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Bioinformatics in the Post-Genomic Era
Bioinformatics is the science of biological information, namely sequences and metadata about organisms and sequences. What's interesting about this field to many people, both in the sciences and outside of it, is the large volume of data that gets analyzed and the results that emerge on a daily basis. Obviously interesting for the medical advances and the rapidly growing business in the life sciences, there's a complex field that has developed in the past ten years or so. And following the sequencing of the human genome, new challenges have arisen for everyone involved. Augen's Bioinformatics provides a good introduction to this new field of research for students in the sciences, and anyone with a decent undergraduate education in modern biology. I think that this accessibility of the material is one of the book's biggest winning points.
After an introduction to the book and the subject area of bioinformatics (chapters 1 and 2), Augen begins at the level of the structure of a gene (chapter 3). Here, anyone with an undergraduate level understanding of genetics or molecular biology can begin using the book and bridging the gap to the new areas of modern bioinformatics. Augen then describes how basic sequence analysis is performed at the DNA sequence level (in chapter 4). The material in Bioinformatics covers some of the higher-level methods for sequence analysis, including hidden Markov models, neural networks, and pattern discovery, and introduces some of the common algorithms found to do this analysis.
Chapter 5 then covers transcription, the process of going from DNA to mRNA. Beginning with the biology behind this activity (the ribosome and the larger "transcriptome"), Bioinformatics then describes how you would perform transcriptional analysis. Here, Augen shows how you go from a wet lab to a computational lab and describes what classes of experiments you perform to gather data and then what kinds of analysis you perform on it. This chapter introduces some of the more common clustering techniques for data aggregation and understanding.
The next step in the DNA -> RNA -> protein chain is found in chapter 6, which covers the translation process. Coupled to chapter 7, which describes protein structure prediction and searching, these two chapters bridge the next gap between laboratory data and computational analysis. Protein folding and structure analysis was one of my pet areas of study as a graduate student, and Augen's text does a decent summarization of the field to date. The resources listed and techniques described are definitely on par with the common practices in the field.
Finally, Bioinformatics gets into the next major area of bioinformatics, medical databases. Augen's bridge from genetics to medical science is complete, and he discusses how medical professionals utilize databases and can begin to predict disease, for example, based on data mining. The final chapter, "New Themes in Bioinformatics," covers exactly that, but also what Augen refers to as "workflow computing," or basically going about being a bioinformatics scientist. One of my favorite emerging areas in bioinformatics, metabolic pathway elucidation, is also covered briefly.
I've shared this book with a few friends who are all studying computer science or practicing computer scientists. I did so because Augen's material does a good job of explaining my background and introducing them to some of the analysis forms I introduce into my own work. It does a good job of that, and gets them quite excited. Bioinformatics really bridges a number of fascinating areas of computer sciences, including data mining and high performance algorithms. Augen's Bioinformatics is a good introduction to the field for them, and really anyone who has studied a couple of biology courses in college.
Where the book falls short, however, can be grouped into two main areas. The first is the failure of Augen's presentation of the algorithms. While the methods used to describe computational algorithms in Bioinformatics is common for non-computer scientists, it's completely unusable for computer scientists who are used to a specific algorithm presentation style that looks more like pseudocode than rambling text. The ambiguities this presents for a technical reader are unfortunate, especially if anyone studying bioinformatics is supposed to be computer science literate. The book itself assumes a life science literacy, so this isn't an unreasonable expectation of the reader.
The second area that consistently falls short in the book is in the utility of the information given. While I am significantly happier with the quality and depth of material presented in Augen's book than in the O'Reilly bioinformatics series, where the book fails to deliver is in showing the reader how to actually use the data they gather. After all, the book shows various sequence analysis algorithms and discusses tools available to do this work, but it only devotes a few pages (out of over 370 in total) to a workflow that can be used. Also, the book fails to point the reader at very worthwhile web resources sometimes, including meta sites like the SDSC Biology Workbench site, and just says "some Perl scripts" for local data analysis. As such, you'll have to go a few extra miles on your own to make use of the data sources.
I guess a third complaint of the book for me is that Augen has ignored or omitted significant bodies of research that fit squarely into the scope of the book. For example, Ken Dill's research into protein folding models, as well as Martin Karplus' work on the subject, receives no mention, nor does the topic of Bayesian network analysis when Augen discusses time series data analysis. These aren't new, they've been around for many years and influenced most of the field, and their absence is noted. The book's spotty coverage in some places, like these, is noticeable.
Bioinformatics does a few things well, but overall reads too much like a biology textbook to be useful to the average computer scientist. More emphasis on the practice of bioinformatics and data analysis would have made this book stronger and complemented the substantive background material well. Finally, using an approach more similar to the computer science approach would have been a tremendous benefit, since the material really is computer science in part. That said, I think this is probably the best introduction to this exciting area of science that I have yet seen.
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It's my feeling from working in EE that the dying fields are EE and software; the future is in the hands of the bio guys. So why did you leave? I'd give everything to get rid of my floaters, but don't give two hoots about the latest hardware. I don't think I'm alone in waiting for the sci-fiesque promises of advanced biotech.
Mostly random stuff.
I'm a Math major, Comp Sci/Physics minor out of university, been working with computer programming and database administration in the past 9 years, but have strongly been looking at changing careers and moving into bioinformatics.
Perhaps it's the DB admin that getting to me, but I've enjoyed being able to work with enormous data sets and putting puzzle pieces together.
It's a big leap. I'm 30. I only have first year chemistry under my belt (no university level biology) and having kids, a mortgage and my own health and sanity to take into account, it seems an enormous career change.
I've started to look into the field by checking out about a couple dozen books on the subject from my university library. (I've since whittled the pile down to just a few books!) I'm plodding along and what I've read to date is really intriguing, even if I'm taking a bizzare Math approach to understanding genetics.
I'm concerned that I have a niave approach to the field: looking at genomics, proteomics and bioinformatics as the biggest and coolest LEGO puzzle ever devised. Yet most books (especially the "Programming for Bioinformatics" types) seem to focus solely on data storage and not actually *using* the data.
Has anyone else here moved from Computing or Mathematics into Bioinformatics? Was the experience what you expected?
"Bioinformatics : a practical guide to the analysis of genes and proteins"
Had much better sections in the third edition, which I got fresh out of the UW Library when it came in, on PSI-BLAST and BioPerl and suchlike.
The only downside to a textbook in our field is that half the database practical sections become out of date within a year or two.
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In bioinformatics, science literacy is so much more important than computer literacy. Computer scientists rarely become good bioinfromaticians. This is the primary reason almost every single peice of commercial bioinformatics software is a complete peice of shit. And why the free stuff is hacky but gets the job done. The free stuff was written by life scientists, the commercial stuff was written by computer scientists with no domain knowledge of the question they were trying to answer.
I have to agree. If you don't have a scientific frame of mind - and "almost" went into Biology but got sidetracked by those shiny techy computers - you'll be in over your head.
During a typical week here - which pays less and some weeks you don't get much sleep - you probably sit in on 2-4 hours of research presentations and doctoral theses - and we have individual researchers assigned to track specific journals and report back to the rest of us what applied to us in summary form.
If you don't like continually relearning things, it can be strange. And you have to realize what the pipeline is, how a lab actually works, how you scale up things, and why E.coli is not just a nasty bug but your best friend in the whole wide world.
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Bioinformatics was something that I "just got into". Really.
I had 5 years under my belt of lab work at MIT, and was learning programming again (I took AP comp-sci in highschool, and had decided to learn some programming for the hell of it with friends who were working in the industry.) There was call at work for me to automate some of the analysis that I needed to do.
Doing some simple tests like a TDT (yeah, I like population genetics) by hand took a long time, and was error prone. I used a bit of my programming knowledge to cook something up in a day to do the work for me.
My boss was pleased, and I soon had another relatively simple project to work on. True, most of these problems were 'parse a file, be an accountant, return a result', but it was fun and exciting to have problems that impacted my work day, and made life easier. The bonus was learning something of programming work, too.
This lead me to take a number of classes at local schools, and start reading a ton of books. A few years later, I was able to get my first full time informatics job (and was at that point a reasonably good scientist, so I was a 'two for one' kinda guy.)
This has lead to more jobs, more difficult projects, and a lot of great learning. Now, I write research projects dealing with selection, rules based frameworks for data analysis, data clustering, etc. Some projects are tools for scientists in my labs. Some projects are my own research.
I just 'got into it'. *shrug*. I don't know how common it is, but my co-worker learned bioinformatics the same way, and we seem to be pretty competent - we've both got papers in Nature/Nature Genetics under our belts, and we're collaborating to be co-first authors on a soon to be reviewed nature paper.