Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set
There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, whi...
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| Format: | Online |
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BioMed Central
2005
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1637034/ |
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pubmed-16370342006-11-16 Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set Wren, Jonathan D Johnson, David Gruenwald, Le Proceedings There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands. BioMed Central 2005-07-15 /pmc/articles/PMC1637034/ /pubmed/16026599 http://dx.doi.org/10.1186/1471-2105-6-S2-S2 Text en Copyright © 2006 Wren et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Wren, Jonathan D Johnson, David Gruenwald, Le |
| spellingShingle |
Wren, Jonathan D Johnson, David Gruenwald, Le Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set |
| author_facet |
Wren, Jonathan D Johnson, David Gruenwald, Le |
| author_sort |
Wren, Jonathan D |
| title |
Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set |
| title_short |
Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set |
| title_full |
Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set |
| title_fullStr |
Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set |
| title_full_unstemmed |
Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set |
| title_sort |
automating genomic data mining via a sequence-based matrix format and associative rule set |
| description |
There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands. |
| publisher |
BioMed Central |
| publishDate |
2005 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1637034/ |
| _version_ |
1611390766767144960 |