Here’s what’s working for me right now: I divide the project up into logical chunks, each with its own folder numbered and named after that chunk (“1. Identify foo homologues”, for example, or “2. Phylogeny of foo proteins”). Each folder contains all the files necessary for that chunk of analysis, even if that means duplicating some files. Each folder also has a README.txt file describing what the other files in the directory are and how they were created, ordered by their place in the workflow. Like this:


Identify foo homologues in Dictyostelid genomes
jeff smith 2013

foo_proteins.fasta
- Protein sequences of identified foo homologues


Right now, I’m focusing on making the work intelligible to future readers (including future me). I’m less concerned with keeping an electronic lab notebook that documents the day-to-day details of the analyses I try and how they turn out. Some people use wikis for this, or revision control software. I’m holding off on that for now.

There’s also “A quick guide to organizing computational biology projects” in PLoS Computational Biology that has some good ideas. I’ve found this part to be especially true:

“Everything you do, you will probably have to do over again. Inevitably, you will discover some flaw in your initial preparation of the data being analyzed, or you will get access to new data, or you will decide that your parameterization of a particular model was not broad enough. This means that the experiment you did last week, or even the set of experiments you’ve been working on over the past month, will probably need to be redone. If you have organized and documented your work clearly, then repeating the experiment with the new data or the new parameterization will be much, much easier.”

Noble WS (2012) PLoS Comp Biol 5:e1000424

In my own field of microbial evolution, one way in which I’d find open data useful has to do with fitness. There are many different ways to measure the survival and reproductive success of organisms, and they each have their uses. I’ve found when reading papers that there are often times in the authors plot their fitness data in one way, and I wonder what it’d look like plotted another way. For example, I often see papers that plots mean group productivity and within-group relative fitness (a multilevel selection partition of social evolution), and I wonder what the data would look as the absolute fitness for each microbial genotype (the neighbor-modulated fitness partition in kin selection theory). Much of the kin selection/group selection debate is about the best way to calculate and think about fitness. I prefer to plot fitness data in a way that’s easily interpreted multiple ways. But it’d be nice to at least grab other people’s data so I can replot it a bit. So here’s my recommendation:

Make sure that the data you archive includes the raw colony counts (or plaque counts, or cell counts). With that, anybody can easily calculate their favored fitness measure.

I’ve been working on a project that involves a large amount of flow cytometry data. How much of my data and calculations should go into Dryad? I’m tempted to say: all of it. From the raw data files, to the flow cytometry gating scripts, to the cell counts to the derived values (growth rates, fitness, etc), to the statistical analyses, to the scripts for making the figures. Why not?

Sharing everything also helps us become better scientists. I often learn a few things when I look at other peoples’ scripts and spreadsheets. I’ve traditionally used Excel for basic data manipulation and plotting. Now, though, I’m thinking that maybe I should try to do as much as possible in R—the scripts are easy to share, it’s easier to use for for large data sets, and it avoids the copy/paste errors that sometimes crop up in spreadsheets.

1. Don’t maintain suspense.

• Present the topic clearly at the very beginning.
• Explain the relevance of the paper at the very beginning.
• Quickly telegraph where the entire paper will be going. Give away all your punchlines in the abstract, and do it again in the Introduction.

2. Make the paper easy to skim.

• Make sure that the “meat”—the core that everyone should read—is well labeled and easy to find.
• Explain your main results using graphs.
• Remove from the main text any technical details that aren’t needed for the flow of ideas. Readers shouldn’t have to stop and think about whether or not they have to think about an equation.
• Use signposting to help people “peel the onion”—get as deep into the paper as they want, but no deeper. Technical sections should be prefaced by an explanation of what and who it’s for, so it’s easy for a reader to tell if they should read it, skim it, or skip it for now.

• If the aliens seeded life on earth with DNA oligomers (PCR primers, basically) like this computer animation is showing us, why did they have to sacrifice one of themselves to do it? Wouldn’t it be easier to just chemically synthesize them the way we do?
• If this is supposed to be the origins of life on earth, then why is it showing us metazoan zygotes? And didn’t they just show us a landscape full of plants, anyway?
• Why aren’t any of these characters saying anything about how wierd it is for a rocky moon to have an atmosphere full of oxygen?
• If this is a barren moon, then why are there earthworms? And why are the earthworms sometimes meal worms (plant-eating insect larvae)?
• Why don’t any of these people act like real scientists, or at least like professionals?
• Why is this movie rehashing trite 1950′s cliches about the difference between humans and robots being emotion and curiosity after just showing us the android character having feelings and being curious?
• If the aliens engineered life on earth, then doesn’t making it look like a species of primate evolved to have the same genome as them seem wierdly narcissistic?
• How did that alien get so big so fast without eating anything?

It’s okay for movies to leave things unexplained. I’m cool with that. Scientists live in a world full of unexplained things. I’d even prefer that movies to leave things unexplained and just chalk it up to alien technology or whatever. Their explanations are usually boring and stupid, anyway. But when the plot revolves around events that any undergrad biology major could poke holes in, well, it’s hard to get past that. Prometheus apparently did have a science consultant, though his involvement seems to have been limited a single conversation. Film makers hire people whose whole job it is to make sure that continuity of appearance is maintained from shot to shot. Can’t they hire someone to make sure the science makes sense, too? Or at least isn’t unnecessarily egregious? Please?

The worst part of Prometheus for me, though, is that the movie is anti-science without even realizing it. Like, for example, the part where the main protagonist couple (archaeologists) claim that aliens engineered life on earth. One of the other characters reasonably asks what evidence they have for this, and the main protagonist says “It’s what I choose to believe”. Ugh. The movie presents this as a heroic act rather than, you know, pants-on-head retarded. To be clear here, this is the equivalent of a professional archaeologist saying that aliens built the pyramids of Egypt. The only response other characters have to this conspiracy idiocy is to whine that it goes against “Darwinism”—as if evolutionary biology were a philosophical belief rather than, you know, science supported by observable facts. In spite of this, the filmmakers inexplicably believe that one of their protagonists is a skeptic.

Dear Mr. Scott: Richard Feynman was right when he said “Science is a long history of learning how not to fool ourselves.” What you show us is self-deception of the worst kind.

Scientists already struggle with a deluge of more papers, posters, and talks than they could ever feasibly process. Give them reason to believe their time and attention will be well-spent on yours.

Also, the whole “if you want to hear more, come by my poster” bit is just wasted time and breath. Advertisements for toothpaste don’t bother saying “Buy FluoroWhite Tooth Creme if you want teeth like this!” because they know their viewers already recognize the ad for what it is—an ad.

To avoid telling “just-so stories”, researchers studying adaptation should actively identify, test, and exclude alternative hypotheses. As George Williams famously put it, “adaptation is a special and onerous concept that should not be used unnecessarily, and an effect should not be called a function unless it is clearly produced by design and not by chance”. Selection is an important mechanism of evolution, but not the only one. Nonadaptive mechanisms like mutation and drift can also play important roles. Mechanisms by which individuals may directly benefit from expressing a trait should also be explored.

This passage seems to evoke strong emotional responses from some people (and not because of the awkward passive voice at the end). I thought we were just describing good scientific practice for studying adaptation. The reviewers apparently thought it was patronizing. One reader even thought that using the phrase “just-so stories”, automatically counted as a full endorsement of Steven J. Gould & Dick Lewontin’s attack on behavioral ecology. The passage seems to make some people really defensive, so I think for the sake of the paper we’re going to take it out. Which is too bad, since I think it’s a message that some researchers could stand to hear (or hear again).

Plasmids are mobile bits of DNA that play a key role in bacterial evolution. They shuttle genes for things like antibiotic resistance and pathogen virulence among different strains or species of bacteria. But not all plasmids carry these genes, and it’s been an outstanding question how plasmids persist in bacterial populations. One possibility is that they’re a kind of genetic parasite, slightly reducing the fitness of the cells they infect but continually infecting new bacteria. One problem with this idea, though, is that their infection rates often don’t seem high enough for a purely parasitic lifestyle.

A new paper by Bärbel Stecher and colleagues at ETH Zürich shows that when Salmonella infect mammalian guts they create an environment that drastically increases plasmid transfer among the bacteria there. They inflame the gut tissue, causing a “bloom” of resident E. coli. All those bacteria bump into each other more, allowing plasmids—which spread by direct contact between bacterial cells—to go gangbusters. The paper has a lot of good experiments showing that it’s the increased bacterial density, and not the inflammation, that causes increased plasmid transfer.

The implication is that plasmids can make a living as parasites if Salmonella and other pathogens cause enough gastrointestinal disturbance, as they might in the developing world or in nonhuman mammal populations. I did find overblown the authors’ claims that their findings “shift the current paradigm” because they show that Salmonella and E. coli share plasmids (which we already knew) and “boost” pathogen evolution (which their findings do not show), but overall this is a pretty cool paper.

Stecher B, Denzler R, Maier L, Bernet F, Sanders MJ, Pickard DJ, Barthel M, Westendorf AM, Krogfelt KA, Walker AW, Ackermann M, Dobrindt U, Thomson NR & Hardt W-D (2012) Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae. PNAS 109: 1269-1274.

Well, systems biology:

• studies dynamic, complex systems whose behavior is governed by the interactions of their component parts
• uses quantitative, data-rich measurements of dynamical behavior
• uses mathematical and computational models to predict and analyze dynamical behavior

Viewed this way, I would argue that systems biology has a lot in common with ecology and evolutionary biology. In some ways, it’s just population biology applied to molecules and cells rather than individuals and species. Asking how genetic regulatory circuits create persistent cycles of gene expression rather than coming to some stable equilibrium is not all that different than asking how predator/prey dynamics create population cycles rather than coming to some stable equilibrium. And with any luck, systems biology will help bridge the traditional divide between population biologists and their more mechanistic colleagues.

smith (2012) Tragedy of the commons among antibiotic resistance plasmids. Evolution. doi: 10.1111/j.1558-5646.2011.01531.x | Early view at Evolution

Abstract: As social interactions are increasingly recognized as important determinants of microbial fitness, sociobiology is being enlisted to better understand the evolution of clinically relevant microbes and, potentially, to influence their evolution to aid human health. Of special interest are situations in which there exists a “tragedy of the commons,” where natural selection leads to a net reduction in fitness for all members of a population. Here, I demonstrate the existence of a tragedy of the commons among antibiotic resistance plasmids of bacteria. In serial transfer culture, plasmids evolved a greater ability to superinfect already-infected bacteria, increasing plasmid fitness when evolved genotypes were rare. Evolved plasmids, however, fell victim to their own success, reducing the density of their bacterial hosts when they became common and suffering reduced fitness through vertical transmission. Social interactions can thus be an important determinant of evolution for the molecular endosymbionts of bacteria. These results also identify an avenue of evolution that reduces proliferation of both antibiotic resistance genes and their bacterial hosts.

Errol Morris has this “Op-Doc” (Opinion-Documentary, as odd as that sounds) over at the New York Times. Technically, it’s about historical research, but I think the phenomenon holds true for biology, as well. As interviewee Tink Thompson puts it, “if you put any event under the microscope, you will find a whole dimension of completely weird, incredible things going on”. I’ve always loved Morris’ work. A lot of it deals with issues of knowing—how we know what we know, how we sometimes deceive ourselves, what evidence does or doesn’t say. Important issues for any scientist.