Leslie Grate

Genome-wide bioinformatic and molecular analysis of introns in Saccharomyces cerevisiae

Introns have typically been discovered in an ad hoc fashion: introns are found as a gene is characterized for other reasons. As complete eukaryotic genome sequences become available, better methods for predicting RNA processing signals in raw sequence will be necessary in order to discover genes and predict their expression. Here we present a catalog of 228 yeast introns, arrived at through a combination of bioinformatic and molecular analysis. Introns annotated in the Saccharomyces Genome Database (SGD) were evaluated, questionable introns were removed after failing a test for splicing in vivo, and known introns absent from the SGD annotation were added. A novel branchpoint sequence, AAUUAAC, was identified within an annotated intron that lacks a six-of-seven match to the highly conserved branchpoint consensus UACUAAC. Analysis of the database corroborates many conclusions about pre-mRNA substrate requirements for splicing derived from experimental studies, but indicates that splicing in yeast may not be as rigidly determined by splice-site conservation as had previously been thought. Using this database and a molecular technique that directly displays the lariat intron products of spliced transcripts (intron display), we suggest that the current set of 228 introns is still not complete, and that additional intron-containing genes remain to be discovered in yeast. The database can be accessed at http://www.cse.ucsc.edu/research/compbi o/yeast_introns.html.

Predicting protein structure using only sequence information.

This paper presents results of blind predictions submitted to the CASP3 protein structure prediction experiment. We made predictions using the SAM‐T98 method, an iterative hidden Markov model–based method for constructing protein family profiles. The method is purely sequence‐based, using no structural information, and yet was able to predict structures as well as all but five of the structure‐based methods in CASP3. Proteins Suppl 1999;3:121–125.

A handful of intron-containing genes produces the lion's share of yeast mRNA

Two studies have provided separate pieces of information that bear on the functional and evolutionary significance of introns in the budding yeast Saccharomyces cerevisiae (Holstege et al., 1998; Spingola et al., 1999). By the measure of the number of introns within its genes, budding yeast seems a disappointing eukaryote. Fewer than 250 of the more than 6,200 annotated genes are known to have introns, and fewer than 10 are known have more than one intron (Spingola et al., 1999). In contrast, metazoan genes are estimated to average nearly 10 introns, and the intronless gene is the exception rather than the rule. Although many essential yeast genes have introns, it would appear that introns are on the way out of the yeast genome, perhaps by a cDNA-directed homologous recombination mechanism proposed by Fink (1987).

What is the value added by human intervention in protein structure prediction

This article presents results of blind predictions submitted to the CASP4 protein structure prediction experiment. We made two sets of predictions: one using the fully automated SAM‐T99 server and one using the improved SAM‐T2K method with human intervention. Both methods use iterative hidden Markov model‐based methods for constructing protein family profiles, using only sequence information. Although the SAM‐T99 method is purely sequence based, the SAM‐T2K method uses the predicted secondary structure of the target sequence and the known secondary structure of the templates to improve fold recognition and alignment. In this article, we try to determine what aspects of the SAM‐T2K method were responsible for its significantly better performance in the CASP4 experiment in the hopes of producing a better automatic prediction server. The use of secondary structure prediction seems to be the most valuable single improvement, though the combined total of various human interventions is probably at least as important. Proteins 2001;Suppl 5:86–91.

Searching yeast intron data at ares lab web site

Publisher Summary It must be obvious to every geneticist by now that the future will be consumed by the need to understand how the elemental properties of genes so elegantly described in the past half-century come together with the environment to produce the subtle differences that are key to the fitness of the organism. This will require a partial abandonment of the reductionism so favored since Mendel, to be replaced by the adoption of a more synthetic view that addresses the molecular underpinnings of complex phenotypes, penetrance, expressivity, and the small contributions of many genes. This chapter tries to embrace this in a small way by setting up a searchable database containing information concerning the introns found in the genome of Saccharomyces cerevisiae. Despite its lack of sophistication and dotcom sheen, the database has found many uses in the laboratory and has been accessed by yeast geneticists, splicers, and bioinformaticists the world around. This chapter explains the browsing and search capabilities of the site, and how to read and interpret the findings.

Sequence analysis with the Kestrel SIMD parallel processor.

Computer aided sequence analysis is a critical aspect of current biological research. Sequence information from the genome sequencing projects fills databases so quickly that humans cannot examine it all. Hence there is a heavy reliance on computer algorithms to point out the few important nuggets for human examination. Sequence search algorithms range from simple to complex, as does the representation of the biological data. Typically though, simple algorithms are used on the simplest of data representations because of the large computational demands of anything more complex. This leads to missed hits because the simple search techniques are often not sufficiently sensitive. Here we describe the implementation of several sensitive sequence analysis algorithms on the Kestrel parallel processor, a single-instruction multiple-data (SIMD) processor developed and built at UCSC. Performance of the Smith-Waterman and Hidden Markov Model algorithms, with both Viterbi and Expectation Maximization methods ranges from 6 to 20 times faster than standard computers.