Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics

• Main Author: Lazarovici, Allan, 1979-
• Other Authors: Christopher Burge.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2006
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/29681

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.