Second author on the paper here and the person who did the analyses that lead the the conclusion of three WGDs since divergence with tomato. We had to phrase the claim like that because tomato was the closest relative with a published genome. These analyses would have been a lot easier if there were genome sequences of plants more closely related to Utricularia, especially if some had more shared WGDs.

Spot on. I just started a tenure track faculty position (80% research) and just spent my first winterbreak trying to catch up on all of the research work that I couldn't do while teaching 300 undergraduates. That means 10 hours a day, every day. I'm fortunate that my wife could take the holiday and visit family, and fill me in on the happenings with everyone. But grants are coming due, paper revisions need writing, new papers are waiting, and conference talks are happening in a week. I'll be lucky to be caught up in on this work in April. But then again, I knew what I was getting myself into accepting this position (I've watched friends ahead of me in this game go through this). Easy position? Hardly. Would I trade it for anything else? Nope. Never a dull moment.

For a good read on this problem, I highly recommend the Fourth Paradigm: http://research.microsoft.com/en-us/collaboration/fourthparadigm/ .

This is a free ebook download from Microsoft and uses a variety of leaders in data driven science to write chapters about a variety of scientific disciplins and what "big data" means to them. The first chapter is especially enlightening! Blurb about the book:

Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets.

The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies.

In The Fourth Paradigm: Data-Intensive Scientific Discovery, the collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

I'm just about to start a tenure track assistant professorship this summer at a research university. Yes, it was a long road to get there, and it continues to be one. For every undergrad that I've mentored, I've done everything I can to discourage them from going into academics. IF there is anything else you can see yourself doing that will make you happy besides being a professor, I would recommend it. It is a lot of thankless work, there is lots of competition, and no guarantee of making it. Getting there requires some luck, being good at politics, and being very good at what you do (hacks are recognized pretty quickly). IF there isn't anything else you can envision yourself doing that will make you happy, then go for it. Just remember that the reward is waking up and doing what you want to do every day (and I mean every waking moment of every day). Though there are many difficult people with whom you have to work, most people are fantastically smart, interesting, and passionate. For me, this was one of the two most important things for becoming a prof (I had spend 5 years in industry as a scientist and was bored silly by my coworkers' water cooler conversations). The other is the opportunity to think up and work on hard problems that no one else had ever done before.

My dad is also an academic. Watching his path was quite inspiring for me, through I didn't appreciate all he had done until I was set on doing the same. He worked wherever he could that would allow him to write grants and do the work he wanted to do. It wasn't until he was 50 that he landed his first profship. He's now been a prof for over 15 years, works harder than before due to department responsibilities, graduate students and post docs, and loves every minute (almost). That showed me that if you keep at it long enough, eventually things would work out.

If you decide to go for the academic life, good luck and enjoy every step along the way. Just don't worry too much about the sad state of affairs for doing basic research.

Well, as others have said, this is kind of correct. After sequencing, the raw reads (short sequences of DNA) are assembled into either transcripts of genome fragments (usually called contigs). This leads to a great reduction in the amount of data, but there is a lot of concern by scientists over whether or not to save all the raw data for future work. My take is that unless the sample is impossible to collect DNA/RNA from again, then toss it and assume that the sequencing technology will be better/faster/cheaper/longer in the future.

I'm actually involved with a large US National Science Foundation project to help build the cyberinfrastructure to help handle these data and analyses: the iPlant Collaborative: http://iplantcollaborative.org./ In addition, I maintain a set of web-based software for comparative genomics: CoGe, http://genomevolution.org./ From the standpoint of genomes, I adopted the philosophy of building a system that can easily accommodate new versions of existing genomes and new genomes. Thus, as new data becomes available, they get quickly loaded into the system and made available for analysis by any of the existing tools or compared to any of the already loaded genomes. So far, the system has scaled quite well and it is storing over 16,000 genomes from over 12,500 organisms. While the science is a lot of fun (sort of like the ultimate video game except no one knows the rules and there are no pre-built user interfaces), it is awesome to see how quickly the number of sequenced genomes has grown over such a short period of time. This is driven by how cheap the technology has become to use and the quantity of data that can be produced. For those interested, the National Human Genome Research Institute keeps track of this and has some very informative graphs: http://www.genome.gov/SequencingCosts/.

While it has also been said, the analyses and interpretation of these data is extremely rate limiting. Lots of opportunity for folks with programming, algorithm, data visualization, web, and user interface experience.

Actually, the best infections are those that cause no symptoms. The bug/bacteria/virus gets in, replicates, and gets out with you and your cells being none the wiser. It is often the infections that cause extreme immune responses (e.g. haunta virus, SARS) that are deadly. The immune system goes haywire and ends up killing you.

Bacteriophages have double stranded circular DNA genomes. And are a bit on the large side for a virus (~100,000 nucleotides). Perhaps a flu virus (orthomyxo, 8-segmented single strand negative sense RNA virus with no DNA stage of its lifecycle?) Sorry, I used teach virology.

I'll need to re-read the article for the specifics, but this was just measuring the background rate of mutations without any form of selection. They found an initial burst of change as the bacteria adapted to the experimental conditions, then not much for the first 20,000 generations, then a continuous burst of new mutations between 20,000 and 40,000 generations. They could attribute those mutations to a mutation that knocked out one of the DNA repair enzymes.

Yes. His group had a recent paper in Nature where they sequenced genomes from their Long Term E. coli Evolution experiment at generations: 0, 2000, 5000, 10000, 15000, 20000, and 40000.

Absolutely stunning piece of work:
http://www.nature.com/nature/journal/v461/n7268/full/nature08480.html

For those that don't know much about either the significance of the science or the technology involved with generating the data, this might be useful. One big gray area in our understanding of evolution is how quickly genomes are changing, where they change, and the types of changes that are occurring. Yes, a genome is usually made up from DNA (RNA viruses being the major exception), and encoded in the DNA are genes, many of which get translated into proteins that do much of the "work" in an organism. However, depending on the organism, much of the DNA does not code for genes. The human genome for example is ~3,100,000,000 nucleotides (DNA's building blocks) long. Of that, ~1.5 percent codes for protein. Of the rest, the vast majority are ancient, dead, "selfish" chunks of DNA such as retroviruses (RNA viruses that convert to DNA and integrate into a genome. HIV is an example of one of these guys) and transposons (a major class of which are just like retroviruses but lack the genes for cell-to-cell transfer). Periodically in the evolution of many multicellular organisms (e.g. plants and animals), there are explosions or blooms of these types of elements that suddenly take off and integrate around a genome. This is one type of mutation (or genome evolution), and there are many others. Single nucleotides can change (e.g. C->T, as discussed in the paper), individual genes can get duplicated through a process known as unequal crossing-over or nonhomologous recombination, and the entire genome can be duplicated (known as polyploidy and is a dominant feature in flowering plant genome evolution.)

Our current understanding of how dynamic a genome is, the types of changes that occur, and the factors that limit these changes is very limited. Much of this is because getting a genome of an organism can be expensive and laborious, depending on the size of the genome (RNA virus 15,000 nt, DNA virus: 150,000 nt, bacteria: 5,000,000 nt, yeast: 20,000,000 nt, multicellular organisms: 100,000,000-10,000,000,000). Since our understanding of how genomes evolve depend on getting genomes sequenced that are appropriately related to one another (e.g. populations of organisms versus diversity of organisms), we can only get answers for those genomes we currently have (current ~8000 for all viruses, bacteria, archaea, and eukaryotes). Fortunately, there is currently a major technological revolution happening in biology: generating DNA sequences fast and cheap. For example, the first human genome was approx a 10 year project and cost ~$1,000,000,000. Now, the record for a human genome takes less than a week and costs ~$15,000.

This project is a major milestone as the authors sequenced 6 plant genomes (a mustard known as Arabidopsis thaliana) that are related to one another by 30 generations. Because of the close evolutionary relationships of these organisms, the authors can characterize the types of genomic change happening over very short time periods.

The emerging picture is that genomes, the fundamental genetic blueprint for a lineage of organisms, are much more dynamic than we had previously thought.