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
 .

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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

Accurate systems biology modeling requires a complete catalog of protein complexes and their constituent proteins. We discuss a graph-theoretic/statistical algorithm for local dynamic modeling of protein complexes using data from affinity purification-mass spectrometry experiments. The algorithm readily accommodates multicomplex membership by individual proteins and dynamic complex composition, two biological realities not accounted for in existing topological descriptions of the overall protein network. A likelihood-based objective function guides the protein complex modeling algorithm. With an accurate complex membership catalog in place, systems biology can proceed with greater precision.

Making sense of high-throughput protein-protein interaction data.

R is a powerful language and widely used software tool for the analysis and visualization of data. Its core capabilities can be extended through many different add-on packages. Among the many packages are some which offer a broad range of facilities for analyzing statistical properties of graphs. This chapter provides a practical tutorial covering the use of R methods for graphs and networks to examine biological data and analyze their topological and statistical properties.

Analyzing biological data using R: methods for graphs and networks.

Motivation: Proteins work together to drive biological processes in cellular machines. Summarizing global and local properties of the set of protein interactions, the interactome, is necessary for describing cellular systems. We consider a relatively simple per-protein feature of the interactome: the number of interaction partners for a protein, which in graph terminology is the degree of the protein.

Results: Using data subject to both stochastic and systematic sources of false positive and false negative observations, we develop an explicit probability model and resultant likelihood method to estimate node degree on portions of the interactome assayed by bait-prey technologies. This approach yields substantial improvement in degree estimation over the current practice that naively sums observed edges. Accurate modeling of observed data in relation to true but unknown parameters of interest gives a formal point of reference from which to draw conclusions about the system under study.

Availability: All analyses discussed in this text can be performed using the ppiStats and ppiData packages available through the Bioconductor project ( http://www.bioconductor.org).

Contact: dscholtens@northwestern.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/estimating-node-degree-in-bait-prey-graphs-1xz5gqgyo0.pdf

Estimating node degree in bait-prey graphs

Expression with the Bioconductor Project

Most computational tools for biologists preferably require data in large amounts. The larger the quantity of data, the more rigorous statistical analyses can support the discovery of new hypotheses for testing in a laboratory. A variety of technological developments during the past two decades have accelerated the rate of deposition of data into databases. Currently there are many public databases where data from, for example, DNA and protein sequences or 3D protein structures, and more complex information types, like ontologies, networks and pathways are deposited, maintained, annotated, curated and stored. Indeed, more recent efforts to store, for example, phenotype (in addition to genotypes) and clinical trials signify a new tendency to gather more complex data types. The data collected in these large public repositories represent valuable and significant resources for ongoing knowledge extraction. Mining of this data using computational tools is an increasingly indispensible part of modern research, and the organized storage of the data in databases is obligatory. Indeed such approaches are likely to have serious impact on the reproducibility of results. Resourceful tools for the establishment, interrogation, rearrangement, display and interpretation of new and large databases are frequently minor points in a publication and are relegated to brief statements in methods sections or in figure legends when the final work is published. However, there are often original and creative computational methods which resulted in these discoveries but which are not communicated in the scientific literature because the description of a database and the tools to interact with it are not deemed essential to the communication.

Accepting that the archiving, curation, analysis and understanding of all of this data is a challenge, DATABASE: the Journal of Biological Databases and Curation will publish articles which describe the construction of novel databases and the software tools designed to interact with these databases. All submissions should describe worthy resources for the scientific research endeavor. We also plan to invite reviews and tutorials that will make the databases described in these pages more user friendly and easier to match with the tasks that need to be accomplished. In addition, manuscripts that describe collections of data and associated tools where a biologically relevant discovery or example is presented will be reviewed more favorably. We would also be prepared to review opinions, discussions and/or demonstrations of how new technologies, new data models (or data exchange models) can be used to address complexities presented by the new large datasets and/or personal identification challenges the new initiatives are presenting. The journal will also accept update reports which describe new features and content of existing databases.

The maintenance and longevity (when appropriate) of databases is an ongoing point of discussion, and we welcome opinion pieces and the presentation of how such problems could best be addressed. Scalability and federation of a number of databases, the Web 2.0 and 3.0 integration and the semantic web are also pertinent discussions for the biological database community, and we hope that DATABASE: the Journal of Biological Databases and Curation becomes the place where some of these ideas are discussed and deliberated. We will provide online commenting and discussion tools on the journal's website to encourage this.

Extensive and ongoing curation of the biological data being stored in public databases ensures that these data can be discovered and used optimally, and facilitates the integration of information from multiple sources. Structured collection of metadata, using standard terminology, will foster more complex and relevant analyses. DATABASE: the Journal of Biological Databases and Curation invites the submission of novel strategies for the efficient and accurate curation of biological data, including systems to support ongoing curation by both individual researchers and research communities in order to ensure long-term availability and reusability of these data.

In support of the new open access policies of many funding agencies as well as the open source software movement which started in the 1980s, DATABASE: the Journal of Biological Databases and Curation will be a fully open access journal from launch. In addition, it will be a condition of publication that all databases and software described in DATABASE articles are made publicly available. The journal will be online-only, providing fast access of its full content to scientists worldwide.

Submissions to DATABASE: the Journal of Biological Databases and Curation are welcomed via the journal's web site at www.database.oxfordjournals.org. We also welcome suggestions for how this new forum can best serve the needs of the increasingly important field it represents.

Robert Gentleman

Papers

Making sense of high-throughput protein-protein interaction data.

Analyzing biological data using R: methods for graphs and networks.

Estimating node degree in bait-prey graphs

Expression with the Bioconductor Project

DATABASE: A new forum for biological databases and curation