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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 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.

SUBMITTER: Langa J

PROVIDER: S-EPMC7452765 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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EXFI: Exon and splice graph prediction without a reference genome.

Langa Jorge J Estonba Andone A Conklin Darrell D

Ecology and evolution 20200728 16

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 ...[more]

PMID: 32884664

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