Computerized polymorphic marker identification: Experimental validation and a predicted human polymorphism catalog

A computational system for the prediction of polymorphic loci directly and efficiently from human genomic sequence was developed and verified. A suite of programs, collectively called pompous (polymorphic marker prediction of ubiquitous simple sequences) detects tandem repeats ranging from dinucleotides up to 250 mers, scores them according to predicted level of polymorphism, and designs appropriate flanking primers for PCR amplification. This approach was validated on an approximately 750-kilobase region of human chromosome 3p21.3, involved in lung and breast carcinoma homozygous deletions. Target DNA from 36 paired B lymphoblastoid and lung cancer lines was amplified and allelotyped for 33 loci predicted by pompous to be variable in repeat size. We found that among those 36 predominately Caucasian individuals 22 of the 33 (67%) predicted loci were polymorphic with an average heterozygosity of 0.42. Allele loss in this region was found in 27/36 (75%) of the tumor lines using these markers. pompous provides the genetic researcher with an additional tool for the rapid and efficient identification of polymorphic markers, and through a World Wide Web site, investigators can use pompous to identify polymorphic markers for their research. A catalog of 13,261 potential polymorphic markers and associated primer sets has been created from the analysis of 141,779,504 base pairs of human genomic sequence in GenBank. This data is available on our Web site (pompous.swmed.edu) and will be updated periodically as GenBank is expanded and algorithm accuracy is improved.

Linking genome and proteome by mass spectrometry: Large-scale identification of yeast proteins from two dimensional gels

The function of many of the uncharacterized open reading frames discovered by genomic sequencing can be determined at the level of expressed gene products, the proteome. However, identifying the cognate gene from minute amounts of protein has been one of the major problems in molecular biology. Using yeast as an example, we demonstrate here that mass spectrometric protein identification is a general solution to this problem given a completely sequenced genome. As a first screen, our strategy uses automated laser desorption ionization mass spectrometry of the peptide mixtures produced by in-gel tryptic digestion of a protein. Up to 90% of proteins are identified by searching sequence data bases by lists of peptide masses obtained with high accuracy. The remaining proteins are identified by partially sequencing several peptides of the unseparated mixture by nanoelectrospray tandem mass spectrometry followed by data base searching with multiple peptide sequence tags. In blind trials, the method led to unambiguous identification in all cases. In the largest individual protein identification project to date, a total of 150 gel spots—many of them at subpicomole amounts—were successfully analyzed, greatly enlarging a yeast two-dimensional gel data base. More than 32 proteins were novel and matched to previously uncharacterized open reading frames in the yeast genome. This study establishes that mass spectrometry provides the required throughput, the certainty of identification, and the general applicability to serve as the method of choice to connect genome and proteome.