Bioinformatics and computational biology researchers at UCSC discover and implement algorithms that facilitate the understanding of biological processes through the application of statistical and machine learning techniques. Because these methods are often compute-intensive, we strive to create algorithms and heuristics that are computationally efficient on serial and parallel computers. Members of the group study the primary (sequence), secondary (folding), and tertiary (3-dimensional) structures of DNA, RNA, and protein sequences.
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B.S. in Bioinformatics
M.S./Ph.D. in Bioinformatics
Grants & fellowships for undergraduates
Grants & fellowships for graduates
Grants & fellowships for postdocs
Karplus' selection of useful WWW pointers
UCSC Science and Engineering Library—bioinformatics page
ISMB99 tutorial on using hidden Markov models
SAM hidden Markov modeling (HMM) suite
UCSC Kestrel parallel processor
HMM applications, such as sequence query against a database or model library
SAM-T08 protein structure prediction system, based on hidden Markov models
E. coli gene prediction with EcoParse hidden Markov models
Small subunit ribosomal RNA secondary structure prediction with RNACAD, a stochastic context-free grammar modeling system
The Intronerator, for cDNA alignments, gene predictions, sequence data, and literature links in C. elegans
Support vector machine classification of microarray gene expression data, a link to the SVM technical report and results
Ares lab intron site, a searchable database of spliceosomal class of introns in Saccharomyces cerevisiae (yeast)
gen sequence, a program for generating random sequences of amino acids with lengths and compositions typical of those found in real protein databases—also includes random number generators for normal, beta, Dirichlet, and mixture of Dirichlet distributions
Index of yeast-protein predictions
Post-CASP bioinformatics workshops, held in December 2000, December 2002, and December 2006