MOTIVATION: Gene alternative splicing plays an important role in development, tissue specialization and disease and differences in splicing patterns can reveal important factors for phenotypic differentiation. While multiple computational methods exist to determine splicing differences, there is a need for user-friendly visualizations that present an intuitive view of the data and work across methods. RESULTS: We developed a toolkit, Jutils, for visualizing differential splicing events at the intron (splice junction) level. Jutils is method-agnostic, converting individual tools' output into a unified representation and using it to create visualizations. Jutils creates three types of visualizations, namely heatmaps of absolute and Z-score normalized splice ratios, sashimi plots and Venn diagrams of results from multiple comparisons. Jutils is lightweight, relying solely on the unified data file for visualizations. AVAILABILITY AND IMPLEMENTATION: Jutils is implemented in Python and is available from https://github.com/Splicebox/Jutils.

Motivation: Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results: We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation: SEVA is implemented in the R/Bioconductor package GSReg. Contact: [email protected] or [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: RNA-seq has revolutionized our ability to survey the cellular transcriptome in great detail. However, while several approaches have been developed, the problem of assembling the short reads into full-length transcripts remains challenging. RESULTS: We developed a novel algorithm and software tool, CLASS (Constraint-based Local Assembly and Selection of Splice variants), for accurately assembling splice variants using local read coverage patterns of RNA-seq reads, contiguity constraints from read pairs and spliced reads, and optionally information about gene structure extracted from cDNA sequence databases. The algorithmic underpinnings of CLASS are: i) a linear program to infer exons, ii) a compact splice graph representation of a gene and its splice variants, and iii) a transcript selection scheme that takes into account contiguity constraints and, where available, knowledge about gene structure. CONCLUSION: In comparisons against leading transcript assembly programs, CLASS is more accurate on both simulated and real reads and produces results that are easier to interpret when applied to large scale real data, and therefore is a promising analysis tool for next generation sequencing data. AVAILABILITY: CLASS is available from http://sourceforge.net/projects/splicebox.

Next-generation sequencing technologies provide unprecedented power to explore the repertoire of genes and their alternative splice variants, collectively defining the transcriptome of a species in great detail. However, assembling the short reads into full-length gene and transcript models presents significant computational challenges. We review current algorithms for assembling transcripts and genes from next-generation sequencing reads aligned to a reference genome, and lay out areas for future improvements.

UNLABELLED: The large number of genomes that will be sequenced will need to be annotated with genes and other functional features. Aligning gene sequences from a related species to the target genome is an economical and highly reliable method to identify genes; unfortunately, existing tools have been lacking in sensitivity and speed. A program we reported, sim4cc, was shown to be highly accurate but is limited to comparing one cDNA with one genomic sequence. We present here an optimization of the tool, implemented in the packages sim4db and leaff. The new tool performs batch alignments of cDNA and genomic sequences in a fraction of the time required by its predecessor, and thus is very well suited for genome-wide analyses. AVAILABILITY: Sim4db and leaff are written in C, C++ and Perl for Linux and other Unix platforms. Source code is distributed free of charge from http://sourceforge.net/projects/kmer/. CONTACT: [email protected]

Abstract Summary: To annotate newly sequenced organisms, cross-species sequence comparison algorithms can be applied to align gene sequences to the genome of a related species. To improve the accuracy of alignment, spaced seeds must be optimized for each comparison. As the number and diversity of genomes increase, an efficient alternative is to cluster pairwise comparisons into groups and identify seeds for groups instead of individual comparisons. Here we investigate a measure of comparison closeness and identify classes of comparisons that show similar seed behavior and therefore can employ the same seed. Availability: Source code is freely available at http://dna.cs.gwu.edu and from Bioinformatics online. Contact: [email protected], [email protected] Supplementary information: Supplementary data are available at http://dna.cs.gwu.edu and Bioinformatics online.

BACKGROUND: To meet the needs of gene annotation for newly sequenced organisms, optimized spaced seeds can be implemented into cross-species sequence alignment programs to accurately align gene sequences to the genome of a related species. So far, seed performance has been tested for comparisons between closely related species, such as human and mouse, or on simulated data. As the number and variety of genomes increases, it becomes desirable to identify a small set of universal seeds that perform optimally or near-optimally on a large range of comparisons. RESULTS: Using statistical regression methods, we investigate the sensitivity of seeds, in particular good seeds, between four cDNA-to-genome comparisons at different evolutionary distances (human-dog, human-mouse, human-chicken and human-zebrafish), and identify classes of comparisons that show similar seed behavior and therefore can employ the same seed. In addition, we find that with high confidence good seeds for more distant comparisons perform well on closer comparisons, within 98-99% of the optimal seeds, and thus represent universal good seeds. CONCLUSION: We show for the first time that optimal and near-optimal seeds for distant species-to-species comparisons are more generally applicable to a wide range of comparisons. This finding will be instrumental in developing practical and user-friendly cDNA-to-genome alignment applications, to aid in the annotation of new model organisms.

The sequencing of the human genome and ensuing wave of data generation have brought new light upon the extent and importance of alternative splicing as an RNA regulatory mechanism. Alternative splicing could potentially explain the complexity of protein repertoire during evolution, and defects in the splicing mechanism are responsible for diseases as complex as cancer. Among the challenges that rise in light of these discoveries are cataloguing splice variation in the human and other eukaryotic genomes, and identifying and characterizing the splicing regulatory elements that control their expression. Bioinformatics efforts tackling these two questions are just at the beginning. This article is a survey of these methods.

Recent sequencing of the human and other mammalian genomes has brought about the necessity to align them, to identify and characterize their commonalities and differences. Programs that align whole genomes generally use a seed-and-extend technique, starting from exact or near-exact matches and selecting a reliable subset of these, called anchors, and then filling in the remaining portions between the anchors using a combination of local and global alignment algorithms, but their choices for the parameters so far have been primarily heuristic. We present a statistical framework and practical methods for selecting a set of matches that is both sensitive and specific and can constitute a reliable set of anchors for a one-to-one mapping of two genomes from which a whole-genome alignment can be built. Starting from exact matches, we introduce a novel per-base repeat annotation, the $Z$-score, from which noise and repeat filtering conditions are explored. Dynamic programming-based chaining algorithms are also evaluated as context-based filters. We apply the methods described here to the comparison of two progressive assemblies of the human genome, NCBI build 28 and build 34 http://genome.ucsc.edu), and show that a significant portion of the two genomes can be found in selected exact matches, with very limited amount of sequence duplication.