EXFI: Exon and splice graph prediction without a reference genome

Abstract 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...

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Bibliographic Details
Main Authors: Jorge Langa, Andone Estonba, Darrell Conklin
Format: Article
Language:English
Published: Wiley 2020-08-01
Series:Ecology and Evolution
Subjects:
Online Access:https://doi.org/10.1002/ece3.6587
Description
Summary:Abstract 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.
ISSN:2045-7758