Methods to study splicing from high-throughput RNA Sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

Optimizing the identification of risk-relevant mutations by multigene panel testing in selected hereditary breast/ovarian cancer families

The introduction of multigene panel testing for hereditary breast/ovarian cancer screening has greatly improved efficiency, speed, and costs. However, its clinical utility is still debated, mostly due to the lack of conclusive evidences on the impact of newly discovered genetic variants on cancer risk and lack of evidence-based guidelines for the clinical management of their carriers. In this pilot study, we aimed to test whether a systematic and multiparametric characterization of newly discovered mutations could enhance the clinical utility of multigene panel sequencing. Out of a pool of 367 breast/ovarian cancer families Sanger-sequenced for BRCA1 and BRCA2 gene mutations, we selected a cohort of 20 BRCA1/2-negative families to be subjected to the BROCA-Cancer Risk Panel massive parallel sequencing. As a strategy for the systematic characterization of newly discovered genetic variants, we collected blood and cancer tissue samples and established lymphoblastoid cell lines from all available individuals in these families, to perform segregation analysis, loss-of-heterozygosity and further molecular studies. We identified loss-of-function mutations in 6 out 20 high-risk families, 5 of which occurred on BRCA1, CHEK2 and ATM and are esteemed to be risk-relevant. In contrast, a novel RAD50 truncating mutation is most likely unrelated to breast cancer. Our data suggest that integrating multigene panel testing with a pre-organized, multiparametric characterization of newly discovered genetic variants improves the identification of risk-relevant alleles impacting on the clinical management of their carriers

PIntron: a Fast Method for Gene Structure Prediction via Maximal Pairings of a Pattern and a Text

Current computational methods for exon-intron structure prediction from a cluster of transcript (EST, mRNA) data do not exhibit the time and space efficiency necessary to process large clusters of over than 20,000 ESTs and genes longer than 1Mb. Guaranteeing both accuracy and efficiency seems to be a computational goal quite far to be achieved, since accuracy is strictly related to exploiting the inherent redundancy of information present in a large cluster. We propose a fast method for the problem that combines two ideas: a novel algorithm of proved small time complexity for computing spliced alignments of a transcript against a genome, and an efficient algorithm that exploits the inherent redundancy of information in a cluster of transcripts to select, among all possible factorizations of EST sequences, those allowing to infer splice site junctions that are highly confirmed by the input data. The EST alignment procedure is based on the construction of maximal embeddings that are sequences obtained from paths of a graph structure, called Embedding Graph, whose vertices are the maximal pairings of a genomic sequence T and an EST P. The procedure runs in time linear in the size of P, T and of the output. PIntron, the software tool implementing our methodology, is able to process in a few seconds some critical genes that are not manageable by other gene structure prediction tools. At the same time, PIntron exhibits high accuracy (sensitivity and specificity) when compared with ENCODE data. Detailed experimental data, additional results and PIntron software are available at http://www.algolab.eu/PIntron

RNA-Seq analysis of splicing in Plasmodium falciparum uncovers new splice junctions, alternative splicing and splicing of antisense transcripts.

Over 50% of genes in Plasmodium falciparum, the deadliest human malaria parasite, contain predicted introns, yet experimental characterization of splicing in this organism remains incomplete. We present here a transcriptome-wide characterization of intraerythrocytic splicing events, as captured by RNA-Seq data from four timepoints of a single highly synchronous culture. Gene model-independent analysis of these data in conjunction with publically available RNA-Seq data with HMMSplicer, an in-house developed splice site detection algorithm, revealed a total of 977 new 5' GU-AG 3' and 5 new 5' GC-AG 3' junctions absent from gene models and ESTs (11% increase to the current annotation). In addition, 310 alternative splicing events were detected in 254 (4.5%) genes, most of which truncate open reading frames. Splicing events antisense to gene models were also detected, revealing complex transcriptional arrangements within the parasite's transcriptome. Interestingly, antisense introns overlap sense introns more than would be expected by chance, perhaps indicating a functional relationship between overlapping transcripts or an inherent organizational property of the transcriptome. Independent experimental validation confirmed over 30 new antisense and alternative junctions. Thus, this largest assemblage of new and alternative splicing events to date in Plasmodium falciparum provides a more precise, dynamic view of the parasite's transcriptome

Tissue resolved, gene structure refined equine transcriptome.

BackgroundTranscriptome interpretation relies on a good-quality reference transcriptome for accurate quantification of gene expression as well as functional analysis of genetic variants. The current annotation of the horse genome lacks the specificity and sensitivity necessary to assess gene expression especially at the isoform level, and suffers from insufficient annotation of untranslated regions (UTR) usage. We built an annotation pipeline for horse and used it to integrate 1.9 billion reads from multiple RNA-seq data sets into a new refined transcriptome.ResultsThis equine transcriptome integrates eight different tissues from 59 individuals and improves gene structure and isoform resolution, while providing considerable tissue-specific information. We utilized four levels of transcript filtration in our pipeline, aimed at producing several transcriptome versions that are suitable for different downstream analyses. Our most refined transcriptome includes 36,876 genes and 76,125 isoforms, with 6474 candidate transcriptional loci novel to the equine transcriptome.ConclusionsWe have employed a variety of descriptive statistics and figures that demonstrate the quality and content of the transcriptome. The equine transcriptomes that are provided by this pipeline show the best tissue-specific resolution of any equine transcriptome to date and are flexible for several downstream analyses. We encourage the integration of further equine transcriptomes with our annotation pipeline to continue and improve the equine transcriptome

An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs.

Reconstructing full-length transcript isoforms from sequence fragments (such as ESTs) is a major interest and challenge for bioinformatic analysis of pre-mRNA alternative splicing. This problem has been formulated as finding traversals across the splice graph, which is a directed acyclic graph (DAG) representation of gene structure and alternative splicing. In this manuscript we introduce a probabilistic formulation of the isoform reconstruction problem, and provide an expectation-maximization (EM) algorithm for its maximum likelihood solution. Using a series of simulated data and expressed sequences from real human genes, we demonstrate that our EM algorithm can correctly handle various situations of fragmentation and coupling in the input data. Our work establishes a general probabilistic framework for splice graph-based reconstructions of full-length isoforms

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

N-terminal proteomics assisted profiling of the unexplored translation initiation landscape in Arabidopsis thaliana

Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high number of protein start sites are currently missing in genome annotations. We constructed a proteogenomic pipeline specific for the analysis of N-terminal proteomics data, with the aim of discovering novel translational start sites outside annotated protein coding regions. In summary, unidentified MS/MS spectra were matched to a specific N-terminal peptide library encompassing protein N termini encoded in the Arabidopsis thaliana genome. After a stringent false discovery rate filtering, 117 protein N termini compliant with N-terminal methionine excision specificity and indicative of translation initiation were found. These include N-terminal protein extensions and translation from transposable elements and pseudogenes. Gene prediction provided supporting protein-coding models for approximately half of the protein N termini. Besides the prediction of functional domains (partially) contained within the newly predicted ORFs, further supporting evidence of translation was found in the recently released Araport11 genome re-annotation of Arabidopsis and computational translations of sequences stored in public repositories. Most interestingly, complementary evidence by ribosome profiling was found for 23 protein N termini. Finally, by analyzing protein N-terminal peptides, an in silico analysis demonstrates the applicability of our N-terminal proteogenomics strategy in revealing protein-coding potential in species with well-and poorly-annotated genomes

Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

As a fruit of the current revolution in sequencing technology, transcriptomes can now be analyzed at an unprecedented level of detail. These advances have been exploited for detecting differential expressed genes across biological samples and for quantifying the abundances of various RNA transcripts within one gene. However, explicit strategies for detecting the hidden differential abundances of RNA transcripts in biological samples have not been defined. In this work, we present two novel statistical tests to address this issue: a 'gene structure sensitive' Poisson test for detecting differential expression when the transcript structure of the gene is known, and a kernel-based test called Maximum Mean Discrepancy when it is unknown. We analyzed the proposed approaches on simulated read data for two artificial samples as well as on factual reads generated by the Illumina Genome Analyzer for two _C. elegans_ samples. Our analysis shows that the Poisson test identifies genes with differential transcript expression considerably better that previously proposed RNA transcript quantification approaches for this task. The MMD test is able to detect a large fraction (75%) of such differential cases without the knowledge of the annotated transcripts. It is therefore well-suited to analyze RNA-Seq experiments when the genome annotations are incomplete or not available, where other approaches have to fail