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

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

/pdf/fast-gapped-read-alignment-with-bowtie-2-2axet5ses2.pdf

Fast gapped-read alignment with Bowtie 2

Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

/pdf/star-ultrafast-universal-rna-seq-aligner-r1vlt61g3c.pdf

STAR: ultrafast universal RNA-seq aligner

http://bartellab.wi.mit.edu/publication_reprints/Bartel_Cell09.pdf

MicroRNAs: Target Recognition and Regulatory Functions

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

Motivation: Read assignment is an important first step in many metagenomic analysis workflows, providing the basis for identification and quantification of species. However ambiguity among the sequences of many strains makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to taxonomic levels where they are unambiguous. We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data in order to develop novel methods for rapid and accurate quantification of metagenomic strains. 

Results: We find that the recent idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics setting. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software, making it possible and practical for the first time to analyze abundances of individual genomes in metagenomics projects.

/pdf/pseudoalignment-for-metagenomic-read-assignment-vu84wde73w.pdf

Pseudoalignment for metagenomic read assignment

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

/pdf/parametric-inference-for-biological-sequence-analysis-2hsytjh6yl.pdf

Parametric inference for biological sequence analysis

Nat. Protoc. 7, 562–578 (2012); doi: 10.1038/nprot.2012.016; published online 1 March 2012; corrected after print 7 August 2014 In the version of this article initially published, the computer script in Box 1 sections B and C, and in Procedure Step 1, contained errors: the last section of the final three lines of the script had 'C1' where it should have been 'C2', as follows:

/pdf/corrigendum-differential-gene-and-transcript-expression-2p4hc05grs.pdf

Corrigendum: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

: The optimal timing of air-to-ground tasks is undertaken. Specifically, a scenario where multiple air vehicles are required to prosecute geographically dispersed targets is considered. The vehicles must perform multiple tasks on each target. The targets must be found, classified, attacked, and verified as destroyed. The optimal performance of these tasks requires cooperation amongst the vehicles such that critical timing constraints are satisfied. In this paper, an optimal task assignment and timing algorithm is developed, using a mixed integer linear program, or MILP, formulation. MILP can be used to assign all tasks to the vehicles in an optimal manner, including variable arrival times, for groups of air vehicles with coupled tasks involving timing and task order constraints. When the air vehicles have sufficient endurance, the existence of a solution is guaranteed.

/pdf/uav-task-assignment-with-timing-constraints-via-mixed-43hgcixyrw.pdf

UAV Task Assignment with Timing Constraints via Mixed-Integer Linear Programming

Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene finding and annotation. Alignment problems can be solved with pair HMMs, while gene finding programs rely on generalized HMMs in order to model exon lengths. In this paper we introduce the generalized pair HMM (GPHMM), which is an extension of both pair and generalized HMMs. We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene finding, and describe applications to DNA-cDNA and DNA-protein alignment. GPHMMs provide a unifying and probabilistically sound theory for modeling these problems.

/pdf/applications-of-generalized-pair-hidden-markov-models-to-1zzsjg2o31.pdf

Lior Pachter

Papers

Pseudoalignment for metagenomic read assignment

Parametric inference for biological sequence analysis

Corrigendum: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

UAV Task Assignment with Timing Constraints via Mixed-Integer Linear Programming

Applications of generalized pair hidden Markov models to alignment and gene finding problems