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Biotech Books Media Programming Book Reviews Technology

Bioinformatics in the Post-Genomic Era 105

Posted by timothy from the nice-genes-on-that-one dept.
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.
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Bioinformatics in the Post-Genomic Era More

Bioinformatics in the Post-Genomic Era

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  • Uh, genomics isn't going anywhere.

    • Re:Post Genomics Era? (Score:5, Informative)

      by killtherat ( 177924 ) on Tuesday April 05, 2005 @04:01PM (#12146909)
      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.
      • Next up is maping protein 3rd and 4th D structure. Cause we only undestand very little about the littlest of proteins.

        • 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 so

    • Re:Post Genomics Era? (Score:5, Funny)

      by AKAImBatman ( 238306 ) * <akaimbatman AT gmail DOT com> on Tuesday April 05, 2005 @04:02PM (#12146918) Homepage Journal
      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. ;-))
    • 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.
      • 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? (Score:4, Informative)

      by habuji ( 846720 ) on Tuesday April 05, 2005 @04:12PM (#12147029)
      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.
      • 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

      • Re:Post Genomics Era? (Score:3, Informative)

        by glwtta ( 532858 )
        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 [nih.gov] (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

        • Re:Post Genomics Era? (Score:2, Informative)

          by Torst ( 22795 )
          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 [nih.gov]. Many of these genomes have also been annotated by human curators - the so called "meta information".

          • 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

    • 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.

    • 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 [amazon.com] (apologies for amazon link) is still unrivalled. Maybe also Felsenstein [sinauer.com] for phylogeny, MacKay [cam.ac.uk] for general probabilistic modeling... anyone recommend anything for the coalescent? Microarrays? Image analys

  • Why would you have left the field? (Score:3, Interesting)

    by 50000BTU_barbecue ( 588132 ) on Tuesday April 05, 2005 @03:57PM (#12146866) Journal
    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? (Score:1, Informative)

      by 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
      • 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.
        • 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
          • 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

            • 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.

  • Important point: (Score:5, Insightful)

    by Neil Blender ( 555885 ) <neilblender@gmail.com> on Tuesday April 05, 2005 @04:07PM (#12146973)
    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.
    • Which is why informatics programs like the one Indiana offers (especially the program at its Indianapolis campus [iupui.edu]) are so important. It gives people the education needed to bridge the two areas.

      BTW, the term is "bioinformaticists", not "bioinformaticians"
      • 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.
        • "By the way, I work for a bioinformatics company."

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

          • 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 experiment
    • 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 furthe
      • 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 sch
        • 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!
    • 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

    • Re:Important point: (Score:2, Insightful)

      by agaznog ( 642529 )
      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 strug

    • disagree (Score:3, Informative)

      by mkcmkc ( 197982 )
      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

      • 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 can
        • 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 devel

          • Re:disagree (Score:4, Insightful)

            by Daniel Dvorkin ( 106857 ) * on Tuesday April 05, 2005 @06:04PM (#12148417) Homepage Journal
            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 (Score:3, Insightful)

              by DShard ( 159067 )
              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
          • Indeed. At my university computer science is under the "Mathematics" department, as it should be.
        • >>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.
    • 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 p
    • 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..
    • 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 an
    • 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 b

  • random quote (Score:2, Insightful)

    by operon ( 688118 )
    bioinformatics is more bio than informatics...
  • "Post-Genomic Era"? What, is Jon Katz back, and ghostwriting this time?

  • Too broad in scope (Score:5, Informative)

    by tOaOMiB ( 847361 ) on Tuesday April 05, 2005 @04:19PM (#12147102)
    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 [wikipedia.org]" 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 (Score:3, Informative)

      by Rei ( 128717 )
      I'll second this. The Biomedical Informatics Research Network [nbirn.net], 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.
    • 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
      • 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 essenti
    • Note: IAAB (I am a bioinformaticist)


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

  • Should I go into Bioinformatics? (Score:4, Interesting)

    by TrevorB ( 57780 ) on Tuesday April 05, 2005 @04:19PM (#12147115) Homepage
    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?
    • 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 migh
    • 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 backg
      • 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.
        • 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 d
    • The university of manchester has a online ms program in bioinformatics that is legitimate.

    • 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: Biologica

    • 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 fe

    • Re:Should I go into Bioinformatics? (Score:4, Insightful)

      by aav ( 117550 ) on Tuesday April 05, 2005 @06:44PM (#12148783)
      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.

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

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

  • 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 so
    • 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/ [http] 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 f

      • 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 [washington.edu] 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 hir
      • 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).
    • "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.

    • 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.

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