Colin N. Dewey

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

BackgroundRNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments.ResultsWe present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEMs ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene.ConclusionsRSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures

Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional element shows characteristic patterns of change, or ‘evolutionary signatures’, dictated by its precise selective constraints. Such signatures enable recognition of new protein-coding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures, including abundant stop-codon readthrough. Similarly, we predict non-protein-coding RNA genes and structures, and new microRNA (miRNA) genes. We provide evidence of miRNA processing and functionality from both hairpin arms and both DNA strands. We identify several classes of pre- and post-transcriptional regulatory motifs, and predict individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies.

RNA-Seq gene expression estimation with read mapping uncertainty

Motivation: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically. Results: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed. Availability: An initial C++ implementation of our method that was used for the results presented in this article is available at http://deweylab.biostat.wisc.edu/rsem. Contact: cdewey@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics on

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.

BUCKy: Gene tree/species tree reconciliation with Bayesian concordance analysis

MOTIVATION BUCKy is a C++ program that implements Bayesian concordance analysis. The method uses a non-parametric clustering of genes with compatible trees, and reconstructs the primary concordance tree from clades supported by the largest proportions of genes. A population tree with branch lengths in coalescent units is estimated from quartet concordance factors. AVAILABILITY BUCKy is open source and distributed under the GNU general public license at www.stat.wisc.edu/∼ane/bucky/.

Genomic Variation in Natural Populations of Drosophila melanogaster

This report of independent genome sequences of two natural populations of Drosophila melanogaster (37 from North America and 6 from Africa) provides unique insight into forces shaping genomic polymorphism and divergence. Evidence of interactions between natural selection and genetic linkage is abundant not only in centromere- and telomere-proximal regions, but also throughout the euchromatic arms. Linkage disequilibrium, which decays within 1 kbp, exhibits a strong bias toward coupling of the more frequent alleles and provides a high-resolution map of recombination rate. The juxtaposition of population genetics statistics in small genomic windows with gene structures and chromatin states yields a rich, high-resolution annotation, including the following: (1) 5′- and 3′-UTRs are enriched for regions of reduced polymorphism relative to lineage-specific divergence; (2) exons overlap with windows of excess relative polymorphism; (3) epigenetic marks associated with active transcription initiation sites overlap with regions of reduced relative polymorphism and relatively reduced estimates of the rate of recombination; (4) the rate of adaptive nonsynonymous fixation increases with the rate of crossing over per base pair; and (5) both duplications and deletions are enriched near origins of replication and their density correlates negatively with the rate of crossing over. Available demographic models of X and autosome descent cannot account for the increased divergence on the X and loss of diversity associated with the out-of-Africa migration. Comparison of the variation among these genomes to variation among genomes from D. simulans suggests that many targets of directional selection are shared between these species.

Evaluation of de novo transcriptome assemblies from RNA-Seq data

De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, we developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown. We show that RSEM-EVAL correctly reflects assembly accuracy, as measured by REF-EVAL, a refined set of ground-truth-based scores that we also developed. Guided by RSEM-EVAL, we assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly. A software package implementing our methods, DETONATE, is freely available at http://deweylab.biostat.wisc.edu/detonate.

Aligning Multiple Whole Genomes with Mercator and MAVID

The availability of an increasing number of whole genome sequences presents us with the need for tools to quickly put them into a nucleotide-level multiple alignment. Mercator and MAVID are two programs that can be combined to accomplish this task. Given multiple whole genomes as input, Mercator is first used to construct an orthology map, which is then used to guide nucleotide-level multiple alignments produced by MAVID. These programs are both fast and freely available, allowing researchers to perform genome alignments on a single laptop. This tutorial will guide the researcher through the steps required for whole-genome alignment with Mercator and MAVID.

Fine-Scale Phylogenetic Discordance across the House Mouse Genome

Population genetic theory predicts discordance in the true phylogeny of different genomic regions when studying recently diverged species. Despite this expectation, genome-wide discordance in young species groups has rarely been statistically quantified. The house mouse subspecies group provides a model system for examining phylogenetic discordance. House mouse subspecies are recently derived, suggesting that even if there has been a simple tree-like population history, gene trees could disagree with the population history due to incomplete lineage sorting. Subspecies of house mice also hybridize in nature, raising the possibility that recent introgression might lead to additional phylogenetic discordance. Single-locus approaches have revealed support for conflicting topologies, resulting in a subspecies tree often summarized as a polytomy. To analyze phylogenetic histories on a genomic scale, we applied a recently developed method, Bayesian concordance analysis, to dense SNP data from three closely related subspecies of house mice: Mus musculus musculus, M. m. castaneus, and M. m. domesticus. We documented substantial variation in phylogenetic history across the genome. Although each of the three possible topologies was strongly supported by a large number of loci, there was statistical evidence for a primary phylogenetic history in which M. m. musculus and M. m. castaneus are sister subspecies. These results underscore the importance of measuring phylogenetic discordance in other recently diverged groups using methods such as Bayesian concordance analysis, which are designed for this purpose.

Gata2 cis-element is required for hematopoietic stem cell generation in the mammalian embryo

Cis-element requirement for the emergence of HSCs in the AGM and for hemogenic endothelium to generate HSC-containing c-Kit+ cell clusters.