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PubMed2005

Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary.

MOTIVATION: High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways. RESULTS: We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available stand-alone Python program that can contribute significantly to genome annotation and microarray analysis.

Artificial Intelligence↗

PubMed2005

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.

Algorithms↗

PubMed2005

Knowledge-based computational search for genes associated with the metabolic syndrome.

MOTIVATION: A methodology to search for genes associated with multifactorial diseases by integrating the large amount of accumulated knowledge is seriously needed. A comprehensive understanding derived from a holistic view of gene relationship structures can be gained from our proposed analysis called the cross-subspace analysis (CSA). In this analysis, gene objects are generated by machine learning using their term occurrence patterns in MEDLINE abstracts and the degree of relationship between gene objects is quantified by matching these patterns. RESULTS: Structuralization of relationships of a set of genes was performed using CSA, which were retrieved using the terms, 'obesity', 'diabetes', 'hypertriglyceridemia' and 'hypertension' that refer to diseases comprising metabolic syndrome, on a 2D plane inferring important biomedical concepts from the gene distribution. Then, we prioritized the significance of 6131 well-annotated human genes in terms of the distance on the plane from the centroid of 'metabolic syndrome'-related genes distribution. The validity was confirmed by comparing the knowledge extracted by the ordering with existing medical knowledge.

Abstracting and Indexing↗

PubMed2005

Discovering patterns to extract protein-protein interactions from the literature: Part II.

MOTIVATION: An enormous number of protein-protein interaction relationships are buried in millions of research articles published over the years, and the number is growing. Rediscovering them automatically is a challenging bioinformatics task. Solutions to this problem also reach far beyond bioinformatics. RESULTS: We study a new approach that involves automatically discovering English expression patterns, optimizing them and using them to extract protein-protein interactions. In a sister paper, we described how to generate English expression patterns related to protein-protein interactions, and this approach alone has already achieved precision and recall rates significantly higher than those of other automatic systems. This paper continues to present our theory, focusing on how to improve the patterns. A minimum description length (MDL)-based pattern-optimization algorithm is designed to reduce and merge patterns. This has significantly increased generalization power, and hence the recall and precision rates, as confirmed by our experiments. AVAILABILITY: http://spies.cs.tsinghua.edu.cn.

Abstracting and Indexing↗

PubMed2005

MeSHer: identifying biological concepts in microarray assays based on PubMed references and MeSH terms.

UNLABELLED: MeSHer uses a simple statistical approach to identify biological concepts in the form of Medical Subject Headings (MeSH terms) obtained from the PubMed database that are significantly overrepresented within the identified gene set relative to those associated with the overall collection of genes on the underlying DNA microarray platform. As a demonstration, we apply this approach to gene lists acquired from a published study of the effects of angiotensin II (Ang II) treatment on cardiac gene expression and demonstrate that this approach can aid in the interpretation of the resulting 'significant' gene set. AVAILABILITY: The software is available at http://www.tm4.org. SUPPLEMENTARY INFORMATION: Results from the analysis of significant genes from the published Ang II study.

Artificial Intelligence↗

PubMed2005

MineBlast: a literature presentation service supporting protein annotation by data mining of BLAST results.

MineBlast is a web service for literature search and presentation based on data-mining results received from UniProt. Users can submit a simple list of protein sequences via a web-based interface. MineBlast performs a BLASTP search in UniProt to identify names and synonyms based on homologous proteins and subsequently queries PubMed, using combined search terms inorder to find and present relevant literature.

Database Management Systems↗

PubMed2005

A semantic analysis of the annotations of the human genome.

The correct interpretation of any biological experiment depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are ubiquitous and used by all life scientists in most experiments. However, it is well known that such databases are incomplete and many annotations may also be incorrect. In this paper we describe a technique that can be used to analyze the semantic content of such annotation databases. Our approach is able to extract implicit semantic relationships between genes and functions. This ability allows us to discover novel functions for known genes. This approach is able to identify missing and inaccurate annotations in existing annotation databases, and thus help improve their accuracy. We used our technique to analyze the current annotations of the human genome. From this body of annotations, we were able to predict 212 additional gene-function assignments. A subsequent literature search found that 138 of these gene-functions assignments are supported by existing peer-reviewed papers. An additional 23 assignments have been confirmed in the meantime by the addition of the respective annotations in later releases of the Gene Ontology database. Overall, the 161 confirmed assignments represent 75.95% of the proposed gene-function assignments. Only one of our predictions (0.4%) was contradicted by the existing literature. We could not find any relevant articles for 50 of our predictions (23.58%). The method is independent of the organism and can be used to analyze and improve the quality of the data of any public or private annotation database.

Chromosome Mapping↗

PubMed2005

GeneInfoMiner--a web server for exploring biomedical literature using batch sequence ID.

GeneInfoMiner is a web-based system for searching Medline abstracts using sequence ID lists such as GenBank accession numbers derived from high-throughput experiments. It will map query results to MeSH topics to facilitate the exploration of the biological significance of the sequence ID lists. GeneInfoMiner is based on a custom gene and protein name identification engine that can map gene and protein names to important molecular biology databases.

Abstracting and Indexing↗

PubMed2005

Applying GIFT, a Gene Interactions Finder in Text, to fly literature.

UNLABELLED: A number of freely available text mining tools have been put together to extract highly reliable Drosophila gene interaction data from text. The system has been tested with The Interactive Fly, showing low recall (27-34%), but very high precision (93-97%). AVAILABILITY: The extracted data and a web interface for submission of texts to GIFT analysis are available at http://gift.cryst.bbk.ac.uk/gift CONTACT: n.domedel_puig@cryst.bbk.ac.uk SUPPLEMENTARY INFORMATION: Additional documentation, such as the dictionaries and the reference sets, are available at the GIFT website.

Artificial Intelligence↗

PubMed2005

Resolving abbreviations to their senses in Medline.

MOTIVATION: Biological literature contains many abbreviations with one particular sense in each document. However, most abbreviations do not have a unique sense across the literature. Furthermore, many documents do not contain the long forms of the abbreviations. Resolving an abbreviation in a document consists of retrieving its sense in use. Abbreviation resolution improves accuracy of document retrieval engines and of information extraction systems. RESULTS: We combine an automatic analysis of Medline abstracts and linguistic methods to build a dictionary of abbreviation/sense pairs. The dictionary is used for the resolution of abbreviations occurring with their long forms. Ambiguous global abbreviations are resolved using support vector machines that have been trained on the context of each instance of the abbreviation/sense pairs, previously extracted for the dictionary set-up. The system disambiguates abbreviations with a precision of 98.9% for a recall of 98.2% (98.5% accuracy). This performance is superior in comparison with previously reported research work. AVAILABILITY: The abbreviation resolution module is available at http://www.ebi.ac.uk/Rebholz/software.html.

Abbreviations as Topic↗

PubMed2005

Extraction of regulatory gene/protein networks from Medline.

MOTIVATION: We have previously developed a rule-based approach for extracting information on the regulation of gene expression in yeast. The biomedical literature, however, contains information on several other equally important regulatory mechanisms, in particular phosphorylation, which we now expanded for our rule-based system also to extract. RESULTS: This paper presents new results for extraction of relational information from biomedical text. We have improved our system, STRING-IE, to capture both new types of linguistic constructs as well as new types of biological information [i.e. (de-)phosphorylation]. The precision remains stable with a slight increase in recall. From almost one million PubMed abstracts related to four model organisms, we manage to extract regulatory networks and binary phosphorylations comprising 3,319 relation chunks. The accuracy is 83-90% and 86-95% for gene expression and (de-)phosphorylation relations, respectively. To achieve this, we made use of an organism-specific resource of gene/protein names considerably larger than those used in most other biology related information extraction approaches. These names were included in the lexicon when retraining the part-of-speech (POS) tagger on the GENIA corpus. For the domain in question, an accuracy of 96.4% was attained on POS tags. It should be noted that the rules were developed for yeast and successfully applied to both abstracts and full-text articles related to other organisms with comparable accuracy. AVAILABILITY: The revised GENIA corpus, the POS tagger, the extraction rules and the full sets of extracted relations are available from http://www.bork.embl.de/Docu/STRING-IE

Abstracting and Indexing↗

PubMed2005

Markov model recognition and classification of DNA/protein sequences within large text databases.

MOTIVATION: Short sequence patterns frequently define regions of biological interest (binding sites, immune epitopes, primers, etc.), yet a large fraction of this information exists only within the scientific literature and is thus difficult to locate via conventional means (e.g. keyword queries or manual searches). We describe herein a system to accurately identify and classify sequence patterns from within large corpora using an n-gram Markov model (MM). RESULTS: As expected, on test sets we found that identification of sequences with limited alphabets and/or regular structures such as nucleic acids (non-ambiguous) and peptide abbreviations (3-letter) was highly accurate, whereas classification of symbolic (1-letter) peptide strings with more complex alphabets was more problematic. The MM was used to analyze two very large, sequence-containing corpora: over 7.75 million Medline abstracts and 9000 full-text articles from Journal of Virology. Performance was benchmarked by comparing the results with Journal of Virology entries in two existing manually curated databases: VirOligo and the HLA Ligand Database. Performance estimates were 98 +/- 2% precision/84% recall for primer identification and classification and 67 +/- 6% precision/85% recall for peptide epitopes. We also find a dramatic difference between the amounts of sequence-related data reported in abstracts versus full text. Our results suggest that automated extraction and classification of sequence elements is a promising, low-cost means of sequence database curation and annotation. AVAILABILITY: MM routine and datasets are available upon request.

Abstracting and Indexing↗

PubMed2005

ChemDB: a public database of small molecules and related chemoinformatics resources.

MOTIVATION: The development of chemoinformatics has been hampered by the lack of large, publicly available, comprehensive repositories of molecules, in particular of small molecules. Small molecules play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes in chemical genomics and systems biology, and for the screening and discovery of new drugs and other useful compounds. RESULTS: We describe ChemDB, a public database of small molecules available on the Web. ChemDB is built using the digital catalogs of over a hundred vendors and other public sources and is annotated with information derived from these sources as well as from computational methods, such as predicted solubility and three-dimensional structure. It supports multiple molecular formats and is periodically updated, automatically whenever possible. The current version of the database contains approximately 4.1 million commercially available compounds and 8.2 million counting isomers. The database includes a user-friendly graphical interface, chemical reactions capabilities, as well as unique search capabilities. AVAILABILITY: Database and datasets are available on http://cdb.ics.uci.edu.

Access to Information↗

PubMed2005

Automatic term list generation for entity tagging.

MOTIVATION: Many entity taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed manually. We show that distributional clustering methods which group words based on the contexts that they appear in, including neighboring words and syntactic relations extracted using a shallow parser, can be used to aid in the construction of term lists. RESULTS: Experiments on learning lists of terms and using them as part of a gene tagger on a corpus of abstracts from the scientific literature show that our automatically generated term lists significantly boost the precision of a state-of-the-art CRF-based gene tagger to a degree that is competitive with using hand curated lists and boosts recall to a degree that surpasses that of the hand-curated lists. Our results also show that these distributional clustering methods do not generate lists as helpful as those generated by supervised techniques, but that they can be used to complement supervised techniques so as to obtain better performance. AVAILABILITY: The code used in this paper is available from http://www.cis.upenn.edu/datamining/software_dist/autoterm/

Abstracting and Indexing↗

PubMed2005

Intelligent client for integrating bioinformatics services.

MOTIVATION: In addition to existing bioinformatics software, a lot of new tools are being developed world wide to supply services for an ever growing, widely dispersed and heterogeneous collection of biological data. The integration of these resources under a common platform is a challenging task. To this end, several groups are developing integration technologies, in which services are usually registered in some sort of catalogue to allow novel discovering and accessing mechanisms to be implemented. However, each service demands specific interfaces to accommodate their parameters and it is a complicated task linking the different service inputs and outputs to solve a biological problem. RESULTS: In this work we address the design and implementation of a versatile web client to access BioMOBY compatible services (a system by which a client can interact with multiple sources of biological data regardless of the underlying format or schema) using the service description stored in the BioMOBY catalogue. The automatic interface generator significantly reduces developing time and produces uniform service access mechanisms. The design and proof of concept (for such a client) including the generic interface generator have been developed and implemented in the National Institute for Bioinformatics in Spain. AVAILABILITY: The INB (National Institute for Bioinformatics, Spain) platform is available at www.inab.org/MOWServ

Automation↗

PubMed2005

Automatic assignment of biomedical categories: toward a generic approach.

MOTIVATION: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent. METHODS: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units. RESULTS AND CONCLUSION: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.

Abstracting and Indexing↗

PubMed2005

PubMed Assistant: a biologist-friendly interface for enhanced PubMed search.

MEDLINE is one of the most important bibliographical information sources for biologists and medical workers. Its PubMed interface supports Boolean queries, which are potentially expressive and exact. However, PubMed is also designed to support simplicity of use at the expense of query expressiveness and exactness. Many PubMed users have never tried explicit Boolean queries. We developed a Java program, PubMed Assistant, to make literature access easier in several ways. PubMed Assistant provides an interface that efficiently displays information about the citations and includes useful functions such as keyword highlighting, export to citation managers, clickable links to Google Scholar and others that are lacking in PubMed.

Artificial Intelligence↗

PubMed2006

ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation.

MOTIVATION: Our understanding of gene regulation is currently limited by our ability to collectively synthesize and catalogue transcriptional regulatory elements stored in scientific literature. Over the past decade, this task has become increasingly challenging as the accrual of biologically validated regulatory sequences has accelerated. To meet this challenge, novel community-based approaches to regulatory element annotation are required. SUMMARY: Here, we present the Open Regulatory Annotation (ORegAnno) database as a dynamic collection of literature-curated regulatory regions, transcription factor binding sites and regulatory mutations (polymorphisms and haplotypes). ORegAnno has been designed to manage the submission, indexing and validation of new annotations from users worldwide. Submissions to ORegAnno are immediately cross-referenced to EnsEMBL, dbSNP, Entrez Gene, the NCBI Taxonomy database and PubMed, where appropriate. AVAILABILITY: ORegAnno is available directly through MySQL, Web services, and online at http://www.oreganno.org. All software is licensed under the Lesser GNU Public License (LGPL).

Binding Sites↗