| | 1 | == Since 15 April 2010 == |
| | 2 | * Submitted Abstract to BOSC2010. (Part of ISMB2010) |
| | 3 | * Fixed problem with Ids. IDFParser OK, SDRFParser and ADFParser to go. |
| | 4 | * Do we allow (example: E-GEOD-1010.idf.txt ) |
| | 5 | {{{ |
| | 6 | Comment[SecondaryAccession] GSE1010 |
| | 7 | Comment[SecondaryAccession] GDS946 |
| | 8 | }}} |
| | 9 | (fixed) |
| | 10 | * (Internet in House! Life is nice again..) |
| | 11 | * In order to parse SDRF we need to parse ADF. Added ADF support |
| | 12 | == Interesting == |
| | 13 | * '''Perl powerful and comprehensive MAGE-TAB API for reading and writing MAGE-TAB files''' and really doing anything you need to do MAGE-TAB wise :) The module also has a few additional utilities like an experiment graph visualizer which is very useful and a database persistence class to store/retrieve your files in and out of an RDBMS. http://search.cpan.org/dist/Bio-MAGETAB/ (Added: 23 March 2010) |
| | 14 | * Version 1.0 Announced. http://search.cpan.org/~tfrayner/Bio-MAGETAB-1.00/ , http://frepan.64p.org/~tfrayner/Bio-MAGETAB-1.00/ , http://cpansearch.perl.org/src/TFRAYNER/Bio-MAGETAB-1.00/README |
| | 15 | |
| | 16 | * '''How to find all GWAS studies that a given gene has been implicated in?''' dbGaP seems to be a primary data repository, but it doesn't store results on the level of genes (or as far as I can tell, even genomic regions). AFAIK, dbGAP is one of the official resource for GWAS studies. A mere gene search may not fetch the exact details about the studies. Dataset from large scale GWAS studies are not available under public access due to sensitive genotype and phenotype data from patients. You have to write to individual investigators to get access to the data. Usually this data will be available only after the Embargo Release date. This is usually after 1year of the submission of the data. I think once you have access to the dataset, you will able to get the p-values of the SNPs genotyped in the whole study with the de-identified case/control and their phenotypes. These SNPs may need further mapping to get the details about the genes. http://www.ncbi.nlm.nih.gov/gap (Added: 24 March 2010) |
| | 17 | |
| | 18 | * There are more false claims made in the medical literature than anybody appreciates. Science and the Shortcomings of Statistics. http://www.sciencenews.org/view/feature/id/57091/title/Odds_Are,_Its_Wrong |
| | 19 | |
| | 20 | * W3C has an interest group called Semantic Web for Health Care and Life Sciences Interest Group (HCLSIC) http://esw.w3.org/HCLSIG , http://www.w3.org/blog/hcls . This Interest Group has (at least) one interesting sub groups: HCLSIG_BioRDF_Subgroup http://esw.w3.org/HCLSIG_BioRDF_Subgroup. Among the use cases that this interest studies are: |
| | 21 | * HCLSIG BioRDF Subgroup/MicroarrayProvenanceUseCase . http://esw.w3.org/HCLSIG_BioRDF_Subgroup/MicroarrayProvenanceUseCase. According to the background: |
| | 22 | * Only a limited number of patient samples can be studied in one microarray experiment. Hence, it is very important to merge together data from different studies in order to increase the sample size and improve the results from statistical analysis of the microarray data. Provenance information about each microarray experiment will facilitate the integration of data while retaining the ownership information. Details of the experiment conditions can be used to infer the dataset quality and to improve the reproducibility of experiments. |
| | 23 | * (Then it goes on referring the known microarray standards (MAGE-ML, ...) and that with some of them we can extract provenance information) |
| | 24 | * This is pretty much one of the things that we want to do with MOLGENIS in the long run. |
| | 25 | * And something I think that is closer to Despoina's interests. http://esw.w3.org/HCLSIG_BioRDF_Subgroup/QueryFederation2. This use case explores the federation of microarray data and related data using the Semantic Web. |
| | 26 | * In general I think we should pay close attention to what W3C plays with.. |
| | 27 | * (Added: 7 April 2010) |
| | 28 | * RDF-izing MAGE-TAB using MAGETAB2RDF (magetab2rdf.googlecode.com). http://prezi.com/pr7qkiw_5yk2/rdf-izing-mage-tab-magetab2magerdf/ (ADDED: 27 April 2010) |
| | 29 | |
| | 30 | * However, patient privacy can be threatened when personal information is linked to genetic information using codes that are available through public databases and electronic medical records, a team of Vanderbilt University researchers in Nashville conclude in a study published Monday in the Proceedings of the National Academy of Sciences (http://www.pnas.org/content/early/2010/04/05/0911686107) The researchers claim to have illustrated this problem as part of their research, where they identified 96 percent of a group of 2,762 patients with the help of the diagnosis codes in the patients' records. http://www.scientificamerican.com/blog/post.cfm?id=researchers-aim-to-prevent-identity-2010-04-12 (Added 13 April 2010) |
| | 31 | * Q&A: Promise and pitfalls of genome-wide association studies. http://www.biomedcentral.com/1741-7007/8/41 |
| | 32 | * Biomatics is a discipline that networks biology, philosophy, mathematics, informatics, and mechanics to describe all biological information structures in holistic ways. It is also a short name of Systems Bioengineering. Unlike bioinformatics which is the science of information retrieval from textual data, biomatics involves engineering, cellular and molecular biology, and hardware. The major difference between biomatics and other biological fields such as bioinformatics is that Biomatics is associated with design and construction of biological objects and machineries under mathematical principles. http://biomatics.org/index.php/Main_Page (Added: 26 April 2010) |
| | 33 | == Papers == |
| | 34 | * Hong Cui. Semantic annotation of morphological descriptions: an overall strategy. BMC Bioinformatics 2010, 11:278. http://www.biomedcentral.com/1471-2105/11/278 (Added 27 May 2010) |
| | 35 | * Daigle BJ Jr, Deng A, McLaughlin T, Cushman SW, Cam MC, et al. (2010) Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance. PLoS Comput Biol 6(3): e1000718. doi:10.1371/journal.pcbi.1000718 http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000718 (Added: 31 March 2010) Comment: Need for a tool for collective management of multiple microarray studies. |
| | 36 | * Fong, C. Ko, D. C. Wasnick, M. Radey, M. Miller, S. I. Brittnacher, M. GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis. Bioinformatics. 2010 Feb 15;26(4):560-4. Epub 2010 Jan 6. (Added: 23 March 2010) . Comment: XGAP - Molgenis similar work. |
| | 37 | * Lavrac N, Novak PK, Mozetic I, Podpecan V, Motaln H, Petek M, Gruden K. Semantic subgroup discovery: using ontologies in microarray data analysis. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:5613-6. (Added 7 April 2010) |
| | 38 | * Tim F. Rayner, Faisal Ibne Rezwan, Margus Lukk, Xiangqun Zheng Bradley, Anna Farne, Ele Holloway, James Malone, Eleanor Williams, and Helen Parkinson. MAGETabulator, a suite of tools to support the microarray data format MAGE-TAB. Bioinformatics. 2009 January 15; 25(2): 279–280. http://ukpmc.ac.uk/articlerender.cgi?artid=1718711 |
| | 39 | * Tim F. Rayner, Faisal Ibne Rezwan, Margus Lukk, Xiangqun Zheng Bradley, Anna Farne, Ele Holloway, James Malone, Eleanor Williams, and Helen Parkinson. MAGETabulator, a suite of tools to support the microarray data format MAGE-TAB. Bioinformatics. 2009 January 15; 25(2): 279–280. http://ukpmc.ac.uk/articlerender.cgi?artid=1718711 |
