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Background: Biclustering 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.

Methods: To 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.

Results: Results 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.

Conclusions: BicPAMS 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.

Software: BicPAMS and its tutorial available in http://www.bicpams.com .

Titel:	BicPAMS: software for biological data analysis with pattern-based biclustering.
Autor/in / Beteiligte Person:	Henriques, R ; Ferreira, FL ; Madeira, SC
Link:	Full Text Finder (Volltext)
Zeitschrift:	BMC bioinformatics, Jg. 18 (2017-02-02), Heft 1, S. 82
Veröffentlichung:	[London] : BioMed Central, 2000-, 2017
Medientyp:	academicJournal
ISSN:	1471-2105 (electronic)
DOI:	10.1186/s12859-017-1493-3
Schlagwort:	Algorithms Cell Line, Tumor Cluster Analysis Gene Regulatory Networks Humans Protein Interaction Mapping Gene Expression Software
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [BMC Bioinformatics] 2017 Feb 02; Vol. 18 (1), pp. 82. <i>Date of Electronic Publication: </i>2017 Feb 02. MeSH Terms: Gene Expression* ; Software* ; Algorithms ; Cell Line, Tumor ; Cluster Analysis ; Gene Regulatory Networks ; Humans ; Protein Interaction Mapping Comments: Erratum in: BMC Bioinformatics. 2017 Mar 9;18(1):162. (PMID: 28279148) References: N Engl J Med. 2002 Jun 20;346(25):1937-47. (PMID: 12075054) ; Nucleic Acids Res. 2015 Jan;43(Database issue):D447-52. (PMID: 25352553) ; Nucleic Acids Res. 2014 Jan;42(Database issue):D161-6. (PMID: 24170807) ; IEEE/ACM Trans Comput Biol Bioinform. 2004 Jan-Mar;1(1):24-45. (PMID: 17048406) ; J Comput Biol. 2003;10(3-4):373-84. (PMID: 12935334) ; Mol Biol Cell. 2000 Dec;11(12):4241-57. (PMID: 11102521) ; Bioinformatics. 2006 May 15;22(10):1282-3. (PMID: 16551664) ; Algorithms Mol Biol. 2011 Jun 23;6(1):18. (PMID: 21699691) ; Nucleic Acids Res. 2016 Jul 8;44(W1):W90-7. (PMID: 27141961) ; Bioinformatics. 2002;18 Suppl 1:S136-44. (PMID: 12169541) ; Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103. (PMID: 10977070) ; Bioinformatics. 2014 Jun 15;30(12):1785-6. (PMID: 24590442) ; Proc Natl Acad Sci U S A. 2000 Oct 24;97(22):12079-84. (PMID: 11035779) ; IEEE/ACM Trans Comput Biol Bioinform. 2015 Jul-Aug;12(4):738-52. (PMID: 26357312) ; Nat Genet. 2007 Oct;39(10):1235-44. (PMID: 17873876) ; Algorithms Mol Biol. 2016 Sep 14;11:23. (PMID: 27651825) ; BMC Bioinformatics. 2014 May 06;15:130. (PMID: 24885271) ; Nucleic Acids Res. 2010 Jan;38(Database issue):D502-7. (PMID: 19880385) ; Algorithms Mol Biol. 2016 May 20;11:14. (PMID: 27213009) ; Bioinformatics. 2010 Jun 15;26(12):1520-7. (PMID: 20418340) ; BMC Genomics. 2007 Nov 29;8:441. (PMID: 18045502) ; Algorithms Mol Biol. 2014 Dec 16;9(1):27. (PMID: 25649207) ; Brief Bioinform. 2013 May;14(3):279-92. (PMID: 22772837) Entry Date(s): Date Created: 20170204 Date Completed: 20170818 Latest Revision: 20240324 Update Code: 20240324 PubMed Central ID: PMC5290636

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