Time-frequency based biological sequence querying

Ravichandran, Lakshminarayan; Papandreou-Suppappola, Antonia; Spanias, Andreas; Lacroix, Zoé; Legendre, Christophe

doi:10.1109/icassp.2010.5495708

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…It has been an active field of research for the past 25 years. While most current GSP methods focus on identifying protein-coding regions in DNA sequences (e.g., [ 1 – 10 ]), other applications include searching for genomic repeats [ 11 ], determining the structural, thermodynamic, and bending properties of DNA [ 12 ], biological sequence querying [ 13 ], estimating of DNA sequence similarity [ 14 – 16 ], and sequence alignment [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

On DNA numerical representations for genomic similarity computation

et al. 2017

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Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

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Section: Introductionmentioning

confidence: 99%

On DNA numerical representations for genomic similarity computation

et al. 2017

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“…Numerical or vector transformations of nucleotide sequences introduce and/or reveal a mathematical structure to certain genomic elements. Mathematical models that capture these structures have previously been used in determining the structural, thermodynamic, and bending properties of DNA [1], biological sequence querying [2], estimating DNA sequence similarity [3][4][5], sequence alignment [6], and identification of repetitive sequences [7].…”

Section: Introductionmentioning

confidence: 99%

DiviSSR: Simple arithmetic for efficient identification of tandem repeats

Avvaru

Mishra

Sowpati

2021

Preprint

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Numerical or vector representations of DNA sequences have been applied for identification of specific sequence characteristics and patterns which are not evident in their character (A, C, G, T) representations. These transformations often reveal a mathematical structure to the sequences which can be captured efficiently using established mathematical methods. One such transformation, the 2-bit format, represents each nucleotide using only two bits instead of eight for efficient storage of genomic data. Here we describe a mathematical property that exists in the 2-bit representation of tandemly repeated DNA sequences. Our tool, DiviSSR (pronounced divisor), leverages this property and subsequent arithmetic for ultrafast and accurate identification of tandem repeats. DiviSSR can process the entire human genome in ~30s, and short sequence reads at a rate of >1 million reads/s on a single CPU thread. Our work also highlights the implications of using simple mathematical properties of DNA sequences for faster algorithms in genomics.

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“…GSP methods require the transformation or mapping of the biological sequences, usually represented as a string of characters (i.e., A, T, G and C) to a numeric representation (i.e., a signal) that can be processed using mathematical functions ( Kwan & Arniker, 2009 ). Examples of the use of GSP methods include the identification of protein-coding regions in DNA sequences ( Das & Turkoglu, 2017 ; Mabrouk, 2017 ; Das & Turkoglu, 2015 ; Inbamalar & Sivakumar, 2012 ; Marhon & Kremer, 2011 ; Akhtar, Epps & Ambikairajah, 2008 ; Akhtar, Epps & Ambikairajah, 2007 ; Rushdi & Tuqan, 2006 ; Yin & Yau, 2005 ; Kotlar, 2003 ; Anastassiou, 2000 ), finding for genomic repeats ( Sharma et al, 2004 ), determining the structural, thermodynamic, and bending properties of DNA ( Gabrielian & Pongor, 1996 ), biological sequence querying ( Ravichandran et al, 2010 ), estimating of DNA sequence similarity ( Mendizabal-Ruiz et al, 2017 ; Hoang, Yin & Yau, 2016 ; Yin, Yin & Wang, 2014 ; Borrayo et al, 2014 ; Cheever et al, 1989 ), and sequence alignment ( Skutkova et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Genomic signal processing for DNA sequence clustering

Mendizabal-Ruiz

Román-Godínez

Torres-Ramos

et al. 2018

PeerJ

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Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

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Time-frequency based biological sequence querying

Cited by 6 publications

References 17 publications

On DNA numerical representations for genomic similarity computation

On DNA numerical representations for genomic similarity computation

DiviSSR: Simple arithmetic for efficient identification of tandem repeats

Genomic signal processing for DNA sequence clustering

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