in peripheral blood mononuclear cells (PBMC) and bronchoalveolar lavage fluid (BALF) cells from control- or mannan oligosaccharide (MOS)-fed pigs with or without porcine reproductive and respiratory syndrome virus (PRRSV) at d 7 postinfection (PI). Weaned pigs (3 wk old) fed 0 or 0.2% MOS (Bio-Mos) diets were intranasally inoculated with PRRSV or a sterile medium at 5 wk of age. Total RNA (3 pigs/treatment) was extracted from cells. Double-stranded cDNA was amplified, labeled, and further hybridized to the Affymetrix GeneChip Porcine Genome Array consisting of 23,937 probe sets representing 20,201 genes. Microarray data were analyzed in R using packages from the Bioconductor project. Differential gene expression was tested by fitting a mixed linear model equivalent to a 2 × 2 factorial ANOVA using the limma package. Dietary MOS and PRRSV changed the expression of thousands of probe sets in PBMC and BALF cells (P < 0.05). The MOS × PRRSV interaction altered the expression of more nonimmune probe sets in PBMC (977 up, 1,128 down) than in BALF cells (117 up, 78 down). The MOS × PRRSV interaction (P < 0.05) for immune probe sets in PBMC affected genes encoding key inflammatory mediators. In uninfected pigs, gene expression of IL-1α, IL-6, myeloid differentiation factor 88, Toll-like receptor (TLR) 4, major histocompatibility complex (MHC) II, and dead box polypeptide 58 increased in PBMC of MOS-fed pigs (P < 0.05). This suggests that MOS enhances disease resistance in pigs and supports the fact that MOS induced a rapid increase in leukocytes at d 3 and 7 PI. Within infected pigs, however, MOS reduced the expression of IL-1β, IL-6, IL-8, macrophage inflammatory protein (MIP)-1α, MIP-1β, monocyte chemotactic protein (MCP)-1, and TLR4 genes in PBMC (P < 0.05). This finding may explain why fever was ameliorated in infected pigs fed MOS by d 7 PI. The expression of IL-1β, IL-6, MIP-1β, MCP-1, and TLR4 genes was confirmed by quantitative real-time reverse-transcription PCR. In BALF cells of infected pigs, MOS reduced the gene expression of TLR4, MHCII, and molecules associated with the complement system, but increased the gene expression of MHCI. In short, MOS regulated the expression of nonimmune and immune genes in pig leukocytes, perhaps providing benefits by enhancing the immune responses of the pigs to an infection, while preventing overstimulation of the immune system.