Bioinformatics in the Post-Genomic Era

nazarijo (Jose Nazario) writes "As a biochemist by training, Jeff Augen's Bioinformatics in the Post-Genomic Era was very interesting to me. Though I left the field some years ago, I was using the bioinformatics tools that are covered in the book daily and still look in from time to time. Naturally I was curious to see a larger perspective, as well as any progressions, that have occurred in the past few years. Augen's book gave me part of the larger picture, but it could have done more." Read on for the rest of Nazario's review. Bioinformatics in the Post-Genomic Era author Jeff Augen pages 388 publisher Addison-Wesley Longman rating 7 reviewer Jose Nazario ISBN 0321173864 summary Genome, Transcriptome, Proteome, and Information-Based Medicine

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.

You can purchase Bioinformatics in the Post-Genomic Era from bn.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.

Comments

Post Genomics Era?

[Neil Blender]

Uh, genomics isn't going anywhere.

Re:Post Genomics Era?

[killtherat]

I think it's referring to the fact that mapping genomes is no longer the future (much like we live in a post-modern world).
Genomics is now part of the game. It used to be that if you sequenced a gene, you could work a PhD off of it. Now that's simply the first step. So now that genomes are a part of every day life science, if you don't know how to run blast, you had better get back to school.

Re:Post Genomics Era?

[AKAImBatman]

Did you hear what the geneticist said when he peered into the dark forest?

"Gee! Gnomes!"

Thank you, I'm here all week. (Actually, I'm not. But I am feeling cheeky today. ;-))

Re:Post Genomics Era?

[TheWhaleShark]

I thought the same thing when I saw this title. As far as I know, and I'm a biologist by training, we are very much still IN the era of genomics. In fact, it would be rather big news if we ever LEFT said era.

Yup, still got my genes.

Re:Post Genomics Era?

[habuji]

I think when biologists refer to the "pre-genomic" era, they're talking about before the Human genome and other genomes are sequenced. Now that many genomes have been sequenced, they call it the "post-genomic era." I think they're referring to the fact that there's not as much sequencing going on. Since there's so much genomic information available, the next step is to weed through it all, searching for gene function, silencing, and other stuff like that.

Re:Post Genomics Era?

[glwtta]

Now that many genomes have been sequenced, they call it the "post-genomic era." I think they're referring to the fact that there's not as much sequencing going on.

I'd say that there is far more sequencing going on right now than ever before, in terms of total output. GenBank provides a nice growth summary (note that the human genome was officially "completed" in 2003). It's just that we now have one nearly complete genome (human) and several largely complete, or getting there.

To me, "post-genomic" sounds like a complete misnomer (probably coined to make it all sound exciting); I mean, finally having a workable genome kinda makes it seem like we just entered the "genomic" era, doesn't it?

Re:Post Genomics Era?

[Torst]

It's just that we now have one nearly complete genome (human) and several largely complete, or getting there.

We have far more than one completed genome! The human genome project gets the most publicity of course, but there are hundreds of bacteria, viruses and plants which have been sequenced, see http://www.ncbi.nlm.nih.gov/Genomes/index.html. Many of these genomes have also been annotated by human curators - the so called "meta information".

Why would you have left the field?

[50000BTU_barbecue]

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.

Important point:

[Neil Blender]

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.

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.

Bioinformatics is not something you 'just get into.' And it is not a natural path to go from CS to bioinformatics.

Re:Important point:

[Neil Blender]

BTW, the term is "bioinformaticists", not "bioinformaticians"

Actually, it is you who is wrong. In the world of bioinformitics, "bioinformatician" is more widely used than "bioinformaticist". By the way, I work for a bioinformatics company.

Re:Important point:

[WillAffleckUW]

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.

Re:Important point:

[agaznog]

To clarify your claim: Computer scientists *in isolation* rarely make good bioinformaticians. As with most application domains, writing code for the sake of writing code without consulting a real-life problem usually produces unusable software. I work in bioinformatics as well, having background in both CS and Bio. I work in a group where there is a development team of ~15 + a team of roughly 30 biologists (MSc + PhDs) that also serve as prototype users. This collaboration is invaluable, yet we still struggle with exchanging ideas. Yes, I have observed biologists-turned-bioinformaticians using a little perl and hard work. Unfortunately, in most cases, the resulting software tends to be fine for specific task at hand, but simplistic or even hideous in terms of software architecture. Therefore, scalability and maintainability are out the door. And the limitations imposed by a naive understanding of computer science results in a limited range that said bioinformatician can explore in searching for solutions to real problems. Sooo, computer scientists working in bioinformatics are essential. But they must remember that *they provide the service to biology*, not the other way around.

Re:Important point:

[espressojim]

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.

random quote

[operon]

bioinformatics is more bio than informatics...

Too broad in scope

[tOaOMiB]

Note: IAAB (I am a bioinformaticist)

Having been in the field for 5 years or so, and matriculating for my PhD next year, I know something about the subject. Unfortunately, the subject "bioinformatics" is way too broad to ever make for a good book.

For example, applying for PhD programs, I found myself looking at program names such as: Biophysics, Bioinformatics and Integrative Genomics, Biomedical Informatics, Computational and Systems Biology, and of course Bioinformatics. And the terms meant something different to each professor I spoke to, and are changing over time yet. Biomedical informatics definitely implies medical databases and EMRs (electronic medical records), while Biophysics implies more of a, well, physical approach (x-ray crystallography, cell movement and membrane forces).

But Bioinformatics and computational biology encompass them all--including other topics such as protein folding, genomics, proteomics, sequence alignment, paper-mining, evolution. Each of these touches on a vastly different aspect of biology and/or computer science and to different degrees. A good book (and plenty long enough for a textbook, I assure you) could be written on any single sub-subject. A book titled bioinformatics isn't going to be worth your while.

My 2 cents and rant. Thanks for bearing with me :)

Re:Too broad in scope

[Rei]

I'll second this. The Biomedical Informatics Research Network, for example, covers everything from studies of how MRI images match up between different scanners at different sites to UMLS mappings of different mouse brain components to developing a distributed filesystem, custom computing racks, and various databases and query tools.

Quite a diverse collection, really.

Re:Too broad in scope

[drmike0099]

I'm a medical informaticist, and I don't completely agree with part of the above. I, too, read the wikipedia entry on bioinformatics and saw that my field is lumped in w/ bioinformatics, which is something I don't agree with. Perhaps to a layperson, the difference between "bio" and "medical" is not a big one, but practically speaking it is quite big. (The parent of this didn't lump, just mentioned it in passing, but I wanted to comment on it.)

Basically, someone like myself might not be too knowledgeable about what I refer to as bioinformatics, which I consider to be everything from DNA->proteins->cell cycles. Bioinformatics focuses on solving problems of data management of the vast amount of information in the above fields, which is a huge undertaking. It also happens to deal more with vast databases and data mining.

A bioinformaticist, on the other hand, is probably not very aware of what I do in my job, which is quite different. I deal on a daily basis with how we manage large datasets of patient-specific data, with the goal of improving medical care. We also deal w/ data mining and database design and all that, but it has a very different focus and uses very different tools. Our solutions are largely focused around caring for patients.

That being said, there is a middle ground of largely research projects that attempt to span that gap. There are some groups working on making data available from the bioinformatics world merge w/ the medical informatics world to be useful in some way to clinicians, but I would estimate the fruit of that labor to be 10-15 years out. These projects are mostly driven by bioinformaticists, since they're more experienced with dealing with their huge datasets and doing the data mining that they do, while the medical informaticists would be more interested in how they feed data into something like that and get something useful out.

At any rate, someone who labels themselves as a "bioinformaticist" and is doing EMR research is clearly just gunning for research money using the bio label (there's more money there).

Should I go into Bioinformatics?

[TrevorB]

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?

Re:Should I go into Bioinformatics?

[spin2cool]

What you need to pick up really depends on what kind of work you want to do in the field. There are absolutely people with little understanding of biology all over. They typically do things like optimize and translate code or tweak algorithms for biologists. To move up to more interesting problems, though, you'll have to teach yourself quite a bit of biology and chemistry.

My advice is to start with the basics. Pick up a college-level Intro to Biology textbook and learn the relevant stuff: Biological molecules, Natural selection and evolution, basic Genetics - the whole pathway from gene -> protein -> enzyme. These kinds of concepts are the foundations of biology that you need to understand before you can get into the hardcore stuff.

If you enjoy chem, keep going through it too - finish general chemistry and work your way up through some organic chemistry and biochemistry. Structural and computational Biochemistry is HUGE right now, and you can definitely choose to go more of a chem path, if that's what floats your boat.

MIT OpenCourseWare has whole sections devoted to Biology, Chemistry, and Biological Engineering. It's probably worth checking out, if nothing else, to guide you to some topics to look more closely at.

Lastly, I'm going to encourage you to do your homework and make the jump. Both Universities and corporations are salivating over anyone with knowledge in both the life and computer sciences that can help bridge the gap between the two. (I should know - I'll be working on my PhD in Computational Biology starting this fall)

Re:Should I go into Bioinformatics?

[SpriteGF]

I'm a 21-year-old CS student that just applied for a double major in Molecular and Cell Biology (MCB), getting into computational biology, and I will say that knowledge of molecular and genetics biology is a must. The people here at Berkeley know their introns and promoters and amino acid interactions, along with (what seems to be) a foundation in statistics and probability. They're juggling enormous data sets to figure out, "What's the probability that alanine is in this protein family?" And sometimes I feel lost, since I don't have a solid background in genetics.

Most of the books you describe (stuff like O'Reilly's "Mastering Perl for Bioinformatics") are geared towards life scientists who aren't computer-savvy or haven't programmed before. They won't go into the background needed to understand the real principles: chemistry, biology, biochemistry and genetics.

If you're interested in getting into bioinformatics (which I really believe is possible for you to do, since you've done CS, math, and physics, so you have at least the technical part down) you can read some textbooks in your spare time. :) I find that textbooks are geared towards teaching, than grim black-and-white technical books and papers on the subject. Skim the text first to gain some familiarity, so that you aren't bogged down with nitty-gritty details.

• Biology by Campbell ~ just read the genetics part of the text
• Lehninger's Principles of Biochemistry by Nelson and Cox has a few sections in the back on genetics metabolism
• Bioinformatics by David Mount. I heard this one was good, but you should read the previous books first :)

Good luck!

Re:Should I go into Bioinformatics?

[aav]

Such a nicely written point deserves an answer, so I hope this helps.

My experience is that formal training in biology and chemistry cannot hurt, but they're not mandatory.

I have degrees in Comp Sci & Math (like a double major in US), but nothing beyond an introduction to biology and chemistry. I have a good understanding of what I know in biology and chemistry, but I'm just a novice in these areas.

I hold a PhD in CS, with a thesis on bioinformatics. I am fairly active in the area, so my experience might be relevant.

Over the years I found that the only necessary skills are good communication and some mathematical intuition. Programming skills are useful, but marginally so. One good idea easily compensates for ten top programmers. I am a good programmer, with years of practice and a few projects of at least 50,000 lines (some published under GPL). So don't think I'm bashing coders because I'm not good at it myself.

However, I always found that the most successful projects followed from good communication between the modellers and the biologists. As long as they were able to tell each other what they wanted and where things weren't going well, all went beautifully.

The quality of the code was a side issue, discussed only when we didn't have anything else to say.

There were some pitfalls I encountered over time, too.

Modellers thinking they understood everything, and that they could do everything on their own. Usually they produced beautiful theories, without much practical application or success.

Biologists thinking the modellers were trying to devise programmes that would replace them. They generally sneered upon our projects and they went back to staring at some experimental results hoping they could sift through thousands of rows in Excel. It rarely worked.

Overly complex programme design because some programmer decided it was useful to use the latest buzzword technology. Usually this failed because it actually wasn't necessary to make the project so complex.

In what concerns the available literature, there are some books that deal with the problems and solutions in the field. One such example would be "Bioinformatics" written by Baldi & Brunak. Another would be "Molecular modelling" by Alan Hinchliffe.
I found these geared more towards presenting the problems at hand, and some of the existing algorithms.

So, all in all: one can work in bioinformatics without much training on life sciences. Some general knowledge is necessary, although mostly for allowing the communication with the experts in biology or chemistry.

From a social perspective, a somewhat modest attitude (not humble, just know your limitations!) is also important, because it facilitates communication. A positive attitude towards group work is also necessary, since I really cannot see anyone being able to do such research alone.

I liked thisbetter - Bioinformatics: practical gde

[WillAffleckUW]

"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.

disagree

[mkcmkc]

I beg to disagree. Computer scientists (i.e., skilled computer programmers, etc.) and biologists both have substantial domain knowledge that they're bringing to the table. A practitioner from either camp that fails to make use of the skills of a partner from the other is likely to leave a trail of serious messes in their wake. I see this a lot, and I think it really slows science down.

Mike

Re:disagree

[Daniel Dvorkin]

All the abstruse stuff that an OS, DBMS, or compiler writer should know about, but that an application programmer does not need.

Well, there are at least two answers to that. The first is general: the idea that "programmers don't need to know all that theory" is, IMNSDGHO, largely responsible for all the crappy bloatware that the computing world has to deal with; if programmers spent more time learning real CS than the latest buzzwords, software would generally be much better than it is.

The second is specific to the topic of discussion: scientific programming, including bioinformatics, is much closer to the theoretical level than is most application programming. Pretty widgets don't matter nearly as much as the fact that you're dealing with complex operations on huge data sets, and if you write your program without any awareness of What's Really Going On, then your program will run like shit.

Re:disagree

[DShard]

The reason software that exists is of poor quality is a function of both those who work on it _and_ the amount of features required for the product. I would argue that MS Office is a great application if you require the list of features the various applications need to provide. The problem with office is that 99% of the list is extraneous for 99% of it's users. I have seen people try to use excel as a database. Others use it as a viewer for slices of a database. Excel is ok at doing this but making it ok at this has detracted from it's actual problem domain, namely analyzing rather small numeric datasets.

Now the other %99 percent of software (domain specific spec software) developed in house for a company will fall into the other category. Most companies do want to hire a scientist to develop for them, they really would rather hire a spreadsheet jocky who can understand what if/then/else does and pay them accordingly. The real problem is the proprietary domain specific applications that are developed by those same spreadsheet jockeys (you know who you are wintam developer). You get neither the skill of a well trained scientist nor the internal expertise of the application.

Bioinformatics book recommendations

[Dioscorea]

Uh, genomics isn't going anywhere

Lots of molecular biologists would say the same thing (perhaps not in the way you meant it). Francis Crick apparently thought genomics was way overhyped.

Seriously though, I sometimes wonder why anyone bothers writing another bioinformatics howto book when Durbin et al (apologies for amazon link) is still unrivalled. Maybe also Felsenstein for phylogeny, MacKay for general probabilistic modeling... anyone recommend anything for the coalescent? Microarrays? Image analysis? I could post book refs for these, but I'm not as fluent in those areas.