EXFI: Exon and splice graph prediction without a reference genome

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Accurate genomic information is crucial for population genetic studies, especially in nonmodel organisms.
• Existing methods may not fully utilize all available genomic data sources.

Purpose of the Study:

• To present EXFI, a novel Python pipeline for predicting splice graphs and exon sequences.
• To enable comprehensive genomic analysis in nonmodel organisms by integrating transcriptomic and whole-genome sequencing data.

Main Methods:

• EXFI utilizes Bloom filters to identify and filter reads belonging to the transcriptome.
• The pipeline predicts intron-exon boundaries and calls exons from the assembled transcriptome.
• It generates a splice graph representing transcript structures.

Main Results:

• EXFI successfully predicts exon sequences and their connections, forming a splice graph.
• The output is provided in GFA1 format, detailing exon sequences and transcript assembly.
• The pipeline is implemented in Python and tested on Linux systems.

Conclusions:

• EXFI offers a valuable tool for population geneticists studying nonmodel organisms.
• The pipeline enhances the utilization of genomic information for understanding genetic diversity and structure.