Segmentation of genomic data through multivariate statistical approaches: comparative analysis
Segmenting a series of measurements along a genome into regions with distinct characteristics is widely used to identify functional components of a genome. The majority of the research on biological data segmentation focuses on the statistical problem of identifying break or change-points in a simu...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Indian Council of Agricultural Research
2022-03-01
|
| Series: | The Indian Journal of Agricultural Sciences |
| Subjects: | |
| Online Access: | https://epubs.icar.org.in/index.php/IJAgS/article/view/118040 |
| _version_ | 1811169091930554368 |
|---|---|
| author | ARFA ANJUM SEEMA JAGGI SHWETANK LALL ELDHO VARGHESE ANIL RAI ARPAN BHOWMIK DWIJESH CHANDRA MISHRA |
| author_facet | ARFA ANJUM SEEMA JAGGI SHWETANK LALL ELDHO VARGHESE ANIL RAI ARPAN BHOWMIK DWIJESH CHANDRA MISHRA |
| author_sort | ARFA ANJUM |
| collection | DOAJ |
| description |
Segmenting a series of measurements along a genome into regions with distinct characteristics is widely used to
identify functional components of a genome. The majority of the research on biological data segmentation focuses on the statistical problem of identifying break or change-points in a simulated scenario using a single variable. Despite the fact that various strategies for finding change-points in a multivariate setup through simulation are available, work on segmenting actual multivariate genomic data is limited. This is due to the fact that genomic data is huge in size and contains a lot of variation within it. Therefore, a study was carried out at the ICAR-Indian Agricultural Statistics Research Institute, New Delhi during 2021 to know the best multivariate statistical method to segment the sequences which may influence the properties or function of a sequence into homogeneous segments. This will reduce the volume of data and ease the analysis of these segments further to know the actual properties of these segments. The genomic data of Rice (Oryza sativa L.) was considered for the comparative analysis of several multivariate approaches and was found that agglomerative sequential clustering was the most acceptable due to its low computational cost and feasibility.
|
| first_indexed | 2024-04-10T16:37:39Z |
| format | Article |
| id | doaj.art-3b83166d769f418cbd876835aba72a13 |
| institution | Directory Open Access Journal |
| issn | 0019-5022 2394-3319 |
| language | English |
| last_indexed | 2024-04-10T16:37:39Z |
| publishDate | 2022-03-01 |
| publisher | Indian Council of Agricultural Research |
| record_format | Article |
| series | The Indian Journal of Agricultural Sciences |
| spelling | doaj.art-3b83166d769f418cbd876835aba72a132023-02-08T11:12:15ZengIndian Council of Agricultural ResearchThe Indian Journal of Agricultural Sciences0019-50222394-33192022-03-0192710.56093/ijas.v92i7.118040Segmentation of genomic data through multivariate statistical approaches: comparative analysisARFA ANJUM0SEEMA JAGGI1SHWETANK LALL2ELDHO VARGHESE3ANIL RAI4ARPAN BHOWMIK5DWIJESH CHANDRA MISHRA6Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New DelhiAssistant Director General (HRD),Education Division,KAB II, ICAR, New DelhiAristocrat Technologies, New DelhiFishery Resources Assessment Division,ICAR-Central Marine Fisheries Research Institute, KochiAssistant Director General (ICT),Khishi Bhavan, ICAR, New Delhi(Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi)Division of Design of Experiments, ICAR-Indian Agricultural Statistics Research Institute, New DelhiCentre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi Segmenting a series of measurements along a genome into regions with distinct characteristics is widely used to identify functional components of a genome. The majority of the research on biological data segmentation focuses on the statistical problem of identifying break or change-points in a simulated scenario using a single variable. Despite the fact that various strategies for finding change-points in a multivariate setup through simulation are available, work on segmenting actual multivariate genomic data is limited. This is due to the fact that genomic data is huge in size and contains a lot of variation within it. Therefore, a study was carried out at the ICAR-Indian Agricultural Statistics Research Institute, New Delhi during 2021 to know the best multivariate statistical method to segment the sequences which may influence the properties or function of a sequence into homogeneous segments. This will reduce the volume of data and ease the analysis of these segments further to know the actual properties of these segments. The genomic data of Rice (Oryza sativa L.) was considered for the comparative analysis of several multivariate approaches and was found that agglomerative sequential clustering was the most acceptable due to its low computational cost and feasibility. https://epubs.icar.org.in/index.php/IJAgS/article/view/118040GenomeSegmentationMultivariate analysisSequential clustering |
| spellingShingle | ARFA ANJUM SEEMA JAGGI SHWETANK LALL ELDHO VARGHESE ANIL RAI ARPAN BHOWMIK DWIJESH CHANDRA MISHRA Segmentation of genomic data through multivariate statistical approaches: comparative analysis The Indian Journal of Agricultural Sciences Genome Segmentation Multivariate analysis Sequential clustering |
| title | Segmentation of genomic data through multivariate statistical approaches: comparative analysis |
| title_full | Segmentation of genomic data through multivariate statistical approaches: comparative analysis |
| title_fullStr | Segmentation of genomic data through multivariate statistical approaches: comparative analysis |
| title_full_unstemmed | Segmentation of genomic data through multivariate statistical approaches: comparative analysis |
| title_short | Segmentation of genomic data through multivariate statistical approaches: comparative analysis |
| title_sort | segmentation of genomic data through multivariate statistical approaches comparative analysis |
| topic | Genome Segmentation Multivariate analysis Sequential clustering |
| url | https://epubs.icar.org.in/index.php/IJAgS/article/view/118040 |
| work_keys_str_mv | AT arfaanjum segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis AT seemajaggi segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis AT shwetanklall segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis AT eldhovarghese segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis AT anilrai segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis AT arpanbhowmik segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis AT dwijeshchandramishra segmentationofgenomicdatathroughmultivariatestatisticalapproachescomparativeanalysis |