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

Seal

From the site:

Seal is a Hadoop-based distributed short read alignment and analysis toolkit. Currently Seal includes tools for: read demultiplexing, read alignment, duplicate read removal, sorting read mappings, and calculating statistics for empirical base quality recalibration. Seal scales, easily handling TB of data.

Features:

• short read alignment (based on BWA)
• duplicate read identification
• sort read mappings
• calculate empirical base quality recalibration tables
• fast, scalable, reliable (runs on Hadoop)

Seal website with extensive documentation.

ISBRA 2012 : International Symposium on Bioinformatics Research and Applications

Dates:

When May 21, 2012 – May 23, 2012
Where Dallas, Texas
Submission Deadline Feb 6, 2012
Notification Due Mar 5, 2012
Final Version Due Mar 15, 2012

From the call for papers:

The International Symposium on Bioinformatics Research and Applications (ISBRA) provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. Submissions presenting original research are solicited in all areas of bioinformatics and computational biology, including the development of experimental or commercial systems.

The Variation Toolkit by Pierre Lindenbaum.

From the post:

During the last weeks, I’ve worked on an experimental C++ package named The Variation Toolkit (varkit). It was originally designed to provide some command lines equivalent to knime4bio but I’ve added more tools over time. Some of those tools are very simple-and-stupid ( fasta2tsv) , reinvent the wheel (“numericsplit“), are part of an answer to biostar, are some old tools (e.g. bam2wig) that have been moved to this package, but some others like “samplepersnp“, “groupbygene” might be useful to people.

The package is available at : http://code.google.com/p/variationtoolkit/.

See the post for documentation.

Pybedtools: a flexible Python library for manipulating genomic datasets and annotations by Ryan K. Dale, Brent S. Pedersen and Aaron R. Quinlan.

Abstract:

Summary: pybedtools is a flexible Python software library for manipulating and exploring genomic datasets in many common formats. It provides an intuitive Python interface that extends upon the popular BEDTools genome arithmetic tools. The library is well documented and efficient, and allows researchers to quickly develop simple, yet powerful scripts that enable complex genomic analyses.

From the documentation:

Formats with different coordinate systems (e.g. BED vs GFF) are handled with uniform, well-defined semantics described in the documentation.

Starting to sound like HyTime isn’t it? Transposition between coordinate systems.

If you venture into this area with a topic map, something to keep in mind.

I first saw this in Christophe Lalanne’s A bag of tweets / Dec 2011.

Galaxy: Data Intensive Biology for Everyone (main site)

Galaxy 101: The first thing you should try (tutorial)

Work through the tutorial, keeping track of where you think subject identity (and any tests you care to suggest) would be useful.

I don’t know but suspect this is representative of what researchers in the field expect in terms of capabilities.

With a little effort I suspect it would make a nice basis to start a conversation about what subject identity could add that would be of interest.

New Entrez Genome Released on November 9, 2011

From the announcement:

Historically the Entrez Genome data model was designed for complete genomes of microorganisms (Archaea, Eubacteria, and Viruses) and a very few eukaryotic genomes such as human, yeast, worm, fly and thale cress (Arabidopsis thaliana). It also included individual complete genomes of organelles and plasmids. Despite the name, the Entrez Genome database record has been a chromosome (or organelle or plasmid) rather than a genome.

The new Genome resource uses a new data model where a single record provides information about the organism (usually a species), its genome structure, available assemblies and annotations, and related genome-scale projects such as transcriptome sequencing, epigenetic studies and variation analysis. As before, the Genome resource represents genomes from all major taxonomic groups: Archaea, Bacteria, Eukaryote, and Viruses. The old Genome database represented only Refseq genomes, while the new resource extends this scope to all genomes either provided by primary submitters (INSDC genomes) or curated by NCBI staff (RefSeq genomes).

The new Genome database shares a close relationship with the recently redesigned BioProject database (formerly Genome Project). Primary information about genome sequencing projects in the new Genome database is stored in the BioProject database. BioProject records of type “Organism Overview” have become Genome records with a Genome ID that maps uniquely to a BioProject ID. The new Genome database also includes all “genome sequencing” records in BioProject.

BTW, just in case you ever wonder about changes in identifiers causing problems:

The new Genome IDs cannot be directly mapped to the old Genome IDs because the data types are very different. Each old Genome ID represented a single sequence that can still be found in Entrez Nucleotide using standard Entrez searches or the E-utilities. We recommend that you convert old Genome IDs to Nucleotide GI numbers using the following remapping file available on the NCBI FTP site:
ftp://ftp.ncbi.nih.gov/genomes/old_genomeID2nucGI

The Genome site.

Standard Measures in Genomic Studies

This news story caught my eye and leads off simply enough:

Standards can make our lives better. We have standards for manufacturing many items — from car parts to nuts and bolts — that improve the reliability and compatibility of all sorts of widgets that we use in our daily lives. Without them, many tasks would be difficult, a bit like trying to fit a square peg into a round hole. There are even standard measures to collect information from participants of large population genomic studies that can be downloaded for free from the Consensus Measures for Phenotype and eXposures (PhenX) Toolkit [phenxtoolkit.org]. However, researchers will only adopt such standard measures if they can be used easily.

That is why the NHGRI’s Office of Population Genomics has launched a new effort called the PhenX Real-world Implementation and Sharing (PhenX RISING) program. The National Human Genome Research Institute (NHGRI) has awarded nearly $900,000, with an additional $100,000 from NIH Office of Behavioral and Social Sciences Research (OBSSR), to seven investigators to use and evaluate the standards. Each investigator will incorporate a variety of PhenX measures into their ongoing genome-wide association or large population study. These researchers will also make recommendations as to how to fine-tune the PhenX Toolkit.

OK, good for them, or at least the researchers who get the grants, but what does that have to do with topic maps?

Just a bit further the announcement says:

GWAS have identified more than a thousand associations between genetic variants and common diseases such as cancer and heart disease, but the majority of the studies do not share standard measures. PhenX standard measures are important because they allow researchers to more easily combine data from different studies to see if there are overlapping genetic factors between or among different diseases. This ability will improve researchers’ understanding of disease and may eventually be used to assess a patient’s genetic risk of getting a disease such as diabetes or cancer and to customize treatment.

OK, so there are existing studies that don’t share standard measures, there will be more studies while the PhenX RISING program goes on that don’t share standard measures and there may be future studies while PhenX RISING is being adjusted that don’t share standard measures.

Depending upon the nature of the measures that are not shared and the importance of mapping between these non-shared standards, this sounds like fertile ground for topic map prospecting.

Visions of a semantic molecular future

I already have a post on the Journal of Cheminformatics but this looked like it needed a separate post.

This thematic issue arose from a symposium held in the Unilever Centre [for Molecular Science Informatics, Department of Chemistry, University of Cambridge] on 2011-01-15/17 to celebrate the career of Peter Murray-Rust. From the programme:

This symposium addresses the creativity of the maturing Semantic Web to the unrealized potential of Molecular Science. The world is changing and we are in the middle of many revolutions: Cloud computing; the Semantic Web; the Fourth Paradigm (data-driven science); web democracy; weak AI; pervasive devices; citizen science; Open Knowledge. Technologies can develop in months to a level where individuals and small groups can change the world. However science is hamstrung by archaic approaches to the publication, redistribution and re-use of information and much of the vision is (just) out of reach. Social, as well as technical, advances are required to realize the full potential. We’ve asked leading scientists to let their imagination explore the possible and show us how to get there.

This is a starting point for all of us – the potential of working with the virtual world of scientists and citizens, coordinated through organizations such as the Open Knowledge Foundation and continuing connection with the Cambridge academic community makes this one of the central points for my future.

The pages in this document represent vibrant communities of practice which are growing and are offered to the world as contributions to a bsemantic molecular future.

We have combined talks from the symposium with work from the Murray-Rust group into 15 articles.

Quickly, just a couple of the articles with abstracts to get you interested:

“Openness as infrastructure”
John Wilbanks Journal of Cheminformatics 2011, 3:36 (14 October 2011)

The advent of open access to peer reviewed scholarly literature in the biomedical sciences creates the opening to examine scholarship in general, and chemistry in particular, to see where and how novel forms of network technology can accelerate the scientific method. This paper examines broad trends in information access and openness with an eye towards their applications in chemistry.

“Open Bibliography for Science, Technology, and Medicine”
Richard Jones, Mark MacGillivray, Peter Murray-Rust, Jim Pitman, Peter Sefton, Ben O’Steen, William Waites Journal of Cheminformatics 2011, 3:47 (14 October 2011)

The concept of Open Bibliography in science, technology and medicine (STM) is introduced as a combination of Open Source tools, Open specifications and Open bibliographic data. An Openly searchable and navigable network of bibliographic information and associated knowledge representations, a Bibliographic Knowledge Network, across all branches of Science, Technology and Medicine, has been designed and initiated. For this large scale endeavour, the engagement and cooperation of the multiple stakeholders in STM publishing – authors, librarians, publishers and administrators – is sought.

It should be interesting when generally realized that the information people have hoarded over the years isn’t important. It is the human mind that perceives, manipulates, and draws conclusions from information that gives it any value at all.

OpenHelix

From the about page:

More efficient use of the most relevant resources means quicker and more effective research. OpenHelix empowers researchers by

• providing a search portal to find the most relevant genomics resource and training on those resources.
• distributing extensive and effective tutorials and training materials on the most powerful and popular genomics resourcs.
• contracting with resource providers to provide comprehensive, long-term training and outreach programs.

If you are interested in learning the field of genomics research, other than by returning to graduate/medical school, this site will figure high on your list of resources.

It offers a very impressive gathering of both commercial and non-commercial resources under one roof.

I haven’t taken any of the tutorials produced by OpenHelix and so would appreciate comments from anyone who has.

Bioinformatics is an important subject area for topic maps for several reasons:

First, the long term (comparatively speaking) interest in the use of computers and the use in fact of computers in biology indicates there is a need for information for which other people will spend money. There is a key phrase in that sentence, “…for which other people will spend money.” You are already spending your time working on topic maps so it is important to identify other people who are willing to part with cash for your software or assistance. Bioinformatics is a field where that is already known to happen, other people spend their money on software or expertise.

Second, for all of the progress on identification issues in bioinformatics, any bioinformatics journal you pick up, will have references to the need for greater integration of biological resources. There is plenty of opportunity now and as far as anyone can tell, for many tomorrows to follow.

Third, for good or ill, any progress in the field attracts a disproportionate amount of coverage. The public rarely reads or sees coverage of discoveries being less than what was initially reported. And not only health professionals hear such news so it would be good PR for topic maps.

Broad Institute

In their own words:

The Eli and Edythe L. Broad Institute of Harvard and MIT is founded on two core beliefs:

1. This generation has a historic opportunity and responsibility to transform medicine by using systematic approaches in the biological sciences to dramatically accelerate the understanding and treatment of disease.
2. To fulfill this mission, we need new kinds of research institutions, with a deeply collaborative spirit across disciplines and organizations, and having the capacity to tackle ambitious challenges.

The Broad Institute is essentially an “experiment” in a new way of doing science, empowering this generation of researchers to:

• Act nimbly. Encouraging creativity often means moving quickly, and taking risks on new approaches and structures that often defy conventional wisdom.
• Work boldly. Meeting the biomedical challenges of this generation requires the capacity to mount projects at any scale — from a single individual to teams of hundreds of scientists.
• Share openly. Seizing scientific opportunities requires creating methods, tools and massive data sets — and making them available to the entire scientific community to rapidly accelerate biomedical advancement.
• Reach globally. Biomedicine should address the medical challenges of the entire world, not just advanced economies, and include scientists in developing countries as equal partners whose knowledge and experience are critical to driving progress.

The Detecting Novel Associations in Large Data Sets software and data is from the Broad Institute.

Sounds like the sort of place that would be interested in enhancing research and sharing of information with topic maps.

Toolset for Genomic Analysis, Data Management

From the post:

The informatics group at the Genome Institute at Washington University School of Medicine has released an integrated analysis and information-management system called the Genome Modeling System.

The system borrows concepts from traditional laboratory information-management systems — such as tracking methods and data-access interfaces, — and applies them to genomic analysis. The result is a standardized system that integrates both analysis and management capabilities, David Dooling, the assistant director of informatics at Wash U and one of the developers of GMS, explained to BioInform.

Not exactly. The tools that will make up the “Genome Modeling System” have been released but melding them into the full package is something we will see near the end of this year. (later in the article)

I remember the WU-FTPD software before it fell into disrepute so I have great expectations for this software. I will keep watch and post a note when it appears for download.

openSNP

Don’t recognize the name? I didn’t either when I came across it on Genoweb under the title Battle Over.

Then I read the homepage blurb:

openSNP allows customers of direct-to-customer genetic tests to publish their test results, find others with similar genetic variations, learn more about their results, find the latest primary literature on their variations and help scientists to find new associations.

I think we will be hearing more about openSNP in the not too distant future.

Sounds like a useful place to talk about topic maps. But in terms of their semantic impedances and their identifiers for subjects.

Hard to sell a product if we are fixing a “problem” that no one sees as a “problem.”