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Novel Sequence Discovery by Subtractive Genomics

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Published on: January 25, 2019

Iterative Variable Gene Discovery from Whole Genome Sequencing with a Bootstrapped Multiresolution Algorithm.

David N Olivieri¹, Francisco Gambón-Deza²

¹Department of Computer Science, University of Vigo, Ourense 32004, Spain.

Computational and Mathematical Methods in Medicine

|March 20, 2019

Summary

This summary is machine-generated.

This study introduces a novel machine learning algorithm to identify variable (V) genes in jawed vertebrates. The method aids in understanding immune receptor evolution by discovering V-genes across diverse species.

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Area of Science:

Immunology
Genomics
Bioinformatics

Background:

Variable (V) genes encode antigen-binding regions for B and T lymphocyte receptors in jawed vertebrates.
Understanding the V-gene repertoire is crucial for studying immune receptor evolution and function.
Current V-gene annotation methods are limited to a few model species due to low sequence homology in distant taxa.

Purpose of the Study:

To develop a computational method for discovering V-genes in jawed vertebrates, overcoming limitations of standard gene-finding algorithms.
To enable the study of V-gene repertoire evolution across a broader range of taxa.
To provide a tool for identifying homologous V-exons from whole genome shotgun (WGS) datasets.

Main Methods:

An iterative supervised machine learning algorithm was developed, starting with a small set of known V-genes.
The algorithm progressively discovers homologous unaligned V-exons from WGS datasets.
Newly identified V-genes are added to the training set in each iteration, improving discovery in more distant taxa.

Main Results:

The algorithm successfully identified V-genes from 14 primate WGS datasets.
Results were validated against existing Ensembl annotations, demonstrating accuracy.
The iterative learning approach proved effective for discovering V-genes in successively more distant taxa.

Conclusions:

The developed iterative machine learning algorithm is effective for discovering V-gene repertoires in jawed vertebrates.
This method significantly advances the ability to study the evolution and function of immune receptors.
The freely available Python implementation facilitates broader research in comparative genomics and immunology.