Open AccessEXFI: Exon and splice graph prediction without a reference genome
Author(s) -
Langa Jorge,
Estonba Andone,
Conklin Darrell
Publication year - 2020
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.6587
Subject(s) - python (programming language) , exon , splice , computational biology , genome , computer science , intron , source code , genomics , biology , population , genetics , exon trapping , alternative splicing , gene , programming language , demography , sociology
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.