James and the Giant Corn Genetics: Studying the Source Code of Nature

Read the whole list here, but I want to highlight #8 in particular:

Evolution has a requirement that things work, not that it’s an elegant engineering solution. Expect jury rigged systems which can be bewildering in their complexity.

This was just on slashdot, so I imagine many will have already read about it, but for those who haven’t, here’s a wonderful metaphor to understand the difference between how scientists (biologists anyway) code, and how professional computer people (some of whom as also scientists) do:

Scientists see their software as a kind of exoskeleton, an extension of themselves. … The software may do heavy lifting, but the scientists remain actively involved in its use. The software is a tool, not a self-contained product.

Programmers see their software as something they will hand over to someone else, more like building a robot than an exoskeleton. Programmers believe it’s their job to encapsulate intelligence in software. If users have to depend on programmers after the software is written, the programmers didn’t finish their job.

The full post was writing by a fellow named John D. Cook and is available over on his website.  (more…)

Papayas genetically engineered to resist papaya ringspot virus were developed in the public/non-profit sector (Cornell University, the University of Hawaii and the US Department of Agriculture). So none of the “I don’t have a problem with the science of genetic engineering, I just don’t like/trust big companies patenting life” arguments apply.

The engineered papayas were released as the ringspot virus began to devastate the Hawaiian papaya crop back in 1998 and have been grown and consumed successfully ever since.

The engineered papayas’ resistance is the result of expressing a protein from the coat of the papaya ringspot virus and engineered papayas contain less of this protein than the fruit of infected trees. Yet the fruit of diseased trees can be sold as “organic” while the fruit of healthy resistant trees (distinguished only by containing less viral protein) cannot.

The engineered papayas even provide herd immunity that makes it possible to grow un-improved organic papayas for export to countries like Japan that reject much genetic engineering.

But the engineered papayas do have one clear (and sometimes fatal) flaw which is only now becoming apparent. They aren’t immune to the machetes of the ignorant.

h/t @Franknfood

I was very excited to read in my twitter feed this morning that google has launched a new service that lets researchers automatically aggregate data on all the papers they’ve published and how often those papers are cited. With a click of a button you can opt in to sharing that data with the world (or at least anyone who searches for your name in google scholar).

Since we’re always told its important to judge the quality of researchers by the impact of their papers (presumably measured by citations or more advanced metrics like the H-index) rather than the impact factor of the journals their papers are published in, I think this represents a big step forward for three reasons:

• Unlike other services that do sort of similar things. Google Scholar profiles are free and visible to anyone (once you opt in).
• The service automatically updates as newly published papers cite your previous work, and as you publish new papers yourself (no need to remember to visit your page and manually enter each new paper). People who have browsed faculty profiles on university websites and realized the professor hasn’t remembered to update their list of new publications in five years or more will appreciate now important a feature this is!
• It’s surprisingly accurate! The only corrections I had to make to my profile were condensing two duplicates of existing papers which were listed with slightly different titles or author lists on different websites. Google scholar didn’t miss a single one of my papers, nor did it include any of the papers published by other people what shared my name back in the 20th century, like so many other searches have.

For those of you who don’t dive into the comments section of posts, I wanted to share some really good points others made commenting on my previous entry:

William Nelson points out that whatever choice you make for computation support (collaboration/hiring in house/farming it out) it makes sense to involve them from the initial design phase, rather than coming to them with a bunch of pre-generated data. Generating a dataset that will play nice with computational pipelines often means an experiment that has a stronger overall design, so this is really a win-win for all involved. The same logic extends to designing follow up analyses. Explain what you’re interested in finding out, not just the specific analysis you’d like run. There may be much more effective or faster ways to find out the answer to your question that someone familiar with the nuts and bolts of computational work can suggest if they understand the whole problem.

He also very correctly points out that as expensive as hiring computational biologists or programmers who understand biology can be, people with those same skill sets make a lot more money outside of academia. So keep your programmers (whether co-workers, employees, or collaborators) feeling happy and valued! (A woman who entered grad school the same time as I did brings her lab’s programmer cookies each time she needs something done, and she’s getting papers so fast she is on schedule to graduate before anyone else in our incoming class. 😉 )

Meanwhile Matt (the scientist gardener) reminds everyone that being a whiz with computers and a whiz with statistics don’t go hand in hand. And for big data projects like most high throughput sequencing experiments, a lack of statistical expertise can hamstring a project just as effectively as a lack of computational skill. So your university’s statistics department is another set of intelligent colleagues you should remember to develop and maintain good relationships with.

He also reminds us that computational analysis isn’t the only talent people can use to get through a PhD without developing the skills necessary to direct a research project of their own as a Principle Investigator later in life. It’s all too easy to fall into the same trap by being the one person in lab who is good at some complicated molecular biology technique. The example he used was chromosome walking… which I’m ashamed to admit I had to look up on wikipedia.

Under absolutely no circumstances should you take your hard drive full of data, walk into lab and drop it on the desk of some new grad student who decided to go to grad school because he loves plants (or whatever your favorite model organism is) and was a wiz at PCRs in his undergrad lab and tell him he’s now in charge of figuring out how to turn it into a paper.  (more…)

“Back in my day,” countless middle aged professors have said, “if you cloned a gene in grad school, that was it, you were done and graduated.”

Well times change, and cloning a gene isn’t quite as hard as it used to be. But don’t let the nostalgia a lot of old school geneticsts give off fool you into thinking identifying the gene responsible for some interesting mutant phenotype isn’t still a big deal.

Here are the three most recent papers I can think of off the top of my head reporting the cloning of maize mutants:

People have been studying the genetics of maize pretty much since the word “genetics” entered the english language at the beginning of the 20th century and the community is full of people, myself included, who can trace their academic lineage back through generations of maize geneticsts to the founder of the field himself R. A. Emerson himself. Each generation laboring for decades (often in the blazing sun and sucking mud of cornfields that are about as far as possible from the air conditions labs and white lab coats that the word “geneticist” usual brings to mind) to increase our community’s understanding of this crazy plant and left a legacy of hundreds of genes whose functions do not need to be inferred by BLAST searches, conserved domains or expression patterns, but have been individually studied and quantified by talents scientists through years of field work and wet-lab experimentation.

The Classical Maize Gene list is an attempt to capture as much of that knowledge as possible and make it accessible and useful to the new generation of genomic researchers — who spend a lot more time in air conditioned comfort than our predecessors in the maize community (although I imagine I’d still get thoroughly laughed at if I showed up to work in a white labcoat).

With the announced release of a new version of the maize genome and maize gene models in august, it’s time for me to update the list again. But I need your help. If there are maize genes which have been cloned, but are missing from the current list (available here), please let me know using the “Contact Me” form at the top of this page and pass this appeal on to others you know who studies (or has studied in the past) maize.