#6. Do you know how your favorite gene’s expression compares in cancer cell line models compared to the normal cell type counterpart for a given cancer?

Example: Microseminoprotein-beta (MSMB), a known tumor suppressor in prostate cancer is over 500-fold less in each of the six prostate cancer cell-lines displayed compared to its expression in either the prostate gland as a whole or in luminal cells of the prostate.

#7. If your favorite gene is suspected to play a role in a cancer type, how does its expression compare across all popular cell line models for that cancer? And which might be the best model in which to study it based on its expression level?

Example: The PIM1 proto-oncogene shows quite variable expression in leukemia cell lines, being about 30-fold higher in the KU812 chronic myelogenous leukemia cell line, for instance, than in the CCRF-CEM T-cell acute lymphoblastic leukemia cell line. Thus, the KU812 cell line may be a better candidate for studying potential PIM1 oncogenic promoting roles and methods to decrease over-active PIM1 expression.

#8. Is there a tissue in which your gene’s expression may be unappreciated and relatively unexplored? Compare the tissue tag cloud in NextBio’s literature search to Body Atlas results and uncover a potentially new avenue of research.

Example: Doublecortin (DCX) is a gene whose expression in immature neurons is well-established but whose potential role in lymphocytes may not be so well understood yet.

#9. If you want to study your favorite pathway in a given cancer, which of the cancer cell line models for that cancer are most strongly correlated to the pathway?

Example: Which glioblastoma cell line is most positively correlated with the EGF signaling pathway? Answer: The SF-268 line. Since there can be multiple paths to achieving the cancer phenotype, glioblastoma cell lines less correlated to the EGF signaling pathway may rely on other signaling pathways to achieve it.

#10. Which normal tissue does a signature generated from your dataset of interest most closely match?

Example: A ChIP-seq-derived signature corresponding to genes which have occupied c-Myc sites in ES cells is most positively correlated to hematopoietic stem cell of bone marrow where it is known to play an essential role [Baena E et al (2007) Exp Hematol 35:1333].

Did we miss some other interesting uses of the Body Atlas? Please send us feedback@nextbio.com and share your insights with us.

Anoop Grewal

Director of Customer Support

#1. How variable is my favorite gene’s expression? Is it tissue-specific or ubiquitously expressed?

Example: GAPDH is a ubiquitously expressed gene showing a gradual decline across a ranking of all tissues. Microseminoprotein-beta (MSMB), on the other hand, is tissue-specific; it is expressed in about five different tissues, including those of lung and prostate gland, and it appears off in most other tissues.

#2. Can a nice graphic of my favorite gene’s expression be depicted for my presentation? Yes, it can.

Example: The figure generated below shows MSMB expression in six selected tissues from the Normal Tissues panel. To generate a similar figure for your favorite gene, use the checkboxes in the right column of results to add a tissue to a graphic, and then download the .png version by clicking the ‘Export Graph’ button.

#3. If I know my favorite gene is expressed in another model organism in a given tissue, say, mouse hippocampus, can I find out if it’s expressed in the corresponding human tissue? Yes.

Example: The PIM1 serine-threonine kinase gene is reported to be expressed in mouse hippocampus [Eichmann et al (2000)Oncogene 19:1215], but it is not expressed above baseline in the human hippocampus. Note that the Wikipedia article on PIM1 refers to this gene as being expressed in hippocampus (paragraph 2) without reference to species. So the Body Atlas can be a quick means to clarify ambiguous statements such as this. BCL2L2 is another gene that’s reported to be expressed in the mouse hippocampus [Murphy et al(2007) Am.J.Pathol. 171:1258] and its expression in human hippocampus is verified by using the Body Atlas.

#4. If you know your favorite gene is expressed in a tissue, say, blood, do you know which specific cell type(s) within it that it’s actually expressed in?

Example: PIM1 is expressed in whole blood, but is found notably highest in basophils.

#5. Is your favorite gene a low-, medium- or high-abundance gene?

Example: Interleukin 2 (IL2) is a low-abundance gene, while the PIM1 proto-oncogene is a moderate-abundance gene and myoglobin is a high abundance gene (NOTE: the differences in expression scale).

Did you find these helpful? Stay tuned and we’ll offer five more questions you can answer using the Body Atlas in next week’s blog.

Anoop Grewal

Director of Customer Support

Effective communication of data is one of the most important and also most overlooked aspects of any research career. In an age of digital technology and free and instant access to databases containing a plethora of information, it is vital that one be able to easily interpret and assemble that data. No longer is it enough to look at data from a single species and/or experiment. One must broaden the scope to look at effects seen in a broad array of organisms and/or experiments carried out under a range of conditions. Additionally, many research outcomes are no longer single gene-centric, but must take entire pathways and systems into account.

As a graduate student nearing completion of a Ph.D., it has become increasingly important to be able to integrate data from numerous studies, both in a variety of conditions for my organism of choice and for single genes/proteins of interest in a range of organisms. The tools available through NextBio have simplified some parts of the integration process. Additionally, NextBio tools facilitate broadening the implications of results by providing correlations and additional studies of interest, making it easy to see how specific results in one organism translate into an adverse effect in another organism.

My research is broadly focused on chromosome segregation, using the opportunistic pathogen Candida albicans as a model. I am interested in both functional aspects of chromosome segregation (the establishment and maintenance of centromere function for proper chromosome segregation) and the end result of chromosome mis-segregation (aneuploidy); both of these topics have huge implications in all eukaryotes. The tools available through NextBio have enabled me to reduce the number of searches I need to perform to find relevant information, and this information is often beyond the scope that I expect.

The NextBio literature search engine is extremely powerful in its searches. Not only does it search the keyword I am interested in (e.g. Cse4), but also searches synonyms and related terms, such that the results include the term I searched, homologs in other species, and processes/functions that are directly related to or influenced by my search term. In addition, the search provides direct access to data correlation studies from high-throughput experiments and single studies alike, where I can view correlations with disease states, compounds, and gene perturbations at a single glance. This alone makes follow-up searches ultimately unnecessary because so many different types of data are available from a single resource. If I do need to make further inquiries, the associated researchers list provides a starting point for additional searches, especially when the search is a recent interest and I am not familiar with the field. Importantly, I am using the associated researchers feature as I begin my search for a laboratory to conduct post-doctoral research. I can search by research interest, get a list of key researchers, and look at the literature that these researchers have published without navigating other search engines.

The NextBio recommendations are a useful place to begin any search. Here, I gain information about the latest news and clinical trials that are relevant to my research. The information about clinical trials is especially pertinent to my research because Candida albicans is an opportunistic pathogen that is increasingly resistant to current methods of treatment in the clinic. New drug targets are always a hot topic in C. albicans research and being connected with information about current clinical trials lets me see the types of processes that are being targeted by potential therapies.

All of the NextBio tools that I have encountered so far have been extremely useful and integrative. Most recently, I have discovered the NextBio Highlight tool, which is one of the most unique and connected add-ons that I have ever experienced. From any website, I can get a list of literature, clinical trials, news, genomic data, and general information about any biological term. The NextBio Highlight feature will help encourage my curiosity about all life science topics and will improve my general knowledge without leaving a website to conduct a secondary search. All of this information is available from a single click of the NextBio Highlight bookmark. NextBio is becoming a tool that I use on a daily basis as I become familiar with the utilities it has to offer.

Congratulations to all the graduate and medical students who entered the NextBio Spring 2010 Travel Grant! It was a great validation and demonstration of  the utility of NextBio to read about the many ways NextBio was being used by real scientists- and doctors-in-training to further their own research. We look forward to more great essays during the next round of travel grants this fall! – Helen Wang, Travel Grant Program Manager

One of the main interests of my PhD work has been a subset of the Argonaute family of proteins called the Piwi proteins.  Piwi proteins have been shown to play a key role in male germ cell maintenance and to function with a novel class of small non-coding RNAs called piRNAs (Piwi-interacting RNAs).  However, the mechanisms by which Piwi proteins control transposable elements and DNA methylation have not been completely elucidated.

When I was introduced to the capabilities of NextBio, there were several key features that I found particularly useful.  I first performed a simple search for the different members of the Piwi protein subfamily.  I was particularly struck by the convenient organization of the search results, allowing me to see the instances in which the different Piwi genes have been analyzed in various tissues, diseases, as well as drug studies.  This has given me insight into the various systems that are being used to study Piwi proteins.  Furthermore, delving deeper into these results allowed me to identify the less obvious studies that did not truly focus on Piwi proteins, yet contained valuable information on their regulation nonetheless.

Secondly, I was particularly impressed with the quantity and detail of data available, and the ability to apply various filters in order to customize the results at which I was looking.  This allowed me to see new and unexpected associations and connections.  Piwi proteins were first identified in Drosophila, however they contain many orthologs in mammals, and the nomenclature is quite confusing across different species.  The fact that I am able to sort my search results by organism is yet another useful feature.  This has helped me focus on mouse and human Piwi proteins separately, where it is believed their functions may be quite distinct compared to their Drosophila counterparts.  NextBio also allowed me to see new links to other proteins that may be involved in the Piwi pathway in model organisms such as C. elegans.  Accessing these data and conveniently highlighting potentially new interactions would have been nearly impossible using more traditional search tools.

Lastly, the literature search on Piwi proteins, which highlights correlated terms, has given me an opportunity to look into new areas of research, many of which I did not realize nor think that Piwi proteins may be relevant to.  Taken all together, the above features of NextBio have allowed me to see new correlations between the expression patterns of several Piwi protein members and a variety of different cancers.  The fact that NextBio search results allow you to see the quantitated fold changes in the expression levels of Piwi proteins has allowed me to prioritize and formulate new ideas on how I can expand my project.  I did not originally envision exploring the role of these proteins in cancer until I found these results using the tools that NextBio provides.

I am particularly interested in expanding my use of NextBio, specifically taking the opportunity to use the data set comparison function that is available.  I am now planning to do extensive expression analyses after altering Piwi protein expression in various cancer cell lines.  I plan to use NextBio to compare these results with the vast amount of publicly available data.  This will hopefully uncover potential pathways in which Piwi proteins function, further expanding both the breadth and depth of my current research.

Tune in next week, for the unveiling of the essay from our first place winner!  – Helen Wang, Travel Grant Program Manager

NextBio: A versatile one-stop-shop for researchers (and a data junkie’s paradise)!

I am currently writing my doctoral dissertation on a gene involved in retinal degeneration at Baylor College of Medicine, and I have had internship experience. Both of these responsibilities involve assimilation and organization of a large amount of information to create mental snapshots that can be recalled and applied to specific contexts that I am interested in looking at. For my dissertation, I need to interpret primary research data in the context of existing literature or “knowledge base”, whereas the internship required integration of technical and clinical data for enumerating commercial utility.

While writing my dissertation, particularly for the introductory chapters, I discovered a salient utility of NextBio: the ability to cross-index references. I have benefited immensely from being able to create mental maps of research results from published papers, identify the thought-leaders in the field who produced the most referenced papers, parse the results of any of my searches with respect to organisms and tissues in which the dataset was observed, find diseases in which the gene network has been implicated, and see chemical moieties that affect components of the pathway involved. A good case-in-point of the utility of NextBio’s information engine is my decision to study the function of Kinesin-2 in the process of cellularization (a process in embryos during development of fruit-flies) as a side project of my major objective to characterize its function in photoreceptor cells. The earliest hint came while using “Data Correlations” for a query filtered by organism and expression changes that led me to an RNA expression profile, which I would have completely missed had I been searching on PubMed alone.

Another very powerful functionality of NextBio that I use very frequently is its Clinical trials information. During my internship, one of the first steps to getting started was to do a deep dive search of existing technologies and drugs in development. NextBio provides me with a “one-stop-shop” for finding clinical trials currently underway, and compounds under trial, each correlated with molecular and genomic data from research studies that led to the development phase of the drug candidate. This rapid access to a snapshot of the current state of drug development through NextBio is a great start to doing diligence on particular diseases.

Any search engine that enables such modular and “on-demand” parsing of information is a very powerful tool for any researcher. NextBio has not only provided me with the right information but has also opened new vistas of thought from the mental connections I made while browsing through results. Thus NextBio has become an integral part of my information search and visualization process.

A NextBio feature that I have not utilized to the fullest extent is the Web 2.0 type data sharing designed for collaborative projects. This interactive aspect of NextBio can be extremely powerful, especially for collaborative brainstorming and reaching out to other NextBio users to get feedback on their areas of expertise.

Stayed tuned for the appearance of the essay from our second place winner on our blog next week.  – Helen Wang, Travel Grant Program Manager

The aerobics, or tagging of datasets with biomedical terms, goes a long way in helping us achieve our mission of rendering the huge volume of public domain data to formats that better serve the needs of all researchers. Typically experimental results are tagged with multiple biological concepts. For example, a leukemia study in which B-cell acute lymphoblastic leukemia is compared to acute myeloid leukemia using human blood samples would be tagged with the source tissue, peripheral blood mononuclear cells,  as well as with the two diseases. And the terms used for tagging aren’t just any biological terms that come to mind. Rather they are derived from accredited biomedical vocabularies.

The benefits are at least two-fold: first, using ontologies allows NextBio to yield accurate, comprehensive search results and second, more sophisticated meta-analyses are now possible. For example, we can differentiate the instances where a gene is expressed in normal tissues versus where it is dysregulated in specific diseases. We can also rank diseases, for instance, according to the significance of a gene’s association among all diseases (see screenshot).

In the case of disease-based tagging, SNOMED-CT® – The Systematized Nomenclature of Medicine – Clinical Terms, which hierarchically organizes over a million terms, has proved an excellent source for purposes such as this.
Using these ontologies, we’ve tagged over 30 000 study results to date. That’s thousands of hours worth of tagging – and while SNOMED-CT® has been a pretty trusty and comprehensive resource for diseases, occasionally we come across terms that make us look twice. Here are some observations that have given us pause and entertained us while we diligently tagged away…
We were surprised to find that injury while boarding or alighting from train was a bona fide disease. Note that there are at least two variations of this: injury while boarding or alighting from moving train and injury while boarding or alighting from stationary train.
Those of us on the NextBio Curation team that are a little heavier were relieved to find that SNOMED® classifies exercise as a disease. That was until we discovered that lack of exercise is a disease too. …looks like there’s no winning this one.
Another one of our team members (hint: her job title is ‘Senior Curation Scientist’) personally cringes every time she has to tag with the disease aging. Figuring that this might work along the same line as exercise, she sought consolation by searching for the disease tags ‘ignorance of youth’ or just plain ‘adolescence’;  alas, SNOMED® failed to comfort her.

What are some of the strangest diseases we’ve come across while data mining? Well, there are many contenders but a lot of the nominations fall into the category of culture-specific syndromes. Here’s a sampling:
• Koro: According to DSM-IV-TR, Koro is “an episode of sudden and intense anxiety that the penis (or, in females, the vulva and nipples) will recede into the body and possibly cause death” (1). This disease has been documented among multiple cultures in Southeast Asia.
• Amok: If you’ve heard the phrase, “to run amok”, it has its origin with the Malay disease of the same name that refers to a syndrome characterized by the sudden appearance of violent behavior in a male who hasn’t shown such a previous inclination. The male typically attempts to kill or injure anyone he encounters (1).
• Mal de ojo or ‘evil eye’ is a sickness caused –usually inadvertently – by a gaze from a more powerful person  looking at a weaker person, often a child, in the eye(2). Symptoms may include crying, colic, diarrhea, or sleeplessness. While the term mal de ojo is specific to the Hispanic culture, ‘evil eye’ appears to be believed by many cultures including those in the Mediterranean, Middle East, Africa and Asia.
We’ve also found SNOMED-CT® is a good place to hunt for self-diagnoses. Some of us are convinced we suffer from:
• Lousy hair –Oh, we thought we were having one of those days. But it turns out lousy hair refers to lice. Okay, that’s not the kind of hair day we were talking about. Really.
• Keratoconjunctivitis sicca – or dry eyes, but then again, the causes don’t have anything to do with the excessive reading that is typical of curation work.
• Achrocordon – After a long day of tagging, we figure there must be some disease corresponding to ‘exhaustion from excessive tagging’. Searching for ‘tag’ led us to this disease, but this refers to skin tags, benign skin growths that look like a small piece of soft hanging skin. Nope, that’s not at all what we’ve got!

And you would think that if SNOMED-CT® contains a million terms they would have included the following, but apparently these diseases are yet to be included:
• Paris Syndrome. Hey, it’s in Wikipedia, so it must be a disease. Paris Syndrome is the sensation that tourists – disproportionately Japanese tourists – experience when the reality of Paris does not match with their initial perception.
• Lima Syndrome. A phenomenon in which an abductor begins to sympathize with his or her hostages; it is the reverse of the better known Stockholm syndrome. We’re starting to detect a SNOMED® bias against diseases with city names.
• Luposlipophobia – A disease first characterized by one Gary Larson, it is the innate fear of running around a kitchen table, being pursued by wolves, while wearing fresh socks on a newly waxed floor. Though well documented on the web, somehow, this one hasn’t made it into SNOMED-CT® either!
• Pneumonoultramicroscopicsilicovolcanoconiosis,  symptomatically the same as silicosis, it is a lung disease contracted from the inhalation of very fine silica particles, specifically from a volcano. Sufferers are advised to do a web search of the term silicosis, not just because the former term is alleged to be a factitious disease by the Oxford English Dictionary, but also because pneumoultramicro…watchamacallit is often accompanied by carpal tunnel syndrome from all the extra typing that occurs when searching the internet for a cure. Okay, we made up that last bit.
Well, even if we have NextBio to thank for giving us an acute case of hyperchondria, every member on the team believes in our mission. We hope our efforts to connect researchers with relevant data in the public and private domains will lead to discoveries that ultimately translate into a genuine difference in the lives of those that suffer.

Anoop Grewal has been managing various curation projects at NextBio since 2006. She’s also a frequent contributor to NextBio online support resources such as web conferences and white papers.

References
1. American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text revision. Washington, DC: American Psychiatric Association.
2. Evil eye. (2010, March 19). In Wikipedia, The Free Encyclopedia. Retrieved 19:56, March 22, 2010, from http://en.wikipedia.org/w/index.php?title=Evil_eye&oldid=350714909

If you ask scientists “Why do science?”many of the answers will be along the lines of “In order to advance human knowledge.”  But if the goal of science is to contribute to the sum of human knowledge, why don’t scientists place more emphasis on sharing data? Wouldn’t the most efficient path to advancing knowledge be to pool our information and work together?  Scientist may have idealistic intentions and magnanimous motivations, but the fact remains, they do not share well.

So why don’t scientist share data well? Nature recently addressed this question in a special issue dedicated to data sharing that  is available free of charge online.

Data sharing is an incredibly important issue and I am very happy to see it being addressed by such a respected and widely-read publication.  With modern, high-throughput techniques bringing us into an age of data-driven science it may be more important now than ever before.  With that said, sharing data has always been central to science, it takes many minds and many perspectives to extract the full value of knowledge from data.  As Cambridge economist Rufus Pollock says : “The best thing to do with your data will be thought of by someone else.” (Voices from the future of science: Rufus Pollock of the Open Knowledge Foundation)

Nature’s special data sharing issue contains:

• An editorial  titled Data’s shameful neglect
• A feature by Bryn Nelson called Data sharing: Empty archives
• Two collaboratively written opinion pieces
  • Prepublication data sharing
  • Post-publication sharing of data and tools

Any one of them provides a thought-provoking read. Taken together they make a thorough statement on where we are now and where we need to get to in order to take full advantage of the massive amount of experimental data being generated.

Guest Blogger Hope Leman: Hope Leman is a Research Information Technologist at the Samaritan Health Services Center for Health Research and Quality, co-founder of Next Generation Science, a staff writer at AltSearchEngines and the Web administrator of ScanGrants. You can find her on NextBio, FriendFeed and Twitter.

Review of Internet Cool Tools for Physicians by Melissa L. Rethlefsen, David L. Rothman and Daniel S. Mojon

Okay, this is going to one of those book reviews full of praise and every conceivable adjective connoting the concept of “must-read.” And don’t be put of by the cutesy use of “cool” in the title—this is a book of substance and not just for physicians, either. Anyone interested in the provision of health information on the Internet should buy a copy of this book and certainly all public libraries should have it on their shelves. Even experienced medical librarians should at least page through it. I can think offhand of quite a number of professionals and students who would benefit from reading it: nurse researchers, pharmacists, physical therapists, dentists, medical students, nursing students, anyone who does any sort of health sciences research. And given the rise of increasingly sophisticated laypeople as typified by the term e-patient there is a need for well-written, authoritative books such as this one that discuss how to find health information beyond Google, MedlinePlus and Wikipedia.

Before I launch into my paean for nearly every aspect of this book, I do have a few grumbles to get out of the way.

First of all, given the time lag these days between when a book is written and when it is published this book is already dated in that neither Twitter nor FriendFeed nor Facebook appear to be mentioned. That is a major flaw in this otherwise superb book. I say “appear” because the book lacks an index, so I could not simply look for the words “Twitter,” “FaceBook,” and “FriendFeed” in the index. The lack of an index is really dismaying and unfortunate in a book whose subject matter is the acquisition of information. Maybe we can blame the lack of an index on the publisher and not the authors. If so, shame on you Springer. We expect better of such a prestigious publisher. Cutting back on book basics such as indexes is penny wise and pound foolish and a no no for publishers who are reputation conscious.

Also, the incredibly rich research tool of ScienceDirect is given all of one paragraph. This is another case in which Internet Cool Tools for Physicians has been overtaken by the march of time. The host of this blog, NextBio, has rendered ScienceDirect immensely more useful and searchable to users. Here is an edifying screencast (that is itself a model of that genre and one that search engine start-ups hoping to build up usership would do well to emulate):ScienceDirect and NextBio

Click here to see what Elsevier itself says.

As someone who revels in the richness of Elsevier’s riches but who has been frustrated over the years by the clunkiness of ScienceDirect I can only say, “Go, NextBio, go!”

These are the kinds of things that can be addressed in the next edition of Internet Cool Tools for Physicians. I am hoping that there will be one, as this is an outstanding book.

Now that I have gotten the grumbles out of the way, I will proceed to the gushing praise part of this review.

One of the wonderful things about this book is that it is a book. Why is that important? Because so much of what physicians and other medical professionals and medical librarians need to know about medical literature searching and current awareness is known by one group by not necessarily by another. As a non-physician, I can’t mosey around the social network for physicians Sermo.

And I may have to learn important developments like that by attending conferences such as Health 2.0 or hoping to learn about them by happening across blog postings and tweets. Far better to invest in a book such as Internet Cool Tools for Physicians and thereby gain a grasp on key technologies, such as the basics of RSS and have a ready reference to suggest to doctors and others in the health sciences who need a comprehensive guide to such important topics.

The book has very concise, busy professional-friendly sections on how to use Google, Google Health and Google Scholar, PubMed and Medline effectively and efficiently. It covers such useful topics as personalized start pages, third-party PubMed tools (that alone is worth price of the book), reference managers (that section is somewhat dated, but all the more reason for this book to be continuously updated and to become a standard text), email alerts, social bookmarking tools, wikis, collaboration tools. Indeed, the book in all of those respects could be read profitably even for those whose primary interest is not medicine.

The authors do such a fine job of discussing leveraging the power of the Web that I wished that they had included a section on searching for grants, scholarships and other types of funding. They could have included the site I help on, ScanGrants, had they done so. (See how I sneaked in a mention of ScanGrants—that is a given in almost everything I write). But seriously, more and more physicians who are not primarily researchers are engaging in clinical research or community/public health projects and info on how to gain funding for such projects would be a useful feature of the next edition of Internet Cool Tools for Physicians.

The next edition should also contain discussion of search engines such as DeepDyve, NextBio, Mednar, Novoseek given that it does discuss their rival, GoPubMed. Also, we need discussion of the rise of the impact of the Smart Phone and the iPhone specifically. There may be applications that may be optimized for or available only on the latter and those will need to be addressed, as will the rise of new services for connecting frontline physicians, researchers and patients via such services as Private Access and services such as Trial-X, which I read about today in the invaluable blog of one of the authors of this book, David Rothman.

Physicians will need to acquaint themselves with the increasing numbers of trial finders because patients are soon going to ask their personal physicians about such things—or bypass them entirely if the physicians don’t seem open to some pretty assertive instances of patient empowerment.

I mention everything I hope to see in the next edition of this book because this book is a contribution to the betterment of health information acquisition and organization. Kudos for the authors for compiling so much information and presenting it in such a clear, straightforward fashion. The title suggests that the book is gee-whiz sophomoric. It is not. It a work of substance and from a purely aesthetic standpoint, it is a pleasure to handle. What a sad commentary on our times that I was actually surprised to handle a book that was a model of copy editing and production.

I wish that this book had been available when I first entered the field of medical librarianship in 2004 and I hope it will be assigned to all library science students. I have just finished my master’s in library and information science, for instance, and can see that reading it would have greatly enhanced the education my classmates and I have just completed.

In short, buy this book.

We were impressed with the quality of essays and very happy to hear about the different ways in which NextBio has been useful to graduate students in the pursuit of their research. The three students who were selected for this quarter’s travel grant are:

Abhishek Tiwari
Sayan Mondal
Yunyun Jiang

Abhishek is a second year PhD student in bioengineering at the University of Auckland who using modeling to study hormone dynamics. In his essay, Abhishek relates how a discovery in NextBio helped direct his research.

In order to develop a mathematical model of the steroid synthesis I was searching for functional mechanism of the steroidogenic acute regulatory protein or StAR. A simple keyword search using StAR returned a well categorized results page and further exploration and filtering of results landed me on a data correlation between StAR and congenital adrenal hyperplasia. Inspired from this small but crucial finding I devoted one section of my model to simulate the mechanistic basis of the congenital adrenal hyperplasia, and based on model sensitivity analysis we found new insights into the underlying process and emerging trends in diseased system. This was possible only because of the NextBio which provided an abstracted data correlation derived from several high-throughput data sources.

He goes on to discuss the collaboration features and the NextBio recommendation service and then wraps up the essay with a wonderful final paragraph on his vision for the future of science. I highly recommend you read the full text of Abhishek’s essay.

Sayan is a graduate student in the Physiology, Biophysics and Systems Biology department at Weill Medical College of Cornell University. His essay describes how NextBio has been helpful to his systems biology research.

My chemical kinetics-based computational model has identified and elucidated a key role of the phosphodiesterase (PDE) -mediated degradation of cyclic adenosine monophosphate (cAMP) to adenosine monophosphate (AMP) in maintaining the acidic pH in the lumen of the male reproductive tract. Now, as the pH homeostasis is necessary for sperm maturation (spermatogenesis), I wanted to find out if phosphodiesterase has previously been related to spermatogenic dysfunction. I used NextBio to look up this information, and it was quick and easy to do so. Interestingly, NextBio’s data correlations utility referenced microarray data showing downregulation of two phosphodiesterase genes (PDE1A and PDE4A) in the diseased condition of infertility due to incomplete spermatogenic arrest. We are greatly encouraged by finding this result, and my experimental collaborators are now moving ahead with biochemical experiments to test my model predictions.

I should add that the aforementioned microarray study performed a global gene expression profiling of testicular biopsies, and mentioned only a few selected genes in the text of the article. In particular, the text makes no mention of phosphodiesterase genes, so it would have been difficult to find this result using a typical Pubmed or Google Scholar search. In general, most papers reporting such high-throughput studies can reasonably focus on only a few genes in the text, so I find it useful that NextBio’s data correlations utility can search across publicly available high-throughput datasets from these papers and connect proteins of my interest to specific diseases.

The essay also discusses other NextBio features Sayan finds useful. It really illustrates the power of NextBio, in fact the next time someone asks me what NextBio can do for a researcher, I might just give them Sayan’s essay.

Yunyun is a biochemistry graduate student at Rice University who works on the cohesion complex. In her essay, Yunyun discusses her appreciation of NextBio’s filtering functions and “high specificity”. (The pun lover in me really appreciates that a biochemist chose to compare NextBio to PubMed in terms of “specificity”) The community features of NextBio are also very appealing to Yunyun.

Studying the published literature is one way to learn about the latest progress, communicating with scientists from related fields provides another great way to keep up with the newest findings. NextBio provides a platform for us to find people or groups with same research interest. It has been a pleasure to meet people with same or similar interest through NextBio and share opinions about our research.

All of the essays submitted to the travel grant were well-written and the judges had a very difficult time choosing just three essays. On behalf of the entire NextBio team, please allow me to offer our sincere thanks to all those who applied and to everyone who helped us to spread the word about the grant.

The NextBio Travel Grant program is ongoing. We give out travel grants four times a year, so if you missed the chance to apply for this one, you can submit to for Fall 2009. The deadline is September 30th 2009 and you can find details here.

ScienceDirect users who are logged into their account will now see a box titled “Relevant Terms extracted from this Article” beneath the familiar “Article Toolbox” box.

The relevant terms are extracted from the text of the article using NextBio’s ontological tools and each is linked to a custom page with a wealth of information on experimental data, literature, clinical trials, news and key researchers.

I am very happy that ScienceDirect has chosen to integrate all these different types of content on a single platform. It will be a powerful tool for researchers. Hopefully, this enhancement of ScienceDirect will help illustrate the power of integrating information and data rather than isolating them in silos. Many people believe that future of life science research lies in making data more open, accessible and integrated. I am among those people and therefore very glad to see Elsevier take this step towards data accessibility and integration.