FineSplice, enhanced splice junction detection and quantification: a novel pipeline based on the assessment of diverse RNA-Seq alignment solutions

The Genome-Wide Characterization of Alternative Splicing and RNA Editing in the Development of Coprinopsis cinerea

Coprinopsis cinerea is one of the model species used in fungal developmental studies. This mushroom-forming Basidiomycetes fungus has several developmental destinies in response to changing environments, with dynamic developmental regulations of the organism. Although the gene expression in C. cinerea development has already been profiled broadly, previous studies have only focused on a specific stage or process of fungal development. A comprehensive perspective across different developmental paths is lacking, and a global view on the dynamic transcriptional regulations in the life cycle and the developmental paths is far from complete. In addition, knowledge on co- and post-transcriptional modifications in this fungus remains rare. In this study, we investigated the transcriptional changes and modifications in C. cinerea during the processes of spore germination, vegetative growth, oidiation, sclerotia formation, and fruiting body formation by inducing different developmental paths of the organism and profiling the transcriptomes using the high-throughput sequencing method. Transition in the identity and abundance of expressed genes drive the physiological and morphological alterations of the organism, including metabolism and multicellularity construction. Moreover, stage- and tissue-specific alternative splicing and RNA editing took place and functioned in C. cinerea. These modifications were negatively correlated to the conservation features of genes and could provide extra plasticity to the transcriptome during fungal development. We suggest that C. cinerea applies different molecular strategies in its developmental regulation, including shifts in expressed gene sets, diversifications of genetic information, and reversible diversifications of RNA molecules. Such features would increase the fungal adaptability in the rapidly changing environment, especially in the transition of developmental programs and the maintenance and balance of genetic and transcriptomic divergence. The multi-layer regulatory network of gene expression serves as the molecular basis of the functioning of developmental regulation.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

2passtools: two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing

Transcription of eukaryotic genomes involves complex alternative processing of RNAs. Sequencing of full-length RNAs using long reads reveals the true complexity of processing. However, the relatively high error rates of long-read sequencing technologies can reduce the accuracy of intron identification. Here we apply alignment metrics and machine-learning-derived sequence information to filter spurious splice junctions from long-read alignments and use the remaining junctions to guide realignment in a two-pass approach. This method, available in the software package 2passtools (https://github.com/bartongroup/2passtools), improves the accuracy of spliced alignment and transcriptome assembly for species both with and without existing high-quality annotations.

RAP: A Web Tool for RNA-Seq Data Analysis

Since 1950 main studies of RNA regarded its role in the protein synthesis. Later insights showed that only a small portion of RNA codes for proteins where the rest could have different functional roles. With the advent of Next Generation Sequencing (NGS) and in particular with RNA-seq technology the cost of sequencing production dropped down. Among the NGS application areas, the transcriptome analysis, that is, the analysis of transcripts in a cell, their quantification for a specific developmental stage or treatment condition, became more and more adopted in the laboratories. As a consequence in the last decade new insights were gained in the understanding of both transcriptome complexity and involvement of RNA molecules in cellular processes. For what concerns computational advances, bioinformatics research developed new methods for analyzing RNA-seq data. The comparison among transcriptome profiles from several samples is often a difficult task for nonexpert programmers. Here, in this chapter, we introduce RAP (RNA-Seq Analysis Pipeline), a completely automated web tool for transcriptome analysis. It is a user-friendly web tool implementing a detailed transcriptome workflow to detect differential expressed genes and transcript, identify spliced junctions and constitutive or alternative polyadenylation sites and predict gene fusion events. Through the web interface the researchers can get all this information without any knowledge of the underlying High Performance Computing infrastructure.

Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach

Background

Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results

In this work, we present a deep learning based splice junction sequence classifier, named DeepSplice, which employs convolutional neural networks to classify candidate splice junctions. We show (I) DeepSplice outperforms state-of-the-art methods for splice site classification when applied to the popular benchmark dataset HS3D, (II) DeepSplice shows high accuracy for splice junction classification with GENCODE annotation, and (III) the application of DeepSplice to classify putative splice junctions generated by Rail-RNA alignment of 21,504 human RNA-seq data significantly reduces 43 million candidates into around 3 million highly confident novel splice junctions.

Conclusions

A model inferred from the sequences of annotated exon junctions that can then classify splice junctions derived from primary RNA-seq data has been implemented. The performance of the model was evaluated and compared through comprehensive benchmarking and testing, indicating a reliable performance and gross usability for classifying novel splice junctions derived from RNA-seq alignment.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5350-1) contains supplementary material, which is available to authorized users.

Efficient and accurate detection of splice junctions from RNA-seq with Portcullis

Next-generation sequencing technologies enable rapid and cheap genome-wide transcriptome analysis, providing vital information about gene structure, transcript expression, and alternative splicing. Key to this is the accurate identification of exon-exon junctions from RNA sequenced (RNA-seq) reads. A number of RNA-seq aligners capable of splitting reads across these splice junctions (SJs) have been developed; however, it has been shown that while they correctly identify most genuine SJs available in a given sample, they also often produce large numbers of incorrect SJs. Here, we describe the extent of this problem using popular RNA-seq mapping tools and present a new method, called Portcullis, to rapidly filter false SJs derived from spliced alignments. We show that Portcullis distinguishes between genuine and false-positive junctions to a high degree of accuracy across different species, samples, expression levels, error profiles, and read lengths. Portcullis is portable, efficient, and, to our knowledge, currently the only SJ prediction tool that reliably scales for use with large RNA-seq datasets and large, highly fragmented genomes, while delivering accurate SJs.

Genome-wide profiling of differentially spliced mRNAs in human fetal cortical tissue exposed to alcohol

Excessive alcohol consumption results in significant changes in gene expression and isoforms due to altered mRNA splicing. As such, an intriguing possibility is that disturbances in alternative splicing are involved in key pathological pathways triggered by alcohol exposure. However, no resources have been available to systematically analyze this possibility at a genome-wide scale. Here, we performed RNA sequencing of human fetal cortical slices that were obtained at the late first trimester and exposed to ethanol or control medium. We report 382 events that were identified as changes affecting the ratio of splicing isoforms in the ethanol-exposed fetal human cortex. Additionally, previously unreported novel isoforms of several genes were also identified. These results provide a broad perspective on the post-transcriptional regulatory network underlying ethanol-induced pathogenesis in the developing human cortex.

Comparison of Alternative Splicing Junction Detection Tools Using RNA-Seq Data

BACKGROUND

Alternative splicing (AS) is a posttranscriptional process that produces differ-ent transcripts from the same gene and is important to produce diverse protein products in response to environmental stimuli. AS occurs at specific sites on the mRNA sequence, some of which have been de-fined. Multiple bioinformatics tools have been developed to detect AS from experimental data.

OBJECTIVES

The goal of this review is to help researchers use specific tools to aid their research and to develop new AS detection tools based on these previously established tools.

METHOD

We selected 15 AS detection tools that were recently published; we classified and delineated them on several aspects. Also, a performance comparison of these tools with the same starting input was conducted.

RESULT

We reviewed the following categorized features of the tools: Publication information, working principles, generic and distinct workflows, running platform, input data requirement, sequencing depth dependency, reads mapped to multiple locations, isoform annotation basis, precise detected AS types, and performance benchmarks.

CONCLUSION

Through comparisons of these tools, we provide a panorama of the advantages and short-comings of each tool and their scopes of application.

Gene expression atlas of pigeonpea and its application to gain insights into genes associated with pollen fertility implicated in seed formation

Pigeonpea (Cajanus cajan) is an important grain legume of the semi-arid tropics, mainly used for its protein rich seeds. To link the genome sequence information with agronomic traits resulting from specific developmental processes, a Cajanus cajan gene expression atlas (CcGEA) was developed using the Asha genotype. Thirty tissues/organs representing developmental stages from germination to senescence were used to generate 590.84 million paired-end RNA-Seq data. The CcGEA revealed a compendium of 28 793 genes with differential, specific, spatio-temporal and constitutive expression during various stages of development in different tissues. As an example to demonstrate the application of the CcGEA, a network of 28 flower-related genes analysed for cis-regulatory elements and splicing variants has been identified. In addition, expression analysis of these candidate genes in male sterile and male fertile genotypes suggested their critical role in normal pollen development leading to seed formation. Gene network analysis also identified two regulatory genes, a pollen-specific SF3 and a sucrose-proton symporter, that could have implications for improvement of agronomic traits such as seed production and yield. In conclusion, the CcGEA provides a valuable resource for pigeonpea to identify candidate genes involved in specific developmental processes and to understand the well-orchestrated growth and developmental process in this resilient crop.

The Calcineurin Variant CnAβ1 Controls Mouse Embryonic Stem Cell Differentiation by Directing mTORC2 Membrane Localization and Activation

Embryonic stem cells (ESC) have the potential to generate all the cell lineages that form the body. However, the molecular mechanisms underlying ESC differentiation and especially the role of alternative splicing in this process remain poorly understood. Here, we show that the alternative splicing regulator MBNL1 promotes generation of the atypical calcineurin Aβ variant CnAβ1 in mouse ESCs (mESC). CnAβ1 has a unique C-terminal domain that drives its localization mainly to the Golgi apparatus by interacting with Cog8. CnAβ1 regulates the intracellular localization and activation of the mTORC2 complex. CnAβ1 knockdown results in delocalization of mTORC2 from the membrane to the cytoplasm, inactivation of the AKT/GSK3β/β-catenin signaling pathway, and defective mesoderm specification. In summary, here we unveil the structural basis for the mechanism of action of CnAβ1 and its role in the differentiation of mESCs to the mesodermal lineage.

Neurogenesis: Regulation by Alternative Splicing and Related Posttranscriptional Processes

The complexity of the mammalian brain requires highly specialized protein function and diversity. As neurons differentiate and the neuronal circuitry is established, several mRNAs undergo alternative splicing and other posttranscriptional changes that expand the variety of protein isoforms produced. Recent advances are beginning to shed light on the molecular mechanisms that regulate isoform switching during neurogenesis and the role played by specific RNA binding proteins in this process. Neurogenesis and neuronal wiring were recently shown to also be regulated by RNA degradation through nonsense-mediated decay. An additional layer of regulatory complexity in these biological processes is the interplay between alternative splicing and long noncoding RNAs. Dysregulation of posttranscriptional regulation results in defective neuronal differentiation and/or synaptic connections that lead to neurodevelopmental and psychiatric disorders.

Computational Identification of Novel Genes: Current and Future Perspectives

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data

The quantification of transcriptomic features is the basis of the analysis of RNA-seq data. We present an integrated alignment workflow and a simple counting-based approach to derive estimates for gene, exon and exon–exon junction expression. In contrast to previous counting-based approaches, EQP takes into account only reads whose alignment pattern agrees with the splicing pattern of the features of interest. This leads to improved gene expression estimates as well as to the generation of exon counts that allow disambiguating reads between overlapping exons. Unlike other methods that quantify skipped introns, EQP offers a novel way to compute junction counts based on the agreement of the read alignments with the exons on both sides of the junction, thus providing a uniformly derived set of counts. We evaluated the performance of EQP on both simulated and real Illumina RNA-seq data and compared it with other quantification tools. Our results suggest that EQP provides superior gene expression estimates and we illustrate the advantages of EQP's exon and junction counts. The provision of uniformly derived high-quality counts makes EQP an ideal quantification tool for differential expression and differential splicing studies. EQP is freely available for download at https://github.com/Novartis/EQP-cluster.

A survey of computational methods in transcriptome-wide alternative splicing analysis

Alternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. Consequently the identification and quantification of differentially spliced transcripts is pivotal for transcriptome analysis. Here, we review the currently available computational approaches for the analysis of RNA-sequencing data with a focus on exon-skipping events of alternative splicing and discuss the novelties as well as challenges faced to perform differential splicing analyses. In accordance with operational needs we have classified the software tools, which may be instrumental for a specific analysis based on the experimental objectives and expected outcomes. In addition, we also propose a framework for future directions by pinpointing more extensive experimental validation to assess the accuracy of the software predictions and improvements that would facilitate visualizations, data processing, and downstream analyses along with their associated software implementations.

Experimental approaches to studying the nature and impact of splicing variation in zebrafish

From a fixed number of genes carried in all cells, organisms create considerable diversity in cellular phenotype through differential regulation of gene expression. One prevalent source of transcriptome diversity is alternative pre-mRNA splicing, which is manifested in many different forms. Zebrafish models of splicing dysfunction due to mutated spliceosome components provide opportunity to link biochemical analyses of spliceosome structure and function with whole organism phenotypic outcomes. Drawing from experience with two zebrafish mutants: cephalophŏnus (a prpf8 mutant, isolated for defects in granulopoiesis) and caliban (a rnpc3 mutant, isolated for defects in digestive organ development), we describe the use of glycerol gradient sedimentation and native gel electrophoresis to resolve components of aberrant splicing complexes. We also describe how RNAseq can be employed to examine relatively rare alternative splicing events including intron retention. Such experimental approaches in zebrafish can promote understanding of how splicing variation and dysfunction contribute to phenotypic diversity and disease pathogenesis.

A Developmental Transcriptome Map for Allotetraploid Arachis hypogaea

The advent of the genome sequences of Arachis duranensis and Arachis ipaensis has ushered in a new era for peanut genomics. With the goal of producing a gene atlas for cultivated peanut (Arachis hypogaea), 22 different tissue types and ontogenies that represent the full development of peanut were sequenced, including a complete reproductive series from flower to peg elongation and peg tip immersion in the soil to fully mature seed. Using a genome-guided assembly pipeline, a homeolog-specific transcriptome assembly for Arachis hypogaea was assembled and its accuracy was validated. The assembly was used to annotate 21 developmental co-expression networks as tools for gene discovery. Using a set of 8816 putative homeologous gene pairs, homeolog expression bias was documented, and although bias was mostly balanced, there were striking differences in expression bias in a tissue-specific context. Over 9000 alterative splicing events and over 6000 non-coding RNAs were further identified and profiled in a developmental context. Together, this work represents a major new resource for cultivated peanut and will be integrated into peanutbase.org as an available resource for all peanut researchers.

Two-pass alignment improves novel splice junction quantification

MOTIVATION

Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions.

RESULTS

Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events.

CONTACT

arul@med.umich.edu, nesvi@med.umich.edu

AVAILABILITY AND IMPLEMENTATION

Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application

Background

The study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.).

Moreover, the huge volume of data generated by NGS platforms introduces unprecedented computational and technological challenges to efficiently analyze and store sequence data and results.

Methods

In order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq).

Results

Through a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs.

RNA-Seq: Improving Our Understanding of Retinal Biology and Disease

Over the past several years, rapid technological advances have allowed for a dramatic increase in our knowledge and understanding of the transcriptional landscape, because of the ability to study gene expression in greater depth and with more detail than previously possible. To this end, RNA-Seq has quickly become one of the most widely used methods for studying transcriptomes of tissues and individual cells. Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. Additionally, RNA-Seq derived transcriptomes provide a basis for widening the scope of research to identify potential targets in the treatment of retinal disease.

Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis

RNA-seq is a methodology for RNA profiling based on next-generation sequencing that enables to measure and compare gene expression patterns at unprecedented resolution. Although the appealing features of this technique have promoted its application to a wide panel of transcriptomics studies, the fast-evolving nature of experimental protocols and computational tools challenges the definition of a unified RNA-seq analysis pipeline. In this review, focused on the study of differential gene expression with RNA-seq, we go through the main steps of data processing and discuss open challenges and possible solutions.