Concordance analysis of microbial genomes.

The set of proteins which are conserved across families of microbes contain important targets of new anti-microbial agents. We have developed a simple and efficient computational tool which determines concordances of putative gene products that show sets of proteins conserved across one set of user specified genomes and not present in another set of user specified genomes. The thresholds and the homology scoring criterion are selectable to allow the user to decide the stringency of the homologies. The system uses a relational database to store protein coding regions from different genomes, and to store the results of a complete comparison of all sequences against all sequences using the FASTA program. Using Web technology, the display of all the related proteins for a given sequence and calculation of multiple sequence alignments (using CLUSTALW) can be performed with the click of a button. The current database holds 97 365 sequences from 19 complete or partial genomes and 8798905 FASTA comparison results. A example concordance is presented which demonstrates that the target of the quinolone antibiotics could have been identified using this tool.

Alternative gene form discovery and candidate gene selection from gene indexing projects.

Several efforts are under way to partition single-read expressed sequence tag (EST), as well as full-length transcript data, into large-scale gene indices, where transcripts are in common index classes if and only if they share a common progenitor gene. Accurate gene indexing facilitates gene expression studies, as well as inexpensive and early gene sequence discovery through assembly of ESTs that are derived from genes that have not been sequenced by classical methods. We extend, correct, and enhance the information obtained from index groups by splitting index classes into subclasses based on sequence dissimilarity (diversity). Two applications of this are highlighted in this report. First it is shown that our method can ameliorate the damage that artifacts, such as chimerism, inflict on index integrity. Additionally, we demonstrate how the organization imposed by an effective subpartition can greatly increase the sensitivity of gene expression studies by accounting for the existence and tissue- or pathology-specific regulation of novel gene isoforms and polymorphisms. We apply our subpartitioning treatment to the UniGene gene indexing project to measure a marked increase in information quality and abundance (in terms of assembly length and insertion/deletion error) after treatment and demonstrate cases where new levels of information concerning differential expression of alternate gene forms, such as regulated alternative splicing, are discovered. [Tables 2 and 3 can be viewed in their entirety as Online Supplements at http://www.genome.org.]

A measure of DNA sequence dissimilarity based on Mahalanobis distance between frequencies of words.

A number of algorithms exist for searching genetic databases for biologically significant similarities in DNA sequences. Past research has shown that word-based search tools are computationally efficient and can find similarities or dissimilarities invisible to other algorithms like FASTA. We characterize a family of word-based dissimilarity measures that define distance between two sequences by simultaneously comparing the frequencies of all subsequences of n adjacent letters (i.e., n-words) in the two sequences. Applications to real data demonstrate that currently used word-based methods that rely on Euclidean distance can be significantly improved by using Mahalanobis distance, which accounts for both variances and covariances between frequencies of n-words. Furthermore, in those cases where Mahalanobis distance may be too difficult to compute, using standardized Euclidean distance, which only corrects for the variances of frequencies of n-words, still gives better performance than the Euclidean distance. Also, a simple way of combining distances obtained at different n-words is considered. The goal is to obtain a single measure of dissimilarity between two DNA sequences. The performance ranking of the preceding three distances still holds for their combined counterparts. All results obtained in this paper are applicable to amino acid sequences with minor modifications.

A novel mouse mitochondrial voltage-dependent anion channel gene localizes to chromosome 8.

Voltage-dependent anion channels (VDACs) are small pore-forming channels found in the outer membrane of mitochondria. VDACs translocate adenine nucleotides and are the binding sites for several cytosolic kinases important in intermediary metabolism. Recently two human VDAC cDNAs (HVDAC1 and HVDAC2) were isolated, and possible orthologues of these genes have been isolated from mouse, bovine, and rat tissues. We report the isolation of a novel third VDAC cDNA from the mouse, designated MVDAC3. The deduced MVDAC3 protein is approximately 70% identical to the previously isolated MVDAC1 and MVDAC2 proteins. The MVDAC3 gene was mapped by an interspecies backcross panel to mouse chromosome 8. A database search using the mouse VDACs identified a second yeast VDAC-like gene that retains about 20% amino acid sequence identity with the mouse VDAC genes and 50% identity with the previously isolated yeast VDAC gene. The phylogenetic relationship of the eukaryotic VDAC genes is presented.

BCM Search Launcher--an integrated interface to molecular biology data base search and analysis services available on the World Wide Web.

The BCM Search Launcher is an integrated set of World Wide Web (WWW) pages that organize molecular biology-related search and analysis services available on the WWW by function, and provide a single point of entry for related searches. The Protein Sequence Search Page, for example, provides a single sequence entry form for submitting sequences to WWW servers that offer remote access to a variety of different protein sequence search tools, including BLAST, FASTA, Smith-Waterman, BEAUTY, PROSITE, and BLOCKS searches. Other Launch pages provide access to (1) nucleic acid sequence searches, (2) multiple and pair-wise sequence alignments, (3) gene feature searches, (4) protein secondary structure prediction, and (5) miscellaneous sequence utilities (e.g., six-frame translation). The BCM Search Launcher also provides a mechanism to extend the utility of other WWW services by adding supplementary hypertext links to results returned by remote servers. For example, links to the NCBI's Entrez data base and to the Sequence Retrieval System (SRS) are added to search results returned by the NCBI's WWW BLAST server. These links provide easy access to auxiliary information, such as Medline abstracts, that can be extremely helpful when analyzing BLAST data base hits. For new or infrequent users of sequence data base search tools, we have preset the default search parameters to provide the most informative first-pass sequence analysis possible. We have also developed a batch client interface for Unix and Macintosh computers that allows multiple input sequences to be searched automatically as a background task, with the results returned as individual HTML documents directly to the user's system. The BCM Search Launcher and batch client are available on the WWW at URL http:@gc.bcm.tmc.edu:8088/search-launcher.html.

Biological evaluation of d2, an algorithm for high-performance sequence comparison.

A number of algorithms exist for searching sequence databases for biologically significant similarities based on the primary sequence similarity of aligned sequences. We have determined the biological sensitivity and selectivity of d2, a high-performance comparison algorithm that rapidly determines the relative dissimilarity of large datasets of genetic sequences. d2 uses sequence-word multiplicity as a simple measure of dissimilarity. It is not constrained by the comparison of direct sequence alignments and so can use word contexts to yield new information on relationships. It is extremely efficient, comparing a query of length 884 bases (INS1ECLAC) with 19,540,603 bases of the bacterial division of GenBank (release 76.0) in 51.77 CPU seconds on a Cray Y/MP-48 supercomputer. It is unique in that subsequences (words) of biological interest can be weighted to improve the sensitivity and selectivity of a search over existing methods. We have determined the ability of d2 to detect biologically significant matches between a query and large datasets of DNA sequences while varying parameters such as word-length and window size. We have also determined the distribution of dissimilarity scores within eukaryotic and prokaryotic divisions of GenBank. We have optimized parameters of the d2 program using Cray hardware and present an analysis of the sensitivity and selectivity of the algorithm. A theoretical analysis of the expectation for scores is presented. This work demonstrates that d2 is a unique, sensitive, and selective method of rapid sequence comparison that can detect novel sequence relationships which remain undetected by alternate methodologies.