PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets

Yu, Qiang; Huo, Hongwei; Feng, Di

doi:10.1155/2016/4986707

Cited by 8 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In SamSelect, we set w and k to 12 and 1, respectively. With this setting, in addition to the guarantee of good space and time performance, we would also like to obtain more motif information, as the probability analysis shows that count 1 (12-mer) for a motif instance is significantly larger than that for a background substring [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…2 . The initial value of threshold f is set to the sum of N r and N m , where N r and N m are count k ( w -mer) for a background substring and a motif instance for a random case, respectively; the calculation method of N r and N m is given in [ 29 ]. For any two overlapped w -mers, if the length of the overlap is greater than or equal to w /2, we combine the two w -mers into one substring.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets

Wei

Huo

2018

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

BackgroundGiven a set of t n-length DNA sequences, q satisfying 0 < q ≤ 1, and l and d satisfying 0 ≤ d < l < n, the quorum planted motif search (qPMS) finds l-length strings that occur in at least qt input sequences with up to d mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g., t = 20 and n = 600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more.ResultsWe analyze the effects of t and q on the time performance of qPMS algorithms and find that a large t or a small q causes a longer computation time. Based on this information, we improve the time performance of existing qPMS algorithms by selecting a sample sequence set D’ with a small t and a large q from the large input dataset D and then executing qPMS algorithms on D’. A sample sequence selection algorithm named SamSelect is proposed. The experimental results on both simulated and real data show (1) that SamSelect can select D’ efficiently and (2) that the qPMS algorithms executed on D’ can find implanted or real motifs in a significantly shorter time than when executed on D.ConclusionsWe improve the ability of existing qPMS algorithms to process large DNA datasets from the perspective of selecting high-quality sample sequence sets so that the qPMS algorithms can find motifs in a short time in the selected sample sequence set D’, rather than take an unfeasibly long time to search the original sequence set D. Our motif discovery method is an approximate algorithm.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets

Wei

Huo

2018

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering current efforts in analyzing large amounts of data, Yu et al ( 2016 ) proposed a new algorithm, PairMotifChIP, for this purpose. This tool can identify motifs by extracting combining pairs of an “l” width in the input sequences that have small Hamming distance, distinguishing the motifs from random overrepresented sequences by probabilistic analysis and then combines the remaining sequences to form motifs.…”

Section: Eukaryotic Promoters and Transcription Factors: The Blocks Tmentioning

confidence: 99%

“…This tool can identify motifs by extracting combining pairs of an “l” width in the input sequences that have small Hamming distance, distinguishing the motifs from random overrepresented sequences by probabilistic analysis and then combines the remaining sequences to form motifs. This tool runs very fast and does not require previous user information (Yu et al, 2016 ). Caldonazzo Garbelini et al ( 2018 ) created a new approach for motif discovery by making use of a genetic algorithm to escape from optimal local solutions.…”

Section: Eukaryotic Promoters and Transcription Factors: The Blocks Tmentioning

confidence: 99%

Systems and Synthetic Biology Approaches to Engineer Fungi for Fine Chemical Production

Martins-Santana

Nora

Sanches-Medeiros

et al. 2018

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Since the advent of systems and synthetic biology, many studies have sought to harness microbes as cell factories through genetic and metabolic engineering approaches. Yeast and filamentous fungi have been successfully harnessed to produce fine and high value-added chemical products. In this review, we present some of the most promising advances from recent years in the use of fungi for this purpose, focusing on the manipulation of fungal strains using systems and synthetic biology tools to improve metabolic flow and the flow of secondary metabolites by pathway redesign. We also review the roles of bioinformatics analysis and predictions in synthetic circuits, highlighting in silico systemic approaches to improve the efficiency of synthetic modules.

show abstract

“…The recent years have witnessed the proposal of some motif discovery algorithms based on new strategies aimed at efficiently processing large datasets. PairMotifChIP [23] discovers motifs by mining and merging pairs of similar substrings in the input sequences. It spends a large portion of running time on the former operation, which shows quadratic growth as the dataset size increases.…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

Zhang

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Quorum planted (l, d) motif search (qPMS) is a challenging computational problem in bioinformatics, mainly for the identification of regulatory elements such as transcription factor binding sites in DNA sequences. Large DNA datasets play an important role in identifying high-quality (l, d) motifs, while most existing qPMS algorithms are too time-consuming to complete the calculation of qPMS in a reasonable time. We propose an approximate qPMS algorithm called APMS to deal with large DNA datasets mainly by accelerating neighboring substring search and filtering redundant substrings. Experimental results on them show that APMS can not only identify the implanted (l, d) motifs, but also run orders of magnitude faster than the state-of-the-art qPMS algorithms. The source code of APMS and the python wrapper for the code are freely available at https://github.com/qyu071/apms. INDEX TERMS Quorum planted (l, d) motif search, large DNA datasets, transcription factor binding sites.

show abstract

PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets

Cited by 8 publications

References 30 publications

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets

Systems and Synthetic Biology Approaches to Engineer Fungi for Fine Chemical Production

A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

Contact Info

Product

Resources

About