BiCluE - Exact and heuristic algorithms for weighted bi-cluster editing of biomedical data

Peng Sun,Jiong Guo,Jan Baumbach

doi:10.1186/1753-6561-7-s7-s9

Peng Sun, Jiong Guo + Show 1 more

Open Access

https://doi.org/10.1186/1753-6561-7-s7-s9

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Abstract

BackgroundThe explosion of biological data has dramatically reformed today's biology research. The biggest challenge to biologists and bioinformaticians is the integration and analysis of large quantity of data to provide meaningful insights. One major problem is the combined analysis of data from different types. Bi-cluster editing, as a special case of clustering, which partitions two different types of data simultaneously, might be used for several biomedical scenarios. However, the underlying algorithmic problem is NP-hard.ResultsHere we contribute with BiCluE, a software package designed to solve the weighted bi-cluster editing problem. It implements (1) an exact algorithm based on fixed-parameter tractability and (2) a polynomial-time greedy heuristics based on solving the hardest part, edge deletions, first. We evaluated its performance on artificial graphs. Afterwards we exemplarily applied our implementation on real world biomedical data, GWAS data in this case. BiCluE generally works on any kind of data types that can be modeled as (weighted or unweighted) bipartite graphs.ConclusionsTo our knowledge, this is the first software package solving the weighted bi-cluster editing problem. BiCluE as well as the supplementary results are available online at http://biclue.mpi-inf.mpg.de.

Highlights

The enormous amount of available data from laboratories around the world has greatly shifted the focus of biologically motivated studies
UniProtKb/Swiss-Prot provides a database containing more than 53,000 annotated sequences, extracted and integrated from 205,244 published references and Protein Data Bank (PDB) has incorporated over 78,400 molecule structures
We focus on the exact and heuristic algorithms that cluster data from different types simultaneously, i.e. so called “bi- cluster editing”

Summary

Introduction

Background The enormous amount of available (sequential) data from laboratories around the world has greatly shifted the focus of biologically motivated studies. UniProtKb/Swiss-Prot provides a database containing more than 53,000 annotated sequences, extracted and integrated from 205,244 published references and Protein Data Bank (PDB) has incorporated over 78,400 molecule structures. Integrating, processing and analyzing large quantities of data from various sources have become the main challenge in modern bioinformatics. The explosion of biological data has dramatically reformed today’s biology research. The biggest challenge to biologists and bioinformaticians is the integration and analysis of large quantity of data to provide meaningful insights. One major problem is the combined analysis of data from different types. Bi-cluster editing, as a special case of clustering, which partitions two different types of data simultaneously, might be used for several biomedical scenarios.

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