In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
 .

/pdf/moderated-estimation-of-fold-change-and-dispersion-for-rna-3ywnbkcan0.pdf

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/edgeR%20bioinformatics%202010.pdf

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

/pdf/limma-powers-differential-expression-analyses-for-rna-4yl7ae3arh.pdf

limma powers differential expression analyses for RNA-sequencing and microarray studies

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

/pdf/the-genome-analysis-toolkit-a-mapreduce-framework-for-4aygfa9b66.pdf

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.

/pdf/moderated-estimation-of-fold-change-and-dispersion-for-rna-59glcgpwlk.pdf

We address the problem of determining all sets which form minimal covers of maximal cliques for interval graphs. We produce an algorithm enumerating all minimal covers using the ⊂-minimal elements of the interval order, as well as an independence Metropolis sampler. We characterize maximal removable sets, which are the complements of minimal covers, and produce a distinct algorithm to enumerate them. We use this last characterization to provide bounds on the maximum number of minimal covers for an interval order with a given number of maximal cliques, and present some simulation results on the number of minimal covers in different settings. This work was supported in part by the National Sciences and Engineering Research Council of Canada, the Fonds quebecois de la recherche sur la nature et les technologies and the New Zealand Marsden Fund.

/pdf/minimal-covers-of-maximal-cliques-for-interval-graphs-54uhjqehpl.pdf

Minimal Covers of Maximal Cliques for Interval Graphs.

In this chapter we cover basic uses of R and begin working with Bioconductor datasets and tools. Topics covered include simple R programming, R graphics, and working with environments as hash tables. We introduce the ExpressionSet class as an example for a basic Bioconductor structure used for holding genomic data, in this case expression microarray data. And we explore some visualization techniques for gene expression data to get a feeling for R’s extensive graphical capabilities.

R and Bioconductor Introduction

Typically, in higher organisms, each chromosome has a centromere and two arms. The short arm is called the p arm and the longer arm the q arm. Chromosome bands (see Figure 1) are identified by differential staining, usually with Giemsa-based stains, and many disease-related defects have been mapped to these bands; such mappings have played an important role in classical cytogenetics. With the availability of complete sequences for several genomes, there have been efforts to link these bands with specific sequence locations (Furey and Haussler, 2003). The estimated location of the bands in the reference genomes can be obtained from the UCSC genome browser, and data linking genes to particular bands can be obtained from a variety of sources such as the NCBI. This vignette demonstrates tools that allow the use of categories derived from chromosome bands, that is, the relevant categories are determined a priori by a mapping of genes to chromosome bands. Figure 1 shows an ideogram of human chromosome 12, with the band 12q21 shaded. As shown in the figure, 12q21 can be divided into more granular levels 12q21.1, 12q21.2, and 12q21.3. 12q21.3 can itself be divided at an even finer level of resolution into 12q21.31, 12q21.32, and 12q21.33. Moving towards less granular bands, 12q21 is a part of 12q2 which is again a part of 12q. We take advantage of this nested structure of the bands in our analysis.

/pdf/using-categories-dened-by-chromosome-bands-5bv55has0w.pdf

Using Categories dened by Chromosome Bands

Addressing the accuracy of direct-to-consumer genetic testing.

Bioconductor is an open source initiative for the creation and dissemination of methods in statistical genomics and computational biology based on R. This article describes the requirements, language features, and methodology of design and development guiding the evolution of this project. Commitments to software interoperability, computable task-oriented documentation, and full transparency of algorithm development and use are found to be valuable in reducing barriers to access faced by statistical, computational, or biological researchers attempting interdisciplinary work. These commitments are expected to foster the propagation of standards of transparency and explicit reproducibility from wet-lab science, where they are well accepted, to in silico biology, where explicit reproduction of important published results is often very difficult.


Keywords:

computational biology;
open source software;
object-oriented programming;
documentation;
network algorithms;
software quality assurance;
reproducible research

Robert Gentleman

Papers

Minimal Covers of Maximal Cliques for Interval Graphs.

R and Bioconductor Introduction

Using Categories dened by Chromosome Bands

Addressing the accuracy of direct-to-consumer genetic testing.

Bioconductor: software and development strategies for statistical genomics