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AnnotaPipeline: An integrated tool to annotate eukaryotic proteins using multi-omics data.

Guilherme Augusto Maia¹, Vilmar Benetti Filho¹, Eric Kazuo Kawagoe¹

¹Laboratório de Bioinformática, Universidade Federal de Santa Catarina (UFSC), Campus João David Ferreira Lima, Florianópolis, Brazil.

Frontiers in Genetics

|December 9, 2022

Summary

This summary is machine-generated.

AnnotaPipeline automates gene function annotation using transcriptomic and proteomic data. This computational workflow enhances genomic sequence accuracy by reducing hypothetical proteins.

Keywords:

functional annotation genome annotation hypothetical proteins proteogenomics workflow

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Manual gene function assignment is a bottleneck in genomics.
Increasing data volume necessitates automated annotation solutions.
Integrating experimental data validates in silico predictions.

Purpose of the Study:

To present AnnotaPipeline, an automated workflow for gene function annotation and validation.
To integrate diverse data types (genomic, transcriptomic, proteomic) for robust annotation.
To improve the accuracy of gene predictions in genomic sequences.

Main Methods:

Developed a Unix-based pipeline using Python.
Integrated FASTA, GFF3, FASTQ RNA-seq, and mzXML data formats.
Employed a proteogenomic approach combining transcriptomic and proteomic evidence.

Main Results:

Reannotated multiple genomes (Arabidopsis thaliana, Caenorhabditis elegans, etc.).
Achieved a higher proportion of annotated proteins compared to public annotations.
Reduced the number of hypothetical proteins in annotated genomes.

Conclusions:

AnnotaPipeline provides an effective automated solution for gene function annotation.
The proteogenomic approach enhances annotation accuracy and reduces ambiguity.
This workflow is crucial for advancing genomic research and data interpretation.