UniProt蛋白质数据库简介

UniProt(https://www.uniprot.org/)是国际知名蛋白质数据库,主要包括UniProtKB知识库、UniParc归档库和UniRef参考序列集三部分。UniProtKB知识库是UniProt的核心,除蛋白质序列数据外,还包括大量注释信息。UniProtKB知识库分Swiss-Prot和TrEMBL两个子库。Swiss-Prot子库中50多万条序列均由人工审阅和注释,而TrEMBL子库中1.4亿多条序列是由核酸序列数据库EMBL中的蛋白质编码序列翻译所得,并由计算机根据一定规则进行注释。UniParc归档库将存放于不同数据库中的同一个蛋白质归并到一个记录中以避免冗余,并赋予序列唯一性特定标识符。UniRef参考序列集按相似性程度将UniProtKB和UniParc中的序列分为UniRef100、UniRef90和UniRef50三个数据集。UniProt网站为用户提供了高效实用的高级检索系统和大量帮助文档。UniProt数据库每4周发布新版的同时也发布统计报表,用户可通过统计报表了解该数据库的数据量及更新情况、数据类别和物种分布等基本信息,查看常规注释信息、序列特征注释信息和数据库交叉链接等统计数据。UniProt是目前国际上序列数据最完整、注释信息最丰富的非冗余蛋白质序列数据库,自本世纪初创建以来,为生命科学领域提供了宝贵资源。     

Analysis of the tryptic search space in UniProt databases

In this article, we provide a comprehensive study of the content of the Universal Protein Resource (UniProt) protein data sets for human and mouse. The tryptic search spaces of the UniProtKB (UniProt knowledgebase) complete proteome sets were compared with other data sets from UniProtKB and with the corresponding International Protein Index, reference sequence, Ensembl, and UniRef100 (where UniRef is UniProt reference clusters) organism‐specific data sets. All protein forms annotated in UniProtKB (both the canonical sequences and isoforms) were evaluated in this study. In addition, natural and disease‐associated amino acid variants annotated in UniProtKB were included in the evaluation. The peptide unicity was also evaluated for each data set. Furthermore, the peptide information in the UniProtKB data sets was also compared against the available peptide‐level identifications in the main MS‐based proteomics repositories. Identifying the peptides observed in these repositories is an important resource of information for protein databases as they provide supporting evidence for the existence of otherwise predicted proteins. Likewise, the repositories could use the information available in UniProtKB to direct reprocessing efforts on specific sets of peptides/proteins of interest. In summary, we provide comprehensive information about the different organism‐specific sequence data sets available from UniProt, together with the pros and cons for each, in terms of search space for MS‐based bottom‐up proteomics workflows. The aim of the analysis is to provide a clear view of the tryptic search space of UniProt and other protein databases to enable scientists to select those most appropriate for their purposes.     

Plant protein annotation in the UniProt Knowledgebase

The Swiss-Prot, TrEMBL, Protein Information Resource (PIR), and DNA Data Bank of Japan (DDBJ) protein database activities have united to form the Universal Protein Resource (UniProt) Consortium. UniProt presents three database layers: the UniProt Archive, the UniProt Knowledgebase (UniProtKB), and the UniProt Reference Clusters. The UniProtKB consists of two sections: UniProtKB/Swiss-Prot (fully manually curated entries) and UniProtKB/TrEMBL (automated annotation, classification and extensive cross-references). New releases are published fortnightly. A specific Plant Proteome Annotation Program (http://www.expasy.org/sprot/ppap/) was initiated to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Through UniProt, our aim is to provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information that will allow the plant community to fully explore and utilize the wealth of information available for both plant and non-plant model organisms.     

Representative proteomes: a stable, scalable and unbiased proteome set for sequence analysis and functional annotation

The accelerating growth in the number of protein sequences taxes both the computational and manual resources needed to analyze them. One approach to dealing with this problem is to minimize the number of proteins subjected to such analysis in a way that minimizes loss of information. To this end we have developed a set of Representative Proteomes (RPs), each selected from a Representative Proteome Group (RPG) containing similar proteomes calculated based on co-membership in UniRef50 clusters. A Representative Proteome is the proteome that can best represent all the proteomes in its group in terms of the majority of the sequence space and information. RPs at 75%, 55%, 35% and 15% co-membership threshold (CMT) are provided to allow users to decrease or increase the granularity of the sequence space based on their requirements. We find that a CMT of 55% (RP55) most closely follows standard taxonomic classifications. Further analysis of this set reveals that sequence space is reduced by more than 80% relative to UniProtKB, while retaining both sequence diversity (over 95% of InterPro domains) and annotation information (93% of experimentally characterized proteins). All sets can be browsed and are available for sequence similarity searches and download at http://www.proteininformationresource.org/rps, while the set of 637 RPs determined using a 55% CMT are also available for text searches. Potential applications include sequence similarity searches, protein classification and targeted protein annotation and characterization.     

UniProtJAPI: a remote API for accessing UniProt data

Programmatic access to the UniProt Knowledgebase (UniProtKB) is essential for many bioinformatics applications dealing with protein data. We have created a Java library named UniProtJAPI, which facilitates the integration of UniProt data into Java-based software applications. The library supports queries and similarity searches that return UniProtKB entries in the form of Java objects. These objects contain functional annotations or sequence information associated with a UniProt entry. Here, we briefly describe the UniProtJAPI and demonstrate its usage.     

A procedure to recruit members to enlarge protein family databases--the building of UECOG (UniRef-Enriched COG Database) as a model

A procedure to recruit members to enlarge protein family databases is described here. The procedure makes use of UniRef50 clusters produced by UniProt. Current family entries are used to recruit additional members based on the UniRef50 clusters to which they belong. Only those additional UniRef50 members that are not fragments and whose length is within a restricted range relative to the original entry are recruited. The enriched dataset is then limited to contain only genomes from selected clades. We used the COG database - used for genome annotation and for studies of phylogenetics and gene evolution - as a model. To validate the method, a UniRef-Enriched COG0151 (UECOG) was tested with distinct procedures to compare recruited members with the recruiters: PSI-BLAST, secondary structure overlap (SOV), Seed Linkage, COGnitor, shared domain content, and neighbor-joining single-linkage, and observed that the former four agree in their validations. Presently, the UniRef50-based recruitment procedure enriches the COG database for Archaea, Bacteria and its subgroups Actinobacteria, Firmicutes, Proteobacteria, and other bacteria by 2.2-, 8.0-, 7.0-, 8.8-, 8.7-, and 4.2-fold, respectively, in terms of sequences, and also considerably increased the number of species.     

In Silico Characterization of Proteins: UniProt,InterPro and Integr8

Nucleic acid sequences from genome sequencing projects are submitted as raw data, from which biologists attempt to elucidate the function of the predicted gene products. The protein sequences are stored in public databases, such as the UniProt Knowledgebase (UniProtKB), where curators try to add predicted and experimental functional information. Protein function prediction can be done using sequence similarity searches, but an alternative approach is to use protein signatures, which classify proteins into families and domains. The major protein signature databases are available through the integrated InterPro database, which provides a classification of UniProtKB sequences. As well as characterization of proteins through protein families, many researchers are interested in analyzing the complete set of proteins from a genome (i.e. the proteome), and there are databases and resources that provide non-redundant proteome sets and analyses of proteins from organisms with completely sequenced genomes. This article reviews the tools and resources available on the web for single and large-scale protein characterization and whole proteome analysis.     

Mapping PDB chains to UniProtKB entries

MOTIVATION: UniProtKB/SwissProt is the main resource for detailed annotations of protein sequences. This database provides a jumping-off point to many other resources through the links it provides. Among others, these include other primary databases, secondary databases, the Gene Ontology and OMIM. While a large number of links are provided to Protein Data Bank (PDB) files, obtaining a regularly updated mapping between UniProtKB entries and PDB entries at the chain or residue level is not straightforward. In particular, there is no regularly updated resource which allows a UniProtKB/SwissProt entry to be identified for a given residue of a PDB file. RESULTS: We have created a completely automatically maintained database which maps PDB residues to residues in UniProtKB/SwissProt and UniProtKB/trEMBL entries. The protocol uses links from PDB to UniProtKB, from UniProtKB to PDB and a brute-force sequence scan to resolve PDB chains for which no annotated link is available. Finally the sequences from PDB and UniProtKB are aligned to obtain a residue-level mapping. AVAILABILITY: The resource may be queried interactively or downloaded from http://www.bioinf.org.uk/pdbsws/.     

A database of unique protein sequence identifiers for proteome studies

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.     

Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB

A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge; during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over sentences. Over sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately are erroneous, whilst appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation.Source code and supplementary data are available from the authors website at http://homepages.cs.ncl.ac.uk/m.j.bell1/sentence_analysis/.     

SNPdbe: constructing an nsSNP functional impacts database

Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe-SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; 'human' being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. AVAILABILITY: http://www.rostlab.org/services/snpdbe.     

UniSave: the UniProtKB sequence/annotation version database

SUMMARY: The UniProtKB Sequence/Annotation Version database (UniSave) is a comprehensive archive of UniProtKB/Swiss-Prot and UniProtKB/TrEMBL entry versions. All changed Swiss-Prot and TrEMBL entries are loaded into the UniSave as part of the public bi-weekly UniProtKB releases. Unlike the UniProtKB, which contains only the latest Swiss-Prot and TrEMBL entry versions, the UniSave provides access to previous versions of these entries. AVAILABILITY: http://www.ebi.ac.uk/uniprot/unisave     

Published and Perished? The Influence of the Searched Protein Database on the Long-Term Storage of Proteomics Data

In proteomics, protein identifications are reported and stored using an unstable reference system: protein identifiers. These proprietary identifiers are created individually by every protein database and can change or may even be deleted over time. To estimate the effect of the searched protein sequence database on the long-term storage of proteomics data we analyzed the changes of reported protein identifiers from all public experiments in the Proteomics Identifications (PRIDE) database by November 2010. To map the submitted protein identifier to a currently active entry, two distinct approaches were used. The first approach used the Protein Identifier Cross Referencing (PICR) service at the EBI, which maps protein identifiers based on 100% sequence identity. The second one (called logical mapping algorithm) accessed the source databases and retrieved the current status of the reported identifier. Our analysis showed the differences between the main protein databases (International Protein Index (IPI), UniProt Knowledgebase (UniProtKB), National Center for Biotechnological Information nr database (NCBI nr), and Ensembl) in respect to identifier stability. For example, whereas 20% of submitted IPI entries were deleted after two years, virtually all UniProtKB entries remained either active or replaced. Furthermore, the two mapping algorithms produced markedly different results. For example, the PICR service reported 10% more IPI entries deleted compared with the logical mapping algorithm. We found several cases where experiments contained more than 10% deleted identifiers already at the time of publication. We also assessed the proportion of peptide identifications in these data sets that still fitted the originally identified protein sequences. Finally, we performed the same overall analysis on all records from IPI, Ensembl, and UniProtKB: two releases per year were used, from 2005. This analysis showed for the first time the true effect of changing protein identifiers on proteomics data. Based on these findings, UniProtKB seems the best database for applications that rely on the long-term storage of proteomics data.     

ProRule: a new database containing functional and structural information on PROSITE profiles

MOTIVATION: Increase the discriminatory power of PROSITE profiles to facilitate function determination and provide biologically relevant information about domains detected by profiles for the annotation of proteins. SUMMARY: We have created a new database, ProRule, which contains additional information about PROSITE profiles. ProRule contains notably the position of structurally and/or functionally critical amino acids, as well as the condition they must fulfill to play their biological role. These supplementary data should help function determination and annotation of the UniProt Swiss-Prot knowledgebase. ProRule also contains information about the domain detected by the profile in the Swiss-Prot line format. Hence, ProRule can be used to make Swiss-Prot annotation more homogeneous and consistent. The format of ProRule can be extended to provide information about combination of domains. AVAILABILITY: ProRule can be accessed through ScanProsite at http://www.expasy.org/tools/scanprosite. A file containing the rules will be made available under the PROSITE copyright conditions on our ftp site (ftp://www.expasy.org/databases/prosite/) by the next PROSITE release.     

Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.     

GeneReporter--sequence-based document retrieval and annotation

GeneReporter is a web tool that reports functional information and relevant literature on a protein-coding sequence of interest. Its purpose is to support both manual genome annotation and document retrieval. PubMed references corresponding to a sequence are detected by the extraction of query words from UniProt entries of homologous sequences. Data on protein families, domains, potential cofactors, structure, function, cellular localization, metabolic contribution and corresponding DNA binding sites complement the information on a given gene product of interest. Availability and implementation: GeneReporter is available at http://www.genereporter.tu-bs.de. The web site integrates databases and analysis tools as SOAP-based web services from the EBI (European Bioinformatics Institute) and NCBI (National Center for Biotechnology Information).     

GenBank.

The GenBank sequence database continues to expand its data coverage, quality control, annotation content and retrieval services for the scientific community. Besides handling direct submissions of sequence data from authors, GenBank also incorporates DNA sequences from all available public sources; an integrated retrieval system, known as Entrez, also makes available data from the major protein sequence and structural databases, and from U.S. and European patents. MIDLINE abstracts from published articles describing the sequences are also included as an additional source of biological annotation for sequence entries. GenBank supports distribution of the data via FTP, CD-ROM, and E-mail servers. Network server-client programs provide access to an integrated database for literature retrieval and sequence similarity searching.     

GenBank.

The GenBank sequence database has undergone an expansion in data coverage, annotation content and the development of new services for the scientific community. In addition to nucleotide sequences, data from the major protein sequence and structural databases, and from U.S. and European patents is now included in an integrated system. MEDLINE abstracts from published articles describing the sequences provide an important new source of biological annotation for sequence entries. In addition to the continued support of existing services, new CD-ROM and network-based systems have been implemented for literature retrieval and sequence similarity searching. Major releases of GenBank are now more frequent and the data are distributed in several new forms for both end users and software developers.     

An open source chimera checker for the fungal ITS region

The internal transcribed spacer (ITS) region of the nuclear ribosomal repeat unit holds a central position in the pursuit of the taxonomic affiliation of fungi recovered through environmental sampling. Newly generated fungal ITS sequences are typically compared against the International Nucleotide Sequence Databases for a species or genus name using the sequence similarity software suite blast . Such searches are not without complications however, and one of them is the presence of chimeric entries among the query or reference sequences. Chimeras are artificial sequences, generated unintentionally during the polymerase chain reaction step, that feature sequence data from two (or possibly more) distinct species. Available software solutions for chimera control do not readily target the fungal ITS region, but the present study introduces a blast -based open source software package (available at http://www.emerencia.org/chimerachecker.html ) to examine newly generated fungal ITS sequences for the presence of potentially chimeric elements in batch mode. We used the software package on a random set of 12 300 environmental fungal ITS sequences in the public sequence databases and found 1.5% of the entries to be chimeric at the ordinal level after manual verification of the results. The proportion of chimeras in the sequence databases can be hypothesized to increase as emerging sequencing technologies drawing from pooled DNA samples are becoming important tools in molecular ecology research.