Investigating 42 candidate orthologous protein groups by molecular evolutionary analysis on genome scale

Prediction of orthologous relationship by functionally important sites

Making accurate functional predictions plays an important role in the era of proteomics. Reliable functional information can be extracted from orthologs in other species when annotating an unknown gene. Here a site-based approach called PORFIS is proposed to predict orthologous relationship. When applied to the bacterial transcription factor PurR/LacI family and the protein kinase AGC family, our method was able to identify, with few false positives, the important sites that agree with those verified by biological experiments. We also tested it on the alpha-proteasome family, the glycoprotein hormone family and the growth hormone family to demonstrate its ability to predict orthologous relationship. Compared with other prediction methods based on phylogenetic analysis or hidden Markov models, PORFIS not only has competitive prediction accuracy, but also provides valuable biological information of functionally important sites associated with orthologs which can be further studied in biological experiments.

OrthoDisease: a database of human disease orthologs

One of the greatest promises of genome sequencing projects is to further the understanding of human diseases and to develop new therapies. Model organism genomes have been sequenced in parallel to human genomes to provide effective tools for the investigation of human gene function. Many of their genes share a common ancestry and function with human genes, and this is particularly true for orthologous genes. Here we present OrthoDisease, a comprehensive database of model organism genes that are orthologous to human disease genes. OrthoDisease was constructed by applying the Inparanoid ortholog detection algorithm to disease genes derived from the Online Mendelian Inheritance in Man database (OMIM). Pairwise whole genome/proteome comparisons between Homo sapiens and six other organisms were performed to identify ortholog clusters. OMIM numbers were extracted from the OMIM Morbid Map and were converted to gene sequences using the Locuslink mim2loc and loc2acc tables. These were mapped to Inparanoid ortholog clusters using Blast. The number of ortholog clusters in OrthoDisease with each respective species is currently: M. musculus, 1,354; D. melanogaster, 724; C. elegans, 533; A. thaliana, 398; S. cerevisiae, 290; and E. coli, 153. The database is accessible online at http://orthodisease.cgb.ki.se, and can be searched with disease or protein names. The web interface presents all ortholog clusters that include a selected disease gene. A capability to download the entire dataset is also provided.

Comprehensive analysis of orthologous protein domains using the HOPS database

One of the most reliable methods for protein function annotation is to transfer experimentally known functions from orthologous proteins in other organisms. Most methods for identifying orthologs operate on a subset of organisms with a completely sequenced genome, and treat proteins as single-domain units. However, it is well known that proteins are often made up of several independent domains, and there is a wealth of protein sequences from genomes that are not completely sequenced. A comprehensive set of protein domain families is found in the Pfam database. We wanted to apply orthology detection to Pfam families, but first some issues needed to be addressed. First, orthology detection becomes impractical and unreliable when too many species are included. Second, shorter domains contain less information. It is therefore important to assess the quality of the orthology assignment and avoid very short domains altogether. We present a database of orthologous protein domains in Pfam called HOPS: Hierarchical grouping of Orthologous and Paralogous Sequences. Orthology is inferred in a hierarchic system of phylogenetic subgroups using ortholog bootstrapping. To avoid the frequent errors stemming from horizontally transferred genes in bacteria, the analysis is presently limited to eukaryotic genes. The results are accessible in the graphical browser NIFAS, a Java tool originally developed for analyzing phylogenetic relations within Pfam families. The method was tested on a set of curated orthologs with experimentally verified function. In comparison to tree reconciliation with a complete species tree, our approach finds significantly more orthologs in the test set. Examples for investigating gene fusions and domain recombination using HOPS are given.

Phylogenetic analysis and gene functional predictions: phylogenomics in action

Making accurate functional predictions for genes is a key step in this era of high throughput gene and genome sequencing. While most functional prediction methods are comparative in nature, many do not take advantage of the power that an evolutionary perspective provides to any comparative biology analysis. Here we review how evolutionary analysis can greatly benefit both homology-based and non-homology-based functional prediction methods. Examples that are discussed include phylogenetic determination of orthology, the use of character state reconstruction analysis of gene function, and evolutionary analysis of rates and patterns of gene evolution.