Mapping of orthologous genes in the context of biological pathways: An application of integer programming

Abstract

Mapping biological pathways across microbial genomes is a highly important technique in functional studies of biological systems. Existing methods mainly rely on sequence-based orthologous gene mapping, which often leads to suboptimal mapping results because sequence-similarity information alone does not contain sufficient information for accurate identification of orthology relationship. Here we present an algorithm for pathway mapping across microbial genomes. The algorithm takes into account both sequence similarity and genomic structure information such as operons and regulons. One basic premise of our approach is that a microbial pathway could generally be decomposed into a few operons or regulons. We formulated the pathway-mapping problem to map genes across genomes to maximize their sequence similarity under the constraint that the mapped genes be grouped into a few operons, preferably coregulated in the target genome. We have developed an integer-programming algorithm for solving this constrained optimization problem and implemented the algorithm as a computer software program, P-MAP. We have tested P-MAP on a number of known homologous pathways. We conclude that using genomic structure information as constraints could greatly improve the pathway-mapping accuracy over methods that use sequence-similarity information alone.genomic structure ͉ operon ͉ regulon ͉ ortholog ͉ pathway mapping C omparative genome analysis represents a powerful technique for functional inference of genes. Its foundation is the ability to identify homologous (or more specifically, orthologous) genes. Here orthologous genes refer to isofunctional and heterospecic genes (1-3) for practical purposes. Several methods have been developed for mapping orthologous genes through sequence comparison. A popular approach is based on reciprocal BLAST searches, the so-called bidirectional best-hit (BDBH) approach (4). Based on this strategy, Wall et al. (5) recently designed a more sophisticated scheme for finding orthologous genes through BLAST searching followed by a more accurate sequence-alignment scheme (e.g., CLUSTALW and PAML). Koonin and co-workers (6) developed a popular method for orthologous gene identification based on the idea of clusters of orthologs groups (COG). Whereas all these approaches have provided useful practical tools for prediction of orthologous genes, their prediction accuracy has been less than optimal, as discussed below. One of the key issues with all these methods is their underlying assumption that sequence similarity alone contains sufficient information for prediction of orthologous gene relationship, which is probably far from being true.One important piece of information for orthologous gene identification across microbial genomes could come from the genomic structures such as operons (7) and regulons (8). Using such information has proven to be very helpful in orthology mapping in our previous studies (9, 10). Based on this observation, we have developed an algorithm, called P-MAP, for mapping orthologous...

Comparative Pathway Annotation With Protein-Dna Interaction and Operon Information via Graph Tree Decomposition

“…For TdPATH, the operon structures are predicted according to the method used in 10 and experimentally confirmed transcriptional regulation information is taken from 6 for B. subtilis 168 and from 11 for E. coli K12. For PMAP, predicted operon and regulon information is obtained according to the method used in 7 . Both TdPATH and PMAP include the COG filtering.…”

“…A more often used technique is by reciprocal BLAST search, called bidirectional best-hit (BBH) 8 , where gene pairs are regarded as orthologues if they are the best hits in both directions of the search. However, these and other sequence similarity based approaches share the same limitation 7 : the best hits may not necessarily be the optimal orthologues, thus compromising the prediction accuracy.…”

Computational Challenges in Deciphering Genomic Structures of Bacteria

“…genome analyses [93] , which can be used to reveal unknown genomic structures by detecting conserved structures, local or global, across multiple genomes.…”

“…Originally, the idea was introduced for identification of orthologous genes, referring to genes in different organisms that have evolved from a common ancestral gene through speciation only [93,95] . This is in contrast to paralogous genes, which refer to genes related by duplication within a genome [99] .…”

“…For example, identification of cis regulatory motifs for transcription regulation relies on identification of promoters of orthologous genes [100] across related organisms, which has been widely used for eukaryotic genomes. Other applications include (a) prediction of protein functions [101] , (b) operon prediction [102] , (c) uber-operon identification [97] , (d) co-evolutionary analyses of genes [103][104] , (e) co-occurrence analyses of genes [105][106] , (f) genome rearrangement analyses [107] , (g) prediction of protein-protein interactions [108] , (h) biological pathway mapping [93] among other applications.…”