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?

[Anonymous Coward]

That joke was so bad I think it gave me cancer.

Re:Post Genomics Era?

[Anonymous Coward]

Feeling cheeky? Well, you're talking like you're dumb as shit. Best reconcile those before someone gets the wrong idea and comes away thinking that you're a stupid fucking jackass.

Re:Post Genomics Era?

[proteonic]

Bioinformatics is very much in the post genomic era, though, biology certainly is not. Specifically, pre-genomics, the big bioinformatics problem was assembling genomic sequences efficiently. Now that's been solved, so bioinformatics questions now deal with how to treat the mountains of genomic data that have been generated. Thats' just my interpretation, but on the record, I hate buzz-words.

Re:Post Genomics Era?

[fshalor]

Next up is maping protein 3rd and 4th D structure. Cause we only undestand very little about the littlest of proteins.

Re:Post Genomics Era?

[Valdrax]

I think they're referring to the fact that genetics is nothing but a bunch of smarmy, self-absorbed in-jokes and structural noodling around that has nothing to do with good biology anymore. Representative genetics -- that is genetics that's actually about living things -- is seen as too commercialized and artless. Modern genetics is only done for the purpose of giving molecular biology grad students something to mentally masturbate about.

Modern genetics doesn't have to actually be about anything as long as it provokes a response, and teasing religious conservatives is seen as the highest form of the science. Just look at those embrionic stem cell researchers. You just know that they're using dead babies just to piss people off, right? Next thing you know, someone's going to get a hold of the Shroud of Turin and clone a half-goat, half-Jesus just to get a NIH grant... and our tax dollars go to pay for it!

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?

[the gnat]

Next up is maping protein 3rd and 4th D structure. Cause we only undestand very little about the littlest of proteins.

Actually, the scope of structural biology has broadened considerably over the past couple of decades, and now membrane proteins and ribosomes are within reach. A number of groups worldwide are trying to apply high-throughput methodology to structure determination, hence "structural genomics." The real problem is that even for small proteins the structure determination process is still somewhat laborious even when it's straightforward. Automation is still very rare, although it's being worked on for specific tasks.

In general, though, structural biology is almost at the point where it can be part of any biologist's toolkit if they're willing to collaborate or spend some extra time.

Re:Post Genomics Era?

[kasparov]

I've gotten so used to pronouncing the 'G' that it took me a while to get the joke... I feel a little sad about that.

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".

Re:Post Genomics Era?

[glwtta]

This is true, I was only thinking of mammalian genomes (which, on the order of several gigabases, take somewhat more effort to sequence than viruses and bacteria).

And there is another distinction to be made: we keep talking about the human genome, whereas we really only are dealing with a human genome (or rather chunks of a few with a lot more coverage for some specific sites). It will get a lot more interesting when we'll have access to thousands of human genomes (along with patient histories) - that will deserve the name "genomic era".

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.

Re:Why would you have left the field?

[Anonymous Coward]

I don't think I'm alone in waiting for the sci-fiesque promises of advanced biotech.

I hate to say it, but my opinion is that very few today are going to live to see the promise realized. The last polls of westerners I saw showed an almost universal dislike over the idea of genetic engineering. Have you ever heard the head of the US bioethics council speak? He's a nutjob who thinks humans are some sort of divine creation which stands apart from the animals. Any tinkering with our genetics is, to those who share his line of thinking, an affront to their God and their own sense of self. Just the knowledge that engineering of humans is possible is a violation of everything they hold dear. I think about the only real hope for westerners is that sneaking treatment in another country becomes possible, or that home hacking becomes commonplace and safe enough to be useful.

Re:Why would you have left the field?

[Anonymous Coward]

Get rid of your floaters? Huh? What does that mean?

Re:Why would you have left the field?

[Anonymous Coward]

I'm an EE and I've worked in Biotech (specifically for a well known genomics company). My direct observations are:

1. EE and CSci are only dying in terms of the repetitive, text-book solution jobs. Many traditional EE/CSci jobs are dying iff you insist on living in 1st world countries and insist on doing the same basic thing you did 5, 10, 20 years ago or expect a diploma mill (includes MSCEs !) and/or cheating to get you somewhere. Start-ups are still happening (I co-founded one 3 years ago and things are going great). For 1st world countries there are still opportunities due to capital markets, legal structures, etc. The next new things will likely be plasmonics, photonics, quantronics and nanotechnologies. In CSci, consider that microprocessor speeds and shrinks (aka Moore's Law) stopped dead three years ago - innovations in speed and applications will have to come from software and architecting existing process geometries. The only issue is lagging R&D in the US could assure non-US companies end up owning these spaces. I've been setting up shops in Asia for "insurance".

2. Biotech, especially genomics will *never* be a boom business like EE or CSci based industries have been during the last 50 years (electronics, semiconductors, computers and software). The simply reasons: patent-centric business models, FDA approval cycles and largely "craft"-based manufacturing limit product life cycles. This in turn limits the maximum possible ROA/ROI and assure biotech/genomics will never be better than 1/10th that of EE/CSci-based industries historic returns. In the genomics company I worked in, the number full-time lawyers on the payroll was comparable to scientist and research priorities were directed and vetoed by the legal department. Not a recipe for innovation yet this company is the market leader - but mostly, as the CTO told me one time, "because our patent position means customers have to buy from us; we don't need to innovate". All this even though this company's supposed "core values" included "innovation". The only possible exceptions in biotech: non-human, non-health biotech or genomic applications.

The major risk with the US is that we are slashing R&D in both the private and public sector, and to boot, driving out foreign and native-born students from our university and immigration offices. The problem is that innovation is only contextual - you must have the right incubation environment which includes direct exposure to the problem space that requires innovation. Couple that with the fact that post-WWII US economic and military power is directly a result of this innovation environment and lots of dumb luck, and it becomes clear the US is at risk of being at the beginning of the end of the American Empire.

Re:Why would you have left the field?

[Anonymous Coward]

Damn it, Google is your friend. First damn link

TOC

[Virtual Karma]

Here is the TOC: Table of Contents: Preface. 1. Introduction. Overview. Computationally Intense Problems: A Central Theme in Modern Biology. Building the Public Infrastructure. The Human Genome's Several Layers of Complexity. Toward Personalized Medicine. Illnesses are Polygenic. New Science, New Infrastructure. The Proactive Future of Information-Based Medicine. 2. Introduction to Bioinformatics. Introduction. The Emergence of Bioinformatics. The Public Database Infrastructure. Building Database Infrastructure for Bioinformatics. Traditional Bioinformatic Tools and Algorithms. Summary. 3. Gene Structure. Introduction. The Central Dogma of Molecular Biology. The Genetic Code. Structure and Content of the Genome. Computational Techniques for the Identification of Genomic Features. High-Throughput Gene Sequencing. Summary. 4. Computational Techniques for Sequence Analysis. Introduction. Hidden Markov Models. Perceptrons and Neural Networks. Pattern Discovery, Single Nucleotide Polymorphisms, and Haplotype Identification. Summary. 5. Transcription. Introduction. The Transcriptome. Technologies for Transcriptional Profiling. Hierarchical Clustering. Summary. 6. Overview of the Proteome and the Protein Translation Process. Introduction. Ribosomal Structure and the Protein Translation Process. Special Features of the Eukaryotic-Translation Process. Summary. 7. Protein Structure Prediction. Introduction. Overview of Ab Initio and Database-Driven Approaches. Overview of Protein Structure. Protein Structure Databases. Ab Initio Structure Prediction. Predicting Lead-Target Interactions. Summary. 8. Medical Informatics and Information-Based Medicine. Introduction. The Continuous Evolution in Understanding that Leads to Genomic Medicine. Electronic Medical Records. Grid Computing and Medical Informatics. Modeling and Predicting Disease. Summary. 9. New Themes in Bioinformatics. Introduction. Overview of Parallel Computing and Workflow Distribution in Bioinformatics. Workflow Computing. High-Performance Computing and Systems Biology. The Delineation of Metabolic Pathways. Systems Biology. Summary. Further Reading. Index.

Re:TOC

[Anonymous Coward]

Here is the TOC (Table of Contents) posted by an AC that knows how to use tabs:

Preface.
1. Introduction.
Overview.
Computationally Intense Problems: A Central Theme in Modern Biology.
Building the Public Infrastructure.
The Human Genome's Several Layers of Complexity.
Toward Personalized Medicine.
Illnesses are Polygenic.
New Science, New Infrastructure.
The Proactive Future of Information-Based Medicine.
2. Introduction to Bioinformatics.
Introduction.
The Emergence of Bioinformatics.
The Public Database Infrastructure.
Building Database Infrastructure for Bioinformatics.
Traditional Bioinformatic Tools and Algorithms.
Summary.
3. Gene Structure.
Introduction.
The Central Dogma of Molecular Biology.
The Genetic Code.
Structure and Content of the Genome.
Computational Techniques for the Identification of Genomic Features.
High-Throughput Gene Sequencing. Summary.
4. Computational Techniques for Sequence Analysis.
Introduction.
Hidden Markov Models.
Perceptrons and Neural Networks.
Pattern Discovery, Single Nucleotide Polymorphisms, and Haplotype Identification.
Summary.
5. Transcription. Introduction.
The Transcriptome.
Technologies for Transcriptional Profiling.
Hierarchical Clustering.
Summary.
6. Overview of the Proteome and the Protein Translation Process.
Introduction.
Ribosomal Structure and the Protein Translation Process.
Special Features of the Eukaryotic-Translation Process.
Summary.
7. Protein Structure Prediction.
Introduction.
Overview of Ab Initio and Database-Driven Approaches.
Overview of Protein Structure.
Protein Structure Databases.
Ab Initio Structure Prediction.
Predicting Lead-Target Interactions.
Summary.
8. Medical Informatics and Information-Based Medicine.
Introduction.
The Continuous Evolution in Understanding that Leads to Genomic Medicine.
Electronic Medical Records.
Grid Computing and Medical Informatics.
Modeling and Predicting Disease.
Summary.
9. New Themes in Bioinformatics.
Introduction.
Overview of Parallel Computing and Workflow Distribution in Bioinformatics.
Workflow Computing.
High-Performance Computing and Systems Biology.
The Delineation of Metabolic Pathways.
Systems Biology.
Summary.
Further Reading.
Index.

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:

[Pionar]

Which is why informatics programs like the one Indiana offers (especially the program at its Indianapolis campus) are so important. It gives people the education needed to bridge the two areas.

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

Re:Important point:

[TrevorB]

Agreed on commercial vs kacky stuff. I've seen a number of books on Perl and Bioinformatics. They've got a good "hacky" feel to them. Also I'd have to agree with the Computer Science vs Life Science. There's a reason why it's "Applied Science", I've often found Comp Sci students at the 3rd year university level struggling with basic high school math.

Hmm... Just posted a question about this in another thread. How about a Mathematics/Physics background?

I'm thinking I'm looking at about 2 years of further undergraduate to bring my basic biology/biochemistry/molecular biology knowledge up to snuff, and pretty much a lifetime career change. I'd wished I'd had a chance to consider this when I decided on my degree over 10 years ago!

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.

Re:Important point:

[Anonymous Coward]

I always thought of Bioinformatics as a tool for Biologists. Something that can be used in the research environment - therefore it should naturally come from people who understand or are willing to learn about this environment.

Does anyone here have an example where Bioinformatics software has been developed by Computer Scientists with little or no input from "wet" researchers that has worked well?

Re:Important point:

[Schwarzchild]

"By the way, I work for a bioinformatics company."

So...does your company produce crappy software or good software?

Re:Important point:

[blueZhift]

My PhD is in Physics, I've worked in IT in various capacities, including software development, for the last several years now. The research background coupled with the IT work helped me get my current bioinformatics position. From what I've seen, I would say make sure that you get a heavy dose of statistics training with your mathematics because you're going to need it! I work closely with a biostatistician to implement computer analyses and have learned a lot in the process, but more statistics in grad school would have helped!

Now call me biased, but I think my Physics training has helped me think about problems in code and science, so I think it would be a nice addition to your biological studies. Heh, we physicists always think we can do anything!

Re:Important point:

[Anonymous Coward]

Good review here of the Oreily bioinformatics book. I found this book makes it much easier for computer scientists to understand some of the basics and get up to speed. The book is about three or four years old, but the fundamentals are still there.

Re:Important point:

[Neil Blender]

So...does your company produce crappy software or good software?

Most of our customers say good. The people who disagree do so mainly because it lacks a feature they want. Which is a huge problem with bioinformatics software - most do far too many things and none of them particularly well. A terrible side affect of this, is it makes them overly complex, often to the point of unusable by only a very experienced bioinformatitician or very savvy computer user. As the nature of certain types of experiments matures and becomes cheaper (microarrays for instance), the amount of analysis features you need actually lessens because you can design better experiments. Early software was complex because the experiments were very expensive and the inability to to replicates necessitated the need for complex statistical analysis. Our customers want to understand the biological meaning behind their data (as opposed to the meaning of data itself, there's a big difference), many have little understanding of statistics and sometimes very little computer experience. We provide something that is easy to use and provide the knowledge and understanding of what they are doing with their data. We don't sell to bioinformaticians (for the most part), we sell to bench scientists. In my company many of us are former bench scientists and we all have a good understanding of what they want. All of our programmers have life sciences degrees.

Re:Important point:

[mrbooze]

How many examples are there of *any* succesful software being developed without knowledge of the subject or input from its target audience? Was photoshop developed by programmers with no knowledge of image manipulation? Was Lotus 123 developed by people with no knowledge of spreadsheets?

Re:Important point:

[Anonymous Coward]

And it is not a natural path to go from CS to bioinformatics.

Then why are you on /.?

Re:Important point:

[TrevorB]

I have about 3 upper division statistics courses as part of my honours math major. I'd likely have to pick up a bit more. And brush up... it's been years.

This all seems so much more realistic if I was 20 instead of 30 though... :) I'm being inspired by Slashdot though, what a sad life I must lead... :)

Thanks for your reply!

Re:Important point:

[Neil Blender]

And it is not a natural path to go from CS to bioinformatics.

Then why are you on /.?

Hmmm. Not sure. Beause the converse of my statement is surely: Bioinformatics people have no interest in programming, linux, etc. Actually, it's a little known fact that all bioinformatics is still done with pencil and paper (we can't even use calculators, the Patriot Act forbids it).

Re:Important point:

[Anonymous Coward]

I hear this kind of macho rubbish all the time. I've worked (and work) in a computer science department. And I've worked in biology labs. There is a lot to get from both sides. And there are is a lot of sillyness of both side.

I've known people move both ways as well. Of course, domain knowledge is enourmously important. But there again so is much CS knowlege; biology has really not helped itself with flat files every where.

We are moving to a new era now. I need large data sets, I need distribution, I need good data models, I need new technologies for representing those data models.

This is not going to happen if computer scientists
think biology is simple and play with toy examples. But it's also not going to happen if biologists think that they can just knock something together quickly. All that wil happen then is that they wil reinvent existing technologies badly. In the past this has been an irritation, in the future it wil be lethal.

Incidentally, the shit commercial software that you have seen; there actually is a lot of good software around, it's just really expensive.

Phil

Re:Important point:

[Anonymous Coward]

Schools like UCSD have begun to offer undergraduate programs in bioinformatics. At UCSD, the course load is more or less the core of computer science (up to advanced data structs/algorithms) plus biochem/molecular bio (genetics/biochem/mobio/cellbio), then a 6-quarter sequence of integrated bioinformatics classes. Programs like these try to produce future scientists at the interface of biology and computer science.

Re:Important point:

[gurnemanz]

Sounds like Mr. Blender prefers to take his Biology straight. Forgive my little joke. As others on this thread have said, just computer science is not enough. However, a strong background in scientific applications along with computer science would serve one well in this field. Bioinformatics brings in whole new skill sets that are more likely to be possessed by slashdotters than rat slashers - string searching, data mining, large databases, algorithm and gui development, massively complex networking and data sharing, and putting this all together in a coherent computer system design. Other skills of a more mathematical or statistical nature are probably a wash - the skimpy math requirements of a CS degree barely eclipse the skimpy math requirements in biology. Statistics is the rub. I may not know Drosophila Melanogaster from Mus Musculus, but if I have an understanding of the theory behind Blast or Hidden Markov Models, then I may bring something to the party. Wearing just minimum flair, G

Re:Important point:

[Anonymous Coward]

A terrible side affect of this

"effect".

Re:Important point:

[cbare]

This misguided attitude is way too common among biologists.

There's a real lack of well engineered bioinformatics software. Most of what's there is quick-and-dirty one-off hackery that got entrenched as standard practice.

Like computer science, though maybe for different reasons, biology attracts personalities that don't play nice with others. That's the real problem. Because, in order to build bioinformatics software that is both well engineered and actually usefull, skills from a lot of disciplines will be needed. And computer science, software engineering, and even us lowly vo-tech coders will be among them.

random quote

[operon]

bioinformatics is more bio than informatics...

Huh?

[TheRealFixer]

"Post-Genomic Era"? What, is Jon Katz back, and ghostwriting this time?

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).

Re:Too broad in scope

[espressojim]

As a bioinformatics guy (see above post by me) who generally works in the field of population genetics, I often get to deal with large sets of patient data. We call 'em phenotypes, and we use them to distinguish between our 'cases' and controls, to stratify our populations.

That's how we do those association studies. True, we don't have the immediate goal of improving medical care, but we manage huge sets of data. Reference: I've got a data warehouse that has over 550M rows of data in it. That's essentially one data set.

Looking at people doing pop genetics may be close to that bridge you're talking about.

Re:Too broad in scope

[Torst]

Note: IAAB (I am a bioinformaticist)

Down here in Melbourne, Australia we tend to refer to them as bioinformaticians for unknown reasons :-)

Re:Too broad in scope

[Anonymous Coward]

I'm also a grad student engaged in bioinformatics (with undergrad degrees in both bio and cs), and I had the same impression of the book from the table of contents and first chapter. This would not be a useful text for learning in the field of bioinformatics because there's no way that any of the topics could be more than glossed over in this text. Each of the chapters requires its own text to be useful (I'd be interested to see what the book says about Markov models).

To address the "post-genomic era" confusion, the post genomic era can be characterized by the following statement: "OK, so we've got the genomes --- now what the hell do we do?" And, rest assured people, we are still sequencing genomes. In fact, we are doing more genome sequencing than we have ever done --- and we won't be done sequencing genomes anytime in the near future.

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?

[WillAffleckUW]

Only having Chemistry and no Biology would be a lot harder.

I just switched myself, with a DA/DBA post-grad certificate, into Bioinformatics, but I had four years of Latin, had worked in Health Care for four years, and had University-level Biology and Chemistry. The one thing you'll really need is stronger Biology.

You could take some audit courses in Biochemistry and Biology, of course. That might help.

All the acronyms will drive you crazy, but the field is so specialized that if you study hard you might make it. The difference from one lab to the next is amazing - one might be using NMR, the next is on the beamline. Even labs working on the same family are almost entirely different - one is into cell-free, the other uses a different method.

Check out a few books and see if it looks understandable or it makes your head hurt. That's a good sign, if your head can hold that in there.

Re:Should I go into Bioinformatics?

[grimner]

I also switched careers into bioinformatics but from a molecular/cell biology background (went back to schoool for a MS in CS). As several previous posters have pointed out, knowledge of biology is not an incidental in this field, it *is* the field. It's easier to learn the CS side than the biology. I often take my own knowledge for granted, figuring hell, anyone can do this job, until I talk to people without a biology background. That being said, if you are willing to learn the biology your math background could be a huge plus. Much of bioinformatics is focused on data mining various sources like sequence databases, microarray results, pubmed, etc so an understanding of pattern recognition, AI, and statistics would be beneficial. Also, biologist tend to be horribly bad with math;)

Re:Should I go into Bioinformatics?

[rib]

What about computational biology? I'm a fairly well seasoned developer and I am looking to transition into comp bio. To do this, I'm going through a molecular cell biology text book and am going to take a graduate Comp Bio course from a good university. What other things should I be doing?

Do you have any suggestions of companies or organizations that write comp bio software? I'd love to find a need and start my own business writing software but I'm not sure how to break into the field.

Re:Should I go into Bioinformatics?

[avandesande]

The university of manchester has a online ms program in bioinformatics that is legitimate.

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.

Re:Should I go into Bioinformatics?

[grimner]

Do a search on Monster and you will see many companies hiring positions with computational biology experience. All pharmaceutical companies and many biotech companies have computational biologists, to some degree. It's a difficult field to excel in since you need a strong background in both CS/Math and biology. Sounds like you are off to a decent start. Look for a software developement position in a pharmaceutical or biotech company. It will be the best way to get your foot in the door. Play up your developer experience and top it off with a class or two in biology and Comp bio. Good luck.

Re:Should I go into Bioinformatics?

[cinnamon colbert]

There is a reason most of bioniformatics is simply DB and looking at the data, and that is because that is what is reasonable.

First, there has been a huge, huge explosion of data, and the bio community was really not prepared, and so simply getting an understanding of what a real DB is, and how to set them up and so forth, took a while.
Let me tell you a true story: This guy tells me, I used to work on parvovirus ( its not important what parvoviruses do)and I am looking in gen bank, and this PV seq is in the database backwards - its obvious. So I call Genbank up, and they say, the rules are, only the original submitter can make a change.
So here we have THE major public database of DNA sequence, and there is no way to flag obvious errors ( i mean raelly ovbious errors like tranposing letters)
So, there has been a painful period where the rather small and insular bio community learned about data storage.
The other, more significant problem, is that we really dont know what to do with all the data - that is, the bottleneck is in finding scientists who can say, it would be interesting to compare this to this, or do this sort of analysis, and who can then write the code. Because a lot of the data we have has no biological function - it is just DNA sequence, which in and of itself is as informative as a page of code without any knowledge of the hardware or OS that run the code.

Another problem is that a lot of the intersting problems are known to require ~ pflop computational power, that is, we know there is a class of very important problems, and (from what i gather) all the experts agree we need ~ 1,000 x more flops (ibms blue gene is supposed to be the first machine to break this barrier.)

If I had to advise you, I would say in the next 5 years, far and away the most interesting place in bioinformatics is in protein folding and drug docking. Proteins are linear strings of 20 chemical building blocks, eg ala gly ala is a protein with three blocks, alanine, glycine, alanine.
most real proteins have >200 strings, and the initial linear molecule folds up into a 3D structure. Currently, the cost of obtaining a 3D structure (x ray crystallography structure, or 2D NMR) typically exceeds the cost of obtaining the linear (sequence) info by two to orders of magnitude.
since it is the 3D structure that is biologically relevant, there is tremendous interest in programs that can predict 3D structure from 1D sequence.

The docking problem is, once you have a 3D structure for your protein, if you are given the 3D structure of 10,000 possible drugs, can you get the computer to tell you which of those drugs binds (key/lock fit) to the protein...whoever can do this will truly revolutionize (I mean really) the drug industry...

another formidable problem is, once you know all the proteins in the human body, can you predict what happens when you raise the concentration in blood of one protein ?
And how do genetic variations affeect this process.

Re:Should I go into Bioinformatics?

[Anonymous Coward]

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)

While there is some truth in this comment, and the rest of Spin2Cool's advice is pretty good, the level of salivation is overstated, at least for the corporate arena. Demand for bioinformaticians is nowhere near what it was five years ago (a symptom of the "post" genome era). I should know - I'm the head of bioinformatics at a top 20 pharmaceuticals firm, and I have done my share of hiring.

That said, bioinformatics remains an excellent career choice for those with the right skills and commitment. IMHO, the most effective industrial bioinformaticians tend to have either a PhD or Masters degree in a biological field like biochemistry or cell biology, as well as having a Masters degree in Comp Sci. In practice, this usually means that they are biologists "first" and programmers or data management specialists "second". It is certainly possible to be successful without those credentials, but the options will tend to be a bit narrower for all but the most exceptional individuals. On the other hand, I have some excellent people on my staff with Comp Sci backgrounds yet with little biological training. The common feature these latter individuals have is a combination of very strong IT skills and the personal initiative to ask scientists lots of questions in order to ensure that their programming work was truly relevant to challenges faced in the labs. Interestingly, such individuals often end up pairing with lab scientists in a manner reminiscent of agile development methodologies.

As an aside, while formal degrees in bioinformatics are becoming more common, their utility to date has been primarily academic. For someone who already has a background in either comp sci or biology and wants to get another degree in order to prepare for a career as an industrial bioinformatician, I would suggest investing in a full degree in the other field, rather than getting a "hybrid" degree in bioinformatics. The bioinformatics degree programs are still fairly new, and their curricula haven't really matured yet. From the job candidates I have seen, I have had the strong sense that given the time they invested to earn the second degree, individuals with biology backgrounds tend not to get a "good enough" comp sci background from bioinformatics degree programs, and similarly, comp sci folks don't get enough biological perspective from bioinformatics degrees to have the kind of impact I am looking for in a new hire. Of course, all caveats about over-generalizations apply, and your mileage may vary.

Wait, I just read about this ..

[Bitmanhome]

Ah yes, here we go:
http://www.chaosmatrix.org/library/humor/pshift.tx t

Looks like this guy has a newer version, I don't see a "bioinformatics" option.

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.

Re:Electronics/Computer Science isn't tapped out

[50000BTU_barbecue]

Yeah, I agree with this. I mean I got started in EE because I was taking things apart as a kid. 20 years ago discrete parts were still common, especially for things in the garbage.

A kid interested in biology will get taken to the psychologist if he takes the neighborhood squirrel apart. Sadly, it's when you're young that it's the best time to learn by tinkering, so if you do like bio, you'll only get to tinker in your 20s when you hit university and put mice in the blender.

Like the other poster mentionned, the local priest also doesn't care if you take a radio apart and engineer it into a television, let's say. The freaky 12th-century religious fuckwits that dominate the US (it seems) are the biggest problem. (Yet they are the first to demand extraordinary measures for keeping corpses alive.)

Post-Genomic Era?

[Anonymous Coward]

Anything that happened after the agricultural revolution?

All the cool stuff is epigenic

[Anonymous Coward]

It seems a lot of cool stuff never appears in the dead sequence, but in the runtime environment. Functional genomics, with all that gooey chromatin and methylized dna is where all the action is, literally.

It's all proteomics nowadays, post-genomics

[WillAffleckUW]

And structural genomics more than functional genomics.

I spend my day covered in protein sequences and worried about docking configurations and charges, quite frankly, working on drug design targets to help cure malaria and other nasty beasties.

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]

Yes, exactly. The reason, I think, that there's a perception that biologists make better bioinformatic[ists|icians] (it's a stupid argument; both terms are well understood) than computer scientists is that the learning curve for hacking is shallower than the learning curve for molecular biology. Someone with no training in CS can pick up a "Teach Yourself $LANGUAGE in 24 Hours" book and turn out code that, even if it's poorly written, at least does something useful; someone with no training in biology cannot pick up "Biology for Dummies" and do meanignful biological research.

But the fact is that both fields exist as serious areas of academic study in their own right for a reason; a bioinformatic___ who doesn't put several years of serious effort into understanding both will do mediocre work at best. Also, I would argue, an equal amount of effort should be put into the chemistry and math which underlie biology and CS, respectively. I've done pretty much all of the above, and I'm still well aware of how much I have to learn. Anyone in any interdisciplinary field who dismisses the knowledge of those in related fields is making a huge mistake.

Re:disagree

[bar-agent]

Computer Science isn't about programming. CS is about applied theory, specifically: algorithm theory, database theory, data- and instruction-flow theory (don't know the technical term), compression and error-detection, etc. All the abstruse stuff that an OS, DBMS, or compiler writer should know about, but that an application programmer does not need.

Programming and professional software engineering practices should really be vocational school territory, in my opinion. Still valuable--essential--for a developer, but light on the more speculative theoretical areas.

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.

Re:disagree

[merdark]

Indeed. At my university computer science is under the "Mathematics" department, as it should be.

Re:disagree

[danudwary]

>>Someone with no training in CS can pick up a "Teach Yourself $LANGUAGE in 24 Hours" book and turn out code that, even if it's poorly written, at least does something useful;

Absolutely true, and I am proof. A large chunk of my PhD is the results I got from a VERY poorly coded Perl script that I wrote after reading "Teach Yourself Perl in 24 Hours". Had some C background, so that helped, and I eventually learned enough Perl/Tk to code up a UI.

University Of Manchester Bioinformatics

[avandesande]

Here is a link to the UM online MSC program...

http://www.bioinf.man.ac.uk/education/MSc.shtml#co urse

Should you go into Bioinformatics?

[Anonymous Coward]

I don't know. Do you love molecular biology? Do you love programming? Do you love research? If so then yeah perhaps you should go into bioinformatics.

If you're looking for a book about bioinformatics then consider Bioinformatics by Baldi and Brunak.

Keep in mind that many if not most such jobs require a master's degree in the field.

I'm currently finishing such a degree. I'm an engineer with a strong interest in molecular biology and I've taken enough math credits to have a Bachelor's degree in mathematics. I think it's good to have a strong background in mathematics, software development and molecular biology to succeed in this field.

bioinformatics?

[Anonymous Coward]

Seems like any other book on bioinformatics - its either too heavy on biology and not enough on quantitative factors, or too heavy on statistics and barely touches the significance of biology. I'd give my kingdom for a book that is bio enough to explain the importance of genetic markers, binding sites, etc, and how to spot them but CS enough to give pseudo-code for pairwise alignments and HMMs.

For those interested in the field, btw, bioinformatics is definitely more toward computational biology/genomics, medical informatics is more EMRs, medical laboratory systems, etc. and (at least IMHO) biomedical informatics encompasses all of the above and others. The days of commercial bioinformatics are over, though. If you're interested in a career in bioinformatics, you're best bet is going to be in academia, working with biologists and whatnot. The cash-money nowadays seems more oriented toward medical informatics.

Change Terms Please!

[Endymion]

Can we please let the term "Bioinformatics" die already?!

I never understood why people think it's special. We used to call these run-time studys, search algorithms, etc "Computer Science", or maybe just "Informatics".

It seems that biologists decided to learn Perl, and discovered (on their own, maybe!) that you could use it to search these sequence files they generate. Suddenly, they decided they needed to create this entire new field, totally ignoring all of the CS research before them.

It shows in the software they use, too. A huge ammount of software that is considered "production", fails in ways you'd expect a fresman CS student to fail.

"Blast" doesn't have consistent return codes!

"cross_match"/"PXM" has no concept of memmap(), and will happly malloc() multi-GB spaces so it can slurp in entire files! ...sigh...

Ok, I'm bitter... Working here, and see this all the time. It's CS people! Grrr...

#include [std_disclamer.h]

Re:Change Terms Please!

[shadowKFC]

Amen..

I'm approaching this from the other side, I'm a biologist not a coder.
What I'm working on right now is alignments of RNA secondary structures. Since this is a relatively new idea there is no really polished software to this yet.
Some of the stuff I experinced in the last days:
RSMatch:
http://http//aria.njit.edu/rnacenter/RSmatch/ Chokes on lower case letters in the sequnce files. Most amusingly it does that when it encounters one, meaning it will happily do seed alignments for 45 minutes then find a 'g' in the ninth out of ten seqs and die.. No problem if you know but it took me some time to figure that one out
Dart:
http://dart.sourceforge.net/
Eats memory like nothing else I have ever seen. I have a Dual G5 with 4 Gigs of RAM here and Dart really manages to fill the whole 4 Gigs with 4000 bytes of data (20 RNAs with 200 bases..)
Don't get me wrong, I really like what I do and I'm really glad there is people out there writing this kind of software and making it available but sometimes it is a bit frustrating ;-)

Re:Change Terms Please!

[Anonymous Coward]

It's just like "biostatistics". I once flipped through a biostatistics book, and it was just like any other statistics book, except that all the examples had something to do with biology.

Re:Change Terms Please!

[Endymion]

With the software, at least, there is some hope...

I'm constantly writing wrappers around things to make them sane, and re-implementing stuff in a hopefully more useable way. Now if only the'd let me BSD licence the results.

Now if I only had time to work on a consed replacement, like I've wanted to do for quite a while. That is the most unholy piece of software I've ever seen... "../chromat_dir" and "../phd_dir" are HARD CODED in the source!

I need to think of a way to convince the higher-ups to let us hire some heavy-CS-background people. Things might work!

Re:Change Terms Please!

[WillAffleckUW]

"Blast" doesn't have consistent return codes!

You mean "BLAST"?

Of course it doesn't have consistent returns - it's a search of the known entries - people are entering new data every second.

Biology and Biochemistry hold still for no man. Or woman.

Re:Change Terms Please!

[telkel]

Things might work!

I think you are too optimistic... There are too many coding people without any CS background (not even self-taught).

Re:Change Terms Please!

[telkel]

Sometimes, it is sufficient to take a look at the HTML source of the homepage of the software (to see that there are some troubles ahead).

Re:Change Terms Please!

[Anonymous Coward]

I never understood why people think it's special.

Computer scientists have been gathering and analyzing data in many domains for half a century. Why a new term for the processing of genetic data?

Because the genetic information was already software before we transcribed it to our machines. A chromosome is just a storage device, and you have six gigabits worth in every cell in your body.

A gene exists only as a file on these devices -- it has no more physical structure than an inode does.

Yes, this new field deserves a new name.

we all have reason to oppose this

[r00t]

Nobody will want to hire us normal humans.
We generally have lots of flaws.

Once made-to-order humans become common, all
of us existing people become obsolete. We'll
be, at best, like chimps or gorillas in the
new world.

Life wouldn't be grand for the new people either,
because then human version 2.1 comes out, etc.

Re:we all have reason to oppose this

[Joe Tie.]

I think this is making a lot of large assumptions. Firstly, the idea that it's only an either or situation as far as enhancements. Somatic might not be 'quite' as effective as germline therapy, at least from my armchair amateur view, but I'd assume it'd at least get someone in the game if they'd not been lucky enough to receive treatment before birth. Secondly, it makes a rather large assumption about the level of changes. We're talking tweaking here, not outright creation of a new species. At most I doubt there'd be, at least initially, as much difference as exists between the lower and upper class already. While I'm just middle class I can certainly say I've never felt the desire to go into a low income neighbourhood and feed peanuts to the people living there! And if I can't get a position because Richie McRicherson bought better enhancements than I could afford, I don't think it's that much of a change from the way things already are. The rich can afford private schools for their kids which beat the hell out of the sorry state public education is now in, they usually have better food for their child during both gestation and later development which gives them a boost in both body and mind from the start, and they have social doors open to them that the rest of us can only dream of. I think the world you're scared of losing never existed in the first place. If anything, I'd say genetic engineering for the purposes of augmentation might even give lower classes a slight head up on an upper class so blindsided by their own ideas of "human dignity" that it'd give others a better chance at the pot for a while.

In any case though, that's pretty much just wild theory at this point. There's people with genetic diseases, or even those like the parent with simple genetic annoyances, that could really use treatment right now. And I really hope they don't wind up missing out of it either because of too much speculation on futures we can't possibly predict, or because of religious beliefs foisted upon nonbelievers because of their country of origin.

Re:we all have reason to oppose this

[r00t]

Even without any invented features, the rest of us are in big trouble.

Consider the smartest non-insane person in the world. (one in 6 billion, with an IQ well above 300) Now fix any obvious defects, such as nearsightedness or a heart valve problem. So this is pretty much a clone of the brightest person to ever live, with the easy-to-identify flaws patched out. Tweak the appearance a bit (eye color, etc.) if desired.

Now imagine that there are lots and lots of people made like this. It's no longer 1 in 6 billion. It's 1 in 6, with the proportion growing rapidly.

Re:we all have reason to oppose this

[Joe Tie.]

I couldn't see something like that happening on a large scale. I don't know how many people would admit it but most, conscious or unconscious of the fact, have kids in order to launch their genes into the future. I could see a minority of people willing to raise a child born from anthers DNA, but for better or for worse, I think people that unselfish are few and far between.

On-line syllabus by key bioinformatician

[Anonymous Coward]

Instead of going for this book, which sounds rather weak, try this syllabus: http://bio5495.wustl.edu/ by Sean Eddy, one of the world's most effective bioinformaticians. You'll learn more. If the biology is incomprehensible, the classic introduction is probably still Watson's Molecular Biology of the Gene, now in its 5th edition: http://www.amazon.com/exec/obidos/tg/detail/-/0805 34635X

Nah, it's the CS folk who coined the damn name

[Jonathan]

When I started grad school (in biology, but I did computational work on evolution and gene finding) in 1992 we called it "computational biology" I never heard the term "bioinformatics" until the CS people discovered the field after the dot-com bust.

Re:Nah, it's the CS folk who coined the damn name

[Endymion]

Oh, great!

ANOTHER thing wrong with this industry I can blame on dotcom!

Woosh! The sound you hear is my sanity slipping away...

Re:Nah, it's the CS folk who coined the damn name

[Anonymous Coward]

IIRC, the first appearance of the term "bioinformatics" was in the early 1980s. Pretty sure it was in a paper out of the Pasteur Institute. Blamez les Francophones.

Why would you [run away from] the field?

[Anonymous Coward]

"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. "

Type Onocology into a search engine. That will be the next growth field. Making human information more accessable to machines.

Should I go into BioBooks?

[Anonymous Coward]

"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."

http://www.amazon.com/exec/obidos/tg/detail/-/1565 926641/qid%3D1112750321/sr%3D11-1/ref%3Dsr_11_1/10 2-2973245-9165750?v=glance&s=books

commercial bio software sucks because

[cinnamon colbert]

the markets are very small, and there are a lot of companies in each market, so they cant actually afford any real programmers to write real algorythms; instead, they take some piece of code written by a grad student , add a fancy but not very usable gui, and sell the result..

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.

Give me your poor, your tired, your Bioinformatics

[WillAffleckUW]

[Why are you on /.?] Hmmm. Not sure. Because the converse of my statement is surely: Bioinformatics people have no interest in programming, linux, etc. Actually, it's a little known fact that all bioinformatics is still done with pencil and paper (we can't even use calculators, the Patriot Act forbids it).

Shhh. Don't mention that. Next thing you know Congress will outlaw our Sliderules and Pencils.

It's hard enough using Polaroids to take pictures of the gels when we PCR ...