AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures
Abstract Motivation Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive e...
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| Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2023-01, Vol.39 (1) |
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| creator | Binet, Thomas Avalle, Bérangère Dávila Felipe, Miraine Maffucci, Irene |
| description | Abstract
Motivation
Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures.
Results
In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure.
Availability and implementation
The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git.
Supplementary information
Supplementary data are available at Bioinformatics online. |
| doi_str_mv | 10.1093/bioinformatics/btac752 |
| format | Article |
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Motivation
Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures.
Results
In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure.
Availability and implementation
The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4811</identifier><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btac752</identifier><identifier>PMID: 36440922</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Availability ; Bioinformatics ; Cluster Analysis ; Clustering ; Humans ; Life Sciences ; Nucleic Acids ; Oligonucleotides ; Original Paper ; Protein Structure, Secondary ; Quantitative Methods ; Sequences ; Software</subject><ispartof>Bioinformatics (Oxford, England), 2023-01, Vol.39 (1)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c518t-bf9e986eaeb23e9fe2a1954dd62ecda2d415964c6bccd9c503076ae44fd57bb93</citedby><cites>FETCH-LOGICAL-c518t-bf9e986eaeb23e9fe2a1954dd62ecda2d415964c6bccd9c503076ae44fd57bb93</cites><orcidid>0000-0002-4524-1137 ; 0000-0002-6481-1799</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805580/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805580/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36440922$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-03879762$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Binet, Thomas</creatorcontrib><creatorcontrib>Avalle, Bérangère</creatorcontrib><creatorcontrib>Dávila Felipe, Miraine</creatorcontrib><creatorcontrib>Maffucci, Irene</creatorcontrib><title>AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures.
Results
In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure.
Availability and implementation
The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Availability</subject><subject>Bioinformatics</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Nucleic Acids</subject><subject>Oligonucleotides</subject><subject>Original Paper</subject><subject>Protein Structure, Secondary</subject><subject>Quantitative Methods</subject><subject>Sequences</subject><subject>Software</subject><issn>1367-4811</issn><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqNkUtv1DAUhS0EoqXwF6pIbGAR6kfsxCyQRhVQpEFsYG35cTPjKrGD7VTl3-NqhqrtipUt-zvHx_cgdE7wB4IluzA--jDGNOvibb4wRdue02folDDRt91AyPMH-xP0KudrjDHHXLxEJ0x0HZaUniKzWYr-rsvHRjfVK_nb1ugMrtHTLiZf9nNTYmPjvOgETfZhN0GbS9LBVShOfhfDaieIxTvITQYbg9PpT1OZ1ZY1QX6NXox6yvDmuJ6hX18-_7y8arc_vn673Gxby8lQWjNKkIMADYYykCNQTSTvnBMUrNPUdYRL0VlhrHXScsxwLzR03eh4b4xkZ-jTwXdZzQzOQqgxJ7UkP9dAKmqvHt8Ev1e7eKPkgDkfcDV4fzDYP5Fdbbbq7gyzoZe9oDeksu-Oj6X4e4Vc1OyzhWnSAeKaFe3rgPHQC1bRt0_Q67imUEehGGGMU0bkUClxoGyKOScY7xMQrO4qV48rV8fKq_D84bfvZf86rgA5AHFd_tf0L-mhwjM</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Binet, Thomas</creator><creator>Avalle, Bérangère</creator><creator>Dávila Felipe, Miraine</creator><creator>Maffucci, Irene</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><general>Oxford University Press (OUP)</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4524-1137</orcidid><orcidid>https://orcid.org/0000-0002-6481-1799</orcidid></search><sort><creationdate>20230101</creationdate><title>AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures</title><author>Binet, Thomas ; Avalle, Bérangère ; Dávila Felipe, Miraine ; Maffucci, Irene</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c518t-bf9e986eaeb23e9fe2a1954dd62ecda2d415964c6bccd9c503076ae44fd57bb93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Availability</topic><topic>Bioinformatics</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Nucleic Acids</topic><topic>Oligonucleotides</topic><topic>Original Paper</topic><topic>Protein Structure, Secondary</topic><topic>Quantitative Methods</topic><topic>Sequences</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Binet, Thomas</creatorcontrib><creatorcontrib>Avalle, Bérangère</creatorcontrib><creatorcontrib>Dávila Felipe, Miraine</creatorcontrib><creatorcontrib>Maffucci, Irene</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Binet, Thomas</au><au>Avalle, Bérangère</au><au>Dávila Felipe, Miraine</au><au>Maffucci, Irene</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2023-01-01</date><risdate>2023</risdate><volume>39</volume><issue>1</issue><issn>1367-4811</issn><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>Abstract
Motivation
Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures.
Results
In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure.
Availability and implementation
The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git.
Supplementary information
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36440922</pmid><doi>10.1093/bioinformatics/btac752</doi><orcidid>https://orcid.org/0000-0002-4524-1137</orcidid><orcidid>https://orcid.org/0000-0002-6481-1799</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Bioinformatics (Oxford, England), 2023-01, Vol.39 (1) |
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| language | eng |
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| source | Oxford Journals Open Access Collection; MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Alma/SFX Local Collection |
| subjects | Algorithms Availability Bioinformatics Cluster Analysis Clustering Humans Life Sciences Nucleic Acids Oligonucleotides Original Paper Protein Structure, Secondary Quantitative Methods Sequences Software |
| title | AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures |
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