Literature DB >> 22024209

PMS5: an efficient exact algorithm for the (ℓ, d)-motif finding problem.

Hieu Dinh1, Sanguthevar Rajasekaran, Vamsi K Kundeti.   

Abstract

BACKGROUND: Motifs are patterns found in biological sequences that are vital for understanding gene function, human disease, drug design, etc. They are helpful in finding transcriptional regulatory elements, transcription factor binding sites, and so on. As a result, the problem of identifying motifs is very crucial in biology.
RESULTS: Many facets of the motif search problem have been identified in the literature. One of them is (ℓ, d)-motif search (or Planted Motif Search (PMS)). The PMS problem has been well investigated and shown to be NP-hard. Any algorithm for PMS that always finds all the (ℓ, d)-motifs on a given input set is called an exact algorithm. In this paper we focus on exact algorithms only. All the known exact algorithms for PMS take exponential time in some of the underlying parameters in the worst case scenario. But it does not mean that we cannot design exact algorithms for solving practical instances within a reasonable amount of time. In this paper, we propose a fast algorithm that can solve the well-known challenging instances of PMS: (21, 8) and (23, 9). No prior exact algorithm could solve these instances. In particular, our proposed algorithm takes about 10 hours on the challenging instance (21, 8) and about 54 hours on the challenging instance (23, 9). The algorithm has been run on a single 2.4GHz PC with 3GB RAM. The implementation of PMS5 is freely available on the web at http://www.pms.engr.uconn.edu/downloads/PMS5.zip.
CONCLUSIONS: We present an efficient algorithm PMS5 that uses some novel ideas and combines them with well-known algorithm PMS1 and PMSPrune. PMS5 can tackle the large challenging instances (21, 8) and (23, 9). Therefore, we hope that PMS5 will help biologists discover longer motifs in the futures.

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Year:  2011        PMID: 22024209      PMCID: PMC3269969          DOI: 10.1186/1471-2105-12-410

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.169


  19 in total

1.  A statistical method for finding transcription factor binding sites.

Authors:  S Sinha; M Tompa
Journal:  Proc Int Conf Intell Syst Mol Biol       Date:  2000

2.  Finding motifs in the twilight zone.

Authors:  U Keich; P A Pevzner
Journal:  Bioinformatics       Date:  2002-10       Impact factor: 6.937

3.  Finding composite regulatory patterns in DNA sequences.

Authors:  Eleazar Eskin; Pavel A Pevzner
Journal:  Bioinformatics       Date:  2002       Impact factor: 6.937

4.  Finding subtle motifs by branching from sample strings.

Authors:  Alkes Price; Sriram Ramabhadran; Pavel A Pevzner
Journal:  Bioinformatics       Date:  2003-10       Impact factor: 6.937

5.  Methods for discovering novel motifs in nucleic acid sequences.

Authors:  R Staden
Journal:  Comput Appl Biosci       Date:  1989-10

6.  Exact algorithms for planted motif problems.

Authors:  S Rajasekaran; S Balla; C-H Huang
Journal:  J Comput Biol       Date:  2005-10       Impact factor: 1.479

7.  Fast and practical algorithms for planted (l, d) motif search.

Authors:  Jaime Davila; Sudha Balla; Sanguthevar Rajasekaran
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2007 Oct-Dec       Impact factor: 3.710

8.  An experimental comparison of PMSprune and other algorithms for motif search.

Authors:  Dolly Sharma; Sanguthevar Rajasekaran; Sudipta Pathak
Journal:  Int J Bioinform Res Appl       Date:  2014

9.  Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment.

Authors:  C E Lawrence; S F Altschul; M S Boguski; J S Liu; A F Neuwald; J C Wootton
Journal:  Science       Date:  1993-10-08       Impact factor: 47.728

10.  Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies.

Authors:  J van Helden; B André; J Collado-Vides
Journal:  J Mol Biol       Date:  1998-09-04       Impact factor: 5.469

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  17 in total

1.  PMS6: A Fast Algorithm for Motif Discovery.

Authors:  Shibdas Bandyopadhyay; Sartaj Sahni; Sanguthevar Rajasekaran
Journal:  IEEE Int Conf Comput Adv Bio Med Sci       Date:  2012

2.  PMS6MC: A Multicore Algorithm for Motif Discovery.

Authors:  Shibdas Bandyopadhyay; Sartaj Sahni; Sanguthevar Rajasekaran
Journal:  Algorithms       Date:  2013-11-18

3.  Efficient algorithms for biological stems search.

Authors:  Tian Mi; Sanguthevar Rajasekaran
Journal:  BMC Bioinformatics       Date:  2013-05-16       Impact factor: 3.169

4.  Efficient sequential and parallel algorithms for planted motif search.

Authors:  Marius Nicolae; Sanguthevar Rajasekaran
Journal:  BMC Bioinformatics       Date:  2014-01-31       Impact factor: 3.169

5.  PairMotif: A new pattern-driven algorithm for planted (l, d) DNA motif search.

Authors:  Qiang Yu; Hongwei Huo; Yipu Zhang; Hongzhi Guo
Journal:  PLoS One       Date:  2012-10-31       Impact factor: 3.240

6.  qPMS7: a fast algorithm for finding (ℓ, d)-motifs in DNA and protein sequences.

Authors:  Hieu Dinh; Sanguthevar Rajasekaran; Jaime Davila
Journal:  PLoS One       Date:  2012-07-24       Impact factor: 3.240

7.  A hybrid method for the exact planted (l, d) motif finding problem and its parallelization.

Authors:  Mostafa M Abbas; Mohamed Abouelhoda; Hazem M Bahig
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

8.  PairMotif+: a fast and effective algorithm for de novo motif discovery in DNA sequences.

Authors:  Qiang Yu; Hongwei Huo; Yipu Zhang; Hongzhi Guo; Haitao Guo
Journal:  Int J Biol Sci       Date:  2013-04-29       Impact factor: 6.580

9.  An Affinity Propagation-Based DNA Motif Discovery Algorithm.

Authors:  Chunxiao Sun; Hongwei Huo; Qiang Yu; Haitao Guo; Zhigang Sun
Journal:  Biomed Res Int       Date:  2015-08-10       Impact factor: 3.411

10.  PMS: a panoptic motif search tool.

Authors:  Hieu Dinh; Sanguthevar Rajasekaran
Journal:  PLoS One       Date:  2013-12-04       Impact factor: 3.240

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