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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Developing a bioinformatics pipeline for comparative protein classification analysis.

Benedetta Pelosi¹

¹Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden. benedetta.pelosi2011@gmail.com.

BMC Genomic Data

|June 6, 2022

Summary

A new bioinformatics pipeline effectively classifies protein families, overcoming limitations of machine learning. This method accurately identifies carotenoid biosynthetic genes in plants, proving useful for diverse biological datasets.

Keywords:

Arabidopsis thaliana Bioinformatics pipeline Biosynthetic pathway Brassica rapa Brassica rapa Pekinensis group Carotenoid biosynthetic genes Comparative genomics

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Protein classification is crucial in biology but faces challenges with machine learning methods like Random Forest and Neural Network due to data issues.
These challenges include difficulties in data collection, unbalanced datasets, and data labeling.

Purpose of the Study:

To propose and evaluate a novel bioinformatics pipeline as an alternative for protein classification.
To assess the pipeline's efficiency and accuracy in identifying carotenoid biosynthetic genes and their orthologs in plants.

Main Methods:

The pipeline integrates commercial software, multiple databases, phylogenetic analysis, Gene Ontology (GO) terms, and Protein Families (Pfam) at the protein level.
Phylogeny is combined with population analysis to identify potential orthologs, generating a final table of conserved genes.

Main Results:

The pipeline identified 43 putative orthologs conserved in Brassica rapa Pekinensis group.
Multiple Arabidopsis thaliana proteins were found to have more than one syntenic ortholog, consistent with prior research.

Conclusions:

The study validates a computational approach for protein classification when biological features are not highly specific.
The developed bioinformatics pipeline demonstrates accuracy and applicability for filtering various biological datasets, as confirmed by its performance on carotenoid biosynthetic genes.