I’ve recently come across an exciting project, OpenPCR that aims to build a PCR machine by using simple elements that you can buy anywhere. You can pre-order the machine for 512$.
We want to produce an open design for a PCR machine. Our goal is to start this project up quickly and get a working prototype made for Maker Faire. After that, add on applications such as SNPs or PCR kits or synthetic biology.
A DIY Xerox machine for DNA: A fast, computer controlled PCR machine that uses normal PCR tubes and may be built mostly with off the shelf components + free schematics. It does thermal cycling as well as boil, cool, and freeze (4C) samples.
This talk will introduce current best practice in biological engineering, including an overview of how to order synthetic DNA and how to use and contribute standard biological parts to an open source collection of genetic functions. The talk will also discuss issues of human practice, including biological safety, biological security, ownership, sharing, and innovation in biotechnology, community organization, and perception across many different publics.
I’ve been planning to write about these wikis that can be very useful tools in the hands of researchers for a long time. The first one is WikiPathway which is an open platform dedicated to the curation of biological pathways by and for the scientific community.
The second is the WikiGenes which aims to build the database of evolutionary knowledge on Nature.com.
The reason why I mention the third one now is there is a new publication focusing on the pros and cons of using a wiki in genetics research. In Wikipedia, Andrew and his friends created the Gene Portal a year ago and later analyzed the usability and the results.
Annotating the function of all human genes is a critical, yet formidable, challenge. Current gene annotation efforts focus on centralized curation resources, but it is increasingly clear that this approach does not scale with the rapid growth of the biomedical literature. The Gene Wiki utilizes an alternative and complementary model based on the principle of community intelligence. Directly integrated within the online encyclopedia, Wikipedia, the goal of this effort is to build a gene-specific review article for every gene in the human genome, where each article is collaboratively written, continuously updated and community reviewed. Previously, we described the creation of Gene Wiki ‘stubs’ for approximately 9000 human genes. Here, we describe ongoing systematic improvements to these articles to increase their utility. Moreover, we retrospectively examine the community usage and improvement of the Gene Wiki, providing evidence of a critical mass of users and editors. Gene Wiki articles are freely accessible within the Wikipedia web site, and additional links and information are available at Wikipedia.
I use WolframAlpha because sometimes (if I know exactly what I want to find) it saves me plenty of time and clicks. If I want to calculate BMI, Google lists me several calculators. WolframAlpha calculates it itself. If I want to find information very fast about a clinical marker, Google gives me resources, WA gives me the best answer in one click. I also use it for ICD classification, as it’s more easily accessible than Wikipedia; for epidemiological data and other calculations.
To sum it up, I think WolframAlpha is for those who perfectly know what they want to find and want to save time and clicks. For other search queries, Google is still the best.
The genome-wide association study (GWAS) publications listed here include only those attempting to assay at least 100,000 single nucleotide polymorphisms (SNPs) in the initial stage. Publications are organized from most to least recent date of publication, indexing from online publication if available. Studies focusing only on candidate genes are excluded from this catalog. Studies are identified through weekly PubMed literature searches, daily NIH-distributed compilations of news and media reports, and occasional comparisons with an existing database of GWAS literature (HuGE Navigator).
In genetic epidemiology, a genome-wide association study (GWA study, or GWAS) – also known as whole genome association study (WGA study) – is an examination of genetic variation across a given genome, designed to identify genetic associations with observable traits. In human studies, this might include traits such as blood pressure or weight, or why some people get a disease or condition.
These studies normally require two groups of participants: people with the disease (cases) and similar people without (controls). After genotyping each participant, the set of markers, such as SNPs, are scanned into computers. Then bioinformatics is applied to survey participants’ genomes for markers of genetic variation.
If genetic variations are more frequent in people with the disease, the variations are said to be “associated” with the disease. The associated genetic variations are then considered as pointers to the region of the human genome where the disease-causing problem is likely to reside.
I’ve recently come across some great genomic tools that may be useful for your research.
Circos: This is a tool from Martin Krzywinski who described this as: Circos is a Perl application for the generation of publication-quality, circularly composited renditions of genomic data and related annotations. Circos is particularly suited for visualizing alignments, conservation and intra and inter-chromosomal relationships.
Genome coverage simulator: This script simulates the manner in which a genome would be covered by the process of mapping or sequencing. In both cases, elements much smaller than the genome size are used to successively cover parts of the genome until (ideally) every area of the genome has been sampled. (Again from Martin Krzywinski)
is a set of tools aiming at connecting biomedical researchers together helping them to find informations about their previous works, collaborators and affiliations. The remarkable fact is that no information is required! Our intelligent software knows how to combine information already available on the web, specially on the Pubmed database, to provide meaningful information.
Now they have:
Single Search: Based on co-authoring of Pubmed publications, find someone’s collaborators, publications, affiliations.
Fight: Who’s got more publications? Whos got more collaborators? When did this fight reach its climax?
Now, you know we have Pubmedfight in our hands to be able to resolve disputes between two scientists. But what about InterMEDI, a collaborative intelligence for biomed professionals. Excerpts from the blog Personomics:
With the “Search for collaborators” applications, you can find who worked with who. With the “Fight” application, you can compare two researchers on the basis of their publication number but also the number of collaborators they’ve had until now.
It is based on the Pubmed API. Based on a writer’s name “A”, you can retrieve all his articles. Based on those articles, you can retrieve all the co-authors. You then have the collaborator’s list of “A”. This tool is in beta version and will/should be developed in the next few months.
And if you would like to use a search engine that is quite different from Pubmed, check Scienceroll Search out and let us know your opinion.
WikiProfessional was officially launched some weeks ago. So I think it’s time to say a few words about it. WikiProfessional is a new kind of a database. It searches in several sources and helps us how to get the most valuable information.
Redundancy of the same facts and opinions within a myriad of web-pages has artificially inflated the size of the Internet. To get a million search results on a query without the ability to separate redundancy of the same information from the incremental knowledge expansions on that query concept is highly inefficient. Within the Concept Web, information is converted to streamlined knowledge where redundancy and newness of idea expansion are properly represented.
The sources it uses (yes, it searches in Wikipedia)
I gave it a try with cystic fibrosis. Here is what I got:
A concept tree with the articles that should be mentioned
A proper definition, functional information, etc.
We still need time to get used to this system but I’ m pretty sure it can be better and more user-friendly than Pubmed itself.