Posts Tagged ‘Bioinformatics’

Modeling Targeted Therapy

Posted in Advanced Drug Manufacturing Technology, Biological Networks, Gene Regulation and Evolution, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, International Global Work in Pharmaceutical, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Pharmacotherapy and Cell Activity, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Bioinformatics, Biology, Biomedical Engineering, Complex network, computational biology, Northwestern University, Technology, Therapy on March 2, 2013| 3 Comments »

Modeling Targeted Therapy

Reporter: Larry H. Bernstein, MD, FCAP
pharmaceuticalintelligence.com/2013/03/02/modeling-targeted-therapy/

Some Perspectives on Network Modeling in Therapeutic Target Prediction
R Albert, B DasGupta and N Mobasheri
Biomedical Engineering and Computational Biology Insights 2013; 5: 17–24    http://dx.doi.org/BECBI/Albert_DasGupta_ Mobasheri
Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior
http://www.la-press.com/Some_Perspectives_on_Network_Modeling_in_Therapeutic_Target_Prediction/

Journal of Computational Biology (Photo credit: Wikipedia)

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Low Bioavailability of Nitric Oxide due to Misbalance in Cell Free Hemoglobin in Sickle Cell Disease – A Computational Model

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, BioSimilars, CANCER BIOLOGY & Innovations in Cancer Therapy, Cardiovascular Pharmacogenomics, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Nitric Oxide in Health and Disease, Uncategorized, tagged Bioinformatics, Cardiovascular disease, Cardiovascular Disorders, computational biology, health, Hemoglobin, Hemolysis, Hypertension, nitric oxide, Nitric oxide synthase, NO, Pulmonary hypertension, Sickle Cell disease, systems biology on November 9, 2012| 15 Comments »

Author and Reporter: Anamika Sarkar, Ph.D.

Nitric Oxide (NO) is highly regulated in the blood such that it can be released as vasodilator when needed. The importance and pathway of Nitric Oxide has been nicely reviewed by. “Discovery of NO and its effects of vascular biology”. Other articles which are good readings for the importance of NO are  – a) regulation of glycolysis b) NO in cardiovascular disease c) NO and Immune responses Part I and Part II d) NO signaling pathways. The  effects of NO in diseased states have been reviewed by the articles – “Crucial role of Nitric Oxide in Cancer”, “Nitric Oxide and Sepsis, Hemodynamic Collapse, and the Search for Therapeutic Options”.. (Also, please see Source for more articles on NO and its significance).

Computational models are very efficient tools to understand complex reactions like NO towards physiological conditions. Among them wall shear stress is one of the major factors which is reviewed in the article – “Differential Distribution of Nitric Oxide – A 3-D Mathematical Model”.

Moreover, decrease in availability of NO can lead to many complications like pulmonary hypertension. Some of the causes of decrease in NO have been identified as clinical hypertension, right ventricular overload which can lead to cardiac heart failure, low levels of zinc and high levels of cardiac necrosis.

Sickle Cell disease patients, a hereditary disease, are also known to have decreased levels of NO which can become physiologically challenging. In USA alone, there are 90,000 people who are affected by Sickle cell disease.

Sickle cell disease is breakage of red blood cells (RBC) membrane and resulting release of the hemoglobin (Hb) into blood plasma. This process is also known as Hemolysis. Sickle cell disease is caused by single mutation of Hb which changes RBC from round shape to sickle or crescent shapes (Figure 1).

Figure 1 (A) shows normal red blood cells flowing freely through veins. The inset shows a cross section of a normal red blood cell with normal hemoglobin. Figure 1 (B) shows abnormal, sickled red blood cells The inset image shows a cross-section of a sickle cell with long polymerized HbS strands stretching and distorting the cell shape. Image Source: http://en.wikipedia.org/wiki/Sickle-cell_disease

Sickle Cell RBCs has much shorter life span of 10-20 days when compared with normal RBCs 100-120 days lifespan. Shorter life span of Sickle cell disease RBC’s are compensated by bone marrow generation of new RBCs. However, many times new blood generation cannot cope with the small life span of Sickle cell RBCs and causes pathological condition of Anemia.

RBCs generally breakdown and release Hbs in blood plasma after they reach their end of life span. Thus, in case of Sickle cell disease, there is more cell free Hb than normal. Furthermore, it is known that NO has a very high affinity towards Hbs, which is one of the ways free NO is regulated in blood. As a result presence of larger amounts of cell free Hb in Sickle cell disease lead to less availability of NO.

However, the question remained “what is the quantitative relationship between cell free Hb and depletion of NO”. Deonikar and Kavdia (J. Appl. Physiol., 2012) addressed this question by developing a 2 dimensional Mathematical Model of a single idealized arteriole, with different layers of blood vessels diffusing nutrients to tissue layers (Figure 2:  Deonikar and Kavdia Figure 1).

cell free Hb in 2 dimensional representations of blood vessels.

The authors used steady state partial differential equation of circular geometry to represent diffusion of NO in blood and in tissues. They used first and second order biochemical reactions to represent the reactions between NO and RBC and NO autooxidation processes. Some of their reaction model parameters were obtained from literature, rest of them were fitted to experimental results from literature. The model and its parameters are explained in the previously published paper by same authors Deonikar and Kavdia, Annals of Biomed., 2010. The authors found that the reaction rate between NO and RBC is 0.2 x 10⁵, M^-1 s^-1 than 1.4 x 10⁵, M^-1 s^-1 as reported before by Butler et.al., Biochim. Biophys. Acta, 1998.

Their results show that even small increase in cell free Hb, 0.5uM, can decrease NO concentrations by 3-7 folds approximately (comparing Fig1(b) and 1(d) of Deonikar and Kavdia, 2012, as shown in Figure 2 of this article). Moreover, their mathematical analysis shows that the increase in diffusion resistance of NO from vascular lumen to cell free zone has no effect on NO distribution and concentration with available levels of cell free Hb.

Deonikar and Kavdia’s mathematical model is a simple representation of actual physiological scenario. However, their model results show that for Sickle cell disease patients, decrease in levels of bioavailable NO is an attribute to cell free Hb, which is in abundant for these patients. Their results show that small increase by 0.5 uM in cell free Hb can cause large decrease in NO concentrations.

These interesting insights from the model can help in further understanding in the context of physiological conditions, by replicating experiments in-vivo and then relating them to other known diseases of Sickle cell disease patients like Anemia, Pulmonary Hypertension. Further, drugs can be targeted towards decreasing free cell Hbs to keep balance in availability of NO, which in turn may help in other related disease like Pulmonary Hypertension of Sickle Cell disease patients.

Sources:

Deonikar and Kavdia (2012) :http://www.ncbi.nlm.nih.gov/pubmed/22223452

Previous model explaining mathematical representation and parameters used in the model :Deonikar and Kavdia, Annals of Biomed., 2010.

Previous paper stating reaction rate of Hb and NO: Butler et.al., Biochim. Biophys. Acta, 1998.

Causes of decrease in NO

Clinical Hypertension : http://www.ncbi.nlm.nih.gov/pubmed/11311074

Right ventricular overload : http://www.ncbi.nlm.nih.gov/pubmed/9559613

Low levels of zinc and high levels of cardiac necrosis : http://www.ncbi.nlm.nih.gov/pubmed/11243421

Sickle Cell Source:

http://en.wikipedia.org/wiki/Sickle-cell_disease

http://www.nhlbi.nih.gov/health/health-topics/topics/sca/

NO Source:

Differential Distribution of Nitric Oxide – A 3-D Mathematical Model:

Discovery of NO and its effects of vascular biology

Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function

Nitric oxide: role in Cardiovascular health and disease

NO signaling pathways

Nitric Oxide and Immune Responses: Part 1

Nitric Oxide and Immune Responses: Part 2

Statins’ Nonlipid Effects on Vascular Endothelium through eNOS Activation

http://pharmaceuticalintelligence.com/2012/10/08/statins-nonlipid-effects-on-vascular-endothelium-through-enos-activation/

Inhibition of ET-1, ETA and ETA-ETB, Induction of NO production, stimulation of eNOS and Treatment Regime with PPAR-gamma agonists (TZD): cEPCs Endogenous Augmentation for Cardiovascular Risk Reduction – A Bibliography

Nitric Oxide, Platelets, Endothelium and Hemostasis

Crucial role of Nitric Oxide in Cancer

The rationale and use of inhaled NO in Pulmonary Artery Hypertension and Right Sided Heart Failure

Nitric Oxide and Sepsis, Hemodynamic Collapse, and the Search for Therapeutic Options

NO Nutritional remedies for hypertension and atherosclerosis. It’s 12 am: do you know where your electrons are?

Clinical Trials Results for Endothelin System: Pathophysiological role in Chronic Heart Failure, Acute Coronary Syndromes and MI – Marker of Disease Severity or Genetic Determination?

Endothelial Function and Cardiovascular Disease

Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium

Endothelial Dysfunction, Diminished Availability of cEPCs,  Increasing  CVD Risk – Macrovascular Disease – Therapeutic Potential of cEPCs

Cardiovascular Disease (CVD) and the Role of agent alternatives in endothelial Nitric Oxide Synthase (eNOS) Activation and Nitric Oxide Production

 

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NATIONAL CENTERS FOR BIOMEDICAL COMPUTING: Resources

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, FDA Regulatory Affairs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, Scientist: Career considerations, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Aviva Lev-Ari, Bioinformatics, Biology, Brigham and Women's Hospital, Cellular network, genetics, genomics, National Centers for Biomedical Computing, National Institutes of Health, Stanford University, systems biology on September 20, 2012| Leave a Comment »

 

Reporter: Aviva Lev-Ari, PhD, RN

NATIONAL CENTERS FOR BIOMEDICAL COMPUTING

An overarching approach to several disciplines:

• Genomics

Major research areas of Genomics: 2.1 Bacteriophage genomics 2.2 Cyanobacteria genomics 2.3 Human genomics 2.4 Metagenomics 2.5 Pharmacogenomics

• Other Genomics related subdisciplines:

• Computational genomics
• Epigenomics
• Whole genome sequencing
• Functional genomics
• Genomics of domestication
• Immunomics
• Metagenomics
• Nitrogenomics
• Personal genomics
• Proteomics
• Psychogenomics

• The Biomedical Computing Space

• Bioinformatics
• Computational biology
• Network Biology
• Synthetic biology
• List of omics topics in biology
• List of systems biology research groups
• Category:Systems biologists
• Systems microbiology
• Systems biomedicine
• Flux balance analysis
• Metabolic network modelling
• System biology

An illustration of the systems approach to biology

http://en.wikipedia.org/wiki/Systems_biology

 

The National Centers for Biomedical Computing (NCBCs) are part of the U.S. NIH plan to develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. Their mission is to create innovative software programs and other tools that will enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease.

Biomedical Information Science and Technology Initiative (BISTI): Recognizing the potential benefits to human health that can be realized from applying and advancing the field of biomedical computing, the Biomedical Information Science and Technology Initiative (BISTI) was launched at the NIH in April 2000. This initiative is aimed at making optimal use of computer science and technology to address problems in biology and medicine. The full text of the original BISTI Report (June 1999) is available.

Current Centers

• Center for Computational Biology
• National Center for Multi-Scale Study of Cellular Networks
• National Center for Biomedical Ontology
• Simbios: Physics-based Simulation of Biological Structures, Stanford University
• National Alliance for Medical Imaging Computing
• Informatics for Integrating Biology and the Bedside
• National Center for Integrative Biomedical Informatics

National Center for Simulation of Biological Structures (SimBioS) at Stanford University

National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University

National Alliance for Medical Image Computing (NA-MIC) at Brigham and Women’s Hospital, Boston, MA

Integrating Biology and the Bedside (I2B2) at Brigham and Women’s Hospital, Boston, MA

National Center for Biomedical Ontology (NCBO) at Stanford University

Integrate Data for Analysis, Anonymization, and Sharing (IDASH) at the University of California, San Diego

https://commonfund.nih.gov/bioinformatics/

A Biositemap is a way for a biomedical research institution of organisation to show how biological information is distributed throughout their Information Technology systems and networks. This information may be shared with other organisations and researchers.

The Biositemap enables web browsers, crawlers and robots to easily access and process the information to use in other systems, media and computational formats. Biositemaps protocols provide clues for the Biositemap web harvesters, allowing them to find resources and content across the whole interlink of the Biositemap system. This means that human or machine users can access any relevant information on any topic across all organisations throughout the Biositemap system and bring it to their own systems for assimilation or analysis.

http://en.wikipedia.org/wiki/Biositemaps

• Home
• NCBC Summary
• Calendar
• All Hands Meetings
• Biological Projects
• Biositemaps Projects
• Working Group Archive

• 
• 
• 
• 
• 
• 
• 
• 
• 

Search for NCBC resources in the new Resource Discovery System (RDS)

The NIH Common Fund

http://www.ncbcs.org/

For

Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

go to

http://pharmaceuticalintelligence.com/2012/09/18/genome-and-genetics-resources/

 

Biomedical Computation Review (BCR) is a quarterly, open-access magazine funded by the National Institutes of Health and published by Simbios, one of the National Centers for Biomedical Computing located at Stanford University. First published in 2005, BCR covers such topics as molecular dynamics, genomics, proteomics, physics-based simulation, systems biology, and other research involvingcomputational biology. BCR’s articles are targeted to those with a general science or biology background, in order to build a community among biomedical computational researchers who come from a variety of disciplines.

http://en.wikipedia.org/wiki/Biomedical_Computation_Review

 

REFERENCES on BIOINFORMATICS

1. ^ Biositemaps online editor
2. ^ ^a b Dinov ID, Rubin D, Lorensen W, et al. (2008). “iTools: A Framework for Classification, Categorization and Integration of Computational Biology Resources”. PLoS ONE 3 (5): e2265. doi:10.1371/journal.pone.0002265. PMC 2386255. PMID 18509477.
3. ^ M.L. Nelson, J.A. Smith, del Campo, H. Van de Sompel, X. Liu (2006). “Efficient, Automated Web Resource Harvesting”. WIDM’06.
4. ^ Brandman O, Cho J, Garcia-Molina H, Shivakumar N (2000). “Crawler-friendly Web Servers”. ACM SIGMETRICS Performance Evaluation Review 28 (2). doi:10.1145/362883.362894.
5. ^ Cannata N, Merelli E, Altman RB (December 2005). “Time to organize the bioinformatics resourceome”. PLoS Comput. Biol. 1 (7): e76.doi:10.1371/journal.pcbi.0010076. PMC 1323464. PMID 16738704.
6. ^ Chen YB, Chattopadhyay A, Bergen P, Gadd C, Tannery N (January 2007). “The Online Bioinformatics Resources Collection at the University of Pittsburgh Health Sciences Library System—a one-stop gateway to online bioinformatics databases and software tools”.Nucleic Acids Res. 35 (Database issue): D780–5. doi:10.1093/nar/gkl781. PMC 1669712. PMID 17108360.
7. • Kitano, Hiroaki (15 October 2001). Foundations of Systems Biology. MIT Press. pp. 320.ISBN 978-0-262-11266-6.
   • Werner, Eric (29 March 2007). “All systems go”. Nature 446 (7135): 493–494. Bibcode2007Natur.446..493W. doi:10.1038/446493a. provides a comparative review of three books:
     • Alon, Uri (7 July 2006). An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall. pp. 301. ISBN 978-1-58488-642-6.
     • Kaneko, Kunihiko (15 September 2006). Life: An Introduction to Complex Systems Biology. Springer-Verlag. pp. 371. ISBN 978-3-540-32666-3.
     • Palsson, Bernhard O (16 January 2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press. pp. 334. ISBN 978-0-521-85903-5.

 REFERENCES on GENOMICS

1. ^ National Human Genome Research Institute (2010-11-08).“FAQ About Genetic and Genomic Science”. Genome.gov. Retrieved 2011-12-03.
2. ^ EPA Interim Genomics Policy
3. ^ [1]
4. ^ Min Jou W, Haegeman G, Ysebaert M, Fiers W (1972). “Nucleotide sequence of the gene coding for the bacteriophage MS2 coat protein”. Nature 237 (5350): 82–88. Bibcode1972Natur.237…82J. doi:10.1038/237082a0.PMID 4555447.
5. ^ Fiers W, Contreras R, Duerinck F, Haegeman G, Iserentant D, Merregaert J, Min Jou W, Molemans F, Raeymaekers A, Van den Berghe A, Volckaert G, Ysebaert M (1976). “Complete nucleotide sequence of bacteriophage MS2 RNA: primary and secondary structure of the replicase gene”. Nature 260 (5551): 500–507.Bibcode 1976Natur.260..500F. doi:10.1038/260500a0.PMID 1264203.
6. ^ Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes CA, Hutchison CA, Slocombe PM, Smith M (1977). “Nucleotide sequence of bacteriophage phi X174 DNA”. Nature 265 (5596): 687–695. Bibcode 1977Natur.265..687S.doi:10.1038/265687a0. PMID 870828.
7. ^ Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, et al. (1995). “Whole-genome random sequencing and assembly of Haemophilus influenzae Rd”. Science 269 (5223): 496–512.Bibcode 1995Sci…269..496F. doi:10.1126/science.7542800.PMID 7542800.
8. ^ “Complete genomes: Viruses”. NCBI. 2011-11-17. Retrieved 2011-11-18.
9. ^ “Genome Project Statistics”. Entrez Genome Project. 2011-10-07. Retrieved 2011-11-18.
10. ^ Hugenholtz, Philip (2002). “Exploring prokaryotic diversity in the genomic era”. Genome Biology 3 (2): reviews0003.1-reviews0003.8. ISSN 1465-6906.
11. ^ BBC article Human gene number slashed from Wednesday, 20 October 2004
12. ^ CBSE News, Thursday, 16 October 2003
13. ^ National Human Genome Research Institute (2004-07-14).“Dog Genome Assembled: Canine Genome Now Available to Research Community Worldwide”. Genome.gov. Retrieved 2012-01-20.
14. ^ McGrath S and van Sinderen D, ed. (2007). Bacteriophage: Genetics and Molecular Biology (1st ed.). Caister Academic Press. ISBN 978-1-904455-14-1.
15. ^ Herrero A and Flores E, ed. (2008). The Cyanobacteria: Molecular Biology, Genomics and Evolution (1st ed.). Caister Academic Press. ISBN 978-1-904455-15-8.
16. ^ McElheny, Victor (2010). Drawing the map of life : inside the Human Genome Project. New York NY: Basic Books. ISBN 978-0-465-04333-0.
17. ^ Hugenholz, P; Goebel BM, Pace NR (1 September 1998).“Impact of Culture-Independent Studies on the Emerging Phylogenetic View of Bacterial Diversity”. J. Bacteriol 180 (18): 4765–74. PMC 107498. PMID 9733676.
18. ^ Eisen, JA (2007). “Environmental Shotgun Sequencing: Its Potential and Challenges for Studying the Hidden World of Microbes”. PLoS Biology 5 (3): e82.doi:10.1371/journal.pbio.0050082. PMC 1821061.PMID 17355177.
19. ^ Marco, D, ed. (2010). Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
20. ^ Marco, D, ed. (2011). Metagenomics: Current Innovations and Future Trends. Caister Academic Press. ISBN 978-1-904455-87-5.
21. ^ Wang L (2010). “Pharmacogenomics: a systems approach”.Wiley Interdiscip Rev Syst Biol Med 2 (1): 3–22.doi:10.1002/wsbm.42. PMID 20836007.
22. ^ Becquemont L (June 2009). “Pharmacogenomics of adverse drug reactions: practical applications and perspectives”.Pharmacogenomics 10 (6): 961–9. doi:10.2217/pgs.09.37.PMID 19530963.
23. ^ “Guidance for Industry Pharmacogenomic Data Submissions” (PDF). U.S. Food and Drug Administration. March 2005. Retrieved 2008-08-27.
24. ^ Squassina A, Manchia M, Manolopoulos VG, Artac M, Lappa-Manakou C, Karkabouna S, Mitropoulos K, Del Zompo M, Patrinos GP (August 2010). “Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice”.Pharmacogenomics 11 (8): 1149–67. doi:10.2217/pgs.10.97.PMID 20712531.

http://en.wikipedia.org/wiki/Genomics

 

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Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

Posted in Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Population Health Management, Genetics & Pharmaceutical, tagged Aviva Lev-Ari, Biochemistry and Molecular Biology, Bioinformatics, Biology, DNA, Illumina, Nucleic acid sequence, Online Services, Population genetics, RNA, Sequence alignment on September 18, 2012| 5 Comments »

Curator: Aviva Lev-Ari, PhD, RN

Population Genetics

HAPAA: a tool for ancestral haploblock reconstruction. Specifically, given the genotype  (for instance, as derived by an Illumina genotyping array) of an individual of admixed ancestry, find the source population for each segment of the individual’s genome.

Protein Interaction Networks

A tool for aligning multiple global protein interaction networks; Graemlin also supports search for homology between a query module of proteins and a database of interaction networks.

Machine Learning

CONTRA: Conditionally trained models for sequence analysis. SeeCONTRAlign, a protein sequence aligner with very high accuracy, especially in twilight alignments. See CONTRAfold, an RNA secondary structure prediction tool. Stay tuned for more…

RNA Structure Prediction

CONTRAfold: Prediction of RNA secondary structure with a Conditional Log-Linear model that relies on automatically trained parameters, rather than on a physics-based energy model of RNA folding.

Protein Alignment

CONTRAlign: A protein sequence aligner that users can optionally train on feature sets such as secondary structure and solvent accessibility; see the CONTRA project above.

A protein multiple sequence aligner that exhibits high accuracy on popular benchmarks.

A protein multiple aligner that automatically finds domain structures of sequences with shuffled and repeated domain architectures.

Motif Finding

MotifCut: a non-parametric graph-based motif finding algorithm. MotifScan: a non-parametric method for representing motifs and scanning DNA sequences for known motifs.

CompareProspector: motif finding with Gibbs sampling & alignment.

Genomic Alignment

Stanford ENCODE: Multiple Alignments of 1% of the Human genome.

Typhon: BLAST-like sequence search to a multiple alignments database.

LAGAN: tools for genomic alignment. These include the MLAGAN multiple alignment tool, and Shuffle-LAGAN for alignment with rearrangements.

Microarray Analysis

Application of Independent Component Analysis (ICA) to microarrays.

Researchers Hope New Database Becomes Universal Cancer Genomics Tool

1. Mike Martin

Swiss scientists hope that a new online database called “arrayMap” will bring cancer genomics to the desktop, laptop, and tablet computers of pathologists and researchers everywhere.

The database combines genomic information from three sources: large repositories such as the NCBI Gene Expression Omnibus (GEO) and Cancer Genome Atlas (CGA); journal literature; and submissions from individual investigators. It incorporates more than 42,000 genomic copy number arrays—normal and abnormal DNA comparisons—from 195 cancer types.

“arrayMap includes a wider range of human cancer copy number samples than any single repository,” said principal investigator Michael Baudis, M.D. Ease of access, visualization, and data manipulation, he added, are top priorities in its ongoing development.

A product of the University of Zurich Institute for Molecular Life Sciences, where Baudis researches bioinformatics and oncogenomics, arrayMap illustrates the importance of copy number abnormalities (CNA)—dysfunctional DNA gains or losses that visibly lengthen or shorten certain chromosomes—in the diagnosis, staging, and treatment of various malignancies.

“I have this particular tumor type—are there any CNAs in it that can tell me anything about prognosis or treatment?” said Michael Rossi, Ph.D., director of the Winship Cancer Institute cancer genomics program at the Emory University School of Medicine in Atlanta. “Data mining tools like arrayMap are incredibly useful to help answer such questions.”

 http://jnci.oxfordjournals.org/content/104/14/1045

arrayMap – genomic arrays for copy number profiling in human cancer

arrayMap is a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides an entry point for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. The current data reflects:

• 42875 genomic copy number arrays
• 634 experimental series
• 256 array platforms
• 197 ICD-O cancer entities
• 480 publications (Pubmed entries)

For the majority of the samples, probe level visualization as well as customized data representation facilitate gene level and genome wide data review. Results from multi-case selections can be connected to downstream data analysis and visualization tools, as we provide through our Progenetix project.

arrayMap is developed by the group “Theoretical Cytogenetics and Oncogenomics” at the Institute of Molecular Life Sciences of the University of Zurich.

ARRAYMAP NEWS

2012-08-29: arrayMap featured at the Journal of the National Cancer Institute

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2012-08-25: New Progenetix article published at PLoS ONE

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2012-07-25: Renal cell carcinoma paper published at BMC Cancer

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More News …

These tools were developed for our research projects. You are welcome to try them out, but there is only sparse documentation. If more support and/or custom analysis is needed, please contact Michael Baudis regarding a collaborative project.

http://www.arraymap.org/cgi-bin/amHome.cgi

MIT: A New Approach Uses Compression to Speed Up Genome Analysis

Public-Domain Computing Resources

• Structural Bioinformatics 
• Genomics 
• Systems Biology 
• Other

Structural Bioinformatics

The BetaWrap program detects the right-handed parallel beta-helix super-secondary structural motif in primary amino acid sequences by using beta-strand interactions learned from non-beta-helix structures.

Wrap-and-pack detects beta-trefoils in protein sequences by using both pairwise beta-strand interactions and 3-D energetic packing information

The BetaWrapPro program predicts right-handed beta-helices and beta-trefoils by using both sequence profiles and pairwise beta-strand interactions, and returns coordinates for the structure.

The MSARi program indentifies conserved RNA secondary structure in non-coding RNA genes and mRNAs by searching multiple sequence alignments of a large set of candidate catalogs for correlated arrangements of reverse-complementary regions

The Paircoil2 program predicts coiled-coil domains in protein sequences by using pairwise residue correlations obtained from a coiled-coil database. The original Paircoil program is still available for use.

The MultiCoil program predicts the location of coiled-coil regions in amino acid sequences and classifies the predictions as dimeric or trimeric. An updated version, Multicoil2, will soon be available.

The LearnCoil Histidase Kinase program uses an iterative learning algorithm to detect possible coiled-coil domains in histidase kinase receptors.

The LearnCoil-VMF program uses an iterative learning algorithm to detect coiled-coil-like regions in viral membrane-fusion proteins.

The Trilogy program discovers novel sequence-structure patterns in proteins by exhaustively searching through three-residue motifs using both sequence and structure information.

The ChainTweak program efficiently samples from the neighborhood of a given base configuration by iteratively modifying a conformation using a dihedral angle representation.

The TreePack program uses a tree-decomposition based algorithm to solve the side-chain packing problem more efficiently. This algorithm is more efficient than SCWRL 3.0 while maintaining the same level of accuracy.

PartiFold: Ensemble prediction of transmembrane protein structures. Using statistical mechanics principles, partiFold computes residue contact probabilities and sample super-secondary structures from sequence only.

tFolder: Prediction of beta sheet folding pathways. Predict a coarse grained representation of the folding pathway of beta sheet proteins in a couple of minutes.

RNAmutants: Algorithms for exploring the RNA mutational landscape.Predict the effect of mutations on structures and reciprocally the influence of structures on mutations. A tool for molecular evolution studies and RNA design.

AmyloidMutants is a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability.

Genomics

GLASS aligns large orthologous genomic regions using an iterative global alignment system. Rosetta identifies genes based on conservation of exonic features in sequences aligned by GLASS.

RNAiCut – Automated Detection of Significant Genes from Functional Genomic Screens.

MinoTar – Predict microRNA Targets in Coding Sequence.

Systems Biology

The Struct2Net program predicts protein-protein interactions (PPI) by integrating structure-based information with other functional annotations, e.g. GO, co-expression and co-localization etc. The structure-based protein interaction prediction is conducted using a protein threading server RAPTOR plus logistic regression.

IsoRank is an algorithm for global alignment of multiple protein-protein interaction (PPI) networks. The intuition is that a protein in one PPI network is a good match for a protein in another network if the former’s neighbors are good matches for the latter’s neighbors.

Other

t-sample is an online algorithm for time-series experiments that allows an experimenter to determine which biological samples should be hybridized to arrays to recover expression profiles within a given error bound.

http://people.csail.mit.edu/bab/computing_new.html#systems

Compressive genomics

• Po-Ru Loh,
• Michael Baym
• & Bonnie Berger

http://www.nature.com/nbt/journal/v30/n7/abs/nbt.2241.html

Nature Biotechnology 30, 627–630 (2012) doi:10.1038/nbt.2241

Published online 10 July 2012

STANFORD UNIVERSITY: Resources

BMIR is committed to the development of research tools as part of its goal to provide reusable, computational building blocks to facilitate the development of a vast array of systems. Some of these resources are described below.

Resources

The National Center for Biomedical Ontology (NCBO)

The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods that allow scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form.

visit site

Protégé

Protégé is a free, open-source platform that provides its community of more than 80,000 users with a suite of tools to construct domain models and knowledge-based applications with ontologies.

visit site

PharmGKB

PharmGKB curates information that establishes knowledge about the relationships among drugs, diseases and genes, including their variations and gene products. Our mission is to catalyze pharmacogenomics research.

visit site

Simbios

About Simbios

Simbios, the National NIH Center for Physics-based Simulation of Biological Structures is devoted to helping biomedical researchers understand biological form and function. It provides infrastructure, software, and training to assist users as they create novel drugs, synthetic tissues, medical devices, and surgical interventions.

Simbios scientists investigate structure-function studies on a wide scale of biology – from molecules to organisms, and are currently focusing on challenging biological problems in RNA folding, myosin dynamics, neuromuscular biomechanics and cardiovascular dynamics.

visit site

 http://bmir.stanford.edu/resources/

Stanford BioMedical Informatics Research (BMIR) – Publications by Project

There are 8 publications for the project “Genomic Nosology for Medicine (GNOMED)”.

BMIR-2009-1362

Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures

L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte

PNAS, 106, 11, 4148-4153. Published in 2009

BMIR-2008-1338

Using SNOMED-CT For Translational Genomics Data Integration

J. Dudley, D. P. Chen, A. J. Butte

Ronald Cornet, Kent Spackman (eds.): Representing and sharing knowledge using SNOMED. Proceedings of the 3rd International Conference on Knowledge Rep, Pheonix (AZ), USA, CEUR Workshop Proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-410/, 91-96. Published in 2008

BMIR-2008-1303

The Ultimate Model Organism

A. J. Butte

Science, 320, 5874, 325-327. Published in 2008

BMIR-2008-1293

Novel Integration of Hopsital Electronic Medical Records and Gene Expression Measurements to Identify Genetic Markers of Maturation

D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte

Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 243-254. Published in 2008

BMIR-2008-1292

Enabling Integrative Genomic Analysis of High-Impact Human Diseases through Text Mining

J. Dudley, A. J. Butte

Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 580-591. Published in 2008

BMIR-2007-1297

Methodologies for Extracting Functional Pharmacogenomic Experiments from International Repository

Y. Lin, A. P. Chiang, P. Yao, R. Chen, A. J. Butte, R. S. Lin

AMIA Annual Symposium, Chicago, IL, 463-467. Published in 2007

BMIR-2007-1296

Clinical Arrays of Laboratory Measures, or “Clinarrays”, Built from an Electronic Health Record Enable Disease Subtyping by Severity

D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte

AMIA Annual Symposium, Chicago, IL, 115-119. Published in 2007

BMIR-2006-1232

Finding Disease-Related Genomic Experiments Within an International Repository: First Steps in Translational Bioinformatics

A. J. Butte, R. Chen

Annual Symposium of the American Medical Informatics Association, Washington, D.C., 106-10. Published in 2006

http://bmir.stanford.edu/publications/project.php/genomic_nosology_for_medicine_gnomed

Featured Publications

BMIR-2011-1468

The National Center for Biomedical Ontology

M. A. Musen, N. F. Noy, C. G. Chute, M. A. Storey, B. Smith, N. H. Shah

. Published in 2011

BMIR-2009-1378

Prototyping a Biomedical Ontology Recommender Service

C. Jonquet, N. H. Shah, M. A. Musen

Bio-Ontologies: Knowledge in Biology, SIG, ISMB ECCB 2009, Stockholm, Sweden. Published in 2009

BMIR-2009-1376

Translational bioinformatics applications in genome medicine

A. J. Butte

Genome Medicine, 1, 6, 64. Published in 2009

BMIR-2009-1362

Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures

L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte

PNAS, 106, 11, 4148-4153. Published in 2009

BMIR-2009-1361

Technology for Building Intelligent Systems: From Psychology to Engineering

M. A. Musen

Modeling Complex Systems, Bill Shuart, Will Spaulding and Jeffrey Poland, U Nebraska P, Lincoln, Nebraska, Vol 52 of the Nebraska Symposium on Motivation, 145-184. Published in 2009

BMIR-2009-1358

Software-Engineering Challenges of Building and Deploying Reusable Problem Solvers

M. J. O’Connor, C. I. Nyulas, A. Okhmatovskaia, D. Buckeridge, S. W. Tu, M. A. Musen

Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24, 3. Published in 2009

BMIR-2009-1355

Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events

A. P. Chiang, A. J. Butte

Clinical Pharmacology and Therapeutics, 28 January 2009, Advance online publication, doi:10.1038/clpt.2008.274. Published in 2009

BMIR-2009-1318

Knowledge-Data Integration for Temporal Reasoning in a Clinical Trial System

M. J. O’Connor, R. D. Shankar, D. B. Parrish, A. K. Das

International Journal of Medical Informatics, 78, Suppl. 1, S77-S85. Published in 2009

BMIR-2008-1353

GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed

R. Chen, R. Mallelwar, A. Thosar, S. Venkatasubrahmanyam, A. J. Butte

BMC Bioinformatics, 9, 1, 548. (doi:10.1186/1471-2105-9-548). Published in 2008

BMIR-2008-1346

FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

R. Chen, A. A. Morgan, J. Dudley, A. M. Deshpande, L. Li, K. Kodama, A. P. Chiang, A. J. Butte

Genome Biology, 9, 12, R170 (doi:10.1186/gb-2008-9-12-r170). Published in 2008

BMIR-2008-1341

Translational Bioinformatics: Coming of Age

A. J. Butte

Journal of the American Medical Informatics Association, JAMIA, 15, 6, 709-14. Published in 2008

BMIR-2008-1329

An Ontology-Driven Framework for Deploying JADE Agent Systems

C. I. Nyulas, M. J. O’Connor, S. W. Tu, A. Okhmatovskaia, D. Buckeridge, M. A. Musen

IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Sydney, Australia, 2, 573-577. Published in 2008

BMIR-2008-1322

Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-Detection Algorithms

D. Buckeridge, A. Okhmatovskaia, S. W. Tu, C. I. Nyulas, M. J. O’Connor, M. A. Musen

Journal of the American Medical Informatics Association, 15, 6, 760-769. Published in 2008

BMIR-2008-1319

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease

K. S. Supekar, V. Menon, M. A. Musen, D. L. Rubin, M. Greicius

Public Library of Science-Computational Biology., PLoS Computational Biology, June 2008. Published in 2008

BMIR-2008-1315

Medical Imaging on the Semantic Web: Annotation and Image Markup

D. L. Rubin, P. Mongkolwat, V. Kleper, K. S. Supekar, D. S. Channin

AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Stanford. Published in 2008

BMIR-2008-1303

The Ultimate Model Organism

A. J. Butte

Science, 320, 5874, 325-327. Published in 2008

BMIR-2008-1298

BioPortal: A Web Portal to Biomedical Ontologies

D. L. Rubin, D. de Abreu Moreira, P. P. Kanjamala, M. A. Musen

AAAI Spring Symposium Series, Symbiotic Relationships between Semantic Web and Knowledge Engineering, Stanford University, (in press). Published in 2008

BMIR-2007-1295

AILUN: reannotating gene expression data automatically

R. Chen, L. Li, A. J. Butte

Nature Methods, 4, 11, 879. Published in 2007

BMIR-2007-1281

Evaluation and Integration of 49 Genome-wide Experiments and the Prediction of Previously Unknown Obesity-related Genes

S. B. English, A. J. Butte

Bioinformatics, Epub. Published in 2007

BMIR-2007-1261

Protege: A Tool for Managing and Using Terminology in Radiology Applications

D. L. Rubin, N. F. Noy, M. A. Musen

Journal of Digital Imaging, J Digit Imaging. Published in 2007

BMIR-2007-1244

Efficiently Querying Relational Databases using OWL and SWRL

M. J. O’Connor, R. D. Shankar, S. W. Tu, C. I. Nyulas, A. K. Das, M. A. Musen

The First International Conference on Web Reasoning and Rule Systems, Innsbruck, Austria, Springer, LNCS 4524, 361-363. Published in 2007

BMIR-2006-1090

Creation and implications of a phenome-genome network

A. J. Butte, I. S. Kohane

Nature Biotechnology, 24, 1, 55 – 62. Published in 2006

http://bmir.stanford.edu/publications/

NATIONAL CENTERS FOR BIOMEDICAL COMPUTING

National Center for Simulation of Biological Structures (SimBioS) at Stanford University

National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University

National Alliance for Medical Image Computing (NA-MIC) at Brigham and Women’s Hospital, Boston, MA

Integrating Biology and the Bedside (I2B2) at Brigham and Women’s Hospital, Boston, MA

National Center for Biomedical Ontology (NCBO) at Stanford University

Integrate Data for Analysis, Anonymization, and Sharing (IDASH) at the University of California, San Diego

https://commonfund.nih.gov/bioinformatics/

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