Ortholog identification in the presence of domain architecture rearrangement

Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed; these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.     

GWFASTA: server for FASTA search in eukaryotic and microbial genomes

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists.     

iMapper: a web application for the automated analysis and mapping of insertional mutagenesis sequence data against Ensembl genomes

SUMMARY: Insertional mutagenesis is a powerful method for gene discovery. To identify the location of insertion sites in the genome linker based polymerase chain reaction (PCR) methods (such as splinkerette-PCR) may be employed. We have developed a web application called iMapper (Insertional Mutagenesis Mapping and Analysis Tool) for the efficient analysis of insertion site sequence reads against vertebrate and invertebrate Ensembl genomes. Taking linker based sequences as input, iMapper scans and trims the sequence to remove the linker and sequences derived from the insertional mutagen. The software then identifies and removes contaminating sequences derived from chimeric genomic fragments, vector or the transposon concatamer and then presents the clipped sequence reads to a sequence mapping server which aligns them to an Ensembl genome. Insertion sites can then be navigated in Ensembl in the context of genomic features such as gene structures. iMapper also generates test-based format for nucleic acid or protein sequences (FASTA) and generic file format (GFF) files of the clipped sequence reads and provides a graphical overview of the mapped insertion sites against a karyotype. iMapper is designed for high-throughput applications and can efficiently process thousands of DNA sequence reads. AVAILABILITY: iMapper is web based and can be accessed at http://www.sanger.ac.uk/cgi-bin/teams/team113/imapper.cgi.     

FASTC: a file format for multi-character sequence data

Here, we define a sequence file format that allows for multi-character elements (FASTC). The format is derived from the FASTA format and the custom alphabet format of POY4/5. The format is more general than either of these formats and can represent a broad variety of sequence-type data. This format should be useful for analyses involving datasets encoded as linear streams such as gene synteny, comparative linguistics, temporal gene expression and development, complex animal behaviours, and general biological time-series data.     

MultiMSOAR 2.0: an accurate tool to identify ortholog groups among multiple genomes

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free.     

SeqMap: mapping massive amount of oligonucleotides to the genome

SeqMap is a tool for mapping large amount of short sequences to the genome. It is designed for finding all the places in a reference genome where each sequence may come from. This task is essential to the analysis of data from ultra high-throughput sequencing machines. With a carefully designed index-filtering algorithm and an efficient implementation, SeqMap can map tens of millions of short sequences to a genome of several billions of nucleotides. Multiple substitutions and insertions/deletions of the nucleotide bases in the sequences can be tolerated and therefore detected. SeqMap supports FASTA input format and various output formats, and provides command line options for tuning almost every aspect of the mapping process. A typical mapping can be done in a few hours on a desktop PC. Parallel use of SeqMap on a cluster is also very straightforward.     

Iterative orthology prediction uncovers new mitochondrial proteins and identifies C12orf62 as the human ortholog of COX14, a protein involved in the assembly of cytochrome c oxidase

Background

Orthology is a central tenet of comparative genomics and ortholog identification is instrumental to protein function prediction. Major advances have been made to determine orthology relations among a set of homologous proteins. However, they depend on the comparison of individual sequences and do not take into account divergent orthologs.

Results

We have developed an iterative orthology prediction method, Ortho-Profile, that uses reciprocal best hits at the level of sequence profiles to infer orthology. It increases ortholog detection by 20% compared to sequence-to-sequence comparisons. Ortho-Profile predicts 598 human orthologs of mitochondrial proteins from Saccharomyces cerevisiae and Schizosaccharomyces pombe with 94% accuracy. Of these, 181 were not known to localize to mitochondria in mammals. Among the predictions of the Ortho-Profile method are 11 human cytochrome c oxidase (COX) assembly proteins that are implicated in mitochondrial function and disease. Their co-expression patterns, experimentally verified subcellular localization, and co-purification with human COX-associated proteins support these predictions. For the human gene C12orf62, the ortholog of S. cerevisiae COX14, we specifically confirm its role in negative regulation of the translation of cytochrome c oxidase.

Conclusions

Divergent homologs can often only be detected by comparing sequence profiles and profile-based hidden Markov models. The Ortho-Profile method takes advantage of these techniques in the quest for orthologs.     

Proteomics FASTA archive and reference resource

A FASTA file archive and reference resource has been added to ProteomeCommons.org. Motivation for this new functionality derives from two primary sources. The first is the recent FASTA standardization work done by the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Second is the general lack of a uniform mechanism to properly cite FASTA files used in a study, and to publicly access such FASTA files post-publication. An extension to the Tranche data sharing network has been developed that includes web-pages, documentation, and tools for facilitating the use of FASTA files. These include conversion to the new HUPO-PSI format, and provisions for both citing and publicly archiving FASTA files. This new resource is available immediately, free of charge, and can be accessed at http://www.proteomecommons.org/data/fasta/. Source-code for related tools is also freely available under the BSD license.     

CDinFusion--submission-ready, on-line integration of sequence and contextual data

State of the art (DNA) sequencing methods applied in "Omics" studies grant insight into the 'blueprints' of organisms from all domains of life. Sequencing is carried out around the globe and the data is submitted to the public repositories of the International Nucleotide Sequence Database Collaboration. However, the context in which these studies are conducted often gets lost, because experimental data, as well as information about the environment are rarely submitted along with the sequence data. If these contextual or metadata are missing, key opportunities of comparison and analysis across studies and habitats are hampered or even impossible. To address this problem, the Genomic Standards Consortium (GSC) promotes checklists and standards to better describe our sequence data collection and to promote the capturing, exchange and integration of sequence data with contextual data. In a recent community effort the GSC has developed a series of recommendations for contextual data that should be submitted along with sequence data. To support the scientific community to significantly enhance the quality and quantity of contextual data in the public sequence data repositories, specialized software tools are needed. In this work we present CDinFusion, a web-based tool to integrate contextual and sequence data in (Multi)FASTA format prior to submission. The tool is open source and available under the Lesser GNU Public License 3. A public installation is hosted and maintained at the Max Planck Institute for Marine Microbiology at http://www.megx.net/cdinfusion. The tool may also be installed locally using the open source code available at http://code.google.com/p/cdinfusion.     

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.     

ASAP: automated sequence annotation pipeline for web-based updating of sequence information with a local dynamic database

The automated sequence annotation pipeline (ASAP) is designed to ease routine investigation of new functional annotations on unknown sequences, such as expressed sequence tags (ESTs), through querying of web-accessible resources and maintenance of a local database. The system allows easy use of the output from one search as the input for a new search, as well as the filtering of results. The database is used to store formats and parameters and information for parsing data from web sites. The database permits easy updating of format information should a site modify the format of a query or of a returned web page.     

OrthoDisease: tracking disease gene orthologs across 100 species

Orthology is one of the most important tools available to modern biology, as it allows making inferences from easily studied model systems to much less tractable systems of interest, such as ourselves. This becomes important not least in the study of genetic diseases. We here review work on the orthology of disease-associated genes and also present an updated version of the InParanoid-based disease orthology database and web site OrthoDisease, with 14-fold increased species coverage since the previous version. Using this resource, we survey the taxonomic distribution of orthologs of human genes involved in different disease categories. The hypothesis that paralogs can mask the effect of deleterious mutations predicts that known heritable disease genes should have fewer close paralogs. We found large-scale support for this hypothesis as significantly fewer duplications were observed for disease genes in the OrthoDisease ortholog groups.     

Benchmarking ortholog identification methods using functional genomics data

Background  

The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the definition of orthology is incorrectly interpreted as a prediction of proteins that are functionally equivalent across species, while in fact it only defines the existence of a common ancestor for a gene in different species. However, it has been demonstrated that orthologs often reveal significant functional similarity. Therefore, the quality of the orthology prediction is an important factor in the transfer of functional annotations (and other related information). To identify protein pairs with the highest possible functional similarity, it is important to qualify ortholog identification methods.     

snpR: User friendly population genomics for SNP data sets with categorical metadata

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.     

HCOP: The HGNC comparison of orthology predictions search tool

The HGNC Comparison of Orthology Predictions search tool, HCOP (), enables users to compare predicted human and mouse orthologs for a specified gene, or set of genes, from either species according to the ortholog assertions from the Ensembl, HGNC, Homologene, Inparanoid, MGI and PhIGs databases. Users can assess the reliability of the prediction from the number of these different sources that identify a particular orthologous pair. HCOP provides a useful one-stop resource to summarise, compare and access various sources of human and mouse orthology data.     

Large-scale assignment of orthology: back to phylogenetics?

Reliable orthology prediction is central to comparative genomics. Although orthology is defined by phylogenetic criteria, most automated prediction methods are based on pairwise sequence comparisons. Recently, automated phylogeny-based orthology prediction has emerged as a feasible alternative for genome-wide studies.     

OrthologID: automation of genome-scale ortholog identification within a parsimony framework

MOTIVATION: The determination of gene orthology is a prerequisite for mining and utilizing the rapidly increasing amount of sequence data for genome-scale phylogenetics and comparative genomic studies. Until now, most researchers use pairwise distance comparisons algorithms, such as BLAST, COG, RBH, RSD and INPARANOID, to determine gene orthology. In contrast, orthology determination within a character-based phylogenetic framework has not been utilized on a genomic scale owing to the lack of efficiency and automation. RESULTS: We have developed OrthologID, a Web application that automates the labor-intensive procedures of gene orthology determination within a character-based phylogenetic framework, thus making character-based orthology determination on a genomic scale possible. In addition to generating gene family trees and determining orthologous gene sets for complete genomes, OrthologID can also identify diagnostic characters that define each orthologous gene set, as well as diagnostic characters that are responsible for classifying query sequences from other genomes into specific orthology groups. The OrthologID database currently includes several complete plant genomes, including Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, as well as a unicellular outgroup, Chlamydomonas reinhardtii. To improve the general utility of OrthologID beyond plant species, we plan to expand our sequence database to include the fully sequenced genomes of prokaryotes and other non-plant eukaryotes. AVAILABILITY: http://nypg.bio.nyu.edu/orthologid/     

MetaPhOrs: orthology and paralogy predictions from multiple phylogenetic evidence using a consistency-based confidence score

Reliable prediction of orthology is central to comparative genomics. Approaches based on phylogenetic analyses closely resemble the original definition of orthology and paralogy and are known to be highly accurate. However, the large computational cost associated to these analyses is a limiting factor that often prevents its use at genomic scales. Recently, several projects have addressed the reconstruction of large collections of high-quality phylogenetic trees from which orthology and paralogy relationships can be inferred. This provides us with the opportunity to infer the evolutionary relationships of genes from multiple, independent, phylogenetic trees. Using such strategy, we combine phylogenetic information derived from different databases, to predict orthology and paralogy relationships for 4.1 million proteins in 829 fully sequenced genomes. We show that the number of independent sources from which a prediction is made, as well as the level of consistency across predictions, can be used as reliable confidence scores. A webserver has been developed to easily access these data (http://orthology.phylomedb.org), which provides users with a global repository of phylogeny-based orthology and paralogy predictions.     

GFPE: gene-finding program evaluation

Gene-finding program evaluation (GFPE) is a set of Java classes for evaluating gene-finding programs. A command-line interface is also provided. Inputs to the program include the sequence data (in FASTA format), annotations of "actual" sequence features, and annotations of "predicted" sequence features. Annotation files are in the General Feature Format promoted by the Sanger center. GFPE calculates a number of metrics of accuracy of predictions at three levels:the coding level, the exon level, and the protein level.     

Fast and accurate database homology search using upper bounds of local alignment scores

MOTIVATION: It is widely recognized that homology search and ortholog clustering are very useful for analyzing biological sequences. However, recent growth of sequence database size makes homolog detection difficult, and rapid and accurate methods are required. RESULTS: We present a novel method for fast and accurate homology detection, assuming that the Smith-Waterman (SW) scores between all similar sequence pairs in a target database are computed and stored. In this method, SW alignment is computed only if the upper bound, which is derived from our novel inequality, is higher than the given threshold. In contrast to other methods such as FASTA and BLAST, this method is guaranteed to find all sequences whose scores against the query are higher than the specified threshold. Results of computational experiments suggest that the method is dozens of times faster than SSEARCH if genome sequence data of closely related species are available.