Improving the specificity of high-throughput ortholog prediction

Background  

Orthologs (genes that have diverged after a speciation event) tend to have similar function, and so their prediction has become an important component of comparative genomics and genome annotation. The gold standard phylogenetic analysis approach of comparing available organismal phylogeny to gene phylogeny is not easily automated for genome-wide analysis; therefore, ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-BLAST-hits (RBH) approach. One problem with RBH is that it will incorrectly predict a paralog as an ortholog when incomplete genome sequences or gene loss is involved. In addition, there is an increasing interest in identifying orthologs most likely to have retained similar function.     

Accelerating comparative genomics using parallel computing

In the past decade there has been an increase in the number of completely sequenced genomes due to the race of multibillion-dollar genome-sequencing projects. The enormous biological sequence data thus flooding into the sequence databases necessitates the development of efficient tools for comparative genome sequence analysis. The information deduced by such analysis has various applications viz. structural and functional annotation of novel genes and proteins, finding gene order in the genome, gene fusion studies, constructing metabolic pathways etc. Such study also proves invaluable for pharmaceutical industries, such as in silico drug target identification and new drug discovery. There are various sequence analysis tools available for mining such useful information of which FASTA and Smith-Waterman algorithms are widely used. However, analyzing large datasets of genome sequences using the above codes seems to be impractical on uniprocessor machines. Hence there is a need for improving the performance of the above popular sequence analysis tools on parallel cluster computers. Performance of the Smith-Waterman (SSEARCH) and FASTA programs were studied on PARAM 10000, a parallel cluster of workstations designed and developed in-house. FASTA and SSEARCH programs, which are available from the University of Virginia, were ported on PARAM and were optimized. In this era of high performance computing, where the paradigm is shifting from conventional supercomputers to the cost-effective general-purpose cluster of workstations and PCs, this study finds extreme relevance. Good performance of sequence analysis tools on a cluster of workstations was demonstrated, which is important for accelerating identification of novel genes and drug targets by screening large databases.     

Comprehensive splice-site analysis using comparative genomics

We have collected over half a million splice sites from five species-Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Arabidopsis thaliana-and classified them into four subtypes: U2-type GT-AG and GC-AG and U12-type GT-AG and AT-AC. We have also found new examples of rare splice-site categories, such as U12-type introns without canonical borders, and U2-dependent AT-AC introns. The splice-site sequences and several tools to explore them are available on a public website (SpliceRack). For the U12-type introns, we find several features conserved across species, as well as a clustering of these introns on genes. Using the information content of the splice-site motifs, and the phylogenetic distance between them, we identify: (i) a higher degree of conservation in the exonic portion of the U2-type splice sites in more complex organisms; (ii) conservation of exonic nucleotides for U12-type splice sites; (iii) divergent evolution of C.elegans 3' splice sites (3'ss) and (iv) distinct evolutionary histories of 5' and 3'ss. Our study proves that the identification of broad patterns in naturally-occurring splice sites, through the analysis of genomic datasets, provides mechanistic and evolutionary insights into pre-mRNA splicing.     

Coding exon detection using comparative sequences.

We introduce a new system, called shortHMM, for predicting exons, which predicts individual exons using two related genomes. In this system, we build a hidden semi-Markov model to identify exons. In the hidden Markov model, we propose joint probability models of nucleotides in introns, splice sites, 5'UTR, 3'UTR, and intergenic regions by exploiting the homology between related genomes. In order to reduce the false positive rate of the hidden Markov model, we develop a screening process which is able to identify intergenic regions. We then build a classifier by combining the statistics from the hidden Markov model and the screening process. We implement shortHMM on human-mouse sequence alignments. The source codes are available at < www.stat.purdue.edu/ jingwu/hmm >. Compared to TWINSCAN and SLAM, shortHMM is substantially more powerful in identifying AT-rich RefSeq exons (8% more AT-rich RefSeq exons were predicted), as well as slightly more powerful in identifying RefSeq exons (3-10% more RefSeq exons were predicted), at a similar or lower false positive rate, with less computing time and with less memory usage. Last, shortHMM is also capable of finding new potential exons.     

Bacterial comparative genomics

A report on 'Genomes 2004: International Conference on the Analysis of Microbial and Other Genomes', Hinxton, UK, 14-17 April 2004.     

ACGT-a comparative genomics tool

SUMMARY: ACGT (a comparative genomics tool) is a genomic DNA sequence comparison viewer and analyzer. It can read a pair of DNA sequences in GenBank, Embl or Fasta formats, with or without a comparison file, and provide users with many options to view and analyze the similarities between the input sequences. It is written in Java and can be run on Unix, Linux and Windows platforms. AVAILABILITY: The ACGT program is freely available with documentation and examples at website: http://db.systemsbiology.net/projects/local/mhc/acgt/     

Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.     

Microarray detection of food-borne pathogens using specific probes prepared by comparative genomics

In order to design a method for the accurate detection and identification of food-borne pathogens, we used comparative genomics to select 70-mer oligonucleotide probes specific for 11 major food-borne pathogens (10 overlapping probes per pathogen) for use in microarray analysis. We analyzed the hybridization pattern of this constructed microarray with the Cy3-labeled genomic DNA of various food-borne pathogens and other bacteria. Our microarray showed a highly specific hybridization pattern with the genomic DNA of each food-borne pathogen; little unexpected cross-hybridization was observed. Microarray data were analyzed and clustered using the GenePix Pro 6.0 and GeneSpring GX 7.3.1 programs. The analyzed dendrogram revealed the discriminating power of constructed microarray. Each food-borne pathogen clustered according to its hybridization specificity and non-pathogenic species were discriminated from pathogenic species. Our method can be applied to the rapid and accurate detection and identification of food-borne pathogens in the food industry. In addition, this study demonstrates that genome sequence comparison and DNA microarray analysis have a powerful application in epidemiologic and taxonomic studies, as well as in the food safety and biodefense fields.     

GRAST: a new way of genome reduction analysis using comparative genomics

MOTIVATION: Establishment of intra-cellular life involved a profound re-configuration of the genetic characteristics of bacteria, including genome reduction and rearrangements. Understanding the mechanisms underlying these phenomena will shed light on the genome rearrangements essential for the development of an intra-cellular lifestyle. Comparison of genomes with differences in their sizes poses statistical as well as computational problems. Little efforts have been made to develop flexible computational tools with which to analyse genome reduction and rearrangements. RESULTS: Investigation of genome reduction and rearrangements in endosymbionts using a novel computational tool (GRAST) identified gathering of genes with similar functions. Conserved clusters of functionally related genes (CGSCs) were detected. Heterogeneous gene and gene cluster non-functionalization/loss are identified between genome regions, functional gene categories and during evolution. Results show that gene non-functionalisation has accelerated during the last 50 MY of Buchnera's evolution while CGSCs have been static.     

GECO--linear visualization for comparative genomics

In order to understand and interpret phylogenetic and functional relationships between multiple prokaryotic species, qualitative and quantitative data must be correlated and displayed. GECO allows linear visualization of multiple genomes using a client/server based approach by dynamically creating .png- or .pdf-formatted images. It is able to display ortholog relations calculated using BLASTCLUST by color coding ortholog representations. Irregularities on the genomic level can be identified by anomalous G/C composition. Thus, this software will enable researchers to detect horizontally transferred genes, pseudogenes and insertions/deletions in related microbial genomes. AVAILABILITY: http://bioinfo.mikrobio.med.uni-giessen.de/geco2/GecoMainServlet     

Datasets for evolutionary comparative genomics

Many decisions about genome sequencing projects are directed by perceived gaps in the tree of life, or towards model organisms. With the goal of a better understanding of biology through the lens of evolution, however, there are additional genomes that are worth sequencing. One such rationale for whole-genome sequencing is discussed here, along with other important strategies for understanding the phenotypic divergence of species.