EXFI: Exon and splice graph prediction without a reference genome

• Published in: Ecology and evolution Vol. 10; no. 16; pp. 8880 - 8893
• Main Authors: Langa, Jorge, Estonba, Andone, Conklin, Darrell
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.08.2020; John Wiley and Sons Inc; Wiley
• Subjects: Algorithms; Annotations; Chromosomes; Datasets; Deoxyribonucleic acid; DNA; exome sequencing; exon; Exons; Experiments; Gene expression; Gene sequencing; Genes; Genetic engineering; Genomes; Graph representations; Original Research; Population genetics; Population studies; sequence capture; SNP discovery; Source code; splice graph; transcriptome; Whole genome sequencing; Zebrafish; sequence capture; exome sequencing; splice graph; SNP discovery; exon; transcriptome
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.6587

For population genetic studies in nonmodel organisms, it is important to use every single source of genomic information. This paper presents EXFI, a Python pipeline that predicts the splice graph and exon sequences using an assembled transcriptome and raw whole‐genome sequencing reads. The main algorithm uses Bloom filters to remove reads that are not part of the transcriptome, to predict the intron–exon boundaries, to then proceed to call exons from the assembly, and to generate the underlying splice graph. The results are returned in GFA1 format, which encodes both the predicted exon sequences and how they are connected to form transcripts. EXFI is written in Python, tested on Linux platforms, and the source code is available under the MIT License at https://github.com/jlanga/exfi. EXFI predicts the splice graph from a transcriptome and selected WGS reads. Transcripts are splitted into exons given the information present in the WGS experiment. Predictions are suitable for downstream bioinformatic analyses and new experimental designs.