Tieng K. Yap

Parallel computation in biological sequence analysis

A massive volume of biological sequence data is available in over 36 different databases worldwide, including the sequence data generated by the Human Genome project. These databases, which also contain biological and bibliographical information, are growing at an exponential rate. Consequently, the computational demands needed to explore and analyze the data contained in these databases is quickly becoming a great concern. To meet these demands, we must use high performance computing systems, such as parallel computers and distributed networks of workstations. We present two parallel computational methods for analyzing these biological sequences. The first method is used to retrieve sequences that are homologous to a query sequence. The biological information associated with the homologous sequences found in the database may provide important clues to the structure and function of the query sequence. The second method, which helps in the prediction of the function, structure, and evolutionary history of biological sequences, is used to align a number of homologous sequences with each other. These two parallel computational methods were implemented and evaluated on an Intel IPSC/860 parallel computer. The resulting performance demonstrates that parallel computational methods can significantly reduce the computational time needed to analyze the sequences contained in large databases.

Childhood-onset schizophrenia/autistic disorder and t(1;7) reciprocal translocation: identification of a BAC contig spanning the translocation breakpoint at 7q21.

Childhood-onset schizophrenia (COS) is defined by the development of first psychotic symptoms by age 12. While recruiting patients with COS refractory to conventional treatments for a trial of atypical antipsychotic drugs, we discovered a unique case who has a familial t(1;7)(p22;q21) reciprocal translocation and onset of psychosis at age 9. The patient also has symptoms of autistic disorder, which are usually transient before the first psychotic episode among 40-50% of the childhood schizophrenics but has persisted in him even after the remission of psychosis. Cosegregating with the translocation, among the carriers in the family available for the study, are other significant psychopathologies, including alcohol/drug abuse, severe impulsivity, and paranoid personality and language delay. This case may provide a model for understanding the genetic basis of schizophrenia or autism. Here we report the progress toward characterization of genomic organization across the translocation breakpoint at 7q21. The polymorphic markers, D7S630/D7S492 and D7S2410/D7S646, immediately flanking the breakpoint, may be useful for further confirming the genetic linkage for schizophrenia or autism in this region. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 96:749-753, 2000. Published 2000 Wiley-Liss, Inc.

High Performance Computational Methods for Biological Sequence Analysis

Preface. 1. Introduction. 2. Biological Background. 3. Sequence Analysis Algorithms. 4. High Performance Computing Architectures and Techniques. 5. Multiprocessor Sequence Alignment. 6. Multiprocessor Sequence Similarity Searching. 7. Biological Sequence Resources on the Internet. 8. Looking to the Future. References. Index.

Parallel homologous sequence searching in large databases

We present a parallel computational method for retrieving similar sequences from large genetic and protein databases using a dynamic programming comparison algorithm. Two previously published parallel methods for performing this task are first discussed and evaluated. The advantages of these two parallel methods are combined and incorporated into our new method to obtain better performance than either of the original two. Using the entire GenBank database (release 80.0), we compare the performance of the three methods on an Intel iPSC/860 parallel computer.<<ETX>>

Parallel Algorithms in Molecular Biology

Scalable parallel computer architectures provide the computational performance needed for advanced computing problems in molecular biology. Many scientific challenges in molecular biology have associated with them a computational requirement that must be solved before scientific progress can be made. We have developed a number of parallel algorithms and techniques useful in determining biological structure and function. Two example applications are the alignment of multiple DNA and protein sequences using speculative computation and the calculation of the solvent accessible surface area of proteins used to predict the three-dimensional conformation of these molecules from their primary structure. Timing results demonstrate substantial performance improvements with parallel implementations compared with conventional sequential systems. As the developed methods allow molecular biologists to perform computational tasks that would not otherwise be possible, we continue to develop parallel algorithms useful to this important scientific field.

Providing computational science and engineering expertise for a large biomedical research program

The Computational Bioscience and Engineering Laboratory of the National Institutes of Health provides computational science and engineering expertise for the NM Intramural Research Program. Laboratory activities include computational algorithm and method development, high-performance parallel computing, biomedical image processing, image management and communication system development, signal processing and control system design, bioinformatics support, and scientific database system implementation.

Sequence Analysis Algorithms

The Human Genome Project has generated a massive volume of biological sequence data which are deposited in a large number of databases around the world and made available to the public. Presently, there are about 189 biological databases [86, 174]. To make sense of the large volume of sequence data available, a large number of algorithms were developed to analyze them. Unlike other branches of science, many discoveries in biology are made by using various types of comparative analyses. For example, the function and structure of a protein can be determined by comparing its sequence to the sequences of other known proteins. In this chapter, we present three basic comparative analysis tools: pairwise sequence alignment, multiple sequence alignment, and the similarity sequence search. These three basic tools, which have many variations, can be used to find answers to many questions in biological research.

High Performance Computing Architectures and Techniques

Since the dawn of the computing era in the mid to late 1940’s, users have continuously demanded the availability of bigger, in terms of primary and secondary storage, and faster, in terms of computational power, computers. In spite of the great advances in digital electronics, the commercial and scientific communities consistently face problems and opportunities whose solutions involve the use of ever-more-powerful computational engines. Biological sequence analysis is one such application domain.

Multiprocessor Sequence Similarity Searching

For maximum sensitivity, researchers use the computationally intensive dynamic programming algorithm [60] to compare two sequences. As the size of the databases continue to grow exponentially, it becomes impractical to use the full dynamic programming algorithm on a conventional sequential computer system. To reduce the search time, some researchers [5, 28, 129] developed alternative algorithms, as described in chapter 3, which are faster than the dynamic programming algorithm but have a lower degree of sensitivity. Other researchers [16, 26, 37, 43, 63, 83, 93, 94, 116, 117, 143, 144, 168] exploit the power of parallel computation systems to reduce search time without reducing sensitivity.

Multiprocessor Sequence Alignment

As stated in Chapter 3, the Human Genome Project has generated a massive volume of sequence data. Furthermore, longer sequences are being generated due to recent advances in sequencing technology. Consequently, the computational performance needed to analyze these new sequences is increasing enormously. It is presently impractical to align two long sequences or to align a large number of sequences of moderate length on a traditional single processor computer. To meet these computational demands, the high performance provided by parallel computers must be exploited.