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SMF: Approximate Algorithm for the Planted (l, d) Motif Finding Problem in DNA Sequences.

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Title: SMF: Approximate Algorithm for the Planted (l, d) Motif Finding Problem in DNA Sequences.
Authors: Al-Shaikhli, Hasnaa, de Doncker, Elise
Source: Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP) (American Council on Science & Education); 2018, p123-129, 7p
Subject Terms: Big data, Nucleotide sequencing, Prediction models, Nucleotide sequence, Electronic data processing, Simulation methods & models
Abstract: Motif discovery is the problem of finding common substrings within a set of biological strings. Therefore, it can be applied to find Transcription Factors Binding Sites (TFBS) that have common patterns (motifs). The Planted (l; d) Motif Problem (PMP) is a classic version of motif discovery where l is the motif length and d represents the maximum allowed mutation distance. In this paper we present an approximate algorithm, Strong Motif Finder (SMF), to return the k highest ranked (strongest) motifs. We propose a scoring function, Motif Strength Score (MSS), which ranks the motifs discovered in the course of the SMF algorithm. We compare the performance of SMF with the APMotif and MEME algorithms with respect to execution time and prediction accuracy. We use several performance metrics at both the nucleotide and the site level. The algorithms are tested on simulated datasets, and results of SMF are also given for real DNA datasets of orthologous regularity regions from multiple species, without using their related phylogenetic tree. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP) (American Council on Science & Education) is the property of American Council on Science & Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Complementary Index
An: 133748944
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PubTypeId: conference
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  Label: Title
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  Data: SMF: Approximate Algorithm for the Planted (l, d) Motif Finding Problem in DNA Sequences.
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  Data: <searchLink fieldCode="AR" term="%22Al-Shaikhli%2C+Hasnaa%22">Al-Shaikhli, Hasnaa</searchLink><br /><searchLink fieldCode="AR" term="%22de+Doncker%2C+Elise%22">de Doncker, Elise</searchLink>
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  Data: Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP) (American Council on Science & Education); 2018, p123-129, 7p
– Name: Subject
  Label: Subject Terms
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  Data: <searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleotide+sequencing%22">Nucleotide sequencing</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleotide+sequence%22">Nucleotide sequence</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Motif discovery is the problem of finding common substrings within a set of biological strings. Therefore, it can be applied to find Transcription Factors Binding Sites (TFBS) that have common patterns (motifs). The Planted (l; d) Motif Problem (PMP) is a classic version of motif discovery where l is the motif length and d represents the maximum allowed mutation distance. In this paper we present an approximate algorithm, Strong Motif Finder (SMF), to return the k highest ranked (strongest) motifs. We propose a scoring function, Motif Strength Score (MSS), which ranks the motifs discovered in the course of the SMF algorithm. We compare the performance of SMF with the APMotif and MEME algorithms with respect to execution time and prediction accuracy. We use several performance metrics at both the nucleotide and the site level. The algorithms are tested on simulated datasets, and results of SMF are also given for real DNA datasets of orthologous regularity regions from multiple species, without using their related phylogenetic tree. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP) (American Council on Science & Education) is the property of American Council on Science & Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 7
        StartPage: 123
    Subjects:
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Nucleotide sequencing
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Nucleotide sequence
        Type: general
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
    Titles:
      – TitleFull: SMF: Approximate Algorithm for the Planted (l, d) Motif Finding Problem in DNA Sequences.
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          Name:
            NameFull: Al-Shaikhli, Hasnaa
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            NameFull: de Doncker, Elise
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          Dates:
            – D: 01
              M: 01
              Text: 2018
              Type: published
              Y: 2018
          Titles:
            – TitleFull: Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP) (American Council on Science & Education)
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