SimSpliceEvol2: alternative splicing-aware simulation of biological sequence evolution and transcript phylogenies.

BACKGROUND: SimSpliceEvol is a tool for simulating the evolution of eukaryotic gene sequences that integrates exon-intron structure evolution as well as the evolution of the sets of transcripts produced from genes. It takes a guide gene tree as input and generates a gene sequence with its transcripts for each node of the tree, from the root to the leaves. However, the sets of transcripts simulated at different nodes of the guide gene tree lack evolutionary connections. Consequently, SimSpliceEvol is not suitable for evaluating methods for transcript phylogeny inference or gene phylogeny inference that rely on transcript conservation. RESULTS: Here, we introduce SimSpliceEvol2, which, compared to the first version, incorporates an explicit model of transcript evolution for simulating alternative transcripts along the branches of a guide gene tree, as well as the transcript phylogenies inferred. We offer a comprehensive software with a graphical user interface and an updated version of the web server, ensuring easy and user-friendly access to the tool. CONCLUSION: SimSpliceEvol2 generates synthetic datasets that are useful for evaluating methods and tools for spliced RNA sequence analysis, such as spliced alignment methods, methods for identifying conserved transcripts, and transcript phylogeny reconstruction methods. The web server is accessible at https://simspliceevol.cobius.usherbrooke.ca , where you can also download the standalone software. Comprehensive documentation for the software is available at the same address. For developers interested in the source code, which requires the installation of all prerequisites to run, it is provided at  https://github.com/UdeS-CoBIUS/SimSpliceEvol .

Orthology and Paralogy Relationships at Transcript Level.

Eukaryotic genes undergo a mechanism called alternative processing, resulting in transcriptome diversity by allowing the production of multiple distinct transcripts from a gene. More than half of human genes are affected, and the resulting transcripts are highly conserved among orthologous genes of distinct species. In this work, we present the definition of orthology and paralogy between transcripts of homologous genes, together with an algorithm to compute clusters of conserved orthologous and paralogous transcripts. Gene-level homology relationships are utilized to define various types of homology relationships between transcripts originating from the same ancestral transcript. A Reciprocal Best Hits approach is employed to infer clusters of isoorthologous and recent paralogous transcripts. We applied this method to transcripts from simulated gene families as well as real gene families from the Ensembl-Compara database. The results are consistent with those from previous studies that compared orthologous gene transcripts. Furthermore, our findings provide evidence that searching for conserved transcripts between homologous genes, beyond the scope of orthologous genes, is likely to yield valuable information.

From pairwise to multiple spliced alignment.

Motivation: Alternative splicing is a ubiquitous process in eukaryotes that allows distinct transcripts to be produced from the same gene. Yet, the study of transcript evolution within a gene family is still in its infancy. One prerequisite for this study is the availability of methods to compare sets of transcripts while accounting for their splicing structure. In this context, we generalize the concept of pairwise spliced alignments (PSpAs) to multiple spliced alignments (MSpAs). MSpAs have several important purposes in addition to empowering the study of the evolution of transcripts. For instance, it is a key to improving the prediction of gene models, which is important to solve the growing problem of genome annotation. Despite its essentialness, a formal definition of the concept and methods to compute MSpAs are still lacking. Results: We introduce the MSpA problem and the SplicedFamAlignMulti (SFAM) method, to compute the MSpA of a gene family. Like most multiple sequence alignment (MSA) methods that are generally greedy heuristic methods assembling pairwise alignments, SFAM combines all PSpAs of coding DNA sequences and gene sequences of a gene family into an MSpA. It produces a single structure that represents the superstructure and models of the gene family. Using real vertebrate and simulated gene family data, we illustrate the utility of SFAM for computing accurate gene family superstructures, MSAs, inferring splicing orthologous groups and improving gene-model annotations. Availability and implementation: The supporting data and implementation of SFAM are freely available at https://github.com/UdeS-CoBIUS/SpliceFamAlignMulti. Supplementary information: Supplementary data are available at Bioinformatics Advances online.