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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

A weighted power framework for integrating multisource information: gene function prediction in yeast.

Shubhra Sankar Ray¹, Sanghamitra Bandyopadhyay, Sankar K Pal

¹Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. shubhra@isical.ac.in

IEEE Transactions on Bio-Medical Engineering

|February 10, 2012

Summary

This study introduces a novel Weighted Power Biological Score (WPBS) to predict functions of unannotated yeast genes. The framework effectively combines diverse biological data, aiding in gene function discovery.

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Predicting gene function is crucial for understanding biological systems.
Unannotated genes present a significant challenge in genomic research.
Integrating diverse biological data can improve functional predictions.

Purpose of the Study:

To develop a novel framework for predicting functions of unannotated genes.
To combine multiple biological data sources effectively.
To identify functional categories for unclassified Saccharomyces cerevisiae genes.

Main Methods:

Developed a Weighted Power Biological Score (WPBS) framework.
Estimated data source weights using yeast Gene Ontology (GO)-Slim annotations.
Applied k-medoids clustering to WPBS for gene grouping.
Predicted functions for 334 unclassified genes using a P-value cutoff of 1 ×10(-5).

Main Results:

Successfully predicted functional categories for 334 unclassified yeast genes.
The WPBS framework demonstrated effectiveness in integrating diverse biological data.
Identified potential functions for genes lacking annotation.

Conclusions:

The WPBS framework provides a robust method for predicting gene function.
This approach aids in the functional annotation of genomes.
The WPBS tool and code are publicly available for broader research application.