Yearly Archives: 2014

Genome-wide Identification of Zero Nucleotide Recursive Splicing in Drosophila

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Genome-wide Identification of Zero Nucleotide Recursive Splicing in Drosophila

Michael O Duff, Sara Olson, Xintao Wei, Ahmad Osman, Alex Plocik, Mohan Bolisetty, Susan Celniker, Brenton Graveley

Recursive splicing is a process in which large introns are removed in multiple steps by resplicing at ratchet points – 5? splice sites recreated after splicing. Recursive splicing was first identified in the Drosophila Ultrabithorax (Ubx) gene and only three additional Drosophila genes have since been experimentally shown to undergo recursive splicing. Here, we identify 196 zero nucleotide exon ratchet points in 130 introns of 115 Drosophila genes from total RNA sequencing data generated from developmental time points, dissected tissues, and cultured cells. Recursive splicing events were identified by splice junctions that map to annotated 5? splice sites and unannotated intronic 3? splice sites, the presence of the sequence AG/GT at the 3? splice site, and a 5? to 3? gradient of decreasing RNA-Seq read density indicative of co-transcriptional splicing. The sequential nature of recursive splicing was confirmed by identification of lariat introns generated by splicing to and from the ratchet points. We also show that recursive splicing is a constitutive process, and that the sequence and function of ratchet points are evolutionarily conserved. Together these results indicate that recursive splicing is commonly used in Drosophila and provides insight into the mechanisms by which some introns are removed.

Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels.

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Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels.

Nicholas E Banovich, Xun Lan, Graham McVicker, Bryce Van de Geijn, Jacob F Degner, John D. Blischak, Jonathan K. Pritchard, Yoav Gilad

DNA methylation is an important epigenetic regulator of gene expression. Recent studies have revealed widespread associations between genetic variation and methylation levels. However, the mechanistic links between genetic variation and methylation remain unclear. To begin addressing this gap, we collected methylation data at ~300,000 loci in lymphoblastoid cell lines (LCLs) from 64 HapMap Yoruba individuals, and genome-wide bisulfite sequence data in ten of these individuals. We identified (at an FDR of 10%) 11,752 methylation QTLs (meQTLs)?i.e., loci in which genetic variation is associated with changes in DNA methylation. We found that meQTLs are frequently associated with changes in methylation at multiple CpGs across regions of up to 3 kb. Interestingly, meQTLs are also frequently associated with variation in other properties of gene regulation, including histone modifications, DNase I accessibility, chromatin accessibility, and expression levels of nearby genes. These observations suggest that genetic variants may lead to coordinated molecular changes in all of these regulatory phenotypes. One plausible driver of coordinated changes in different regulatory mechanisms is variation in transcription factor (TF) binding. Indeed, we found that SNPs that change predicted TF binding affinities are significantly enriched for associations with DNA methylation at nearby CpGs. Taken together, our observations are consistent with a model whereby changes in TF binding may frequently drive coordinated changes in DNA methylation, histone modification, and gene expression levels.

Validation of methods for Low-volume RNA-seq

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Validation of methods for Low-volume RNA-seq

Peter Acuña Combs, Michael B Eisen

Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in metrics of interest to us than a more standard protocol, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample

Reducing INDEL errors in whole-genome and exome sequencing

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Reducing INDEL errors in whole-genome and exome sequencing

Han Fang, Giuseppe Narzisi, Jason A. O’Rawe, Yiyang Wu, Julie Rosenbaum, Michael Ronemus, Ivan Iossifov, Michael C. Schatz, Gholson J. Lyon

Background INDELs, especially those disrupting protein-coding regions of the genome, have been associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts. We have recently developed a new INDEL-calling algorithm, Scalpel, with substantially improved accuracy. Results We characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate false-positive and false-negative INDEL errors. We developed a classification scheme utilizing validation data to define a class of low-quality INDELs with ~2.7-fold higher error rates than high-quality INDELs. The mean concordance of INDEL detection between WGS and WES data was ~52%, while WGS data uniquely identified ~10.8-fold more high-quality INDELs. Concordance of INDEL detection between standard and PCR-free sequencing data was ~71%, while PCR-free data uniquely yielded ~6.3-fold fewer low-quality INDELs. We demonstrate that these INDEL errors are significantly reduced with a PCR-free library protocol, implying that these errors are introduced with PCR amplification. We calculated that 60X WGS data from the HiSeq 2000 platform are needed to recover ~95% of INDELs, much higher than that for SNP detection. Accurate detection of heterozygous INDELs requires ~1.2-fold higher coverage than that for homozygous INDELs. Conclusions Homopolymer A/T INDELs are a major source of low quality and/or uncertain INDEL calls, and these are highly enriched in the WES data. We recommend WGS for human genomes at 60X mean coverage with PCR-free protocols, which can substantially improve the quality of personal genomes.

Natural selection helps explain the small range of genetic variation within species

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Natural selection helps explain the small range of genetic variation within species

Russell B. Corbett-Detig, Daniel L. Hartl, Timothy B. Sackton

The range of genetic diversity observed within natural populations is much more narrow than expected based on models of neutral molecular evolution. Although the increased efficacy of natural selection in larger populations has been invoked to explain this paradox, to date no tests of this hypothesis have been conducted. Here, we present an analysis of whole-genome polymorphism data and genetic maps from 39 species to estimate for each species the reduction in genetic variation attributable to the operation of natural selection on the genome. We find that species with larger population sizes do in fact show greater reductions in genetic variation. This finding provides the first experimental support for the hypothesis that natural selection contributes to the restricted range of within-species genetic diversity.

Recombination impacts damaging and disease mutations accumulation in human populations

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Recombination impacts damaging and disease mutations accumulation in human populations

Julie Hussin, Alan Hodgkinson, Youssef Idaghdour, Jean-Christophe Grenier, Jean-Philippe Goulet, Elias Gbeha, Elodie Hip-Ki, Philip Awadalla

Many decades of theory have demonstrated that in non-recombining systems, slightly deleterious mutations accumulate non-reversibly, potentially driving the extinction of many asexual species. Non-recombining chromosomes in sexual organisms are thought to have degenerated in a similar fashion, however it is not clear the extent to which these processes operate along recombining chromosomes with highly variable rates of crossing over. Using high coverage sequencing data from over 1400 individuals, we show that recombination rate modulates the genomic distribution of putatively deleterious variants across the entire human genome. We find that exons in regions of low recombination are significantly enriched for deleterious and disease variants, a signature that varies in strength across worldwide human populations with different demographic histories. As low recombining regions are enriched for highly conserved genes with essential cellular functions and show an excess of mutations with demonstrated effect on health, this phenomenon likely affects disease susceptibility in humans.

Transcriptomic analysis of the lesser spotted catshark (Scyliorhinus canicula) pancreas, liver and brain reveals molecular level conservation of vertebrate pancreas function

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Transcriptomic analysis of the lesser spotted catshark (Scyliorhinus canicula) pancreas, liver and brain reveals molecular level conservation of vertebrate pancreas function

John F Mulley, Adam D Hargreaves, Matthew J Hegarty, R. Scott Heller, Martin T Swain

Background Understanding the evolution of the vertebrate pancreas is key to understanding its functions. The chondrichthyes (cartilaginous fish such as sharks and rays) have been suggested to possess the most ancient example of a distinct pancreas with both hormonal (endocrine) and digestive (exocrine) roles, although the lack of genetic, genomic and transcriptomic data for cartilaginous fish has hindered a more thorough understanding of the molecular-level functions of the chondrichthyan pancreas, particularly with respect to their “unusual” energy metabolism (where ketone bodies and amino acids are the main oxidative fuel source) and their paradoxical ability to both maintain stable blood glucose levels and tolerate extensive periods of hypoglycemia. In order to shed light on some of these processes we have carried out the first large-scale comparative transcriptomic survey of multiple cartilaginous fish tissues: the pancreas, brain and liver of the lesser spotted catshark, Scyliorhinus canicula. Results We generated a mutli-tissue assembly comprising 86,006 contigs, of which 44,794 were assigned to a particular tissue or combination of tissue based on mapping of sequencing reads. We have characterised transcripts encoding genes involved in insulin regulation, glucose sensing, transcriptional regulation, signaling and digestion, as well as many peptide hormone precursors and their receptors for the first time. Comparisons to published mammalian pancreas transcriptomes reveals that mechanisms of glucose sensing and insulin regulation used to establish and maintain a stable internal environment are conserved across jawed vertebrates and likely pre-date the vertebrate radiation. Conservation of pancreatic hormones and genes encoding digestive proteins support the single, early evolution of a distinct pancreatic gland with endocrine and exocrine functions in vertebrates, although the peptide diversity of the early vertebrate pancreas has been overestimated as a result of the use of cross-reacting antisera in earlier studies. A three hormone islet organ is therefore the basal vertebrate condition, later elaborated upon only in the tetrapod lineage. Conclusions The cartilaginous fish are a great untapped resource for the reconstruction of patterns and processes of vertebrate evolution and new approaches such as those described in this paper will greatly facilitate their incorporation into the rank of “model organism”.

iRAP – an integrated RNA-seq Analysis Pipeline

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iRAP – an integrated RNA-seq Analysis Pipeline

Nuno A. Fonseca, Robert Petryszak, John Marioni, Alvis Brazma

RNA-sequencing (RNA-Seq) has become the technology of choice for whole-transcriptome profiling. However, processing the millions of sequence reads generated requires considerable bioinformatics skills and computational resources. At each step of the processing pipeline many tools are available, each with specific advantages and disadvantages. While using a specific combination of tools might be desirable, integrating the different tools can be time consuming, often due to specificities in the formats of input/output files required by the different programs. Here we present iRAP, an integrated RNA-seq analysis pipeline that allows the user to select and apply their preferred combination of existing tools for mapping reads, quantifying expression, testing for differential expression. iRAP also includes multiple tools for gene set enrichment analysis and generates web browsable reports of the results obtained in the different stages of the pipeline. Depending upon the application, iRAP can be used to quantify expression at the gene, exon or transcript level. iRAP is aimed at a broad group of users with basic bioinformatics training and requires little experience with the command line. Despite this, it also provides more advanced users with the ability to customise the options used by their chosen tools.

Author post: Predicting evolution from the shape of genealogical trees

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This guest post by Richard Neher discusses his preprint Predicting evolution from the shape of genealogical trees. Richard A. Neher, Colin A. Russell, Boris I. Shraiman. arXived here. This is cross-posted from the Neher lab website.

In this preprint — a collaboration with Colin Russell and Boris Shraiman — we show that it is possible to predict which individual from a population is most closely related to future populations. To this end, we have developed a method that uses the branching pattern of genealogical trees to estimate which part of the tree contains the “fittest” sequences, where fit means rapidly multiplying. Those that multiply rapidly, are most likely to take over the population. We demonstrate the power of our method by predicting the evolution of seasonal influenza viruses.

How does it work?
Individuals adapt to a changing environment by accumulating beneficial mutations, while avoiding deleterious mutations. We model this process assuming that there are many such mutations which change fitness in small increments. Using this model, we calculate the probability that an individual that lived in the past at time t leaves n descendants in the present. This distributions depends critically on the fitness of the ancestral individual. We then extend this calculation to the probability of observing a certain branch in a genealogical tree reconstructed from a sample of sequences. A branch in a tree connects an individual A that lived at time tA and had fitness xA and with an individual B that lived at a later time tB with fitness xB as illustrated in the figure. B has descendants in the sample, otherwise the branch would not be part of the tree. Furthermore, all sampled descendants of A are also descendants of B, otherwise the connection between A and B would have branched between tA and tB. We call the mathematical object describing fitness evolution between A and B “branch propagator” and propagatordenote it by g(xB,tB|xA,tA). The joint probability distribution of fitness values of all nodes of the tree is given by a product of branch propagators. We then calculate the expected fitness of each node and use it to rank the sampled sequences. The top ranked sequence is our prediction for the sequence of the progenitor of the future population.

Why do we care?
flu_tree Being able to predict evolution could have immediate applications. The best example is the seasonal influenza vaccine, that needs to be updated frequently to keep up with the evolving virus. Vaccine strains are chosen among sampled virus strains, and the more closely this strain matches the future influenza virus population, the better the vaccine is going to be. Hence by predicting a likely progenitor of the future, our method could help to improve influenza vaccines. One of our predictions is shown in the figure, with the top ranked sequence marked by a black arrow. Influenza is not the only possible application. Since the algorithm only requires a reconstructed tree as input, it can be applied to other rapidly evolving pathogens or cancer cell populations. In addition, to being useful, the ability to predict also implies that the model captures an essential aspect of evolutionary dynamics: influenza evolution is to a substantial degree — enough to enable prediction — dependent on the accumulation of small effect mutations.

Comparison to other approaches
Given the importance of good influenza vaccines, there has been a number of previous efforts to anticipate influenza virus evolution, typically based on using patterns of molecular evolution from historical data. Along these lines, Luksza and Lässig have recently presented an explicit fitness model for influenza virus evolution that rewards mutations at positions known to convey antigenic novelty and penalizes likely deleterious mutations (+a few other things). By using molecular influenza specific signatures, this model is complementary to ours that uses only the tree reconstructed from nucleotide sequences. Interestingly, the two models do more or less equally well and combining different methods of prediction should result in more reliable results.

Polyester: simulating RNA-seq datasets with differential transcript expression

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Polyester: simulating RNA-seq datasets with differential transcript expression

Alyssa C Frazee, Andrew E Jaffe, Ben Langmead, Jeffrey Leek

Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially-constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data. Polyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with col- lections of RNA-seq reads. The main advantage of Polyester is the ability to simulate isoform-level differential expression across biological replicates for a variety of experimental designs at the read level. Differential expression signal can be simulated with either built-in or user-defined statistical models. Polyester is available on GitHub at https://github.com/alyssafrazee/polyester.