Biomedical subjects

S H Sze

Publications and source records attributed to S H Sze.

5 recordsLinked to original sources

PubMed2002

Finding weak motifs in DNA sequences.

Recognition of regulatory sites in unaligned DNA sequences is an old and well-studied problem in computational molecular biology. Recently, large-scale expression studies and comparative genomics brought this problem into a spotlight by generating a large number of samples with unknown regulatory signals. Here we develop algorithms for recognition of signals in corrupted samples (where only a fraction of sequences contain sites) with biased nucleotide composition. We further benchmark these and other algorithms on several bacterial and archaeal sites in a setting specifically designed to imitate the situations arising in comparative genomics studies.

Base Sequence↗

PubMed2000

Combinatorial approaches to finding subtle signals in DNA sequences.

Signal finding (pattern discovery in unaligned DNA sequences) is a fundamental problem in both computer science and molecular biology with important applications in locating regulatory sites and drug target identification. Despite many studies, this problem is far from being resolved: most signals in DNA sequences are so complicated that we don't yet have good models or reliable algorithms for their recognition. We complement existing statistical and machine learning approaches to this problem by a combinatorial approach that proved to be successful in identifying very subtle signals.

Algorithms↗

PubMed1998

Gene hunting without sequencing genomic clones: finding exon boundaries in cDNAs.

We propose a new experimental protocol, ExonPCR, which is able to identify exon boundaries in a cDNA even in the absence of any genomic clones. ExonPCR can bypass the isolation, characterization, and DNA sequencing of subclones of genomic DNA to determine exon boundaries: a major effort in the process of positional cloning. Given a cDNA sequence, ExonPCR uses a series of "adaptive" steps to analyze the PCR products from cDNA and genomic DNA thereby revealing the approximate positions of "hidden" exon boundaries in the cDNA. The nucleotide sequence of adjacent intronic regions is determined by ligation-mediated PCR. Primers adjacent to the "hidden" exon boundaries are used to amplify genomic DNA followed by limited DNA sequencing of the PCR product. The method was successfully tested on the 3-kb hMSH2 cDNA with 16 known exons and the 9-kb PRDII-BF1 cDNA with a previously unknown number of exons. We subsequently developed the ExonPCR algorithm and software to direct the experimental protocol using a strategy that is analogous to that used in the game "Twenty Questions." Through the use of ExonPCR, the search for disease-causing mutations can be initiated almost immediately after cDNA clones in a genetically mapped region become available. This approach would be most valuable in gene discovery strategies that focus initially on cDNA isolation.

Animals↗

PubMed1998

Algorithms and software for support of gene identification experiments.

MOTIVATION: Gene annotation is the final goal of gene prediction algorithms. However, these algorithms frequently make mistakes and therefore the use of gene predictions for sequence annotation is hardly possible. As a result, biologists are forced to conduct time-consuming gene identification experiments by designing appropriate PCR primers to test cDNA libraries or applying RT-PCR, exon trapping/amplification, or other techniques. This process frequently amounts to 'guessing' PCR primers on top of unreliable gene predictions and frequently leads to wasting of experimental efforts. RESULTS: The present paper proposes a simple and reliable algorithm for experimental gene identification which bypasses the unreliable gene prediction step. Studies of the performance of the algorithm on a sample of human genes indicate that an experimental protocol based on the algorithm's predictions achieves an accurate gene identification with relatively few PCR primers. Predictions of PCR primers may be used for exon amplification in preliminary mutation analysis during an attempt to identify a gene responsible for a disease. We propose a simple approach to find a short region from a genomic sequence that with high probability overlaps with some exon of the gene. The algorithm is enhanced to find one or more segments that are probably contained in the translated region of the gene and can be used as PCR primers to select appropriate clones in cDNA libraries by selective amplification. The algorithm is further extended to locate a set of PCR primers that uniformly cover all translated regions and can be used for RT-PCR and further sequencing of (unknown) mRNA.

Algorithms↗

PubMed1997

Las Vegas algorithms for gene recognition: suboptimal and error-tolerant spliced alignment.

Recently, Gelfand, Mironov and Pevzner (1996) proposed a spliced alignment approach to gene recognition that provides 99% accurate recognition of human genes if a related mammalian protein is available. However, even 99% accurate gene predictions are insufficient for automated sequence annotation in large-scale sequencing projects and therefore have to be complemented by experimental gene verification. One hundred percent accurate gene predictions would lead to a substantial reduction of experimental work on gene identification. Our goal is to develop an algorithm that either predicts an exon assembly with accuracy sufficient for sequence annotation or warns a biologist that the accuracy of a prediction is insufficient and further experimental work is required. We study suboptimal and error-tolerant spliced alignment problems as the first steps towards such an algorithm, and report an algorithm which provides 100% accurate recognition of human genes in 37% of cases (if a related mammalian protein is available). In 52% of genes, the algorithm predicts at least one exon with 100% accuracy.

Algorithms↗