Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics
Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2006
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| Online Access: | http://hdl.handle.net/1721.1/29681 |
| _version_ | 1826211643229470720 |
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| author | Lazarovici, Allan, 1979- |
| author2 | Christopher Burge. |
| author_facet | Christopher Burge. Lazarovici, Allan, 1979- |
| author_sort | Lazarovici, Allan, 1979- |
| collection | MIT |
| description | Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003. |
| first_indexed | 2024-09-23T15:09:14Z |
| format | Thesis |
| id | mit-1721.1/29681 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:09:14Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/296812019-04-10T21:36:10Z Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics Lazarovici, Allan, 1979- Christopher Burge. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003. Includes bibliographical references (leaves 60-62). A computer program called FUNSCAN was developed which identifies protein coding regions in fungal genomes. Gene structural and compositional properties are modeled using a Hidden Markov Model. Separate training and testing sets for FUNSCAN were obtained by aligning cDNAs from an organism to their genomic loci, generating a 'gold standard' set of annotated genes. The performance of FUNSCAN is competitive with other computer programs design to identify protein coding regions in fungal genomes. A technique called 'Training Set Augmentation' is described which can be used to train FUNSCAN when only a small training set of genes is available. Techniques that combine alignment algorithms with FUNSCAN to identify novel genes are also discussed and explored. by Allan Lazarovici. M.Eng.and S.B. 2006-03-24T16:14:44Z 2006-03-24T16:14:44Z 2003 2003 Thesis http://hdl.handle.net/1721.1/29681 53843099 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 62 leaves 2572412 bytes 2572221 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Lazarovici, Allan, 1979- Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics |
| title | Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics |
| title_full | Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics |
| title_fullStr | Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics |
| title_full_unstemmed | Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics |
| title_short | Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics |
| title_sort | development of gene finding algorithms for fungal genomes dealing with small datasets and leveraging comparative genomics |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/29681 |
| work_keys_str_mv | AT lazaroviciallan1979 developmentofgenefindingalgorithmsforfungalgenomesdealingwithsmalldatasetsandleveragingcomparativegenomics |