BicPAMS: software for biological data analysis with pattern-based biclustering

Abstract

BackgroundBiclustering has been largely applied for the unsupervised analysis of biological data, being recognised today as a key technique to discover putative modules in both expression data (subsets of genes correlated in subsets of conditions) and network data (groups of coherently interconnected biological entities). However, given its computational complexity, only recent breakthroughs on pattern-based biclustering enabled efficient searches without the restrictions that state-of-the-art biclustering algorithms place on the structure and homogeneity of biclusters. As a result, pattern-based biclustering provides the unprecedented opportunity to discover non-trivial yet meaningful biological modules with putative functions, whose coherency and tolerance to noise can be tuned and made problem-specific.MethodsTo enable the effective use of pattern-based biclustering by the scientific community, we developed BicPAMS (Biclustering based on PAttern Mining Software), a software that: 1) makes available state-of-the-art pattern-based biclustering algorithms (BicPAM (Henriques and Madeira, Alg Mol Biol 9:27, 2014), BicNET (Henriques and Madeira, Alg Mol Biol 11:23, 2016), BicSPAM (Henriques and Madeira, BMC Bioinforma 15:130, 2014), BiC2PAM (Henriques and Madeira, Alg Mol Biol 11:1–30, 2016), BiP (Henriques and Madeira, IEEE/ACM Trans Comput Biol Bioinforma, 2015), DeBi (Serin and Vingron, AMB 6:1–12, 2011) and BiModule (Okada et al., IPSJ Trans Bioinf 48(SIG5):39–48, 2007)); 2) consistently integrates their dispersed contributions; 3) further explores additional accuracy and efficiency gains; and 4) makes available graphical and application programming interfaces.ResultsResults on both synthetic and real data confirm the relevance of BicPAMS for biological data analysis, highlighting its essential role for the discovery of putative modules with non-trivial yet biologically significant functions from expression and network data.ConclusionsBicPAMS is the first biclustering tool offering the possibility to: 1) parametrically customize the structure, coherency and quality of biclusters; 2) analyze large-scale biological networks; and 3) tackle the restrictive assumptions placed by state-of-the-art biclustering algorithms. These contributions are shown to be key for an adequate, complete and user-assisted unsupervised analysis of biological data.SoftwareBicPAMS and its tutorial available in http://www.bicpams.com.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1493-3) contains supplementary material, which is available to authorized users.

Moving from Formal Towards Coherent Concept Analysis: Why, When and How

“…This behavior explains why this class of biclustering algorithms are receiving an increasing attention in recent years [13,18]. BicPAMS [14] consistently combines such state-of-the-art contributions on pattern-based biclustering.…”

“…Dissimilarity criteria can be further placed to comprehensively cover the vector space with non-redundant biclusters [14].…”

“…In recent years, a clearer understanding of the synergies between biclustering and pattern mining paved the rise for a new class of algorithms, referred to as pattern-based biclustering algorithms [13]. Pattern-based biclustering algorithms are inherently prepared to efficiently find exhaustive solutions of biclusters and offer the unprecedented possibility to affect their structure, coherency and quality [12,14]. This behavior explains why this class of biclustering algorithms are receiving an increasing attention in recent years [13,18].…”

“…parameterizable homogeneity and guarantees of statistical significance [14]. Despite their relevance, their use to explore digital collections remains largely unassessed.…”

BicGO: a new biclustering algorithm based on global optimization

“…In the real world, the biclusters to be detected are usually very narrow of few columns and great many rows. However, all the existing tools but BicPAMS [30] and EBIC [24] tend to collapse in this situation. It is thus imperative to develop a new biclustering algorithm which is more powerful for recognizing not only broader but also narrower biclusters.…”