Almost a year ago, I wrote NextBio was just like using Pubmed but in a more dynamic way. Now the public version was launched so it’s free for everyone.
With NextBio, in just one click users can search through thousands of studies with billions of data points spanning across different experimental platforms, organisms and data types. It also searches across millions of publications to help find new articles pertaining to your search query. NextBio’s data and literature search engine makes massive amounts of disparate biological, clinical and chemical data from public and proprietary sources searchable, regardless of data type and origin, empowering researchers to quickly understand their own experimental results within the context of other research.
I gave it a try by searching for psoriasis and it looked impressive as it offered me the subtypes of psoriasis to search for (auto-complete list).
There were some genes that can play a role in the pathogenesis of the disease and some groups as well. It would be interesting to see how and why it shows only these genes (take a look at Gene2Mesh for a clearer example).
And one more thing:
We process the world’s publicly available high-throughput data through a semi-automated analysis pipeline which involves comprehensive quality control steps and the manual review of studies by our experienced scientific team to ensure the highest quality final output. NextBio correlates gene ontology, pathway and other functional information within the context of the world’s experimental data.
I still need time to get used to this system but looks quite useful and can really ease the job of a scientist. Give it a try!
Some weeks ago, I mentioned on Twitter how hard it is to find proper gene-disease associations in Pubmed, the database of health-science data. Some days later, P. F. Anderson sent me this link: Gene2MeSH.
According to her:
Gene2MeSH was described 2 me as mapping various terms used 2 describe the same gene, or genes assoc w/ medical term
I gave it a try and made a search for psoriasis:
It looks like an interesting and useful idea, while the MeSH heading column seems to be totally unnecessary, for example.
I will keep on using it and will let you know how it goes.
Attila Csordás came up today with the great idea to create a group for biotech Twitterers.
Do you think blogs are the best resources of information?
No more… Join us on Twitter!
Last year, I came up with a list containg 10 tips on how to search for genetic conditions. Now, after weeks of tagging and browsing, I’d like to improve that list with some new tips. But this time, I’d like to show you databases dedicated not only to genetic conditions, but gene-disease associations and human genome epidemiology as well.
A global collaboration of individuals & organizations committed to the assessment of the impact of human genome variation on population health & how genetic information can be used to improve health & prevent disease.
It provides access to a continuously updated knowledge base in human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests.
GAIN is taking the next step in the search to understand the genetic factors influencing risk for complex diseases. Through a series of whole genome association studies, using samples from existing case-control studies of patients with common diseases, GAIN will contribute to the identification of genetic pathways that make us more susceptible to these diseases and thus facilitate discovery of new molecular targets for prevention, diagnosis, and treatment.
It archives and distributes the results of studies that have investigated the interaction of genotype and phenotype. Such studies include genome-wide association studies, medical sequencing, molecular diagnostic assays, as well as association between genotype and non-clinical traits.
A website which assigns molecular functional effects of non-synonymous SNPs based on structure and sequence analysis. You should also check out the Disease-Gene mapping tool.
It will conduct genome wide association studies and analyses in several large NHLBI Cohort studies to identify genes underlying cardiovascular and lung disease and other disorders like osteoporosis and diabetes.
Let’s finish with a great idea, the human disease network published at PNAS.
A network of disorders and disease genes linked by known disorder–gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules.
Let me know please if you happen to know more useful databases and tools.
I’ve been using Firefox for years now and I would never use any other web browser. One of my reasons is the fantastic database of useful extensions. For example, here is bioFOX:
Code bioFOX aims at implementing various bioinformatics tools as an extension on the Firefox browser. Analysis of your favorite gene(s) usually require(s) retrieving it from a database like NCBI or Swiss-Prot and then performing one or more tasks including but not limited to:
- Translation of a nucleotide sequence
- Blast search (For eg. blastn, blastp etc.) of the desired nucleotide/protein sequence.
- Calculation of properties (like PI, charge, molecular weight, AT/GC content etc.) of a protein/nucleotide sequence.
- Conversion between formats (Genbank, Fasta, Swiss-Prot etc.)
- Prediction of sequence for sub-cellular localization (PREDOTAR, TargetP, pSORT etc)
You can download it here.
Another tool is Biobar which provides access to major biological data resources (Genomic, Proteomic, Functional, Literature, Taxonomic, Structural, Plant and Animal-specific databases).
(Hat tip: GooMedic.com)
The most famous article ever written about web 2.0 and medicine belongs to Dean Giustini (UBC biomedical branch librarian) who now made an other big step in this special field. Check out the new article (Web 3.0 and medicine) on British Medical Journal online.
A great honor for me is that Scienceroll has been used as a reference:
Social software enthusiasts may well find that the new web will be fertile ground for the creation of knowledge. Although already popular, wikis may well serve as platforms for the exploration of web 3.0. One innovative wiki—Wikiproteins—is already using semantic technologies. In contrast to other wikis, Wikiproteins imports data mined from several of the world’s leading biomedical databases, such as PubMed, UniProt, and the National Library of Medicine. Its integrated entries are a useful combination of genetic information and scientific literature. Notably, the confluence of databases in Wikiproteins yields more than two million factual associations for data mining and over five billion associated pairs.9
9.) Mesko B. Web 3.0 and medicine. ScienceRoll blog. 2007. http://scienceroll.com/2007/04/06/web-30-and-medicine/.
If you want to read more on the subject:
When I took a look at GoPubMed.org, my first reaction was “Wow”! Transinsight tells you what happens when you mix PubMed with semantic web.
Biomedical research happens in networks of researchers. Social networking web sites like FaceBook, LinkedIn and Xing use personal networks to establish contacts. On these sites, however, connections must be defined by the users themselves. For the first time, GoPubMed now completely and automatically extracts collaboration networks from millions of biomedical science publications.
For each concept in the selected semantic background knowledge, GoPubMed’s “Hot-Topic-View” shows the collaboration network between top authors in this field of research. Collaboration networks can now be experienced and visualized.
GoPubMed also now allows these networks to be searched for possible experts and collaboration partners, a feature which leads to tremendous time saving when searching for appropriate experts. This feature is especially important in a specialized scientific world where it is becoming more and more vital to set up temporary teams of highly specialized experts.
You really should give it a try!
I’ve already talked about my dream to have a software or service that could help physicians how to find out a diagnosis more easily based on symptoms or how to avoid misdiagnosis more efficiently. Now, I came across Isabel on Constructive Medicine where Rahul Shetty, MD said:
Now they can use a web tool named ISABEL,
What is Isabel, it is a Web-based medical technology that generates a list of possible diagnoses based on the patients’ symptoms. The cost for using this system for a 300 bed hospital is $50,000
According to their About Us page:
The clinicians who ‘Isabel’ their patients at an early stage are able to offer a higher quality of care and reduce clinical risk by ensuring that important possible diagnoses have not been missed.
Isabel uniquely adds intelligence to the electronic medical record (EMR) by processing extracted relevant clinical information automatically thereby providing the clinician with diagnosis support instantly with no additional data entry.
What’s next? Maybe DrFirst (via Medgadget):
As physicians are becoming more tech savvy, and a younger flock is graduating from medical school more comfortable with gizmos, many companies are releasing mobile productivity tools specifically designed for physicians. DrFirst™, for example, just released its attractive e-prescribing system for the iPhone, allowing doctors to very easily, and securely, send a prescription to a patient’s pharmacy.
Before you’d say, no, these will never substitute the work of physicians. These are just meant to assist medical professionals in order to be able to use the incredible amount of information of the web. That’s all…
Yesterday, David Rothman shared an interesting application with me. Query Gene is a Google-powered search engine with which you can combine text and gene sequence fragment web searches. What can you do if you have a sequence and you’d like to know whether this sequence has ever been associated with genetic diseases?
Query Gene is a web-based program that searches for information about genetic sequences on the web. It is distinctive because it is not limited to a single database, but instead captures genetic information on the entire Internet using Google. Query Gene works by taking a gene sequence in combination with other search terms, finds similar sequences using NCBI’s MegaBlast, retrieves the descriptions of those matching genes from NCBI’s Entrez Nucleotide database, and performs a series of Google searches using the combination of your original search terms and each gene description.
In their example, they inserted a nucleotide sequence and a search term (genetic disease associated with). This application identified the sequence as human hemoglobin beta and listed search results like sickle cell disease.
Isn’t it fantastic?
As I’ve been mentioned several times by New Media Medicine, I thought I should share this slideshow with you that I found on the blog of Chris Paton:
Here are some links to other similar presentations. Enjoy!
Let us know please if you know more!
Here are other ones mentioning Medicine 2.0 or Genetics and Web 2.0.
Update from Jan Martens:
Google: Friend or Enemy?