Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index
<p>Abstract</p> <p>Background</p> <p>The identification of functional regions contained in a given multiple sequence alignment constitutes one of the major challenges of comparative genomics. Several studies have focused on the identification of conserved regions and mo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2011-10-01
|
| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/S9/S9 |
| _version_ | 1811246237148512256 |
|---|---|
| author | Makarenkov Vladimir Diallo Abdoulaye Boc Alix Badescu Dunarel |
| author_facet | Makarenkov Vladimir Diallo Abdoulaye Boc Alix Badescu Dunarel |
| author_sort | Makarenkov Vladimir |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>The identification of functional regions contained in a given multiple sequence alignment constitutes one of the major challenges of comparative genomics. Several studies have focused on the identification of conserved regions and motifs. However, most of existing methods ignore the relationship between the functional genomic regions and the external evidence associated with the considered group of species (e.g., carcinogenicity of Human Papilloma Virus). In the past, we have proposed a method that takes into account the prior knowledge on an external evidence (e.g., carcinogenicity or invasivity of the considered organisms) and identifies genomic regions related to a specific disease.</p> <p>Results and conclusion</p> <p>We present a new algorithm for detecting genomic regions that may be associated with a disease. Two new variability functions and a bipartition optimization procedure are described. We validate and weigh our results using the Adjusted Rand Index (ARI), and thus assess to what extent the selected regions are related to carcinogenicity, invasivity, or any other species classification, given as input. The predictive power of different hit region detection functions was assessed on synthetic and real data. Our simulation results suggest that there is no a single function that provides the best results in all practical situations (e.g., monophyletic or polyphyletic evolution, and positive or negative selection), and that at least three different functions might be useful. The proposed hit region identification functions that do not benefit from the prior knowledge (i.e., carcinogenicity or invasivity of the involved organisms) can provide equivalent results than the existing functions that take advantage of such a prior knowledge. Using the new algorithm, we examined the <it>Neisseria meningitidis</it> FrpB gene product for invasivity and immunologic activity, and human papilloma virus (HPV) E6 oncoprotein for carcinogenicity, and confirmed some well-known molecular features, including surface exposed loops for <it>N. meningitidis</it> and PDZ domain for HPV.</p> |
| first_indexed | 2024-04-12T14:50:45Z |
| format | Article |
| id | doaj.art-754efeede7eb4bcb93e9e20751b3dee8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T14:50:45Z |
| publishDate | 2011-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-754efeede7eb4bcb93e9e20751b3dee82022-12-22T03:28:27ZengBMCBMC Bioinformatics1471-21052011-10-0112Suppl 9S910.1186/1471-2105-12-S9-S9Detecting genomic regions associated with a disease using variability functions and Adjusted Rand IndexMakarenkov VladimirDiallo AbdoulayeBoc AlixBadescu Dunarel<p>Abstract</p> <p>Background</p> <p>The identification of functional regions contained in a given multiple sequence alignment constitutes one of the major challenges of comparative genomics. Several studies have focused on the identification of conserved regions and motifs. However, most of existing methods ignore the relationship between the functional genomic regions and the external evidence associated with the considered group of species (e.g., carcinogenicity of Human Papilloma Virus). In the past, we have proposed a method that takes into account the prior knowledge on an external evidence (e.g., carcinogenicity or invasivity of the considered organisms) and identifies genomic regions related to a specific disease.</p> <p>Results and conclusion</p> <p>We present a new algorithm for detecting genomic regions that may be associated with a disease. Two new variability functions and a bipartition optimization procedure are described. We validate and weigh our results using the Adjusted Rand Index (ARI), and thus assess to what extent the selected regions are related to carcinogenicity, invasivity, or any other species classification, given as input. The predictive power of different hit region detection functions was assessed on synthetic and real data. Our simulation results suggest that there is no a single function that provides the best results in all practical situations (e.g., monophyletic or polyphyletic evolution, and positive or negative selection), and that at least three different functions might be useful. The proposed hit region identification functions that do not benefit from the prior knowledge (i.e., carcinogenicity or invasivity of the involved organisms) can provide equivalent results than the existing functions that take advantage of such a prior knowledge. Using the new algorithm, we examined the <it>Neisseria meningitidis</it> FrpB gene product for invasivity and immunologic activity, and human papilloma virus (HPV) E6 oncoprotein for carcinogenicity, and confirmed some well-known molecular features, including surface exposed loops for <it>N. meningitidis</it> and PDZ domain for HPV.</p>http://www.biomedcentral.com/1471-2105/12/S9/S9 |
| spellingShingle | Makarenkov Vladimir Diallo Abdoulaye Boc Alix Badescu Dunarel Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index BMC Bioinformatics |
| title | Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index |
| title_full | Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index |
| title_fullStr | Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index |
| title_full_unstemmed | Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index |
| title_short | Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index |
| title_sort | detecting genomic regions associated with a disease using variability functions and adjusted rand index |
| url | http://www.biomedcentral.com/1471-2105/12/S9/S9 |
| work_keys_str_mv | AT makarenkovvladimir detectinggenomicregionsassociatedwithadiseaseusingvariabilityfunctionsandadjustedrandindex AT dialloabdoulaye detectinggenomicregionsassociatedwithadiseaseusingvariabilityfunctionsandadjustedrandindex AT bocalix detectinggenomicregionsassociatedwithadiseaseusingvariabilityfunctionsandadjustedrandindex AT badescudunarel detectinggenomicregionsassociatedwithadiseaseusingvariabilityfunctionsandadjustedrandindex |