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In two classically affected families, affected males were identified with two mutations: one with two novel mutations, D264Y and V269M and the other with one novel (Q312H) and one previously reported (A143T) mutation. In 18 families, 17 previously reported mutations were identified, with R112C occurring in two families. The molecular lesions in the α-Gal A gene causing the classic phenotype of Fabry disease in 66 unrelated families were determined. The drawback is.Fabry disease, an X-linked recessive inborn error of glycosphingolipid catabolism, results from the deficient activity of the lysosomal exoglycohydrolase, α-galactosidase A (EC 3.2.1.22 α-Gal A). FASTA is more sensitive but slower, as it starts with smaller chunks and therefore must scan more segments before validating its selections. BLAST is faster, scanning chunks that are 11 base pairs long in phase one. Then they go back to the candidate regions and test their choices. First they break the data into chunks, scanning regions of nucleotide or protein to find those likely to hold a sequence similar to the query. Each sacrifices exactitude for speed, searching databases in stages. 'They are terrific, but they are extremely slow and are only useable for searching small databases,' says Stein.Ĭonsequently, similarity searches often start with one of two heuristic algorithms that were published later: FASTA (pronounced 'fast-A') in 1988 and BLAST in 1990. Still in use, both algorithms are precise and time-consuming. Smith-Waterman, published in 1981, does local alignment, searching for pieces that correspond to sequences in the query segment.

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The earliest algorithm, Needleman-Wunsch (1970), aligns the full length of two known segments, performing what is called a global alignment. 'Computer science doesn't have a good way of finding similarities short of doing an exhaustive search of every possibility.'

'The basic problem is computer science is good at finding exact matches very quickly,' explains Stephen Altschul, a key figure in the development of BLAST at the National Center for Biotechnology Information (NCBI). Sometimes only portions of sequences match, but in ways that suggest they have a common ancestor and are, therefore, homologous. The process matches segments of nucleic acid and protein to find sequences that are identical or similar to each other. NOT AN EXACT SCIENCE Sequence alignment is not an exact science, experts agree, but rather a tradeoff between speed and accuracy. 'Different problems require different algorithms by necessity.' Why so many.) 'This is a fallacy to think there is any one algorithm that solves all the problems,' says UK-based bioinformatics programmer Ewan Birney of EMBL. Indeed, the European Bioinformatics Institute (EBI) in Hinxton, UK, offers 11 choices under 'homology and similarity' in the toolbox on its Web site, and links to 12 options at its European Molecular Biology Laboratory (EMBL). 'I can only come to grips with a small piece of the field.'

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'It would be a full-time job just to update a Web page of bioinformatics tools,' he says. The result is computational evolution, and even as formidable an expert as Lincoln Stein, at Cold Spring Harbor Laboratory in New York, finds he cannot keep up. Upstarts claim to be better, faster, and smarter at taking on the huge and growing piles of data in public and private repositories. Newer, more specialized versions create handy choices that unsophisticated users overlook. Old stalwarts among the more familiar algorithms go through subtle changes.

Peel away the information technology jargon surrounding these alignment algorithms, and a powerful dynasty becomes evident.Īnd this dynasty is anything but stagnant. In the beginning, there was Needleman-Wunsch, which begat Smith-Waterman, which begat FASTA, which begat BLAST, and so on.

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