Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs

Zea, Diego Javier; Laskina, Sofya; Richard, Hugues; Laine, Élodie

doi:10.1101/2020.11.14.382820

Cited by 3 publications

(4 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While orthologs usually maintain the ancestral function, paralogs are free to mutate and evolve new functions, as long as the ancestral role is preserved [12,13]. Remarkably, although multiple bioinformatic tools have been developed to identify homology relationships at the gene level [14][15][16][17][18], and some are emerging to investigate the conservation of splicing patterns [19], no corresponding tool currently exists to infer exon homologies, particularly over deep evolutionary timescales. Nonetheless, even in the absence of general-purpose tools, a variety of studies have underscored the importance of this approach.…”

Section: Introductionmentioning

confidence: 99%

ExOrthist: a tool to infer exon orthologies at any evolutionary distance

Márquez

Mantica

Cozzuto

et al. 2021

Preprint

View full text Add to dashboard Cite

Several bioinformatic tools have been developed for genome-wide identification of orthologous and paralogous genes among species. However, no existing tool allows the detection of orthologous/paralogous exons. Here, we present ExOrthist, a fully reproducible Nextflow-based software enabling to (i) infer exon homologs and orthogroups, (ii) visualize evolution of exon-intron structures, and (iii) assess conservation of alternative splicing patterns. ExOrthist not only evaluates exon sequence conservation but also considers the surrounding exon-intron context to derive genome-wide multi-species exon homologies at any evolutionary distance. We demonstrate its use in various evolutionary scenarios, from whole genome duplication to convergence of alternative splicing networks.

show abstract

Section: Introductionmentioning

confidence: 99%

ExOrthist: a tool to infer exon orthologies at any evolutionary distance

Márquez

Mantica

Cozzuto

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We estimate the evolutionary conservation of a s-exon (node) or a s-exon junction (edge) by quantifying the amount of species and transcripts including it, respectively. The heuristic for computing the ESG unfolds in five main steps (Zea et al, 2021): (1) cluster similar exons together, (2) define sub-exons within each species, (3) create a multiple sequence alignment (MSA) for each cluster, (4) identify s-exons as continuous blocks in the MSA, (5) refine s-exons. The TPF is a transcript-centred representation of the evolution of AS (Figure 1B).…”

Section: Definitions and Implementationmentioning

confidence: 99%

“…To this aim, we have recently developed a couple of efficient scalable computational methods. The first one, ThorAxe (Zea et al, 2021), adds an evolutionary dimension to the analysis of transcript variability by extending the notion of splicing graph to an ensemble of genes/species. It establishes a mapping between orthologous regions in a set of transcripts coming from different genes/species, and uses this mapping to decompose the transcripts into building blocks.…”

Section: Introductionmentioning

confidence: 99%

ASES: visualizing evolutionary conservation of alternative splicing in proteins

2022

Self Cite

View full text Add to dashboard Cite

Summary ASES is a versatile tool for assessing the impact of alternative splicing, initiation and termination of transcription on protein diversity in evolution. It identifies exon and transcript orthogroups from a set of input genes/species for comparative transcriptomics analyses. It computes an evolutionary splicing graph, where the nodes are exon orthogroups, allowing for a direct evaluation of alternative splicing conservation. It also reconstructs a transcripts’ phylogenetic forest to date the appearance of specific transcripts and explore the events that have shaped them. ASES web server features a highly interactive interface enabling the synchronous selection of events, exons or transcripts in the different outputs, and the visualisation and retrieval of the corresponding amino acid sequences, for subsequent 3D structure prediction. Availability and Implementation http://www.lcqb.upmc.fr/Ases. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

“…There is growing interest in how to approach transcripts in a phylogenetic context (Zea, et al 2021). Modes of splicing variation may differ across taxa (Clark and Thanaraj 2002;Kan, et al 2002;Ner-Gaon, et al 2004;Mei, et al 2017;Freese, et al 2019;Jia, et al 2020).…”

Section: Introductionmentioning

confidence: 99%

The evolution of splicing: transcriptome complexity and transcript distances implemented inTranD

Nanni

Titus-McQuillan

Moskalenko

et al. 2021

Preprint

View full text Add to dashboard Cite

Alternative splicing contributes to organismal complexity. Comparing transcripts between and within species is an important first step toward understanding questions about how evolution of transcript structure changes between species and contributes to sub-functionalization. These questions are confounded with issues of data quality and availability. The recent explosion of affordable long read sequencing of mRNA has considerably widened the ability to study transcriptional variation in non-model species. In this work, we develop a computational framework that uses nucleotide resolution distance metrics to compare transcript models for structural phenotypes: total transcript length, intron retention, donor/acceptor site variation, alternative exon cassettes, alternative 5'/3' UTRs are each scored qualitatively and quantitatively in terms of number of nucleotides. For a single annotation file, all differences among transcripts within a gene are summarized and transcriptome-level complexity metrics: number of variable nucleotides, unique exons per gene, exons per transcript, and transcripts per gene are calculated. To compare two transcriptomes on the same co-ordinates, a weighted total distance between pairs of transcripts for the same gene is calculated. The weight function proposed has larger penalties for intron retention and exon skipping than alternative donor/acceptor sites. Minimum distances can be used to identify both transcript pairs and transcripts missing structural elements in either of the two annotations. This enables a broad range of functionality from comparing sister species to comparing different methods of building and summarizing transcriptomes. Importantly, the philosophy here is to output metrics, enabling others to explore the nucleotide-level distance metrics. Single transcriptome annotation summaries and pairwise comparisons are implemented in a new tool, TranD, distributed as a PyPi package and in the open-source web-based Galaxy (www.galaxyproject.org) platform.

show abstract

Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs

Cited by 3 publications

References 86 publications

ExOrthist: a tool to infer exon orthologies at any evolutionary distance

ExOrthist: a tool to infer exon orthologies at any evolutionary distance

ASES: visualizing evolutionary conservation of alternative splicing in proteins

The evolution of splicing: transcriptome complexity and transcript distances implemented inTranD

Contact Info

Product

Resources

About