Haven’t done one of these for a bit, so let’s clear out some useful bookmarks:
ClusterMed
Another really nice improvement on PubMed searches. Like GoPubMed, ClusterMed provides a variety of categories to narrow down your searches to find the paper you’re seeking. I found this site through Bitesize Bio, which is still consistently one of the best biology blogs out there. Instead of the usual opinion pieces or off-topic rants, Bitesize Bio publishes a constant stream of really useful information and tips for the bench.

A sea of digital cameras
This photo made me feel old, and at the same time reminded me of hiring a wedding photographer, because if you don’t have pictures of an event, did it really happen?

Online Lab Notebooks
Good post by Cameron Neylon looking at the requirements for keeping your lab notebook online. As you can tell from the comment I left, I worry about either the IT overhead this is going to cause, or that we’d be placing our data in the very shaky hands of “the cloud”. Great article on how much you should trust cloud computing here.

Social Networks for Scientists
That post and this one from Richard Grant on the failure of “Myspace for scientists” got me thinking–are there any features unique to the myriad social networks for scientists sites that are useful? Are they offering any tools beyond what you could get on Facebook or LinkedIn that you find valuable?

Costs for e-Books
I think this points out what’s going to be a major problem for the e-book market–price. For us, paper, printing and binding are not the biggest expense when producing a book. The heavy level of editorial input, rewriting, development, design, indexing, etc., are the biggest costs. And those don’t go away when you’re doing an e-book instead of a print one. Will consumers be satisfied with e-books that cost 10% less than paper ones, if that’s truly reflective of the costs of production?

The death of journalism
Lots of recent articles have come out on the death of newspapers, particularly Seth Godin’s one about the real loss, quality journalism. The usually right-on-the-money Scholarly Kitchen responded with this article, which I think is way off base. Blogs don’t come close to replicating real, quality journalism. It reminded me of a recent piece by Warren Ellis, in which he discusses recent events in Mumbai and a talk by David Simon, co-creator of The Wire:

“His argument is that journalism is an honest-to-god job, with skills, that you have to learn in order to do it right. Citizen journalism just doesn’t cut it….Citizen journalism ate it in the US. Dan Gillmor, who had been talking of nothing else for years, launched Bayosphere–because what the world needed, see, was another website about people talking about the San Francisco Bay Area–which fell apart five minutes later. Citizen journalism looks like sites like westportnow.com, whose above-the-fold right now blazes with the hottest news story in town–local church members knitted some woolen caps for charity… The metroblogging sites…are great fun, but at best they’re arts journalism and in general they’re a listings magazine and linkbloggers. They’re very rarely working their own sources, doing original reporting or in broad terms, doing the work of journalists. The five rules of journalism–who, what, where, when and why–aren’t there because people like pissing you off with rules. They’re there because that’s how you learn things and that’s how you explain things, and that, eventually, is how you see that events and people are connected…and that’s how we build up a picture of the world and begin to understand where we are today and what it really looks like.

Linkblogs and Wikis are great for pointing you to original source material, but what purpose will they serve without that source material? A citizen journalist in the early 1970’s at the Watergate hotel might have sent out a tweet that the police were arresting someone for breaking and entering, but would that have led to the downfall of a president? I think good investigative journalism is something of value. But then again, what do I know, I’m a luddite, I still pay for music.

Way back in 2003, we published RNAi: A Guide To Gene Silencing, which was one of, if not the first major treatises on the subject. One of the problems with being the first to publish on a fast-moving field is that a book can date quickly. While there’s still much valuable information in RNAi, I’ve been asking authors to update their protocols, which have evolved over the last 5 years or so.

Last month, Esther Stoeckli and colleagues provided an update to her method for Gene Silencing by Injection and Electroporation of dsRNA in Avian Embryos.

This month’s issue brings a tour de force updating and expansion of Petr Svoboda and Paula Stein’s chapter on RNAi in mouse oocytes and early embryos. They’ve written up a general topic introduction on the subject, explanations of how to choose the sequence of dsRNA for RNAi and how to clone and sequence an inverted repeat, and protocols for Cloning a Transgene for Transgenic RNAi in Mouse Oocytes, Preparation of dsRNA for Microinjection, Microinjection of dsRNA into Fully-Grown Mouse Oocytes, Microinjection of dsRNA into Mouse One-Cell Embryos, and Microinjection of Plasmids into Meiotically Incompetent Mouse Oocytes.

Next month will bring an update of Savithramma Dinesh-Kumar’s protocol for using viral vectors for RNAi in plants. More on that in February.

Given some of the comment reactions from my last posting, perhaps I wasn’t clear enough in what I was trying to say, so a bit more here. As many have pointed out, scientists have long been bombarded with large amounts of potentially useful information, and have developed a sophisticated set of filters to deal with it, both on and offline. That’s not the issue. The issue is that, due to the exponential growth in the amount of research being done and published, even with highly effective filters that eliminate everything extraneous, one is often still left with more information than can be dealt with in a reasonable amount of time. Let me try to explain with a hypothetical example:

I’m a professor at University X. I have a busy schedule, between doing my own bench research, writing grants, managing my students/postdocs and my faculty duties. I have time in my schedule to read (choosing this number randomly) 10 papers a week in depth for a full understanding. 25 years ago, this was fine. The filters I had built pointed me toward 4 quality papers a week directly relevant to my research, and this allowed me to read 6 other papers in other fields. I had complete knowledge of the important work in my own field, plus a good working knowledge of many other fields that could be applied to my own. Fast forward to today, using even better filters, including Connotea, Digg, Science Blogs, what-have-you, I am now pointed toward 12 quality papers a week directly relevant to my research. This is not a filter failure–my filters are better than ever. They’re discarding more than ever before. But the quantity of research published has increased so much that even with more powerful filters, there’s more directly relevant information out there that I need to take in. I have no time for papers outside of my own field, not even enough time for the papers within my field.

That’s what most scientists I know mean by “information overload”. They’re filtering like crazy, but due to the exponential growth in research and journals, there’s more knowledge to assimilate. The solutions available seem to be:

1) Specialization–this is basically the answer I’m being given by those who just say that we merely need to improve our filters and eliminate more material. Doing so means a shallower knowledge of our own field, and a much shallower knowledge of other fields. This is not good for science, and seems contradictory to the cross-disciplinary world that science has become, where the skill set required is much bigger than ever. The more one filters, the more one narrows one’s focus.

2) Spend more time with the literature–this seems to be the approach most scientists are taking, and other parts of their careers and lives are suffering for it. Either their students, their universities or their families end up neglected.

Yes, it’s true, as AJ Cann notes, “every scientist since Aristotle has suffered from information overload,” but the quantity of that overload has grown exponentially. It’s one thing to follow the dozens of labs doing molecular biology in the late 1950’s, it’s another to follow the tens of thousands (if not hundreds) of molecular biology labs today. At some point, even the most sophisticated filters become overwhelmed, or at least they return more information than one can read without sacrificing elsewhere. And many are finding this frustrating, finding that it takes away from some other part of their research/lives. Solving the problem with more filters just means more specialization, which is also a sacrifice, and a way toward doing less important, less interesting science.

This has been bothering me for a while now, dating back to last year, when I first heard Clay Shirky’s very pithy statement that information overload isn’t a real problem, the real problem is a failure to build effective filters. It’s a catchy little phrase, and like most theories from Web 2.0 gurus, it seems reasonable on the surface, but when applied to the world of scientists, it’s less than useful.

The O’Reilly Radar blog had a link this week to an interview with Shirky where he discusses the concept in detail, which was helpful to finally get a handle on what he means and why it’s irrelevant in my world:

“…the information overload people are the most narcissistic because information overload started in Alexandria, in the library of Alexandria, right? That was the first example where we have concrete archaeological evidence that there was more information in one place than one human being could deal with in one lifetime, which is almost the definition of information overload. And the first deep attempt to categorize knowledge so that you could subset; the first take on the information filtering problem appears in the library of Alexandria.

By the time that the publishing industries spun up in Venice in the early- to mid-1500s, the ability to have access to more reading material than you could finish in a lifetime is now starting to become a general problem of the educated classes. And by the 1800s, it’s a general problem of the middle class. So there is no such thing as information overload, there’s only filter failure, right? Which is to say the normal case of modern life is information overload for all educated members of society.

If you took the contents of an average Barnes and Noble, and you dumped it into the streets and said to someone, “You know what’s in there? There’s some works of Auden in there, there’s some Plato in there. Wade on in and you’ll find what you like.” And if you wade on in, you know what you’d get? You’d get Chicken Soup for the Soul. Or, you’d get Love’s Tender Fear. You’d get all this junk. The reason we think that there’s not an information overload problem in a Barnes and Noble or a library is that we’re actually used to the cataloging system. On the Web, we’re just not used to the filters yet, and so it seems like “Oh, there’s so much more information.” But, in fact, from the 1500s on, that’s been the normal case.

Okay, so if by “information overload”, you mean that there’s more interesting stuff out there than I could ever handle if I tried to read all of it, fine, Shirky’s comments make sense. But that’s not what the scientists I talk to on a daily basis mean by “information overload”. What they mean is that we’re seeing huge increases in both the numbers of people doing scientific research, and the numbers of scientific papers being published. While I hate to quote Wikipedia, the numbers listed there (take these with a grain of salt as one should all Wikipedia content) show an estimate of 11,500 total scientific journals in 1981, and over 40,000 listed in 2008 in PubMed in fields related to medical science alone.

Now, most scientists are familiar with the “cataloging system” of scientific journals, they’ve been reading them their entire careers. Everyone has their own filters, their own rankings of which journals are more interesting, or publish better work than others. And all kinds of tools are available for filtering things down to just the relevant essentials for keeping up with your own field. But even so, most people that I talk to are left with more useful, relevant articles that they need to read than they have time to get to. These are not articles that should be filtered out. These are important, quality findings of direct relevance to their own work. And there are too many of them without even factoring in a need to keep up with science in general and see what developments in other fields can be applied to one’s own.

So no, it’s not a filter failure. It’s a genuine overload. A “filter failure” implies that scientists are just not tossing out the less relevant material, but that’s not what’s happening (as an example, almost no scientists I know read science blogs–those are something filtered out as being of less value than the primary literature). Is it so hard to believe that as science and technology move forward, that more and more research is being done, and that there’s more knowledge generated that one should take in? Is it wrong to want to be as informed as possible of one’s own field, and to seek ways of assimilating more research, rather than ways of discarding valuable information?

Shirky’s suggested solution is of no help here:

“So, the real question is, how do we design filters that let us find our way through this particular abundance of information? And, you know, my answer to that question has been: the only group that can catalog everything is everybody. One of the reasons you see this enormous move towards social filters, as with Digg, as with del.icio.us, as with Google Reader, in a way, is simply that the scale of the problem has exceeded what professional catalogers can do.”

I don’t know about you, but I’m not sure how much I’m willing to trust a random group of strangers to tell me how relevant a particular paper is to my own research. Sure, you can get some sense of the quality of the work, perhaps even a decent summary. But no one knows your work as well as you do, and no one is going to be able to tell you what tiny details in a paper will or won’t act as a springboard for new avenues of research. I’d also argue that the top researchers are probably better at discerning those details, and if they leave the paper-reading to others, they’re going to miss out on much of what makes them better than their peers and science is going to suffer.

So while social filtering like that described does have its uses, it’s not the solution here. Social filtering is nice for discovery, for finding papers you might not have read on your own, but that’s not the problem I hear from most scientists. Most aren’t looking for more to read.

Shirky’s point may be relevant in some situations (certainly anyone looking to read every book in the Library of Alexandria will learn a valuable lesson from him), but like most Web 2.0 wisdom, it fails when applied to the particular needs of scientists. As the old phrase goes, “To a hammer the world looks like nails” and Shirky often strikes me as yet another Web 2.0 evangelist trying to convince us that our individual needs are all the same easily hammered nails.

Update: in response to some of the comments, I’ve tried to clarify things with a further posting on this subject, part 2, available here.

The chicken has long been a superb model system for developmental biology. The patterns of gene expression and overall development of avians and mammals are close enough to make comparisons meaningful. And windowing an egg to view an embryo, then sealing it with scotch tape is a lot easier than performing survival surgery on a pregnant mouse. The big drawback to chicken as a model system has been the lack of genetics, the inability to generate transgenic and knockout lines of birds. Though some success has been reported with chicken ES cells, the large size of the animals, the space requirements and the long generational times makes them unfeasible as laboratory animals for this purpose.

The Japanese Quail, however (Coturnix coturnix japonica), has all of the advantages of the chicken, but with a smaller sized adult, short time to sexual maturity, and prodigious egg production. In the January issue of CSH Protocols, Caltech’s Rusty Lansford and colleagues have contributed a set of papers detailing methods for generating transgenic quail via lentiviral vectors. The resultant transgenic birds can be housed and raised in a standard animal facility, with no more space requirements than mouse.

An overview is available here, and protocols for Generation of High-Titer Lentivirus, Injection of Lentivirus and Screening for Transgenic Offspring are available.

Our series on emerging model organisms continues this month, bringing you a set of articles on two systems that may be new to you, and one that’s a long-time classic.

Marianne Bronner-Fraser and colleagues have written up a guide to using the Sea Lamprey, Petromyzon marinus in the laboratory. The unique evolutionary position of the lamprey makes it a fascinating animal for comparative studies, and there are also lamprey-specific systems that are being investigated, like its variable lymphocyte receptor-mediated immune system. Protocols for culturing embryos, microinjection of RNA and morpholinos, DiI cell labeling, whole-mount in situ hybridization and immunohistochemistry are available.

Nipam Patel’s group at Berkeley brings us a look at the amphipod crustacean Parhyale hawaiensis. This crustacean is extremely amenable to laboratory studies, producing large amounts of embryos year round. The establishment of the segemented body plan is a particular area of interest for studies of P. hawaiensis. Protocols are provided for fixing and dissecting embryos, injection with fluorescent dyes, antibody staining and in situ hybridization.

Rusty Lansford and colleagues have written up their methods for using the classic developmental biology system, the Japanese Quail, Coturnix coturnix japonica. Their transgenic system is a big breakthrough, and deserves its own blog article, which I’ll post next week.

This month’s issue of Cold Spring Harbor Protocols has posted, and it our features for January are two articles detailing experimental methods for the analysis of molecular processes involved in DNA repair and post-translational modification of proteins.

Homologous recombination is an important mechanism for the repair of damaged chromosomes. When this occurs, a Displacement Loop, or “D-loop,” is formed as the two strands of the DNA molecule are separated and held apart by a third strand of DNA. Patrick Sung’s laboratory at Yale University has detailed a method for generating these structures in their article, Assay for Human Rad51-Mediated DNA Displacement Loop Formation. This reconstituted system provides researchers a biochemical means to dissect the mechanisms of the homologous recombination machinery.

Sumoylation involves the attachment of Small Ubiquitin-like Modifier or “SUMO” proteins to other proteins in a cell. Sumoylation modifies these target proteins and can affect a variety of activities, including stability, transport, and transcriptional regulation. James Manley’s laboratory at Columbia University provides In Vitro Sumoylation of Recombinant Proteins and Subsequent Purification for Use in Enzymatic Assays, a protocol for modifying proteins in this manner, allowing one to assess the impact of sumoylation.

Like all of our featured articles, these protocols can be freely accessed by subscribers and non-subscribers alike.