Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics
Name
53843099-MIT.pdf
Description
Full printable version
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3.53 MB
Format
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Checksum (MD5)
05a699015bdcd27fa21a2bfaee9d61fa
Author(s)
Lazarovici, Allan, 1979-
Advisor(s)
Christopher Burge.
Date Issued
2003
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaves 60-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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