High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.

/pdf/transcript-assembly-and-quantification-by-rna-seq-reveals-3e2dpnsxxg.pdf

Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 × 10 5 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices. The mRNA population specifies a cell’s identity and helps to govern its present and future activities. This has made transcriptome analysis a general phenotyping method, with expression microarrays of many kinds in routine use. Here we explore the possibility that transcriptome analysis, transcript discovery and transcript refinement can be done effectively in large and complex mammalian genomes by ultra-high-throughput sequencing. Expression microarrays are currently the most widely used methodology for transcriptome analysis, although some limitations persist. These include hybridization and cross-hybridization artifacts 1–3 , dye-based detection issues and design constraints that preclude or seriously limit the detection of RNA splice patterns and previously unmapped genes. These issues have made it difficult for standard array designs to provide full sequence comprehensiveness (coverage of all possible genes, including unknown ones, in large genomes) or transcriptome comprehensiveness (reliable detection of all RNAs of all prevalence classes, including the least abundant ones that are physiologically relevant). Other

/pdf/mapping-and-quantifying-mammalian-transcriptomes-by-rna-seq-1egh5oivst.pdf

Mapping and quantifying mammalian transcriptomes by RNA-Seq.

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.

https://www.cs.utexas.edu/~tandy/velvet.pdf

Velvet: Algorithms for de novo short read assembly using de Bruijn graphs

Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk

/pdf/bayesian-phylogenetics-with-beauti-and-the-beast-1-7-2xkb29qdvu.pdf

Bayesian Phylogenetics with BEAUti and the BEAST 1.7

Recent genome-wide analyses of alternative splicing indicate that 40-60% of human genes have alternative splice forms, suggesting that alternative splicing is one of the most significant components of the functional complexity of the human genome. Here we review these recent results from bioinformatics studies, assess their reliability and consider the impact of alternative splicing on biological functions. Although the 'big picture' of alternative splicing that is emerging from genomics is exciting, there are many challenges. High-throughput experimental verification of alternative splice forms, functional characterization, and regulation of alternative splicing are key directions for research. We recommend a community-based effort to discover and characterize alternative splice forms comprehensively throughout the human genome.

A genomic view of alternative splicing

Motivation: Progressive Multiple Sequence Alignment (MSA) methods depend on reducing an MSA to a linear profile for each alignment step. However, this leads to loss of information needed for accurate alignment, and gap scoring artifacts. Results: We present a graph representation of an MSA that can itself be aligned directly by pairwise dynamic programming, eliminating the need to reduce the MSA to a profile. This enables our algorithm (Partial Order Alignment (POA)) to guarantee that the optimal alignment of each new sequence versus each sequence in the MSA will be considered. Moreover, this algorithm introduces a new edit operator, homologous recombination, important for multidomain sequences. The algorithm has improved speed (linear time complexity) over existing MSA algorithms, enabling construction of massive and complex alignments (e.g. an alignment of 5000 sequences in 4 h on a Pentium II). We demonstrate the utility of this algorithm on a family of multidomain SH2 proteins, and on EST assemblies containing alternative splicing and polymorphism. Availability: The partial order alignment program POA is available at http://www.bioinformatics.ucla.edu/poa.

/pdf/multiple-sequence-alignment-using-partial-order-graphs-2rqe6c3wsm.pdf

Multiple sequence alignment using partial order graphs

We have identified 6201 alternative splice relationships in human genes, through a genome-wide analysis of expressed sequence tags (ESTs). Starting with ∼2.1 million human mRNA and EST sequences, we mapped expressed sequences onto the draft human genome sequence and only accepted splices that obeyed the standard splice site consensus. A large fraction (47%) of these were observed multiple times, indicating that they comprise a substantial fraction of the mRNA species. The vast majority of the detected alternative forms appear to be novel, and produce highly specific, biologically meaningful control of function in both known and novel human genes, e.g. specific removal of the lysosomal targeting signal from HLA-DM β chain, replacement of the C-terminal transmembrane domain and cytoplasmic tail in an FC receptor β chain homolog with a different transmembrane domain and cytoplasmic tail, likely modulating its signal transduction activity. Our data indicate that a large proportion of human genes, probably 42% or more, are alternatively spliced, but that this appears to be observed mainly in certain types of molecules (e.g. cell surface receptors) and systemic functions, particularly the immune system and nervous system. These results provide a comprehensive dataset for understanding the role of alternative splicing in the human genome, accessible at http://www.bioinformatics.ucla.edu/HASDB.

/pdf/genome-wide-detection-of-alternative-splicing-in-expressed-36r9tyk3d7.pdf

Genome-wide detection of alternative splicing in expressed sequences of human genes.

One of the most interesting opportunities in comparative genomics is to compare not only genome sequences but additional phenomena, such as alternative splicing, using orthologous genes in different genomes to find similarities and differences between organisms. Recently, genomics studies have suggested that 40-60% of human genes are alternatively spliced and have catalogued up to 30,000 alternative splice relationships in human genes. Here we report an analysis of 9,434 orthologous genes in human and mouse, which indicates that alternative splicing is associated with a large increase in frequency of recent exon creation and/or loss. Whereas most exons in the mouse and human genomes are strongly conserved in both genomes, exons that are only included in alternative splice forms (as opposed to the constitutive or major transcript form) are mostly not conserved and thus are the product of recent exon creation or loss events. A similar comparison of orthologous exons in rat and human validates this pattern. Although this says nothing about the complex question of adaptive benefit, it does indicate that alternative splicing in these genomes has been associated with increased evolutionary change.

Christopher Lee

Papers

A genomic view of alternative splicing

Multiple sequence alignment using partial order graphs

Genome-wide detection of alternative splicing in expressed sequences of human genes.

Alternative splicing in the human, mouse and rat genomes is associated with an increased frequency of exon creation and/or loss

Dynamic behavior of endoplasmic reticulum in living cells