Inferring sub-cellular localization through automated lexical analysis

MOTIVATION: The SWISS-PROT sequence database contains keywords of functional annotations for many proteins. In contrast, information about the sub-cellular localization is available for only a few proteins. Experts can often infer localization from keywords describing protein function. We developed LOCkey, a fully automated method for lexical analysis of SWISS-PROT keywords that assigns sub-cellular localization. With the rapid growth in sequence data, the biochemical characterisation of sequences has been falling behind. Our method may be a useful tool for supplementing functional information already automatically available. RESULTS: The method reached a level of more than 82% accuracy in a full cross-validation test. Due to a lack of functional annotations, we could infer localization for fewer than half of all proteins in SWISS-PROT. We applied LOCkey to annotate five entirely sequenced proteomes, namely Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Arabidopsis thaliana (plant) and a subset of all human proteins. LOCkey found about 8000 new annotations of sub-cellular localization for these eukaryotes.     

TMCompare: transmembrane region sequence and structure.

TMCompare is an alignment and visualization tool for comparison of sequence information for membrane proteins contained in SWISS-PROT entries, with structural information contained in PDB files. The program can be used for: detection of breaks in alpha helical structure of transmembrane regions; examination of differences in coverage between PDB and SWISS-PROT files; examination of annotation differences between PDB files and associated SWISS-PROT files; examination and comparison of assigned PDB alpha helix regions and assigned SWISS-PROT transmembrane regions in linear sequence (one letter code) format; examination of these differences in 3D using the CHIME plugin, allowing; analysis of the alpha and non-alpha content of transmembrane regions. AVAILABILITY: TMCompare is available for use through selection of a query protein via the internet (http://www.membraneproteins.org/TMCompare) CONTACT: tmcompare@membraneproteins.org     

The role SWISS-PROT and TrEMBL play in the genome research environment

SWISS-PROT, a curated protein sequence data bank, contains not only sequence data but also annotation relevant to a particular sequence. The annotation added to each entry is done by a team of biologists and comes, primarily, from articles in journals reporting the actual sequencing and sometimes characterisation. Review articles and collaboration with external experts also play a role along with the use of secondary databases like PROSITE and Pfam in addition to a variety of feature prediction methods. Annotation added by these methods is checked for relevance and likelihood to a particular sequence. The onset of genome sequencing has led to a dramatic increase in sequence data to be included in SWISS-PROT. This has led to the production of TrEMBL (Translation of the EMBL database). TrEMBL consists of entries in a SWISS-PROT format that are derived from the translation of all coding sequences in the EMBL nucleotide sequence database, that are not in SWISS-PROT. Unlike SWISS-PROT entries those in TrEMBL are awaiting manual annotation. However, rather than just representing basic sequence and source information, steps have been taken to add features and annotation automatically. In taking these steps it is hoped that TrEMBL entries are enhanced with some indication as to what a protein is, could or may be.     

PRECIS: protein reports engineered from concise information in SWISS-PROT

MOTIVATION: There have been several endeavours to address the problem of annotating sequence data computationally, but the task is non-trivial and few tools have emerged that gather useful information on a given sequence, or set of sequences, in a simple and convenient manner. As more genome projects bear fruit, the mass of uncharacterized sequence data accumulating in public repositories grows ever larger. There is thus a pressing need for tools to support the process of automatic analysis and annotation of newly determined sequences. With this in mind, we have developed PRECIS, which automatically creates protein reports from sets of SWISS-PROT entries, collating results into structured reports, detailing known biological and medical information, literature and database cross-references, and relevant keywords.     

Novel search method for the discovery of functional relationships

MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.     

一种新的基因注释语义相似度计算方法

基因本体(GO)数据库为基因提供了统一的注释,有效地解决了不同数据库描述相同基因的不一致问题。但是,根据基因注释如何比较基因的功能相似性,这个问题仍然没有得到有效解决。本文提出一种新的基因注释语义相似度计算方法,这种方法在本质上是基于基因的生物学特性,其特点在于结点的语义相似度与结点所在集合无关,只与结点在GO图的位置有关,语义相似度可被重复利用。它既考虑了基因所映射的GO结点深度,又考虑了两GO结点之间所有路径对结点语义相似度的影响。文中以酵母菌的异亮氨酸降解代谢通路和谷氨酸合成代谢通路为实验,实验结果表明这种算法能准确地计算基因注释语义相似度。     

Functional annotation prediction: all for one and one for all

In an era of rapid genome sequencing and high-throughput technology, automatic function prediction for a novel sequence is of utter importance in bioinformatics. While automatic annotation methods based on local alignment searches can be simple and straightforward, they suffer from several drawbacks, including relatively low sensitivity and assignment of incorrect annotations that are not associated with the region of similarity. ProtoNet is a hierarchical organization of the protein sequences in the UniProt database. Although the hierarchy is constructed in an unsupervised automatic manner, it has been shown to be coherent with several biological data sources. We extend the ProtoNet system in order to assign functional annotations automatically. By leveraging on the scaffold of the hierarchical classification, the method is able to overcome some frequent annotation pitfalls.     

The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003

The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org.     

Sources of systematic error in functional annotation of genomes: domain rearrangement, non-orthologous gene displacement and operon disruption

Functional annotation of proteins encoded in newly sequenced genomes can be expected to meet two conflicting objectives: (i) provide as much information as possible, and (ii) avoid erroneous functional assignments and over-predictions. The continuing exponential growth of the number of sequenced genomes makes the quality of sequence annotation a critical factor in the efforts to utilize this new information. When dubious functional assignments are used as a basis for subsequent predictions, they tend to proliferate, leading to "database explosion". It is therefore important to identify the common factors that hamper functional annotation. As a first step towards that goal, we have compared the annotations of the Mycoplasma genitalium and Methanococcus jannaschii genomes produced in several independent studies. The most common causes of questionable predictions appear to be: i) non-critical use of annotations from existing database entries; ii) taking into account only the annotation of the best database hit; iii) insufficient masking of low complexity regions (e.g. non-globular domains) in protein sequences, resulting in spurious database hits obscuring relevant ones; iv) ignoring multi-domain organization of the query proteins and/or the database hits; v) non-critical functional inferences on the basis of the functions of neighboring genes in an operon; vi) non-orthologous gene displacement, i.e. involvement of structurally unrelated proteins in the same function. These observations suggest that case by case validation of functional annotation by expert biologists remains crucial for productive genome analysis.     

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

MOTIVATION: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. RESULTS: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. AVAILABILITY: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/Spearmint/ Source code is available upon request. CONTACT: kretsch@ebi.ac.uk.     

NEWT,a new taxonomy portal

NEWT is a new taxonomy portal to the SWISS-PROT protein sequence knowledgebase. It contains taxonomy data, which is updated daily, for the complete set of species represented in SWISS-PROT, as well as those stored at the NCBI. Users can navigate through the taxonomy tree and access corresponding SWISS-PROT protein entries. In addition, a manually curated selection of external links allows access to specific information on selected species. NEWT is available at http://www.ebi.ac.uk/newt/.     

A case study for large-scale human microbiome analysis using JCVI's metagenomics reports (METAREP)

As metagenomic studies continue to increase in their number, sequence volume and complexity, the scalability of biological analysis frameworks has become a rate-limiting factor to meaningful data interpretation. To address this issue, we have developed JCVI Metagenomics Reports (METAREP) as an open source tool to query, browse, and compare extremely large volumes of metagenomic annotations. Here we present improvements to this software including the implementation of a dynamic weighting of taxonomic and functional annotation, support for distributed searches, advanced clustering routines, and integration of additional annotation input formats. The utility of these improvements to data interpretation are demonstrated through the application of multiple comparative analysis strategies to shotgun metagenomic data produced by the National Institutes of Health Roadmap for Biomedical Research Human Microbiome Project (HMP) (http://nihroadmap.nih.gov). Specifically, the scalability of the dynamic weighting feature is evaluated and established by its application to the analysis of over 400 million weighted gene annotations derived from 14 billion short reads as predicted by the HMP Unified Metabolic Analysis Network (HUMAnN) pipeline. Further, the capacity of METAREP to facilitate the identification and simultaneous comparison of taxonomic and functional annotations including biological pathway and individual enzyme abundances from hundreds of community samples is demonstrated by providing scenarios that describe how these data can be mined to answer biological questions related to the human microbiome. These strategies provide users with a reference of how to conduct similar large-scale metagenomic analyses using METAREP with their own sequence data, while in this study they reveal insights into the nature and extent of variation in taxonomic and functional profiles across body habitats and individuals. Over one thousand HMP WGS datasets and the latest open source code are available at http://www.jcvi.org/hmp-metarep.     

Applying negative rule mining to improve genome annotation

Background  

Unsupervised annotation of proteins by software pipelines suffers from very high error rates. Spurious functional assignments are usually caused by unwarranted homology-based transfer of information from existing database entries to the new target sequences. We have previously demonstrated that data mining in large sequence annotation databanks can help identify annotation items that are strongly associated with each other, and that exceptions from strong positive association rules often point to potential annotation errors. Here we investigate the applicability of negative association rule mining to revealing erroneously assigned annotation items.     

Adding some SPICE to DAS

The distributed annotation system (DAS) defines a communication protocol used to exchange biological annotations. It is motivated by the idea that annotations should not be provided by single centralized databases but instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is carried out by DAS clients. The original DAS protocol was designed to serve annotation of genomic sequences. We have extended the protocol to be applicable to macromolecular structures. Here we present SPICE, a new DAS client that can be used to visualize protein sequence and structure annotations. AVAILABILITY: http://www.efamily.org.uk/software/dasclients/spice/     

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     

Functional modelling of an equine bronchoalveolar lavage fluid proteome provides experimental confirmation and functional annotation of equine genome sequences

The equine genome sequence enables the use of high-throughput genomic technologies in equine research, but accurate identification of expressed gene products and interpreting their biological relevance require additional structural and functional genome annotation. Here, we employ the equine genome sequence to identify predicted and known proteins using proteomics and model these proteins into biological pathways, identifying 582 proteins in normal cell-free equine bronchoalveolar lavage fluid (BALF). We improved structural and functional annotation by directly confirming the in vivo expression of 558 (96%) proteins, which were computationally predicted previously, and adding Gene Ontology (GO) annotations for 174 proteins, 108 of which lacked functional annotation. Bronchoalveolar lavage is commonly used to investigate equine respiratory disease, leading us to model the associated proteome and its biological functions. Modelling of protein functions using Ingenuity Pathway Analysis identified carbohydrate metabolism, cell-to-cell signalling, cellular function, inflammatory response, organ morphology, lipid metabolism and cellular movement as key biological processes in normal equine BALF. Comparative modelling of protein functions in normal cell-free bronchoalveolar lavage proteomes from horse, human, and mouse, performed by grouping GO terms sharing common ancestor terms, confirms conservation of functions across species. Ninety-one of 92 human GO categories and 105 of 109 mouse GO categories were conserved in the horse. Our approach confirms the utility of the equine genome sequence to characterize protein networks without antibodies or mRNA quantification, highlights the need for continued structural and functional annotation of the equine genome and provides a framework for equine researchers to aid in the annotation effort.