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

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.

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.

Simultaneous estimation of transcript abundances and transcript specific fragment distributions of RNA-Seq data with the Mix2 model

Andreas Tuerk, Gregor Wiktorin

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragmentation bias, which is not represented appropriately by current statistical models of RNA-Seq data. Another, less investigated, source of error is the inaccuracy of transcript start and end annotations. This article introduces the Mix2 (rd. ”mixquare”) model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2 model can be efficiently trained with the EM algorithm and are tied between similar transcripts. Transcript specific shift and scale parameters allow the Mix2 model to automatically correct inaccurate transcript start and end annotations. Experiments are conducted on synthetic data covering 7 genes of different complexity, 4 types of fragment bias and correct as well as incorrect transcript start and end annotations. Abundance estimates obtained by Cufflinks 2.2.0, PennSeq and the Mix2 model show superior performance of the Mix2 model in the vast majority of test conditions.