Annotating genomes with massive-scale RNA sequencing

Next generation technologies enable massive-scale cDNA sequencing (so-called RNA-Seq). Mainly because of the difficulty of aligning short reads on exon-exon junctions, no attempts have been made so far to use RNA-Seq for building gene models de novo, that is, in the absence of a set of known genes and/or splicing events. We present G-Mo.R-Se (Gene Modelling using RNA-Seq), an approach aimed at building gene models directly from RNA-Seq and demonstrate its utility on the grapevine genome.     

A serine–arginine-rich (SR) splicing factor modulates alternative splicing of over a thousand genes in Toxoplasma gondii

Single genes are often subject to alternative splicing, which generates alternative mature mRNAs. This phenomenon is widespread in animals, and observed in over 90% of human genes. Recent data suggest it may also be common in Apicomplexa. These parasites have small genomes, and economy of DNA is evolutionarily favoured in this phylum. We investigated the mechanism of alternative splicing in Toxoplasma gondii, and have identified and localized TgSR3, a homologue of ASF/SF2 (alternative-splicing factor/splicing factor 2, a serine-arginine–rich, or SR protein) to a subnuclear compartment. In addition, we conditionally overexpressed this protein, which was deleterious to growth. qRT-PCR was used to confirm perturbation of splicing in a known alternatively-spliced gene. We performed high-throughput RNA-seq to determine the extent of splicing modulated by this protein. Current RNA-seq algorithms are poorly suited to compact parasite genomes, and hence we complemented existing tools by writing a new program, GeneGuillotine, that addresses this deficiency by segregating overlapping reads into distinct genes. In order to identify the extent of alternative splicing, we released another program, JunctionJuror, that detects changes in intron junctions. Using this program, we identified about 2000 genes that were constitutively alternatively spliced in T. gondii. Overexpressing the splice regulator TgSR3 perturbed alternative splicing in over 1000 genes.     

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

Alternative splicing is the main mechanism governing protein diversity. The recent developments in RNA-Seq technology have enabled the study of the global impact and regulation of this biological process. However, the lack of standardized protocols constitutes a major bottleneck in the analysis of alternative splicing. This is particularly important for the identification of exon–exon junctions, which is a critical step in any analysis workflow. Here we performed a systematic benchmarking of alignment tools to dissect the impact of design and method on the mapping, detection and quantification of splice junctions from multi-exon reads. Accordingly, we devised a novel pipeline based on TopHat2 combined with a splice junction detection algorithm, which we have named FineSplice. FineSplice allows effective elimination of spurious junction hits arising from artefactual alignments, achieving up to 99% precision in both real and simulated data sets and yielding superior F₁ scores under most tested conditions. The proposed strategy conjugates an efficient mapping solution with a semi-supervised anomaly detection scheme to filter out false positives and allows reliable estimation of expressed junctions from the alignment output. Ultimately this provides more accurate information to identify meaningful splicing patterns. FineSplice is freely available at https://sourceforge.net/p/finesplice/.     

Splice site prediction with quadratic discriminant analysis using diversity measure

Based on the conservation of nucleotides at splicing sites and the features of base composition and base correlation around these sites we use the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to study the dependence structure of splicing sites and predict the exons/introns and their boundaries for four model genomes: Caenorhabditis elegans, Arabidopsis thaliana, Drosophila melanogaster and human. The comparison of compositional features between two sequences and the comparison of base dependencies at adjacent or non-adjacent positions of two sequences can be integrated automatically in the increment of diversity (ID). Eight feature variables around a potential splice site are defined in terms of ID. They are integrated in a single formal framework given by IDQD. In our calculations 7 (8) base region around the donor (acceptor) sites have been considered in studying the conservation of nucleotides and sequences of 48 bp on either side of splice sites have been used in studying the compositional and base-correlating features. The windows are enlarged to 16 (donor), 29 (acceptor) and 80 bp (either side) to improve the prediction for human splice sites. The prediction capability of the present method is comparable with the leading splice site detector—GeneSplicer.     

Splice junctions are constrained by protein disorder

We have discovered that positions of splice junctions in genes are constrained by the tolerance for disorder-promoting amino acids in the translated protein region. It is known that efficient splicing requires nucleotide bias at the splice junction; the preferred usage produces a distribution of amino acids that is disorder-promoting. We observe that efficiency of splicing, as seen in the amino-acid distribution, is not compromised to accommodate globular structure. Thus we infer that it is the positions of splice junctions in the gene that must be under constraint by the local protein environment. Examining exonic splicing enhancers found near the splice junction in the gene, reveals that these (short DNA motifs) are more prevalent in exons that encode disordered protein regions than exons encoding structured regions. Thus we also conclude that local protein features constrain efficient splicing more in structure than in disorder.     

Detection, annotation and visualization of alternative splicing from RNA-Seq data with SplicingViewer

Alternative splicing is a crucial mechanism by which diverse gene products can be generated from a limited number of genes, and is thought to be involved in complex orchestration of eukaryotic gene expression. Next-generation sequencing technologies, with reduced time and cost, provide unprecedented opportunities for deep interrogation of alternative splicing at the genome-wide scale. In this study, an integrated software SplicingViewer has been developed for unambiguous detection, annotation and visualization of splice junctions and alternative splicing events from RNA-Seq data. Specifically, it allows easy identification and characterization of splice junctions, and holds a versatile computational pipeline for in-depth annotation and classification of alternative splicing with different patterns. Moreover, it provides a user-friendly environment in which an alternative splicing landscape can be displayed in a straightforward and flexible manner. In conclusion, SplicingViewer can be widely used for studying alternative splicing easily and efficiently. SplicingViewer can be freely accessed at http://bioinformatics.zj.cn/splicingviewer.     

Structural organization,classification and phylogenetic relationship of cytochrome P450 genes in Citrus clementina and Citrus sinensis

The genus Citrus is an important fruit crop and nutritional source for the good health of humans. Cytochrome P450s represent about 1 % of the proteome and mediate diverse biochemical reactions pertaining to both primary and secondary metabolism. Analysis of Citrus genomic resources identified 296 plant cytochrome P450s (CYP) coding genes in Citrus clementina, 272 in double haploid (dh) Citrus sinensis, and 202 in C. sinensis. In C. clementina and dh C. sinensis, CYP genes are distributed into nine clans. In the three genomes, single intron containing CYP genes are predominant in the A-type families. Among non-A-type CYP families, multiple intron containing genes are predominant. More number of genes in CYP A-type families over non-A-type families is attributed to rapid evolution of A-type genes facilitated by their gene organization. Further, complex gene organization of non-A-type genes with the presence of multiple introns might have contributed to the slower evolvement of paralogs. Majority of introns (1,660) from three genomes showed canonical GT-AG splice sites. However, 33 introns showed non-conventional GC… PyAG splice sites and functionality of these splice sites is confirmed by the ESTs lacking this intron. Across the families, gene organization is conserved between the three genomes. In dh C. sinensis, 22 genes were identified to have alternate splicing. Examination of scaffolds in C. clementina revealed that majority of the Citrus CYP genes are solitary and a few of them are in clusters of 3–8 genes. PCR amplification of C. sinensis genomic DNA with gene-specific primers failed to amplify out-grouped genes Ccl-CYP706A16 and Ccl-CYP706B1, confirming that they are specific to C. clementina. Differential number of CYP genes observed between C. clementina and C. sinensis is attributed to the extent of variability between their parents representing ancestral taxa.     

Discovery and analysis of evolutionarily conserved intronic splicing regulatory elements

Knowledge of the functional cis-regulatory elements that regulate constitutive and alternative pre-mRNA splicing is fundamental for biology and medicine. Here we undertook a genome-wide comparative genomics approach using available mammalian genomes to identify conserved intronic splicing regulatory elements (ISREs). Our approach yielded 314 ISREs, and insertions of ~70 ISREs between competing splice sites demonstrated that 84% of ISREs altered 5′ and 94% altered 3′ splice site choice in human cells. Consistent with our experiments, comparisons of ISREs to known splicing regulatory elements revealed that 40%–45% of ISREs might have dual roles as exonic splicing silencers. Supporting a role for ISREs in alternative splicing, we found that 30%–50% of ISREs were enriched near alternatively spliced (AS) exons, and included almost all known binding sites of tissue-specific alternative splicing factors. Further, we observed that genes harboring ISRE-proximal exons have biases for tissue expression and molecular functions that are ISRE-specific. Finally, we discovered that for Nova1, neuronal PTB, hnRNP C, and FOX1, the most frequently occurring ISRE proximal to an alternative conserved exon in the splicing factor strongly resembled its own known RNA binding site, suggesting a novel application of ISRE density and the propensity for splicing factors to auto-regulate to associate RNA binding sites to splicing factors. Our results demonstrate that ISREs are crucial building blocks in understanding general and tissue-specific AS regulation and the biological pathways and functions regulated by these AS events.     

Gene identification in novel eukaryotic genomes by self-training algorithm

Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting in DNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.