Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

October 2, 2013

ExpressionBlast:… [Value of Mapping and Interchanging Mappings]

Filed under: Genomics,Merging,Topic Maps — Patrick Durusau @ 3:25 pm

ExpressionBlast: mining large, unstructured expression databases by Guy E Zinman, Shoshana Naiman, Yariv Kanfi, Haim Cohen and Ziv Bar-Joseph. (Nature Methods 10, 925–926 (2013))

From a letter to the editor:

To the Editor: The amount of gene expression data deposited in public repositories has grown exponentially over the last decade (Supplementary Fig. 1). Specifically, Gene Expression Omnibus (GEO)1 is one of largest expression-data repositories (Supplementary Table 1), containing hundreds of thousands of microarray and RNA-seq experiment results grouped into tens of thousands of series. Although accessible, data deposited in GEO are not well organized. Even among data sets for a single species there are many different platforms with different probe identifiers, different value scales and very limited annotations of the condition profiled by each array. Current methods for using GEO data to study signaling and other cellular networks either do not scale or cannot fully use the available information (Supplementary Table 2 and Supplementary Results).

To enable queries of such large expression databases, we developed ExpressionBlast (http://www.expression.cs.cmu.edu/): a computational method that uses automated text analysis to identify and merge replicates and determine the type of each array in the series (treatment or control; Fig. 1a and Supplementary Methods). Using this information, ExpressionBlast uniformly processes expression data sets in GEO across all experiments, species and platforms. This is achieved by standardizing the data in terms of gene identifiers, the meaning of the expression values (log ratios) and the distribution of these values (Fig. 1b and Supplementary Methods). Our processing steps achieved a high accuracy in identifying replicates and treatment control cases (Supplementary Results and Supplementary Table 3). We applied these processing steps to arrays from more than 900,000 individual samples collected from >40,000 studies in GEO (new series are updated on a weekly basis), which allowed us to create, to our knowledge, the largest collection of computationally annotated expression data currently available (Supplementary Results and Supplementary Table 4) (emphasis in original).

Now there is a letter to the editor!

Your first question:

How did the team create:

…to our knowledge, the largest collection of computationally annotated expression data currently available….?

Hint: It wasn’t by creating a new naming system and then convincing the authors of > 40,000 studies to adopt a new naming system.

They achieved that result by:

This is achieved by standardizing the data in terms of gene identifiers, the meaning of the expression values (log ratios) and the distribution of these values (Fig. 1b and Supplementary Methods).

The benefit from this work begins where “merging” in the topic map sense ends.

One point of curiosity, among many, is the interchangeability of their rule based pattern expressions for merging replicates?

Even if the pattern expression language left execution up to the user, reliably exchanging mappings would be quite useful.

Perhaps a profile of an existing pattern expression language?

To avoid having to write one from scratch?

September 26, 2013

Computational Chemogenomics

Filed under: Bioinformatics,Biomedical,Genomics — Patrick Durusau @ 11:00 am

Computational Chemogenomics by Edgar Jacoby (Novartis Pharma AG, Switzerland).

Description:

In the post-genomic era, one of the key challenges for drug discovery consists in making optimal use of comprehensive genomic data to identify effective new medicines. Chemogenomics addresses this challenge and aims to systematically identify all ligands and modulators for all gene products expressed, besides allowing accelerated exploration of their biological function.

Computational chemogenomics focuses on applications of compound library design and virtual screening to expand the bioactive chemical space, to target hopping of chemotypes to identify synergies within related drug discovery projects or to repurpose known drugs, to propose mechanisms of action of compounds, and to identify off-target effects by cross-reactivity analysis.

Both ligand-based and structure-based in silico approaches, as reviewed in this book, play important roles in all these applications. Computational chemogenomics is expected to increase the quality and productivity of drug discovery and lead to the discovery of new medicines.

If you are on the cutting edge of bioinformatics or want to keep up with the cutting edge in bioinformatics, this is a volume to consider.

The hard copy price is $149.95 so it may be a while before I acquire a copy of it.

August 4, 2013

The 97% Junk Part of Human DNA

Filed under: Bioinformatics,Biomedical,Gene Ontology,Genome,Genomics — Patrick Durusau @ 9:21 am

Researchers from the Gene and Stem Cell Therapy Program at Sydney’s Centenary Institute have confirmed that, far from being “junk,” the 97 per cent of human DNA that does not encode instructions for making proteins can play a significant role in controlling cell development.

And in doing so, the researchers have unravelled a previously unknown mechanism for regulating the activity of genes, increasing our understanding of the way cells develop and opening the way to new possibilities for therapy.

Using the latest gene sequencing techniques and sophisticated computer analysis, a research group led by Professor John Rasko AO and including Centenary’s Head of Bioinformatics, Dr William Ritchie, has shown how particular white blood cells use non-coding DNA to regulate the activity of a group of genes that determines their shape and function. The work is published today in the scientific journal Cell.*

There’s a poke with a sharp stick to any gene ontology.

Roles in associations of genes have suddenly expanded.

Your call:

  1. Wait until a committee can officially name the new roles and parts of the “junk” that play those roles, or
  2. Create names/roles on the fly and merge those with subsequent identifiers on an ongoing basis as our understanding improves.

Any questions?

*Justin J.-L. Wong, William Ritchie, Olivia A. Ebner, Matthias Selbach, Jason W.H. Wong, Yizhou Huang, Dadi Gao, Natalia Pinello, Maria Gonzalez, Kinsha Baidya, Annora Thoeng, Teh-Liane Khoo, Charles G. Bailey, Jeff Holst, John E.J. Rasko. Orchestrated Intron Retention Regulates Normal Granulocyte Differentiation. Cell, 2013; 154 (3): 583 DOI: 10.1016/j.cell.2013.06.052

June 30, 2013

The DNA Data Deluge

Filed under: BigData,Genomics,Semantics — Patrick Durusau @ 5:47 pm

The DNA Data Deluge by Michael C. Schatz & Ben Langmead.

From the post:

We’re still a long way from having anything as powerful as a Web search engine for sequencing data, but our research groups are trying to exploit what we already know about cloud computing and text indexing to make vast sequencing data archives more usable. Right now, agencies like the National Institutes of Health maintain public archives containing petabytes of genetic data. But without easy search methods, such databases are significantly underused, and all that valuable data is essentially dead. We need to develop tools that make each archive a useful living entity the way that Google makes the Web a useful living entity. If we can make these archives more searchable, we will empower researchers to pose scientific questions over much larger collections of data, enabling greater insights.

A very accessible article that makes a strong case for the “DNA Data Deluge.” Literally.

The deluge of concern to the authors is raw genetic data.

They don’t address how we will connect genetic data to the semantic quagmire of clinical data and research publications.

Genetic knowledge disconnected from clinical experience will be interesting but not terribly useful.

If you want more complex data requirements, include other intersections with our genetic makeup, such as pollution, additives, lifestyle, etc.

June 1, 2013

1000 Genomes…

Filed under: 1000 Genomes,Bioinformatics,Genomics — Patrick Durusau @ 3:44 pm

1000 Genomes: A Deep Catalog of Human Genetic Variation

From the overview:

Recent improvements in sequencing technology (“next-gen” sequencing platforms) have sharply reduced the cost of sequencing. The 1000 Genomes Project is the first project to sequence the genomes of a large number of people, to provide a comprehensive resource on human genetic variation.

As with other major human genome reference projects, data from the 1000 Genomes Project will be made available quickly to the worldwide scientific community through freely accessible public databases. (See Data use statement.)

The goal of the 1000 Genomes Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. This goal can be attained by sequencing many individuals lightly. To sequence a person’s genome, many copies of the DNA are broken into short pieces and each piece is sequenced. The many copies of DNA mean that the DNA pieces are more-or-less randomly distributed across the genome. The pieces are then aligned to the reference sequence and joined together. To find the complete genomic sequence of one person with current sequencing platforms requires sequencing that person’s DNA the equivalent of about 28 times (called 28X). If the amount of sequence done is only an average of once across the genome (1X), then much of the sequence will be missed, because some genomic locations will be covered by several pieces while others will have none. The deeper the sequencing coverage, the more of the genome will be covered at least once. Also, people are diploid; the deeper the sequencing coverage, the more likely that both chromosomes at a location will be included. In addition, deeper coverage is particularly useful for detecting structural variants, and allows sequencing errors to be corrected.

Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing.

If you are looking for a large data set other than CiteSeer and DBpedia, you should consider something from the 1000 Genomes project.

Lots of significant data with more on the way.

May 16, 2013

HAL: a hierarchical format for storing…

Filed under: Bioinformatics,Genomics,Graphs — Patrick Durusau @ 12:27 pm

HAL: a hierarchical format for storing and analyzing multiple genome alignments by Glenn Hickey, Benedict Paten, Dent Earl, Daniel Zerbino and David Haussler. (Bioinformatics (2013) 29 (10): 1341-1342. doi: 10.1093/bioinformatics/btt128)

Abstract:

Motivation: Large multiple genome alignments and inferred ancestral genomes are ideal resources for comparative studies of molecular evolution, and advances in sequencing and computing technology are making them increasingly obtainable. These structures can provide a rich understanding of the genetic relationships between all subsets of species they contain. Current formats for storing genomic alignments, such as XMFA and MAF, are all indexed or ordered using a single reference genome, however, which limits the information that can be queried with respect to other species and clades. This loss of information grows with the number of species under comparison, as well as their phylogenetic distance.

Results: We present HAL, a compressed, graph-based hierarchical alignment format for storing multiple genome alignments and ancestral reconstructions. HAL graphs are indexed on all genomes they contain. Furthermore, they are organized phylogenetically, which allows for modular and parallel access to arbitrary subclades without fragmentation because of rearrangements that have occurred in other lineages. HAL graphs can be created or read with a comprehensive C++ API. A set of tools is also provided to perform basic operations, such as importing and exporting data, identifying mutations and coordinate mapping (liftover).

Availability: All documentation and source code for the HAL API and tools are freely available at http://github.com/glennhickey/hal.

Important work for bioinformatics and genome alignment as well as specializing graphs for that work.

Graphs are a popular subject these days but successful projects will rely on graphs with particular properties and structures to be useful.

The more examples of graph-based projects, the more we learn about general principles of graphs for particular applications or requirements.

April 20, 2013

PhenoMiner:..

Filed under: Authoring Topic Maps,Bioinformatics,Biology,Genomics,Interface Research/Design — Patrick Durusau @ 9:21 am

PhenoMiner: quantitative phenotype curation at the rat genome database by Stanley J. F. Laulederkind, et.al. (Database (2013) 2013 : bat015 doi: 10.1093/database/bat015)

Abstract:

The Rat Genome Database (RGD) is the premier repository of rat genomic and genetic data and currently houses >40 000 rat gene records as well as human and mouse orthologs, >2000 rat and 1900 human quantitative trait loci (QTLs) records and >2900 rat strain records. Biological information curated for these data objects includes disease associations, phenotypes, pathways, molecular functions, biological processes and cellular components. Recently, a project was initiated at RGD to incorporate quantitative phenotype data for rat strains, in addition to the currently existing qualitative phenotype data for rat strains, QTLs and genes. A specialized curation tool was designed to generate manual annotations with up to six different ontologies/vocabularies used simultaneously to describe a single experimental value from the literature. Concurrently, three of those ontologies needed extensive addition of new terms to move the curation forward. The curation interface development, as well as ontology development, was an ongoing process during the early stages of the PhenoMiner curation project.

Database URL: http://rgd.mcw.edu

The line:

A specialized curation tool was designed to generate manual annotations with up to six different ontologies/vocabularies used simultaneously to describe a single experimental value from the literature.

sounded relevant to topic maps.

Turns out to be five ontologies and the article reports:

The ‘Create Record’ page (Figure 4) is where the rest of the data for a single record is entered. It consists of a series of autocomplete text boxes, drop-down text boxes and editable plain text boxes. All of the data entered are associated with terms from five ontologies/vocabularies: RS, CMO, MMO, XCO and the optional MA (Mouse Adult Gross Anatomy Dictionary) (13)

Important to note that authoring does not require the user to make explicit the properties underlying any of the terms from the different ontologies.

Some users probably know that level of detail but what is important is the capturing of their knowledge of subject sameness.

A topic map extension/add-on to such a system could flesh out those bare terms to provide a basis for treating terms from different ontologies as terms for the same subjects.

That merging/mapping detail need not bother an author or casual user.

But it increases the odds that future data sets can be reliably integrated with this one.

And issues with the correctness of a mapping can be meaningfully investigated.

If it helps, think of correctness of mappping as accountability, for someone else.

April 3, 2013

Biological Database of Images and Genomes

Filed under: Associations,Biology,Genome,Genomics — Patrick Durusau @ 4:48 am

Biological Database of Images and Genomes: tools for community annotations linking image and genomic information by Andrew T Oberlin, Dominika A Jurkovic, Mitchell F Balish and Iddo Friedberg. (Database (2013) 2013 : bat016 doi: 10.1093/database/bat016)

Abstract:

Genomic data and biomedical imaging data are undergoing exponential growth. However, our understanding of the phenotype–genotype connection linking the two types of data is lagging behind. While there are many types of software that enable the manipulation and analysis of image data and genomic data as separate entities, there is no framework established for linking the two. We present a generic set of software tools, BioDIG, that allows linking of image data to genomic data. BioDIG tools can be applied to a wide range of research problems that require linking images to genomes. BioDIG features the following: rapid construction of web-based workbenches, community-based annotation, user management and web services. By using BioDIG to create websites, researchers and curators can rapidly annotate a large number of images with genomic information. Here we present the BioDIG software tools that include an image module, a genome module and a user management module. We also introduce a BioDIG-based website, MyDIG, which is being used to annotate images of mycoplasmas.

Database URL: BioDIG website: http://biodig.org

BioDIG source code repository: http://github.com/FriedbergLab/BioDIG

The MyDIG database: http://mydig.biodig.org/

Linking image data to genomic data. Sounds like associations to me.

You?

Not to mention the heterogeneity of genomic data.

Imagine extending an image/genomic data association by additional genomic data under a different identification.

March 16, 2013

MetaNetX.org…

Filed under: Bioinformatics,Biomedical,Genomics,Modeling,Semantic Diversity — Patrick Durusau @ 1:42 pm

MetaNetX.org: a website and repository for accessing, analysing and manipulating metabolic networks by Mathias Ganter, Thomas Bernard, Sébastien Moretti, Joerg Stelling and Marco Pagni. (Bioinformatics (2013) 29 (6): 815-816. doi: 10.1093/bioinformatics/btt036)

Abstract:

MetaNetX.org is a website for accessing, analysing and manipulating genome-scale metabolic networks (GSMs) as well as biochemical pathways. It consistently integrates data from various public resources and makes the data accessible in a standardized format using a common namespace. Currently, it provides access to hundreds of GSMs and pathways that can be interactively compared (two or more), analysed (e.g. detection of dead-end metabolites and reactions, flux balance analysis or simulation of reaction and gene knockouts), manipulated and exported. Users can also upload their own metabolic models, choose to automatically map them into the common namespace and subsequently make use of the website’s functionality.

http://metanetx.org.

The authors are addressing a familiar problem:

Genome-scale metabolic networks (GSMs) consist of compartmentalized reactions that consistently combine biochemical, genetic and genomic information. When also considering a biomass reaction and both uptake and secretion reactions, GSMs are often used to study genotype–phenotype relationships, to direct new discoveries and to identify targets in metabolic engineering (Karr et al., 2012). However, a major difficulty in GSM comparisons and reconstructions is to integrate data from different resources with different nomenclatures and conventions for both metabolites and reactions. Hence, GSM consolidation and comparison may be impossible without detailed biological knowledge and programming skills. (emphasis added)

For which they propose an uncommon solution:

MetaNetX.org is implemented as a user-friendly and self-explanatory website that handles all user requests dynamically (Fig. 1a). It allows a user to access a collection of hundreds of published models, browse and select subsets for comparison and analysis, upload or modify new models and export models in conjunction with their results. Its functionality is based on a common namespace defined by MNXref (Bernard et al., 2012). In particular, all repository or user uploaded models are automatically translated with or without compartments into the common namespace; small deviations from the original model are possible due to the automatic reconciliation steps implemented by Bernard et al. (2012). However, a user can choose not to translate his model but still make use of the website’s functionalities. Furthermore, it is possible to augment the given reaction set by user-defined reactions, for example, for model augmentation.

The bioinformatics community recognizes the intellectual poverty of lock step models.

Wonder when the intelligence community is going to have that “a ha” moment?

February 3, 2013

Scribl: an HTML5 Canvas-based graphics library…

Filed under: Genomics,Graphics,HTML5,Javascript,Visualization — Patrick Durusau @ 6:57 pm

Scribl: an HTML5 Canvas-based graphics library for visualizing genomic data over the web by Chase A. Miller, Jon Anthony, Michelle M. Meyer and Gabor Marth. (Bioinformatics (2013) 29 (3): 381-383. doi: 10.1093/bioinformatics/bts677)

Abstract:

Motivation: High-throughput biological research requires simultaneous visualization as well as analysis of genomic data, e.g. read alignments, variant calls and genomic annotations. Traditionally, such integrative analysis required desktop applications operating on locally stored data. Many current terabyte-size datasets generated by large public consortia projects, however, are already only feasibly stored at specialist genome analysis centers. As even small laboratories can afford very large datasets, local storage and analysis are becoming increasingly limiting, and it is likely that most such datasets will soon be stored remotely, e.g. in the cloud. These developments will require web-based tools that enable users to access, analyze and view vast remotely stored data with a level of sophistication and interactivity that approximates desktop applications. As rapidly dropping cost enables researchers to collect data intended to answer questions in very specialized contexts, developers must also provide software libraries that empower users to implement customized data analyses and data views for their particular application. Such specialized, yet lightweight, applications would empower scientists to better answer specific biological questions than possible with general-purpose genome browsers currently available.

Results: Using recent advances in core web technologies (HTML5), we developed Scribl, a flexible genomic visualization library specifically targeting coordinate-based data such as genomic features, DNA sequence and genetic variants. Scribl simplifies the development of sophisticated web-based graphical tools that approach the dynamism and interactivity of desktop applications.

Availability and implementation: Software is freely available online at http://chmille4.github.com/Scribl/ and is implemented in JavaScript with all modern browsers supported.

Contact: gabor.marth@bc.edu

A step towards the “virtual observatory” model of modern astronomy. Free remote access to data in astronomy has long been a fact. It was soon realized that access to data wasn’t enough, remote users needs the power of remote clusters to process large amounts of data.

The intermediate stage of remote access to data and even remote processing models are both going to require easy visualization capabilities.

Are you ready to move to remote access to topic map data?

January 28, 2013

…Everything You Always Wanted to Know About Genes

Filed under: Bioinformatics,Biomedical,Genomics — Patrick Durusau @ 1:18 pm

Toward a New Model of the Cell: Everything You Always Wanted to Know About Genes

From the post:

Turning vast amounts of genomic data into meaningful information about the cell is the great challenge of bioinformatics, with major implications for human biology and medicine. Researchers at the University of California, San Diego School of Medicine and colleagues have proposed a new method that creates a computational model of the cell from large networks of gene and protein interactions, discovering how genes and proteins connect to form higher-level cellular machinery.

…

“Our method creates ontology, or a specification of all the major players in the cell and the relationships between them,” said first author Janusz Dutkowski, PhD, postdoctoral researcher in the UC San Diego Department of Medicine. It uses knowledge about how genes and proteins interact with each other and automatically organizes this information to form a comprehensive catalog of gene functions, cellular components, and processes.

“What’s new about our ontology is that it is created automatically from large datasets. In this way, we see not only what is already known, but also potentially new biological components and processes — the bases for new hypotheses,” said Dutkowski.

Originally devised by philosophers attempting to explain the nature of existence, ontologies are now broadly used to encapsulate everything known about a subject in a hierarchy of terms and relationships. Intelligent information systems, such as iPhone’s Siri, are built on ontologies to enable reasoning about the real world. Ontologies are also used by scientists to structure knowledge about subjects like taxonomy, anatomy and development, bioactive compounds, disease and clinical diagnosis.

A Gene Ontology (GO) exists as well, constructed over the last decade through a joint effort of hundreds of scientists. It is considered the gold standard for understanding cell structure and gene function, containing 34,765 terms and 64,635 hierarchical relations annotating genes from more than 80 species.

“GO is very influential in biology and bioinformatics, but it is also incomplete and hard to update based on new data,” said senior author Trey Ideker, PhD, chief of the Division of Genetics in the School of Medicine and professor of bioengineering in UC San Diego’s Jacobs School of Engineering.

The conclusion to A gene ontology inferred from molecular networks (Janusz Dutkowski, Michael Kramer, Michal A Surma, Rama Balakrishnan, J Michael Cherry, Nevan J Krogan & Trey Ideker, Nature Biotechnology 31, 38–45 (2013) doi:10.1038/nbt.2463), illustrates a difference between ontology in the GO sense and that produced by the authors:

The research reported in this manuscript raises the possibility that, given the appropriate tools, ontologies might evolve over time with the addition of each new network map or high-throughput experiment that is published. More importantly, it enables a philosophical shift in bioinformatic analysis, from a regime in which the ontology is viewed as gold standard to one in which it is the major result. (emphasis added)

Ontology as representing reality as opposed to declaring it.

That is a novel concept.

January 5, 2013

Semantically enabling a genome-wide association study database

Filed under: Bioinformatics,Biomedical,Genomics,Medical Informatics,Ontology — Patrick Durusau @ 2:20 pm

Semantically enabling a genome-wide association study database by Tim Beck, Robert C Free, Gudmundur A Thorisson and Anthony J Brookes. Journal of Biomedical Semantics 2012, 3:9 doi:10.1186/2041-1480-3-9.

Abstract:

Background

The amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central — a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data.

Results

A pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications.

Conclusions

We present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.

Rather than:

The benefits of employing ontologies for standardising and structuring data are widely accepted.

I would rephrase that to read:

The benefits and limitations of employing ontologies for standardising and structuring data are widely known.

Decades of use of relational database schemas, informal equivalents of ontologies, leave no doubt governing structures for data have benefits.

Less often acknowledged is those same governing structures impose limitations on data and what may be represented.

That’s not a dig at relational databases.

Just an observation that ontologies and their equivalents aren’t unalloyed precious metals.

December 21, 2012

New Public-Access Source With 3-D Information for Protein Interactions

Filed under: Bioinformatics,Biomedical,Genome,Genomics — Patrick Durusau @ 5:24 pm

New Public-Access Source With 3-D Information for Protein Interactions

From the post:

Researchers have developed a platform that compiles all the atomic data, previously stored in diverse databases, on protein structures and protein interactions for eight organisms of relevance. They apply a singular homology-based modelling procedure.

The scientists Roberto Mosca, Arnaud Ceol and Patrick Aloy provide the international biomedical community with Interactome3D (interactome3d.irbbarcelona.org), an open-access and free web platform developed entirely by the Institute for Research in Biomedicine (IRB Barcelona). Interactome 3D offers for the first time the possibility to anonymously access and add molecular details of protein interactions and to obtain the information in 3D models. For researchers, atomic level details about the reactions are fundamental to unravel the bases of biology, disease development, and the design of experiments and drugs to combat diseases.

Interactome 3D provides reliable information about more than 12,000 protein interactions for eight model organisms, namely the plant Arabidopsis thaliana, the worm Caenorhabditis elegans, the fly Drosophila melanogaster, the bacteria Escherichia coli and Helicobacter pylori, the brewer’s yeast Saccharomyces cerevisiae, the mouse Mus musculus, and Homo sapiens. These models are considered the most relevant in biomedical research and genetic studies. The journal Nature Methods presents the research results and accredits the platform on the basis of it high reliability and precision in modelling interactions, which reaches an average of 75%.

Further details can be found at:

Interactome3D: adding structural details to protein networks by Roberto Mosca, Arnaud Céol and Patrick Aloy. (Nature Methods (2012) doi:10.1038/nmeth.2289)

Abstract:

Network-centered approaches are increasingly used to understand the fundamentals of biology. However, the molecular details contained in the interaction networks, often necessary to understand cellular processes, are very limited, and the experimental difficulties surrounding the determination of protein complex structures make computational modeling techniques paramount. Here we present Interactome3D, a resource for the structural annotation and modeling of protein-protein interactions. Through the integration of interaction data from the main pathway repositories, we provide structural details at atomic resolution for over 12,000 protein-protein interactions in eight model organisms. Unlike static databases, Interactome3D also allows biologists to upload newly discovered interactions and pathways in any species, select the best combination of structural templates and build three-dimensional models in a fully automated manner. Finally, we illustrate the value of Interactome3D through the structural annotation of the complement cascade pathway, rationalizing a potential common mechanism of action suggested for several disease-causing mutations.

Interesting not only for its implications for bioinformatics but for the development of homology modeling (superficially, similar proteins have similar interaction sites) to assist in their work.

The topic map analogy would be to show a subject domain, different identifications of the same subject tend to have the same associations or to fall into other patterns.

Then constructing a subject identity test based upon a template of associations or other values.

November 26, 2012

Collaborative biocuration… [Pre-Topic Map Tasks]

Filed under: Authoring Topic Maps,Bioinformatics,Biomedical,Curation,Genomics,Searching — Patrick Durusau @ 9:22 am

Collaborative biocuration—text-mining development task for document prioritization for curation by Thomas C. Wiegers, Allan Peter Davis and Carolyn J. Mattingly. (Database (2012) 2012 : bas037 doi: 10.1093/database/bas037)

Abstract:

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems for the biological domain. The ‘BioCreative Workshop 2012’ subcommittee identified three areas, or tracks, that comprised independent, but complementary aspects of data curation in which they sought community input: literature triage (Track I); curation workflow (Track II) and text mining/natural language processing (NLP) systems (Track III). Track I participants were invited to develop tools or systems that would effectively triage and prioritize articles for curation and present results in a prototype web interface. Training and test datasets were derived from the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) and consisted of manuscripts from which chemical–gene–disease data were manually curated. A total of seven groups participated in Track I. For the triage component, the effectiveness of participant systems was measured by aggregate gene, disease and chemical ‘named-entity recognition’ (NER) across articles; the effectiveness of ‘information retrieval’ (IR) was also measured based on ‘mean average precision’ (MAP). Top recall scores for gene, disease and chemical NER were 49, 65 and 82%, respectively; the top MAP score was 80%. Each participating group also developed a prototype web interface; these interfaces were evaluated based on functionality and ease-of-use by CTD’s biocuration project manager. In this article, we present a detailed description of the challenge and a summary of the results.

The results:

“Top recall scores for gene, disease and chemical NER were 49, 65 and 82%, respectively; the top MAP score was 80%.”

indicate there is plenty of room for improvement. Perhaps even commercially viable improvement.

In hindsight, not talking about how to make a topic map along with ISO 13250, may have been a mistake. Even admitting there are multiple ways to get there, a technical report outlining one or two ways would have made the process more transparent.

Answering the question: “What can you say with a topic map?” with “Anything you want.” was, a truthful answer but not a helpful one.

I should try to crib something from one of those “how to write a research paper” guides. I haven’t looked at one in years but the process is remarkably similar to what would result in a topic map.

Some of the mechanics are different but the underlying intellectual process is quite similar. Everyone who has been to college (at least of my age), had a course that talked about writing research papers. So it should be familiar terminology.

Thoughts/suggestions?

November 25, 2012

STAR: ultrafast universal RNA-seq aligner

Filed under: Bioinformatics,Genomics,String Matching — Patrick Durusau @ 9:32 am

STAR: ultrafast universal RNA-seq aligner
 by Stephen Turner.

From the post:

There’s a new kid on the block for RNA-seq alignment.

Dobin, Alexander, et al. “STAR: ultrafast universal RNA-seq aligner.” Bioinformatics (2012).

Aligning RNA-seq data is challenging because reads can overlap splice junctions. Many other RNA-seq alignment algorithms (e.g. Tophat) are built on top of DNA sequence aligners. STAR (Spliced Transcripts Alignment to a Reference) is a standalone RNA-seq alignment algorithm that uses uncompressed suffix arrays and a mapping algorithm similar to those used in large-scale genome alignment tools to align RNA-seq reads to a genomic reference. STAR is over 50 times faster than any other previously published RNA-seq aligner, and outperforms other aligners in both sensitivity and specificity using both simulated and real (replicated) RNA-seq data.

I had a brief exchange of comments with Lars Marius Garshol on string matching recently. Another example of a string processing approach you may adapt to different circumstances.

November 23, 2012

BLAST – Basic Local Alignment Search Tool

Filed under: Bioinformatics,BLAST,Genomics — Patrick Durusau @ 11:27 am

BLAST – Basic Local Alignment Search Tool (Wikipedia)

From Wikipedia:

In bioinformatics, Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of different proteins or the nucleotides of DNA sequences. A BLAST search enables a researcher to compare a query sequence with a library or database of sequences, and identify library sequences that resemble the query sequence above a certain threshold. Different types of BLASTs are available according to the query sequences. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. The BLAST program was designed by Stephen Altschul, Warren Gish, Webb Miller, Eugene Myers, and David J. Lipman at the NIH and was published in the Journal of Molecular Biology in 1990.[1]

I found the uses of BLAST of particular interest:

Uses of BLAST

BLAST can be used for several purposes. These include identifying species, locating domains, establishing phylogeny, DNA mapping, and comparison.

Identifying species
With the use of BLAST, you can possibly correctly identify a species and/or find homologous species. This can be useful, for example, when you are working with a DNA sequence from an unknown species.
Locating domains
When working with a protein sequence you can input it into BLAST, to locate known domains within the sequence of interest.
Establishing phylogeny
Using the results received through BLAST you can create a phylogenetic tree using the BLAST web-page. Phylogenies based on BLAST alone are less reliable than other purpose-built computational phylogenetic methods, so should only be relied upon for “first pass” phylogenetic analyses.
DNA mapping
When working with a known species, and looking to sequence a gene at an unknown location, BLAST can compare the chromosomal position of the sequence of interest, to relevant sequences in the database(s).
Comparison
When working with genes, BLAST can locate common genes in two related species, and can be used to map annotations from one organism to another.

Not just for the many uses of BLAST in genomics, but what of using similar techniques with other data sets?

Are they not composed of “sequences?”

November 22, 2012

eGIFT: Mining Gene Information from the Literature

Filed under: Curation,Genomics,Search Engines,Search Interface,Search Requirements,Text Mining — Patrick Durusau @ 10:23 am

eGIFT: Mining Gene Information from the Literature by Catalina O Tudor, Carl J Schmidt and K Vijay-Shanker.

Abstract:

Background

With the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.

Results

In this paper, we present eGIFT (http://biotm.cis.udel.edu/eGIFT webcite), a web-based tool that associates informative terms, called iTerms, and sentences containing them, with genes. To associate iTerms with a gene, eGIFT ranks iTerms about the gene, based on a score which compares the frequency of occurrence of a term in the gene’s literature to its frequency of occurrence in documents about genes in general. To retrieve a gene’s documents (Medline abstracts), eGIFT considers all gene names, aliases, and synonyms. Since many of the gene names can be ambiguous, eGIFT applies a disambiguation step to remove matches that do not correspond to this gene. Another additional filtering process is applied to retain those abstracts that focus on the gene rather than mention it in passing. eGIFT’s information for a gene is pre-computed and users of eGIFT can search for genes by using a name or an EntrezGene identifier. iTerms are grouped into different categories to facilitate a quick inspection. eGIFT also links an iTerm to sentences mentioning the term to allow users to see the relation between the iTerm and the gene. We evaluated the precision and recall of eGIFT’s iTerms for 40 genes; between 88% and 94% of the iTerms were marked as salient by our evaluators, and 94% of the UniProtKB keywords for these genes were also identified by eGIFT as iTerms.

Conclusions

Our evaluations suggest that iTerms capture highly-relevant aspects of genes. Furthermore, by showing sentences containing these terms, eGIFT can provide a quick description of a specific gene. eGIFT helps not only life scientists survey results of high-throughput experiments, but also annotators to find articles describing gene aspects and functions.

Website: http://biotm.cis.udel.edu/eGIFT

Another lesson for topic map authoring interfaces: Offer domain specific search capabilities.

Using a ****** search appliance is little better than a poke with a sharp stick in most domains. The user is left to their own devices to sort out ambiguities, discover synonyms, again and again.

Your search interface may report > 900,000 “hits,” but anything beyond the first 20 or so are wasted.

(If you get sick, get something that comes up in the first 20 “hits” in PubMed. Where most researchers stop.)

Developing a biocuration workflow for AgBase… [Authoring Interfaces]

Filed under: Bioinformatics,Biomedical,Curation,Genomics,Text Mining — Patrick Durusau @ 9:50 am

Developing a biocuration workflow for AgBase, a non-model organism database by Lakshmi Pillai, Philippe Chouvarine, Catalina O. Tudor, Carl J. Schmidt, K. Vijay-Shanker and Fiona M. McCarthy.

Abstract:

AgBase provides annotation for agricultural gene products using the Gene Ontology (GO) and Plant Ontology, as appropriate. Unlike model organism species, agricultural species have a body of literature that does not just focus on gene function; to improve efficiency, we use text mining to identify literature for curation. The first component of our annotation interface is the gene prioritization interface that ranks gene products for annotation. Biocurators select the top-ranked gene and mark annotation for these genes as ‘in progress’ or ‘completed’; links enable biocurators to move directly to our biocuration interface (BI). Our BI includes all current GO annotation for gene products and is the main interface to add/modify AgBase curation data. The BI also displays Extracting Genic Information from Text (eGIFT) results for each gene product. eGIFT is a web-based, text-mining tool that associates ranked, informative terms (iTerms) and the articles and sentences containing them, with genes. Moreover, iTerms are linked to GO terms, where they match either a GO term name or a synonym. This enables AgBase biocurators to rapidly identify literature for further curation based on possible GO terms. Because most agricultural species do not have standardized literature, eGIFT searches all gene names and synonyms to associate articles with genes. As many of the gene names can be ambiguous, eGIFT applies a disambiguation step to remove matches that do not correspond to this gene, and filtering is applied to remove abstracts that mention a gene in passing. The BI is linked to our Journal Database (JDB) where corresponding journal citations are stored. Just as importantly, biocurators also add to the JDB citations that have no GO annotation. The AgBase BI also supports bulk annotation upload to facilitate our Inferred from electronic annotation of agricultural gene products. All annotations must pass standard GO Consortium quality checking before release in AgBase.

Database URL: http://www.agbase.msstate.edu/

Another approach to biocuration. I will be posting on eGift separately but do note this is a domain specific tool.

The authors did not set out to create the universal curation tool but one suited to their specific data and requirements.

I think there is an important lesson here for semantic authoring interfaces. Word processors offer very generic interfaces but consequently little in the way of structure. Authoring annotated information requires more structure and that requires domain specifics.

Now there is an idea, create topic map authoring interfaces on top of a common skeleton, instead of hard coding interfaces as users “should” use the tool.

November 18, 2012

… text mining in the FlyBase genetic literature curation workflow

Filed under: Curation,Genomics,Text Mining — Patrick Durusau @ 5:47 pm

Opportunities for text mining in the FlyBase genetic literature curation workflow by Peter McQuilton. (Database (2012) 2012 : bas039 doi: 10.1093/database/bas039)

Abstract:

FlyBase is the model organism database for Drosophila genetic and genomic information. Over the last 20 years, FlyBase has had to adapt and change to keep abreast of advances in biology and database design. We are continually looking for ways to improve curation efficiency and efficacy. Genetic literature curation focuses on the extraction of genetic entities (e.g. genes, mutant alleles, transgenic constructs) and their associated phenotypes and Gene Ontology terms from the published literature. Over 2000 Drosophila research articles are now published every year. These articles are becoming ever more data-rich and there is a growing need for text mining to shoulder some of the burden of paper triage and data extraction. In this article, we describe our curation workflow, along with some of the problems and bottlenecks therein, and highlight the opportunities for text mining. We do so in the hope of encouraging the BioCreative community to help us to develop effective methods to mine this torrent of information.

Database URL: http://flybase.org

Would you believe that ambiguity is problem #1 and describing relationships is another one?

The most common problem encountered during curation is an ambiguous genetic entity (gene, mutant allele, transgene, etc.). This situation can arise when no unique identifier (such as a FlyBase gene identifier (FBgn) or a computed gene (CG) number for genes), or an accurate and explicit reference for a mutant or transgenic line is given. Ambiguity is a particular problem when a generic symbol/ name is used (e.g. ‘Actin’ or UAS-Notch), or when a symbol/ name is used that is a synonym for a different entity (e.g. ‘ras’ is the current FlyBase symbol for the ‘raspberry’ gene, FBgn0003204, but is often used in the literature to refer to the ‘Ras85D’ gene, FBgn0003205). A further issue is that some symbols only differ in case-sensitivity for the first character, for example, the genes symbols ‘dl’ (dorsal) and ‘Dl’ (Delta). These ambiguities can usually be resolved by searching for associated details about the entity in the article (e.g. the use of a specific mutant allele can identify the gene being discussed) or by consulting the supplemental information for additional details. Sometimes we have to do some analysis ourselves, such as performing a BLAST search using any sequence data present in the article or supplementary files or executing an in-house script to report those entities used by a specified author in previously curated articles. As a final step, if we cannot resolve a problem, we email the corresponding author for clarification. If the ambiguity still cannot be resolved, then a curator will either associate a generic/unspecified entry for that entity with the article, or else omit the entity and add a (non-public) note to the curation record explaining the situation, with the hope that future publications will resolve the issue.

One of the more esoteric problems found in curation is the fact that multiple relationships exist between the curated data types. For example, the ‘dppEP2232 allele’ is caused by the ‘P{EP}dppEP2232 insertion’ and disrupts the ‘dpp gene’. This can cause problems for text-mining assisted curation, as the data can be attributed to the wrong object due to sentence structure or the requirement of back- ground or contextual knowledge found in other parts of the article. In cases like this, detailed knowledge of the FlyBase proforma and curation rules, as well as a good knowledge of Drosophila biology, is necessary to ensure the correct proforma field is filled in. This is one of the reasons why we believe text-mining methods will assist manual curation rather than replace it in the near term.

I like the “manual curation” line. Curation is a task best performed by a sentient being.

November 17, 2012

Visualising associations between paired `omics’ data sets

Filed under: Associations,Bioinformatics,Biomedical,Genomics,Interactomics,Metabolomics,Visualization — Patrick Durusau @ 5:31 pm

Visualising associations between paired `omics’ data sets by Ignacio González, Kim-Anh Lê Cao, Melissa J Davis and Sébastien Déjean.

Abstract:

Background

Each omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities.

Results

The multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis.

Conclusions

Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole.

Availability

The graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application.

Just in case you are looking for something a little more challenging this weekend than political feeds on Twitter. 😉

Is “higher dimensional” data everywhere? Just more obvious in the biological sciences?

If so, there are lessons here for manipulation/visualization of higher dimensional data in other areas as well.

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