“Ome” and “omics” are suffixes that are derived from genome (the whole collection of a person’s DNA, as coined by Hans Winkler, as a combinaion of “gene” and “chromosome”¹) and genomics (the study of the genome). Scientists like to append to these to any large-scale system (or really, just about anything complex), such as the collection of proteins in a cell or tissue (the proteome), the collection of metabolites (the metabolome), and the collection of RNA that’s been transcribed from genes (the transcriptome). High-throughput analysis is essential considering data at the “omic” level, that is to say considering all DNA sequences, gene expression levels, or proteins at once (or, to be slightly more precise, a significant subset of them). Without the ability to rapidly and accurately measure tens and hundreds of thousands of data points in a short period of time, there is no way to perform analyses at this level.

There are four major types of high-throughput measurements that are commonly performed: genomic SNP analysis (i.e., the large-scale genotyping of single nucleotide polymorphisms), transcriptomic measurements (i.e., the measurement of all gene expression values in a cell or tissue type simultaneously), proteomic measurements (i.e., the identification of all proteins present in a cell or tissue type), and metabolomic measurements (i.e., the identification and quantification of all metabolites present in a cell or tissue type). Each of these four is distinct and offers a different perspective on the processes underlying disease initiation and progression as well as on ways of predicting, preventing, or treating disease.

Genomic SNP genotyping measures a person’s genotypes for several hundred thousand single nucleotide polymorphisms spread throughout the genome. Other assays exists to genotype ten thousand or so polymorphic sites that are near known genes (under the assumption that these are more likely to have some effect on these genes). The genotyping technology is quite accurate, but the SNPs themselves offer only limited information. These SNPs tend to be quite common (with typically at least 5% of the population having at least one copy of the less frequent allele), and not strictly causal of the disease. Rather, SNPs can act in unison with other SNPs and with environmental variables to increase or decrease a person’s risk of a disease. This makes identifying important SNPs difficult; the variation in a trait that can be accounted for by a single SNP is fairly small relative to the total variation in the trait. Even so, because genotypes remain constant (barring mutations to individual cells) throughout life, SNPs are potentially among the most useful measurements for predicting risk.

Transcriptomic measurements (often referred to as gene expression microarrays or “gene chips” are the oldest and most established of the high-throughput methodologies. The most common are commercially produced “oligonucleotide arrays”, which have hundreds of thousands of small (25 bases) probes, between 11 and 20 per gene. RNA that has been extracted from cells is then hybridized to the chip, and the expression level of ~30,000 different mRNAs can be assessed simultaneously. More so than SNP genotypes, there is the potential for a significant amount of noise in transcriptomic measurements. The source of the RNA, the preparation and purification methods, and variations in the hybridization and scanning process can lead to differences in expression levels; statistical methods to normalize, quantify, and analyze these measures has been one of the hottest areas of research in the last five years. Gene expression levels influence traits more directly than than SNPs, and so significant associations are easier to detect. While transcriptomic measures are not as useful for pre-disease prediction (because a person’s gene expression levels very far in advance of disease initiation are not likely to be informative because they have the potential to change so significantly), they are very well-suited for either early identification of a disease (i.e., finding people who have gene expression levels characteristic of a disease but who have not yet manifested other symptoms) or classifying patients with a disease into subgroups (by identifying gene expression levels that are associated with either better or worse outcomes or with higher or lower values of some disease phenotype).

Proteomics is similar in character to transcriptomics. The most significant difference is in regards to the measurements. Unlike transcriptomics, where the gene expression levels are assessed simultaneously, protein identification is done in a rapid serial fashion. After a sample has been prepared, the proteins are separated using chromatography, 2 dimensional protein gels (which separate proteins based on charge and then size) or 1 dimensional protein gels (which separate based on size alone), and digested, typically with trypsin (which cuts proteins after each arginine and lysine), and then run through mass spectroscopy. The mass spec identifies the size of each of the peptides, and the proteins can be identified by comparing the size of the peptides created with the theoretical digests of all know proteins in a database. This searching is the key to the technology, and a number of algorithms both commercial and open-source have been created for this. Unlike transcriptomic measures, the overall quantity of a protein cannot be assessed, just its presence or absence. Like transcriptomic measures, though, proteomic measures are excellent for early identification of disease or classifying people into subgroups.

Last up is metabolomics, the high-throughput measure of the metabolites present in a cell or tissue. As with proteomics, the metabolites are measured in a very fast serial process. NMR is typically used to both identify and quantify metabolites. This technology is newer and less frequently used than the other technologies, but similar caveats apply. Measurements of metabolites are dynamic as are gene expression levels and proteins, and so are best suited for either early disease detection or disease subclass identification.

So, above was a brief introduction to all the “omics”. Would include details on each in my next posts.

Till then Happy Xmas Season !!

Author: Anonymous
Source: free-articles

ARLINGTON, VA July 18, 2003 โ€” According to a recent survey, over 40% of researchers who currently use tissue microarrays are working on cancer research or diagnosis. Since tissue microarrays, per se, were developed by researchers, power tools
, at the National Cancer Institute, it is not surprising that early, power tools
, adopters, power tools
, of this technology are using them, power tools
, in oncology.

Future market growth will be driven by adoption of tissue microarrays in other areas of research, such as neurobiology and infectious disease, as well as their increased utilization in high-throughput analysis of tissue sections, validation of DNA microarray data and biomarker discovery.

These findings were, power tools
, recently published by BioInformatics, LLC (Arlington, VA) in a new life science market report, โ€œThe Market,, power tools
, power tools
, for Tissue Microarrays.โ€ Based on a comprehensive survey of more than 250 researchers and clinicians, power tools
, who currently use tissue microarrays, the report details the products and techniques most commonly used and reveals the product attributes and suppliers with which they are most satisfied.

Histology is taking on new importance in clinical and research laboratories as the growing use of DNA microarrays and other rapid genomic analysis techniques creates increasing amounts of data that need to be validated. Until the development of the tissue microarray, informative tissue analysis required the production of individual tissue, power tools
, sections one at a time, which, power tools
, created a bottleneck in the research pipeline.

Now, tissue microarrays allow hundreds of tissue samples to be analyzed simultaneously on one microscope slide in the same amount of time that was previously required for a single specimen. Besides increasing throughput, tissue microarrays offer researchers the ability to conserve precious tissue resources, improve internal experimental control and reduce the consumption of reagents.

โ€œScience ultimately benefits since tissue microarraysโ€”by their very natureโ€”often necessitate multi-center research studies, which bring the expertise and experience of many talented researchers to bear on tough problems,โ€ observes Dr. Robin Rothrock, Director of Market Research at BioInformatics, LLC.

Realizing the benefits, an increasing number, power tools
, of scientists are starting to use, power tools
, tissue, power tools
, microarrays. In fact, within the study population, the number of users has at least doubled each year over the past two years. In the next 12 months, this number is projected to grow over 40% with much of the near-term, power tools
, growth being driven by new entrants rather than by increased consumption by current users.

Many scientists produce, process and analyze their own tissue microarrays, citing their control over array content and cost as key reasons. To create their arrays, over one-half of these respondents indicated that they use in-house developed instruments and hand tools, while others use specially designed instrumentation from Beecher and Chemicon, power tools
, .

In addition to the tools for creating arrays, scientists frequently employ antibodies, stains and colorimetric, power tools
, or fluorescent detection reagents to visualize target RNA, DNA and protein molecules in tissue microarray sections. โ€œInvitrogen is a leading supplier of signal detection, power tools
, kits and reagents, and as of yet, few other suppliers appear, power tools
, to have targeted their signal detection products specifically to tissue microarray users,โ€ says Rothrock.

The most pervasive stumbling blocks that scientists encounter when employing tissue microarrays are the detachment of specimens during processing and the fact that the fixative medium is not optimized for the detection, power tools
, of specific target molecules. โ€œI believe that these two reasons likely explain why over half of the researchers surveyed outsource the production of tissue microarrays, preferring to rely on someone else’s expertise,โ€ claims Rothrock.

Despite the fact that 52% and 21% of the respondents outsource the production and processing/analysis of tissue microarrays, respectively, a dominant commercial provider has yet to emerge in this highly fragmented market. Of the researchers who obtain, power tools
, tissue microarrays externally, only 48% purchase premade arrays and another 15% order custom-made arrays from commercial sourcesโ€”with Invitrogen, BD Biosciences-Pharmingen and Ambion among the top suppliers, power tools
, .

Suppliers are meeting users’ requirements for choice of species, number of samples per, power tools
, slide and choice of tissues; however, scientists are less satisfied with, power tools
, other important attributes like batch-to-batch consistency and the number of slides per order. With regards to the respondents who outsource the processing/analysis of their tissue microarrays, only 31% rely on commercial service providers such as Biocat and Zymed. These respondents indicated that cost-effectiveness and quality of service were factors in their selection of a particular provider.

โ€œTissue microarrays and related products or services represent a new opportunity for many companies to further expand, power tools
, their presence in the lucrative pharmaceutical and clinical markets. Companies should use this report to capitalize on the increasing popularity of this powerful technology,โ€ concludes Rothrock.

For a complimentary Executive Summary of this report, please visit http://www.gene2drug.com/reports

ABOUT BIOINFORMATICS, LLC

BioInformatics, LLC is a market research firm located in Arlington, Virginia. BioInformatics supports marketing, sales, power tools
, and R&D executives in the life science, medical device and pharmaceutical industries through published research reports, custom research and consulting.

BioInformatics sponsors the world’s largest market research panel of scientific, power tools
, customers โ€” The Science Advisory Board (http://www.scienceboard.net) โ€” which consists of more than 15,500 scientists, physicians and other life science and medical professionals from 62 countries who participate in surveys that address emerging technologies, test customer reactions to new product concepts, measure brand awareness and assess advertising effectiveness.

For more information, please contact:

Alyssa Martin

BioInformatics, LLC

2111 Wilson Blvd., Suite 250

Arlington, VA 22201

703.778.3080 x12 (phone)

703.778.3081 (fax)

a.martin@gene2drug.com

http://www.gene2drug.com/

Daniel Reda, co-Chair of the Biotechnology and Bioinformatics track at Singularity University, introduces the key concepts and breakthroughs in biotechnology and bioinformatics. Filmed during the November 2009 Executive Program at Singularity University.

Shows how R can be used to solve bioinformatics and computational biology problems. Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.

Ducument Link

R programming language from wikipedia

La semana pasada asistí al curso del instrumento de secuenciación por ligación “SOLiD 3 Plus” impartido por Applied Biosystems, el curso se llevo a cabo en el Instituto Nacional de Medicina Genomica. El viernes colabore un poco impartiendo una platica introductoria sobre el análisis de los datos via offline y los formatos de las lecturas de SOLiD.

Fue un enorme gusto y placer el colaborar con Applied Biosystems y el Inmegen en esta pequeña platica que impartí.

A mediados del pasado año 2007 rápidamente desarrolle un programa que permite embeber código html dentro de los reportes que BLAST genera al terminar de hacer un alineamiento y surge como necesidad para la interfaz Query Sequence Visualizer ya que la interfaz tiene un modulo que permite alinear secuencias mediante blast, pero el reporte resultante necesita vincular las secuencias que dieron hit y posteriormente verlas mediante un pequeño browser web que desarrolle  el cual aun permanece  en estado beta ya que hay nuevos cambios y mejoras.

El código html puede embeberse donde sea y como sea necesario.

Sitio de red: http://hackob.openenchilada.com/projects/soft/bio/bhtmlembed/

Bioinformatics:  RP’s Big Step in Drug Discovery

By Bernie Cahiles-Magkilat

Bioinformatics techniques in drug discovery are making a way for Filipino scientists to take a hard look at developing the country’s drug-making potentials. It could be a small step in the world of the giants, but nevertheless a step in the right direction.

   The Wikipedia defines bioinformatics as the application of information technology to the field of molecular biology.

   Common activities in bioinformatics are mapping and analyzing DNA and protein sequences, aligning different DNA and protein sequences to compare them and creating and viewing 3-D models of protein structures.

   “If you don’t have a laboratory to conduct a full-blown experiment, you can do it on the computer using a specialized software,” said Dr. Junie B. Billones, University of the Philippines (UP)-Manila Learning Resource Center director who is spearheading this effort..

   The process is time-and-cost-efficient.  It allows the compounds to be examined first through computer simulation to find out if there are active (curative) component in a natural ingredient that can be developed for a targeted drug to treat a particular disease.

   Bioinformatics techniques are already widely employed abroad. A cost-effective system in drug synthesis is an important factor in the commercial success of a drug.  This process must integrate chemistry, biology, pharmacokinetics, and other disciplines needed in drug design and development.

   For this, the Philippines needs to establish a BioChem Informatics Center (BIC).  UP Manila is pushing for the funding of the BIC by the Philippine Council for Advanced Science and Technology Research and Development (PCASTRD-DOST).

   “We plan to acquire Accelrys’ complete suite of computational programs for drug design and discovery.  With these programs, one can easily screen potential drug candidates from a database of natural and synthetic products for any target biomolecules,” he said.

   The Philippines has so much natural resources to tap for drug development. But it needs the equipment and the training of people skilled in bioinformatics and related disciplines, according to Billones, a graduate of BS Agricultural Chemistry (magna cum laude) at the Visayas State University and a Ph.D. in Chemistry at the Australian National University at Canberra.

   With our very limited resources, Billones hopes that the academe, industry, and the government can

collaborate on the long process of drug development.

   For instance, the output of UP Manila’s simulation works can be used by UP Diliman or Ateneo where they have synthetic capability.

   Isn’t it about time that the Philippines develop its own drug manufacturing industry? Bioinformatics is paving the way. Let there be a strong determination to proceed.  

Pipeline Pilot solutions are based around a powerful client-server platform that lets you construct workflows by graphically combining components for data retrieval, filtering, analysis, and reporting. Different client interfaces to the Pipeline Pilot platform enable you to work in the environment that best suits your needs.

Presentation document: Pipeline Pilot(8.9M)

Product Link

* Please download the document rather than click the document link. If you just click the link, it seems to be waiting forever because the document file size is big.

There are lots of lectures in bioinformatics and molecular biology and also for free. You can even download the lecture files in your desktop which has video and slides. This site is awesome.

http://s-star.org/lectures.html

This is a research paper published in 2008.

Full Text Link

This document introduces microarray data analyses.

Document Link

This paper published in 2008.

Document Link

Bioinformatics – An Introduction for Computer Scientists
JACQUES COHEN / Brandeis University

The article aims to introduce computer scientists to the new field of bioinformatics. This area has arisen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research—and its more recent counterparts, proteomics and functional genomics.

Document Link

One day Helicos would make $1000 genome possible

N.M. Luscombe,D. Greenbaum, M. Gerstein
Department of Molecular Biophysics and Biochemistry Yale University New Haven, USA

Abstract

A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies.
In this review we provide an introduction and overview of the current state of the field. We discuss the main principles that underpin bioinformatics analyses, look at the types of biological information and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems.

Full Text Document Link

Craic Computing is pleased to announce that our SQIP Patent Sequence Database is officially open for business.

You can find out more information and sign up for a Free Account by going to http://sqipdb.com.

SQIP (pronounced ’skip’) consists of a carefully curated database of DNA, RNA and Protein sequences derived from patents and a sophisticated web interface that makes the task of patent sequence searching much more efficient and cost effective than is currently possible.

SQIP includes a project management system that helps you organize and keep track of your sequence assessments. It lets you run several types of search against the sequence database and provides a rich interface for viewing the voluminous and detailed search results. By carefully managing duplication in the database SQIP can help you avoid frustration and save you lots of time in assessing search results, while still allowing you see all the available data.

Having selected relevant sequence matches, you can view all the linked patents organized in a timeline, with extensive links to external resources.

SQIP is updated every two weeks and users can choose to resubmit existing searches whenever new data is added. New matches, relative to the previous search, are highlighted in the search results and users can opt to be notified by email whenever new relevant matches are found. This helps you keep your projects up to date with no extra effort.

SQIP is extremely cost-effective compared to the competition. There is no large annual commitment, you simply pay for the searches that you use and the cost per search ($150/BLAST search) extremely competitive. This straightforward approach to pricing makes SQIP ideal for companies that have occasional needs for patent sequence searching and that don’t want to commit tens of thousands of dollars per year.

There is no charge to create new accounts and you can evaluate all the features of SQIP, at no charge, using a preloaded example project.

Please consider SQIP for your patent sequence search needs and sign up for your free account at http://sqipdb.com.

Machine learning-based methods have been proven to be powerful in developing new fold recognition tools.
DescFold(Descriptor-based Fold Recognition System) is a web server for protein fold recognition,which can predict a protein’s fold type from its amino acid sequence. The server combines six effictive descriptors : a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), a descriptor based on the occurrence of PROSITE functional motifs, a descriptor based on profile-profile-alignment(PPA) and a descriptor based on Profile-structural-profile-alignment (PSPA) .

When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset.

The DESC server is freely available at: http://202.112.170.199/DescFold/index.html

Earlier  I showed a very simple way to perform a BLAST  using Ruby. Today I would like to revisit that topic for two reasons.

1. The “using ruby with blast” search term seems to be very common and actually one of the ways that people reach my blog.
2. The original post was not very through.

BLAST aka Basic Local Alignment Tool is used to search a sequence (either DNA or protein) against a database of other sequences (either all nucleotide or all protein) in order to identify similar sequences. BLAST has many different flavors and can  search DNA against DNA or protein against protein and also can translate a nucleotide query and search it against a protein database  and vice versa. It can also compute a “profile” for the query sequence and use that for further searches as well as search the query against a database of profiles.

The BLAST tool is fundamental to molecular biologists and bioinformaticians. There are excellent books and tutorials on how to and when to use BLAST, so i will assume all you need is to automated your work and parse the results. The actual algorithm is implemented in C and freely  available from the NCBI website.The first thing  to do is to download the appropriate binaries for your platform. Instructions for setting up and installing BLAST

Once installed on your system  the primary method of interaction is using the command line. Use formatdb to create blast databases and blastall to search for sequence homology for a given sequence against a given blast database.

In Ruby, there are two ways you can call the BLAST program. First using the Bioruby library and second by writing your own ruby wrapper for the BLAST command line parameters and execution. Most often, one executes BLAST from the command line and then process the results file which is in either one of the many BLAST output formats. Bioruby is excellent  at parsing the results file. Using Bioruby with BLAST is  very straightforward:

#blasting the bioruby way
  #query_file: a list of query sequences in fasta format
  #database_path: a path to the actual BLAST formatted database
  #program: The BLAST program to call, either of blastp,blastn,tblastn e.t.c.
    def bio_blast(program, database_path,query_file)
        factory = Bio::Blast.local(program,database_path)

        ff = Bio::FlatFile.open(Bio::FastaFormat, query_file)
        ff.each do |entry|
           report = factory.query(entry) # report will be a Blast::Report object
          # iterate trough the hits
          report.each do |hit|
            puts hit.bit_score        # bit score (*)
            puts hit.query_seq        # query sequence (TRANSLATOR'S NOTE: sequence of homologous region of query sequence)
            puts hit.midline          # middle line string of alignment of homologous region (*)
            puts hit.target_seq       # hit sequence (TRANSLATOR'S NOTE: sequence of homologous region of query sequence)
            puts hit.evalue           # E-value
            puts hit.identity         # % identity
            puts hit.overlap          # length of overlapping region
           puts hit.query_id         # identifier of query sequence
           puts hit.query_def        # definition(comment line) of query sequence
           puts hit.query_len        # length of query sequence
           puts hit.target_id        # identifier of hit sequence
           puts hit.target_def       # definition(comment line) of hit sequence
           puts hit.target_len       # length of hit sequence
           puts hit.query_start      # start position of homologous region in query sequence
           puts hit.query_end        # end position of homologous region in query sequence
           puts hit.target_start     # start position of homologous region in hit(target) sequence
           puts hit.target_end       # end position of homologous region in hit(target) sequence
           puts hit.lap_at           # array of above four numbers

hit.each do |hsp|
             puts hsp.query_from
            end
         end
      end
    end

The method will execute BLAST and also print the hits and the high scoring potions start coordinates for each hit. How ever you may want to just run BLAST without the bioruby overhead. The line below will work as well:

  input = query_path
    #execute blast and store the results in the blast_results  variable
    #-p blast program to run
    #-d blast database to query against
    #-T gives a html output
    #-i query file path

  #execution
blast_result = %x(blastall -p #{program} -d #{database} -e #{expectation} -M #{matrix}
                 -i #{input} -T  T)
#blast_result will be the output from the system execution of the above command. You can choose to write it 
to a file or process it using the Bio::Blast::Report object.

You can use a similar style command like the one above to create BLAST databases using the formatdb command.

I would recommend the use of the bio-ruby blast report parsing classes to automate the process. Please look at the Bio-ruby API documentation for more details.

Después de mucho tiempo sin anunciar una aplicación que desarrolle en mi actual empleo, finalmente me anime a platicar un poco sobre esta aplicación.
Prácticamente el programa no hace gran cosa, solo interpreta los archivos resultantes, en formato xml, del alineamiento que realiza blast, y los va automáticamente almacenando en una base de datos como MySql o PostgreSql , a elección del usuario, técnicamente el proceso lo realiza sin volcar a memoria nada mas que el objeto en si ,de esta manera es posible usar archivos de gran tamaño arriba de 2 GB y el consumo de memoria es mínimo, el uso de cpu si es aprovechado lo más posible.

¿Quien está usando el programa? gracias a Fran Gonzalez, el Centro de Investigación del Cancer en (Universidad de Salamanca-Consejo Superior de Investigaciones Científicas en España), quien le resulto provechoso y sus  resultados se llevaron con éxito.

¿ Sitio de red ? temporalmente he elaborado rápidamente el sitio web del proyecto, más adelante moveré la aplicación al lugar donde pertenecen, los binarios y fuentes del programa no están disponibles si no por solicitud , la cual más adelante comentare al respecto de los detalles, la ayuda del programa esta disponible en el icono de la bandera en la sección de documentación.

Haz clic aquí para entrar al sitio oficial

¿En que está escrito el algoritmo? en C# y corre en Mono sobre Linux, Windows, Mac. podría sin mucha complicación migrarlo a Vala.

El hospedaje del servidor fue gracias a Mauro, de nueva cuenta muchas gracias Mauro

]]> http://biointelligence.wordpress.com/2009/12/14/applications-system-biology-drug-discovery/ Mon, 14 Dec 2009 04:33:25 +0000 biointelligence http://biointelligence.wordpress.com/2009/12/14/applications-system-biology-drug-discovery/

Till date we have made a lot of posts on Systems Biology, its applications and it scope. Indeed, Systems Biology has brought a big revolution in cell biology and pathway analysis. When seen in combination with treatment of diseases and drug discovery, it proves even more handy. Here we discuss Systems Biology in combination with drug discovery.

The goal of modern systems biology is to understand physiology and disease from the level of molecular pathways, regulatory networks, cells, tissues, organs and ultimately the whole organism. As currently employed, the term ’systems biology’ encompasses many different approaches and models for probing and understanding biological complexity, and studies of many organisms from bacteria to man. Much of the academic focus is on developing fundamental computational and informatics tools required to integrate large amounts of reductionist data (global gene expression, proteomic and metabolomic data) into models of regulatory networks and cell behavior. Because biological complexity is an exponential function of the number of system components and the interactions between them, and escalates at each additional level of organization.

There are basically three advances in the practical applications of systems biology to drug discovery. These are:

1. Informatic integration of ‘omics’ data sets (a bottom-up approach)

Omics approaches to systems biology focus on the building blocks of complex systems (genes, proteins and metabolites). These approaches have been adopted wholeheartedly by the drug industry to complement traditional approaches to target identification and validation, for generating hypotheses and for experimental analysis in traditional hypothesis-based methods.

2. Computer modeling of disease or organ system physiology from cell and organ response level information available in the literature (a top-down approach to target selection, clinical indication and clinical trial design).
The goal of modeling in systems biology is to provide a framework for hypothesis generation and prediction based on in silico simulation of human disease biology across the multiple distance and time scales of an organism. More detailed understanding of the systems behavior of intercellular signaling pathways, such as the identification of key nodes or regulatory points in networks or better understanding of crosstalk between pathways, can also help predict drug target effects and their translation to organ and organism level physiology.

3.  The use of complex human cell systems themselves to interpret and predict the biological activities of drugs and gene targets (a direct experimental approach to cataloguing complex disease-relevant biological responses).

Pathway modeling as yet remains too disconnected from systemic disease biology to have a significant impact on drug discovery. Top-down modeling at the cell-to-organ and organism scale shows promise, but is extremely dependent on contextual cell response data. Moreover, to bridge the gap between omics and modeling, we need to collect a different type of cell biology data—data that incorporate the complexity and emergent properties of cell regulatory systems and yet ideally are reproducible and amenable to storing in databases, sharing and quantitative analysis.

This is how Systems Biology has aided in Drug Discovery Research and paved its path to cure many vital diseases.

Read our other posts on Systems Biology – http://biointelligence.wordpress.com/category/systems-biology/

After the ontology languages and general aspects of ontology engineering, we now will delve into one specific application area: SWT for health care and life sciences. Its frontrunners in bioinformatics were adopters of some of the Semantic Web ideas even before Berners-Lee, Hendler, and Lassila wrote their Scientific American paper in 2001, even though they did not formulate their needs and intentions in the same terminology: they did want to have shared, controlled vocabularies with the same syntax, to facilitate data integration—or at least interoperability—across Web-accessible databases, have a common space for identifiers, it needing to be a dynamic, changing system, to organize and query incomplete biological knowledge, and, albeit not stated explicitly, it all still needed to be highly scalable [1].

Bioinformaticians and domain experts in genomics already organized themselves together in the Gene Ontology Consortium, which was set up officially in 1998 to realize a solution for these requirements. The results exceeded anyone’s expectations in its success for a range of reasons. Many tools for the Gene Ontology (GO) and its common KR format, .obo, have been developed, and other research groups adopted the approach to develop controlled vocabularies either by extending the GO, e.g., rice traits, or adding their own subject domain, such as zebrafish anatomy and mouse developmental stages. This proliferation, as well as the OWL development and standardization process that was going on at about the same time, pushed the goal posts further: new expectations were put on the GO and its siblings and on their tools, and the proliferation had become a bit too wieldy to keep a good overview what was going on and how those ontologies would be put together. Put differently, some people noticed the inferencing possibilities that can be obtained from moving from obo to OWL and others thought that some coordination among all those obo bio-ontologies would be advantageous given that post-hoc integration of ontologies of related and overlapping subject domains is not easy. Thus came into being the OBO Foundry to solve such issues, proposing a methodology for coordinated evolution of ontologies to support biomedical data integration [2].

People in related disciplines, such as ecology, have taken on board experiences of these very early adopters, and instead decided to jump on board after the OWL standardization. They, however, were not only motivated by data(base) integration. Referring to Madin et al’s paper [3] again, I highlight three points they made: “terminological ambiguity slows scientific progress, leads to redundant research efforts, and ultimately impedes advances towards a unified foundation for ecological science”, i.e., identification of some serious problems they have in ecological research; “Formal ontologies provide a mechanism to address the drawbacks of terminological ambiguity in ecology”, i.e., what they expect that ontologies will solve for them (disambiguation); and “and fill an important gap in the management of ecological data by facilitating powerful data discovery based on rigorously defined, scientifically meaningful terms”, i.e., for what purpose they want to use ontologies and any associated computation (discovery). That is, ontologies not as a—one of many possible—tool in the engineering/infrastructure means, but as a required part of a method in the scientific investigation that aims to discover new information and knowledge about nature (i.e., in answering the who, what, where, when, and how things are the way they are in nature).

What has all this to do with actual Semantic Web technologies? On the one hand, there are multiple data integration approaches and tools that have been, and are being, tried out by the domain experts, bioinformaticians, and interdisciplinary-minded computer scientists [4], and, on the other hand, there are the W3C Semantic Web standards XML, RDF(S), SPARQL, and OWL. Some use these standards to achieve data integration, some do not. Since this is a Semantic Web course, we shall take a look at two efforts who (try to) do, which came forth from the activities of the W3C’s Health Care and Life Sciences Interest Group. More precisely, we take a closer look at a paper written about 3 years ago [5] that reports on a case study to try to get those Semantic Web Technologies to work for them in order to achieve data integration and a range of other things. There is also a more recent paper from the HCLS IG [6], where they aimed at not only linking of data but also querying of distributed data, using a mixture of RDF triple stores and SKOS. Both papers reveal their understanding of the purposes of SWT, and, moreover, what their goals are, their experimentation with various technologies to achieve them, and where there is still some work to do. There are notable achievements described in these, and related, papers, but the sought-after “killer app” is yet to be announced.

The lecture will cover a ‘historical’ overview and what more recent ontology-adopters focus on, the very basics of data integration approaches that motivated the development of ontologies, and we shall analyse some technological issues and challenges mentioned in [5] concerning Semantic Web (or not) technologies.

References:

[1] The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics, May 2000;25(1):25-9.

[2] Barry Smith, Michael Ashburner, Cornelius Rosse, Jonathan Bard, William Bug, Werner Ceusters, Louis J. Goldberg, Karen Eilbeck, Amelia Ireland, Christopher J Mungall, The OBI Consortium, Neocles Leontis, Philippe Rocca-Serra, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H Scheuermann, Nigam Shah, Patricia L. Whetzel, Suzanna Lewis. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25, 1251-1255 (2007).

[3] Joshua S. Madin, Shawn Bowers, Mark P. Schildhauer and Matthew B. Jones. (2008). Advancing ecological research with ontologies. Trends in Ecology & Evolution, 23(3): 159-168.

[4] Erhard Rahm. Data Integration in Bioinformatics and Life Sciences. EDBT Summer School, Bolzano, Sep. 2007.

[5] Ruttenberg A, Clark T, Bug W, Samwald M, Bodenreider O, Chen H, Doherty D, Forsberg K, Gao Y, Kashyap V, Kinoshita J, Luciano J, Scott Marshall M, Ogbuji C, Rees J, Stephens S, Wong GT, Elizabeth Wu, Zaccagnini D, Hongsermeier T, Neumann E, Herman I, Cheung KH. Advancing translational research with the Semantic Web, BMC Bioinformatics, 8, 2007.

[6] Kei-Hoi Cheung, H Robert Frost, M Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Jun Zhao, and Adrian Paschke. A journey to Semantic Web query federation in the life sciences. BMC Bioinformatics 2009, 10(Suppl 10):S10

Note: references 1, 2, and (5 or 6) are mandatory reading, and 3 and 4 are recommended to read.

Lecture notes: lecture 8 – SWLS background and data integration

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