Knowledge-based voting algorithm for automated protein functional annotation

Automated annotation of high-throughput genome sequences is one of the earliest steps toward a comprehensive understanding of the dynamic behavior of living organisms. However, the step is often error-prone because of its underlying algorithms, which rely mainly on a simple similarity analysis, and lack of guidance from biological rules. We present herein a knowledge-based protein annotation algorithm. Our objectives are to reduce errors and to improve annotation confidences. This algorithm consists of two major components: a knowledge system, called "RuleMiner," and a voting procedure. The knowledge system, which includes biological rules and functional profiles for each function, provides a platform for seamless integration of multiple sequence analysis tools and guidance for function annotation. The voting procedure, which relies on the knowledge system, is designed to make (possibly) unbiased judgments in functional assignments among complicated, sometimes conflicting, information. We have applied this algorithm to 10 prokaryotic bacterial genomes and observed a significant improvement in annotation confidences. We also discuss the current limitations of the algorithm and the potential for future improvement.     

Complexity of automated gene annotation

Describing the determinants of robustness of biological systems has become one of the central questions in systems biology. Despite the increasing research efforts, it has proven difficult to arrive at a unifying definition for this important concept. We argue that this is due to the multifaceted nature of the concept of robustness and the possibility to formally capture it at different levels of systemic formalisms (e.g., topology and dynamic behavior). Here we provide a comprehensive review of the existing definitions of robustness pertaining to metabolic networks. As kinetic approaches have been excellently reviewed elsewhere, we focus on definitions of robustness proposed within graph-theoretic and constraint-based formalisms.     

Statistically rigorous automated protein annotation

MOTIVATION: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g. sequence identity, keywords or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins. RESULTS: A dataset of 27 000 annotated Protein Data Bank (PDB) polypeptide chains (of 36 000 chains currently in the PDB) was generated from 23 000 chains classified a priori. AVAILABILITY: PDB annotations and sample software implementation are freely accessible on the Web at http://pmr.sdsc.edu/go     

An automated protein annotation filter for integrating web-based annotation tools

A wide range of web based prediction and annotation tools are frequently used for determining protein function from sequence. However, parallel processing of sequences for annotation through web tools is not possible due to several constraints in functional programming for multiple queries. Here, we propose the development of APAF as an automated protein annotation filter to overcome some of these difficulties through an integrated approach.     

GoFigure: automated Gene Ontology annotation

SUMMARY: We have developed a web tool to predict Gene Ontology (GO) terms. The tool accepts an input DNA or protein sequence, and uses BLAST to identify homologous sequences in GO annotated databases. A graph is returned to the user via email. AVAILABILITY: The tool is freely available at: http://udgenome.ags.udel.edu/frm_go.html/     

Ways to improve the prediction quality in the adaptive algorithm of automated annotation (A 4)

Modifications for improving the prediction quality in a previously described adaptive algorithm of automated annotation (A ⁴) were considered. First, the direct use of the basis statistic η ensures a higher prediction quality than the use of a previously proposed statistic γ. Second, the quality is improved when only some of the found similar sequences, rather than all of them, are used for prediction, since this reduces the data noise.     

A categorization approach to automated ontological function annotation

Automated function prediction (AFP) methods increasingly use knowledge discovery algorithms to map sequence, structure, literature, and/or pathway information about proteins whose functions are unknown into functional ontologies, typically (a portion of) the Gene Ontology (GO). While there are a growing number of methods within this paradigm, the general problem of assessing the accuracy of such prediction algorithms has not been seriously addressed. We present first an application for function prediction from protein sequences using the POSet Ontology Categorizer (POSOC) to produce new annotations by analyzing collections of GO nodes derived from annotations of protein BLAST neighborhoods. We then also present hierarchical precision and hierarchical recall as new evaluation metrics for assessing the accuracy of any predictions in hierarchical ontologies, and discuss results on a test set of protein sequences. We show that our method provides substantially improved hierarchical precision (measure of predictions made that are correct) when applied to the nearest BLAST neighbors of target proteins, as compared with simply imputing that neighborhood's annotations to the target. Moreover, when our method is applied to a broader BLAST neighborhood, hierarchical precision is enhanced even further. In all cases, such increased hierarchical precision performance is purchased at a modest expense of hierarchical recall (measure of all annotations that get predicted at all).     

PhyloGena--a user-friendly system for automated phylogenetic annotation of unknown sequences

MOTIVATION: Phylogenomic approaches towards functional and evolutionary annotation of unknown sequences have been suggested to be superior to those based only on pairwise local alignments. User-friendly software tools making the advantages of phylogenetic annotation available for the ever widening range of bioinformatically uninitiated biologists involved in genome/EST annotation projects are, however, not available. We were particularly confronted with this issue in the annotation of sequences from different groups of complex algae originating from secondary endosymbioses, where the identification of the phylogenetic origin of genes is often more problematic than in taxa well represented in the databases (e.g. animals, plants or fungi). RESULTS: We present a flexible pipeline with a user-friendly, interactive graphical user interface running on desktop computers that automatically performs a basic local alignment search tool (BLAST) search of query sequences, selects a representative subset of them, then creates a multiple alignment from the selected sequences, and finally computes a phylogenetic tree. The pipeline, named PhyloGena, uses public domain software for all standard bioinformatics tasks (similarity search, multiple alignment, and phylogenetic reconstruction). As the major technological innovation, selection of a meaningful subset of BLAST hits was implemented using logic programming, mimicing the selection procedure (BLAST tables, multiple alignments and phylogenetic trees) are displayed graphically, allowing the user to interact with the pipeline and deduce the function and phylogenetic origin of the query. PhyloGena thus makes phylogenomic annotation available also for those biologists without access to large computing facilities and with little informatics background. Although phylogenetic annotation is particularly useful when working with composite genomes (e.g. from complex algae), PhyloGena can be helpful in expressed sequence tag and genome annotation also in other organisms. AVAILABILITY: PhyloGena (executables for LINUX and Windows 2000/XP as well as source code) is available by anonymous ftp from http://www.awi.de/en/phylogena.     

Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies.     

Sequence annotation of nuclear receptor ligand-binding domains by automated homology modeling

The quality of three-dimensional homology models derived from protein sequences provides an independent measure of the suitability of a protein sequence for a certain fold. We have used automated homology modeling and model assessment tools to identify putative nuclear hormone receptor ligand-binding domains in the genome of Caenorhabditis elegans. Our results indicate that the availability of multiple crystal structures is crucial to obtaining useful models in this receptor family. The majority of annotated mammalian nuclear hormone receptors could be assigned to a ligand-binding domain fold by using the best model derived from any of four template structures. This strategy also assigned the ligand-binding domain fold to a number of C.elegans. sequences without prior annotation. Interestingly, the retinoic acid receptor crystal structure contributed most to the number of sequences that could be assigned to a ligand-binding domain fold. Several causes for this can be suggested, including the high quality of this protein structure in terms of our assessment tools, similarity between the biological function or ligand of this receptor and the modeled genes and gene duplication in C.elegans.     

Steady progress and recent breakthroughs in the accuracy of automated genome annotation

The sequencing of large, complex genomes has become routine, but understanding how sequences relate to biological function is less straightforward. Although much attention is focused on how to annotate genomic features such as developmental enhancers and non-coding RNAs, there is still no higher eukaryote for which we know the correct exon-intron structure of at least one ORF for each gene. Despite this uncomfortable truth, genome annotation has made remarkable progress since the first drafts of the human genome were analysed. By combining several computational and experimental methods, we are now closer to producing complete and accurate gene catalogues than ever before.     

TRAP: automated classification, quantification and annotation of tandemly repeated sequences

TRAP, the Tandem Repeats Analysis Program, is a Perl program that provides a unified set of analyses for the selection, classification, quantification and automated annotation of tandemly repeated sequences. TRAP uses the results of the Tandem Repeats Finder program to perform a global analysis of the satellite content of DNA sequences, permitting researchers to easily assess the tandem repeat content for both individual sequences and whole genomes. The results can be generated in convenient formats such as HTML and comma-separated values. TRAP can also be used to automatically generate annotation data in the format of feature table and GFF files.     

TEnest: automated chronological annotation and visualization of nested plant transposable elements

Organisms with a high density of transposable elements (TEs) exhibit nesting, with subsequent repeats found inside previously inserted elements. Nesting splits the sequence structure of TEs and makes annotation of repetitive areas challenging. We present TEnest, a repeat identification and display tool made specifically for highly repetitive genomes. TEnest identifies repetitive sequences and reconstructs separated sections to provide full-length repeats and, for long-terminal repeat (LTR) retrotransposons, calculates age since insertion based on LTR divergence. TEnest provides a chronological insertion display to give an accurate visual representation of TE integration history showing timeline, location, and families of each TE identified, thus creating a framework from which evolutionary comparisons can be made among various regions of the genome. A database of repeats has been developed for maize (Zea mays), rice (Oryza sativa), wheat (Triticum aestivum), and barley (Hordeum vulgare) to illustrate the potential of TEnest software. All currently finished maize bacterial artificial chromosomes totaling 29.3 Mb were analyzed with TEnest to provide a characterization of the repeat insertions. Sixty-seven percent of the maize genome was found to be made up of TEs; of these, 95% are LTR retrotransposons. The rate of solo LTR formation is shown to be dissimilar across retrotransposon families. Phylogenetic analysis of TE families reveals specific events of extreme TE proliferation, which may explain the high quantities of certain TE families found throughout the maize genome. The TEnest software package is available for use on PlantGDB under the tools section (http://www.plantgdb.org/prj/TE_nest/TE_nest.html); the source code is available from (http://wiselab.org).     

An automated annotation tool for genomic DNA sequences using GeneScan and BLAST

Genomic sequence data are often available well before the annotated sequence is published. We present a method for analysis of genomic DNA to identify coding sequences using the GeneScan algorithm and characterize these resultant sequences by BLAST. The routines are used to develop a system for automated annotation of genome DNA sequences.     

RiceGAAS: an automated annotation system and database for rice genome sequence

An extensive effort of the International Rice Genome Sequencing Project (IRGSP) has resulted in rapid accumulation of genome sequence, and >137 Mb has already been made available to the public domain as of August 2001. This requires a high-throughput annotation scheme to extract biologically useful and timely information from the sequence data on a regular basis. A new automated annotation system and database called Rice Genome Automated Annotation System (RiceGAAS) has been developed to execute a reliable and up-to-date analysis of the genome sequence as well as to store and retrieve the results of annotation. The system has the following functional features: (i) collection of rice genome sequences from GenBank; (ii) execution of gene prediction and homology search programs; (iii) integration of results from various analyses and automatic interpretation of coding regions; (iv) re-execution of analysis, integration and automatic interpretation with the latest entries in reference databases; (v) integrated visualization of the stored data using web-based graphical view. RiceGAAS also has a data submission mechanism that allows public users to perform fully automated annotation of their own sequences. The system can be accessed at http://RiceGAAS.dna.affrc.go.jp/.     

EST Express: PHP/MySQL based automated annotation of ESTs from expression libraries

Background  

Several biological techniques result in the acquisition of functional sets of cDNAs that must be sequenced and analyzed. The emergence of redundant databases such as UniGene and centralized annotation engines such as Entrez Gene has allowed the development of software that can analyze a great number of sequences in a matter of seconds.     

Novel algorithm for automated genotyping of microsatellites

Microsatellites or short tandem repeats (STRs) are abundant in the human genome with easily assayed polymorphisms, providing powerful genetic tools for mapping both Mendelian and complex traits. Microsatellite genotyping requires detection of the products of polymerase chain reaction (PCR) amplification by electrophoresis, and analysis of the peak data for discrimination of the true allele. A high-throughput genotyping approach requires computer-based automation at both the detection and analysis phases. In order to achieve this, complicated peak patterns from individual alleles must be interpreted in order to assign alleles. Previous methods consider limited types of noise peaks and cannot provide enough accuracy. By pattern recognition of various types of noise peaks, such as stutter peaks and additional peaks, we have achieved an overall average accuracy of 94% for allele calling in our actual data. Our algorithm is crucial for a high-throughput genotyping system for microsatellite markers by reducing manual editing and human errors.