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- Definitions: What is Bioinformatics?
- What is Bioinformatics?---The Tight Definition
- What is Bioinformatics?---The Loose Definition
- Definitions of Fields Related to Bioinformatics
- What is Biophysics?
- What is Cheminformatics?
- What is Computational Biology?
- What is Genomics?
- What is Mathematical Biology?
- What is Medical informatics/Medinformatics?
- What is Pharmacogenetics?
- What is Pharmacogenomics?
- What is Proteomics?
- The Tight Definition
- The Loose Definition
- Definitions of fields related to bioinformatics
- What is Biophysics?
- What is Cheminformatics?
- What is Computational Biology?
- What is Genomics?
- What is Medical informatics/Medinformatics?
- What is Pharmacogenetics?
- What is Pharmacogenomics?
- What is Proteomics?
Roughly, bioinformatics describes any use of computers to handle biological information.
In practice, the definition used by most people is narrower; bioinformatics to them is a synonym for "computational molecular biology"---the use of computers to characterize the molecular components of living things.
Most biologists talk about "doing bioinformatics" when they use computers to store, compare, retrieve, analyze or predict the composition or the structure of biomolecules. As computers become more powerful you could probably add simulate to this list of bioinformatics verbs. "Biomolecules" include your genetic material---nucleic acids---and the products of your genes: proteins. These are the concerns of "classical" bioinformatics, dealing primarily with sequence analysis.
Khairuddin Itam drew my attention to this crisp definition of bioinformatics dating back to 1987, from P. Hogeweg:"[Bioinformatics is] the study of informatic processes in biotic systems"
Fredj Tekaia at the Institut Pasteur offers this definition of bioinformatics:
"The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information."
It is a mathematically interesting property of most large biological molecules that they are polymers; ordered chains of simpler molecular modules called monomers. Think of the monomers as beads or building blocks which, despite having different colours and shapes, all have the same thickness and the same way of connecting to one another.
Monomers that can combine in a chain are of the same general class, but each kind of monomer in that class has its own well-defined set of characteristics.
Many monomer molecules can be joined together to form a single, far larger, macromolecule. Macromolecules can have exquisitely specific informational content and/or chemical properties.
According to this scheme, the monomers in a given macromolecule of DNA or protein can be treated computationally as letters of an alphabet, put together in pre-programmed arrangements to carry messages or do work in a cell.
The greatest achievement of bioinformatics methods, the Human Genome Project, is currently being completed. Because of this the nature and priorities of bioinformatics research and applications are changing. People often talk portentously of our living in the " post-genomic" era. My personal view is that this will affect bioinformatics in several ways:
- Now we possess multiple whole genomes we can look for differences and similarities between all the genes of multiple species. From such studies we can draw particular conclusions about species and general ones about evolution. This kind of science is often referred to as comparative genomics.
- There are now technologies designed to measure the relative number of copies of a genetic message (levels of gene expression) at different stages in development or disease or in different tissues. Such technologies, such as DNA microarrays will grow in importance.
- Other, more direct, large-scale ways of identifying gene functions and associations (for example yeast two-hybrid methods) will grow in significance and with them the accompanying bioinformatics of functional genomics.
- There will be a general shift in emphasis (of sequence analysis especially) from genes themselves to gene products. This will lead to:
- attempts to catalogue the activities and characterize interactions between all gene products (in humans): proteomics ).
- attempts to crystallize and or predict the structures of all proteins (in humans): structural genomics.
- fewer DNA double-helices in bad sci-fi movies.
- What some people refer to as research or medical informatics, the management of all biomedical experimental data associated with particular molecules or patients---from mass spectroscopy, to in vitro assays to clinical side-effects---will move from the concern of those working in drug company and hospital I.T. (information technology) into the mainstream of cell and molecular biology and migrate from the commercial and clinical to academic sectors.
"an interdisciplinary field which applies techniques from the physical sciences to understanding biological structure and function"More information about the various facets of the discipline can be found at the society's site hosted at Birkbeck College, London.
Mike Goodrich wrote to ask what the status of biophysics was given the definition of computational biology submitted by Paul Schulte (below). A recent article in The Scientist [free registration required] dealt with this question---thanks to Jo Wixon (Managing Editor of Comparative and Functional Genomics) for the reference.
Computational biologists might object (please do), but, I find that people use "computational biology" when discussing that subset of bioinformatics (in the broadest sense) closest to the field of classical general biology.
Computational biologists interest themselves more with evolutionary, population and theoretical biology rather than cell and molecular biomedicine. It is inevitable that molecular biology is profoundly important in computational biology, but it is certainly not what computational biology is all about (see next paragraph). In these areas of computational biology it seems that computational biologists have tended to prefer statistical models for biological phenomena over physico-chemical ones. This is often wise...
One computational biologist (Paul J Schulte) did object to the above and makes the entirely valid point that this definition derives from a popular use of the term, rather than a correct one. Paul works on water flow in plant cells. He points out that biological fluid dynamics is a field of computational biology in itself. He argues that this, and any application of computing to biology, can be described as "computational biology" (see also the "loose" definition of bioinformatics below). Where we disagree, perhaps, is in the conclusion he draws from this---which I reproduce in full:
"Computational biology is not a "field", but an "approach" involving the use of computers to study biological processes and hence it is an area as diverse as biology itself."
"I do not think all biological computing is bioinformatics, e.g. mathematical modelling is not bioinformatics, even when connected with biology-related problems. In my opinion, bioinformatics has to do with management and the subsequent use of biological information, particular genetic information."
The Medical Informatics FAQ (no relation) provides the following definition:
"Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information."
That FAQ also points here
Aamir Zakaria, the author of the FAQ, emphasises that medical informatics is more concerned with structures and algorithms for the manipulation of medical data, rather than with the data itself.
This suggests that one difference between bioinformatics and medical informatics as disciplines lies with their approaches to the data; there are bioinformaticians interested in the theory behind the manipulation of that data and there are bioinformatics scientists concerned with the data itself and its biological implications. (I believe that a good bioinformatics researcher should be interested in both of these aspects of the field.)
Medical informatics, for practical reasons, is more likely to deal with data obtained at "grosser" biological levels---that is information from super-cellular systems, right up to the population level---while most bioinformatics is concerned with information about cellular and biomolecular structures and systems.
On both of these points I'd be happy for any medical informatics specialists to correct me.
What is Cheminformatics?
The Web advertisement for Cambridge Healthtech Institute's Sixth Annual Cheminformatics conference describes the field thus:
"the combination of chemical synthesis, biological screening, and data-mining approaches used to guide drug discovery and development"
but this, again, sounds more like a field being identified by some of its most popular (and lucrative) activities, rather than by including all the diverse studies that come under its general heading.
The story of one of the most successful drugs of all time, penicillin, seems bizarre, but the way we discover and develop drugs even now has similarities, being the result of chance, observation and a lot of slow, intensive chemistry. Until recently, drug design always seemed doomed to continue to be a labour-intensive, trial-and-error process. The possibility of using information technology, to plan intelligently and to automate processes related to the chemical synthesis of possible therapeutic compounds is very exciting for chemists and biochemists. The rewards for bringing a drug to market more rapidly are huge, so naturally this is what a lot of cheminformatics works is about.
Here is a page with a commercial slant which links to some interesting discussions of the term "cheminformatics", what it means, whether or not it exists as a distinct discipline, and even whether it should be replaced by "chemoinformatics".
The span of academic cheminformatics is wide and is exemplified by the interests of the cheminiformatics groups at the Centre for Molecular and Biomolecular Informatics at the University of Nijmegen in the Netherlands. These interests include:
- Synthesis Planning
- Reaction and Structure Retrieval
- 3-D Structure Retrieval
- Computational Chemistry
- Visualisation Tools and Utilities
Genomics is a field which existed before the completion of the sequences of genomes, but in the crudest of forms, for example the oft-re-referenced estimate of 100 000 genes in the human genome derived from a(n) (in)famous piece of "back of an envelope" genomics, guessing the weight of chromosomes and the density of the genes they bear. Genomics is any attempt to analyze or compare the entire genetic complement of a species or species (plural). It is, of course possible to compare genomes by comparing more-or-less representative subsets of genes within genomes.
Mathematical biology is easier to distinguish from bioinformatics than computational biology. Mathematical biology also tackles biological problems, but the methods it uses to tackle them need not be numerical and need not be implemented in software or hardware. Indeed, such methods need not "solve" anything; in mathematical biology it would be considered reasonable to publish a result which merely establishes that a biological problem belongs to a particular general class.
The distinction between bioinformatics and mathematical biology was illuminated by an email I received from Alex Kasman at the College of Charleston. According to his working definition, he distinguished bioinformatics which (under the tight definition at least)...
"...seems to focus almost exclusively on specific algorithms that can be applied to large molecular biological data sets..."
...from mathematical biology which...
"...includes things of theoretical interest which are not necessarily algorithmic, not necessarily molecular in nature, and are not necessarily useful in analyzing collected data."
A recent review on proteomics in the journal Nature defined the field this way:
"The term proteome was first coined to describe the set of proteins encoded by the genome1. The study of the proteome, called proteomics, now evokes not only all the proteins in any given cell, but also the set of all protein isoforms and modifications, the interactions between them, the structural description of proteins and their higher-order complexes, and for that matter almost everything 'post-genomic'."
Michael J.Dunn, the Editor-in-Chief of Proteomics defines the "proteome" as:
"the PROTEin complement of the genOME"
and proteomics to be concerned with:
"qualitative and quantitative studies of gene expression at the level of the functional proteins themselves"
"an interface between protein biochemistry and molecular biology"
Characterizing the many tens of thousands of proteins expressed in a given cell type at a given time---whether measuring their molecular weights or isoelectric points, identifying their ligands or determining their structures---involves the storage and comparison of vast numbers of data. Inevitably this requires bioinformatics. Here is a constructively skeptical review by Lukas Huber.
Pharmacogenomics is the application of genomic approaches and technologies to the identification of drug targets. Examples include trawling entire genomes for potential receptors by bioinformatics means, or by investigating patterns of gene expression in both pathogens and hosts during infection, or by examining the characteristic expression patterns found in tumours or patients samples for diagnostic purposes (possibly in the pursuit of potential cancer therapy targets).
The term "pharmacogenomics" is used for the more "trivial"---but arguably more useful---application of bioinformatics approaches to the cataloguing and processing of information relating to pharmacology and genetics, for example the accumulation of information in databases like this one. (Thanks to Ivanovi.)
All individuals respond differently to drug treatments; some positively, others with little obvious change in their conditions and yet others with side effects or allergic reactions. Much of this variation is known to have a genetic basis. Pharmacogenetics is a subset of pharmacogenomics which uses genomic/bioinformatic methods to identify genomic correlates, for example SNPs (Single Nucleotide Polymorphisms), characteristic of particular patient response profiles and use those markers to inform the administration and development of therapies. Strikingly, such approaches have been used to "resurrect" drugs thought previously to be ineffective, but subsequently found to work with in subset of patients. They can also be used for optimizing the doses of chemotherapy for particular patients.
Overview of most common bioinformatics programs
Everyday bioinformatics is done with sequence search programs like BLAST, sequence analysis programs, like the EMBOSS and Staden packages, structure prediction programs like THREADER or PHD or molecular imaging/modelling programs like RasMol and WHATIF.
Overview of most common bioinformatics technology
Currently, a lot of bioinformatics work is concerned with the technology of databases (Thanks again to Ivanovi.) These databases include both "public" repositories of gene data like GenBank or the Protein DataBank (the PDB), and private databases, like those used by research groups involved in gene mapping projects or those held by biotech companies. Making such databases accessible via open standards is very important. Consumers of bioinformatics data use a range of computer platforms: from the more powerful and forbidding UNIX boxes favoured by the developers and curators to the far friendlier Macs often found populating the labs of computer-wary biologists.
Databases of existing sequencing data can be used to identify homologues of new molecules that have been amplified and sequenced in the lab. The property of sharing a common ancestor, homology, can be a very powerful indicator in bioinformatics (see below).
Acquisition of sequence data
Bioinformatics tools can be used to obtain sequences of genes or proteins of interest, either from material obtained, labelled, prepared and examined in electric fields by individual researchers/groups or from repositories of sequences from previously investigated material.
Analysis of data
Both types of sequence can then be analysed in many ways with bioinformatics tools.
They can be assembled. Note that this is one of the occasions when the meaning of a biological term differs markedly from a computational one (see the amusing confusion over the issue at Web-based geek forum Slashdot). Computer scientists, banish from your mind any thought of assembly language. Sequencing can only be performed for relatively short stretches of a biomolecule and finished sequences are therefore prepared by arranging overlapping "reads" of monomers (single beads on a molecular chain) into a single continuous passage of "code". This is the bioinformatic sense of assembly.
They can be mapped---that is, their sequences can be parsed to find sites where so-called "restriction enzymes" will cut them.
They can be compared, usually by aligning corresponding segments and looking for matching and mismatching letters in their sequences. Genes or proteins that are sufficiently similar are likely to be related and are therefore said to be "homologous" to each other---the whole truth is rather more complicated than this. Such cousins are called "homologues".
If a homologue (a related molecule) exists, then a newly discovered protein may be modelled---that is the three dimensional structure of the gene product can be predicted without doing laboratory experiments.
Bioinformatics is used in primer design. Primers are short sequences needed to make many copies of (amplify) a piece of DNA as used in PCR (the Polymerase Chain Reaction).
Bioinformatics is used to attempt to predict the function of actual gene products.
Information about the similarity, and, by implication, the relatedness of proteins is used to trace the "family trees" of different molecules through evolutionary time.
There are various other applications of computer analysis to sequence data, but, with so much raw data being generated by the Human Genome Project and other initiatives in biology, computers are presently essential for many biologists just to manage their day-to-day results
Molecular modelling / structural biology is a growing field which can be considered part of bioinformatics. There are, for example, tools which allow you (often via the Net) to make pretty good predictions of the secondary structure of proteins arising from a given amino acid sequence, often based on known "solved" structures and other sequenced molecules acquired by structural biologists.
Structural biologists use "bioinformatics" to handle the vast and complex data from X-ray crystallography, nuclear magnetic resonance (NMR) and electron microscopy investigations and create the 3-D models of molecules that seem to be everywhere in the media.
Unfortunately the word "map" is used in several different ways in biology/genetics/bioinformatics. The definition given above is the one most frequently used in this context, but a gene can be said to be "mapped" when its parent chromosome has been identified, when its physical or genetic distance from other genes is established and---less frequently---when the structure and locations of its various coding components (its "exons") are established.
There are other fields---for example medical imaging / image analysis which might be considered part of bioinformatics. There is also a whole other discipline of biologically-inspired computation; genetic algorithms, AI, neural networks. Often these areas interact in strange ways. Neural networks, inspired by crude models of the functioning of nerve cells in the brain, are used in a program called PHD to predict, surprisingly accurately, the secondary structures of proteins from their primary sequences.
What almost all bioinformatics has in common is the processing of large amounts of biologically-derived information, whether DNA sequences or breast X-rays.
"How old is bioinformatics?" The answer to this one depends on which source you choose to read.
From T K Attwood and D J Parry-Smith's "Introduction to Bioinformatics", Prentice-Hall 1999 [Longman Higher Education; ISBN 0582327881]:
"The term bioinformatics is used to encompass almost all computer applications in biological sciences, but was originally coined in the mid-1980s for the analysis of biological sequence data."
From Mark S. Boguski's article in the "Trends Guide to Bioinformatics" Elsevier, Trends Supplement 1998 p1:
"The term "bioinformatics" is a relatively recent invention, not appearing in the literature until 1991 and then only in the context of the emergence of electronic publishing...
"...However, some of my role models when I was a graduate student (Margaret O. Dayhoff, Russell F. Doolittle, Walter M. Fitch and Andrew D. McLachlan) had been building databases, developing algorithms and making biological discoveries by sequence analysis since the 1960s---long before anyone thought to label this activity with a special term (if anything it was called `molecular evolution'). Even a relatively new kid on the block, the National Center for Biotechnology Information (NCBI), is celebrating its 10th anniversary this year, having been written into existence by US Congressman Claude Pepper and President Ronald Reagan in 1988. So bioinformatics has, in fact, been in existence for more than 30 years and is now middle-aged."
- General introductions
- Computational/Mathematical aspects of bioinformatics
- Applying bioinformatics in biological research
- Other lists of bioinformatics books
It's notoriously difficult to find any books on bioinformatics itself that cater well for all of those coming from computing, from mathematics and from biology backgrounds. The few textbooks available in the field tend to be eyewateringly expensive as well. I've divided suggested reading into books of general interest, those best suited to people coming from a computational/mathematical background and books for biologists interested in bioinformatics. Where a book is also listed in Bioinformatics.Org's books section I have linked the title to the relevant entry there. Links to other lists of bioinformatics books follow this section of suggested reading.
Many people are curious about the Human Genome (Project). The completion of the first draft probably represents bioinformatics' coming of age as a discipline. The first couple of books are aimed at the intelligent layperson.
A gossipy and insightful account of the race to sequence the genome can be found in "The Sequence" by Kevin Davies [Weidenfeld; ISBN 0297646982]. Matt Ridley's "Genome" [Fourth Estate; ISBN 185702835X] is both an interesting layperson's introduction to the issues raised by the bioinformatic revolution and an overview of its biology and enormous scope. If I remember rightly, Ridley's book received a slightly snooty review from Walter Bodmer. This is understandable, since his and Robin McKie's excellent "pre-genomic" guide to the Human Genome Mapping Project, "The Book of Life" [Oxford Paperbacks; ISBN 0195114876] was undeservedly in a remainders bin when I bought my copy a couple of years ago.
If you are a non-biological scientist (or a non-scientist) and are hooked by these, why not go back to the "real beginning" of the race and read James Watson's entertaining and indiscreet memoir of his and Francis Crick's determination of the structure of DNA, "The Double Helix" [Penguin; ISBN 0140268774]---now updated with an introduction by media don Steve Jones.
Nigel Barber at Peterborough Regional College in the UK recommends Gary Zweiger's "Transducing the Genome" [McGraw-Hill Professional Publishing: ISBN 0071369805]. The summary at Amazon makes it sound a tad pretentious, but all the reviews seem pretty positive so it might be worth a read.
If you are a quantitative scientist and would like a deeper knowledge of contemporary (molecular) biology, but you want to acquire it as painlessly as possible you could try the following:
- Donna Rae Siegfried's Biology for Dummies [Wiley; ISBN 0-7645-5326-7] is fun, well thought out and a lot more informative than the title might suggest. If only all biology textbooks were this entertaining and unpretentious.
- If you already have some biological knowledge and would like to get a grip on modern biomolecular science then Richard J. Epstein's Human Molecular Biology is an elegant, colourful and detailed guide.
There are two classic competing texts in cell and molecular biology which Maximilian Haeussler reminds me to include: Alberts et al's Molecular Biology of the Cell [Garland Science: ISBN 0815340729] and Molecular Biology of the Gene [Benjamin Cummings: ISBN 0321248643].
If you are a hardcore maths/computing person Michael Waterman's "Introduction to Computational Biology" [Chapman & Hall/CRC Statistics and Mathematics; ISBN 0412993910] and Pavel Pevzner's "Computational Molecular Biology - An Algorithmic Approach" [The MIT Press (A Bradford Book); ISBN 0262161974] will give you all the discrete maths you can shake a stick at, but perfunctory introductions to the biology.
Bioinformatics.Org's very own Jeff Bizzaro recommends Dan Gusfield's "Algorithms on Strings, Trees and Sequences" [Cambridge, 1997 ISBN 0-52158-519-8], Richard Durbin, S. Eddy, A. Krogh, G. Mitchison "Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids" [Cambridge, 1997 ISBN 0-52162-971-3] (which I think is one of the clearest and most comprehensive guides to alignment algorithms) and---for that full "computers-to-biology conversion"--- Geoffrey M. Cooper "The Cell: A Molecular Approach" [ASM Press, 1996 ISBN 0-87893-119-8]. Jeff Ames writes that a second edition of this book is now available [Sinauer Associates, Incorporated, 2000 ISBN 0-87893-106-6] and that this version---if you can find it in the shops---comes with a CD.
One outstanding general text for the biologist is David W. Mount's "Bioinformatics" [Cold Spring Harbor Press; ISBN 0879696087]. It's not cheap, but it's the best I've seen if you are studying bioinformatics itself.
Bioinformatics has been dismissed by some as "the science of BLAST searches". The best collection of advice so far on doing BLAST searches is O'Reilly's BLAST book by Ian Korf, Mark Yandell and Joseph Bedell [O'Reilly ISBN 0-596-00299-8]. I reviewed it enthusiastically, but not uncritically, for the UK UNIX Users' Group magazine. I'd go as far as to say that all biologists thinking of using BLAST in their research should read the relevant sections before they even go near a computer.
If you wish to use general bioinformatics tools, especially if you are a little wary of computers, my new "best" book is "Bioinformatics for Dummies" [John Wiley and Sons ISBN 0764516965]. It is (obviously) aimed at people who are beginners, who are happier using the Web rather than typing commands, and who are more interested in learning than in impressing people---the writing is friendly clear and unpretentious. However, like several of my other tips (below) it concentrates on Web-based resources so it will, inevitably, date. (This is partially compensated for by there being a companion Website.)
Also, if you're coming to the subject as a computer user with a biological background, looking to exploit the many tools available, you might want to try Terry Attwood and David Parry-Smith's "Introduction to Bioinformatics" [Longman Higher Education; ISBN 0582327881], or Des Higgins and Willie Taylor's "Bioinformatics: Sequence Structure and Databanks" [Oxford University Press; ISBN 0199637903]. Another excellent practical introduction is Andreas Baxevanis and Francis Oulette's "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins" [Wiley-Interscience; ISBN 0471383910], now in its new and improved second edition. Bax teaches bioinformatics all over Canada and the experience shows. Arthur Lesk has also produced an excellent teaching book particularly for protein bioinformatics in his Introduction to Bioinformatics
Bioinformatics.Org also recommends Cynthia Gibas and Per Jambeck's "Developing Bioinformatics Skills" [O'Reilly, 2001 ISBN 1-56592-664-1].
Stuart Brown recommends his own book "Bioinformatics: A Biologist's Guide to Biocomputing and the Internet" [Eaton Pub Co; ISBN: 188129918X]. If he sends me a review copy I might recommend it too ;-) .
"Darwin's Radio" by Greg Bear [Ballantine Books, ISBN: 0345435249] is a wonderful hard SF thriller which stretches ideas derived from genome discoveries to their breaking point. It's gripping and humane.
Leonard Crane, the author of Ninth Day of Creation kindly sent me a copy for review. So far it's an excellent read. I haven't finished it yet, not because it isn't a rattling good story, but because, like "Darwin's Radio", it is very long and because I am very busy. If you'd like to read a well-researched, but speculative, novel containing actual scenes of practising bioinformatics then try it.
Ken Allen contributed the following reviews:
"Frameshift [Tor Books, ISBN: 0812571088] by Robert J. Sawyer---based around the HGP---reasonable read, but poor / confused ending."
Calculating God [Tor Books, ISBN: 0812580354]by the same author---has a subtler bio connection and is a much better read. Near the start an alien spacecraft lands, the alien emerges and says 'take me to your paleontologist'
Further suggestions for this section are welcome.
Other lists of bioinformatics books
The biggest and best source of bioinformatics links I have encountered is the Genome Web at the Rosalind Franklin Centre for Genomics Research at the Genome Campus near Cambridge, UK. Most of the links below come from that resource. My list is necessarily limited by comparison.
- Research centres
- Sequencing centres
- Standards centres
- Are there any standards in bioinformatics?
- "Virtual" centres (for example consortia and communities)
- Centro Nacional de Biotecnologia (CNB)
- Computational Biology and Informatics Laboratory at the University of Pennysylvania
- CIRB: Centro Interdipartimentale di Ricerche Biotecnologiche
- Cold Spring Harbor Labs
- European Molecular Biology Laboratory (EMBL)
- GIRI: Genetic Information Research Institute
- MRC Human Genetics Unit
- MRC Rosalind Franklin Centre for Genomics Research(RFCGR)
- [XXXX INSERT DETAILS OF RESOURCE CENTRES BEGINNING WITH LETTERS IN THE ALPHABET LATER THAN 'I' HERE]
- The Department of Genome Analysis at the Institute of Molecular Biotechnology, Jena, Germany
- The Australian Genome Resesarch Facility
- Baylor College of Medicine
- Michael Smith Genome Sciences Centre, Canada
- Laboratório Nacional de Computação Científica, Brazil
[XXXX INSERT DETAILS OF MORE SEQUENCING CENTRES HERE]
[XXXX INSERT DETAILS OF STANDARDS CENTRES HERE]
[XXXX INSERT MORE DETAILS OF VIRTUAL BIOINFORMATICS CENTRES HERE]
A great place to start, whether you come from a biological, physical or computational background is at Martin Vingron's superb online bioinformatics tutorial. (Begin by choosing a section from the left-hand-side menu bar.)
Tom Smith and Don Emmeluth have produced a nice little exploration of bioinformatics using NCBI resources and tools.
I recently stumbled upon a promising set of online lecture notes currently under construction by B. Steipe at the Genzentrum (Gene Center) at the Ludwig-Maximilians-Universität München (University of Munich).
A defiantly frames-free chemistry tutorial site.
First of all, an almost completely painless introduction to the horrors of the quadratic equation by Peter Whalen, James Walker, and Drew Marticorena.
Estrella Mountain Community College in the States offers this excellent short introduction to biology (actually "The Nature of Science and Biology". It's a great place for keyboard jockeys to start their journey to enlightenment. Thanks to Alex O'Neill for pointing out the broken link.
The Dolan DNA Learning Center at Cold Spring Harbor has an outstanding interactive tutorial introducing genetics. To take full advantage of the multimedia elements you should download the Flash and Real players.
The Institute of Arable Crop Research Beginner's Guide to Molecular Biology
Unilever Education Advanced Series tutorial on proteins.
The University of Arizona has made available a high-quality tutorial in cell biology. Not only does it cover the facts, but it also attempts to introduce some of the philosophy of the field---recommended. Even better, it's also available en Español and in Italiano.
Bob Patterson maintains his "Darwiniana" with amazing diligence.
- ...in Africa?
- ...in the Americas?
- ...in Brazil?
- ...in Canada?
- ...in California?
- ...in Connecticut?
- ...in Georgia?
- ...in Illinois?
- ...in Indiana?
- ...in Iowa?
- ...in Maine?
- ...in Maryland?
- ...in Mexico?
- ...in Minnesota?
- ...in New Jersey?
- ...in New York State?
- ...in North Carolina?
- ...in Ohio?
- ...in Pennysylvania?
- ...in Texas?
- ...in Virginia?
- ...in Asia?
- ...in Australasia?
- ...in Europe?
This section is not complete, but contributions to broaden its coverage are welcome. Please do not direct questions about eligibility, course quality or admissions policy to me, but to ask the individual institutions directly. Use the links to obtain contact details. If an institution doesn't provide telephone numbers/email addresses or snailmail details on its Web site it doesn't deserve your patronage.
This resource focuses on complete, full-time degree programmes rather than on individual study modules. Curating a list of the latter would be a full-time job. You can go to other places, however, if you are looking for short courses. Thanks to various contributors, including Wentian Li who pointed me to this list at Rockefeller which is mirrored at various other sites. And to Humberto Ortiz Zuazaga for mailing me a link to the ICSB, where you can find this list.
Those wanting to find programmes in the Asia Pacific region could have a look at this resource maintained by the Asia Pacific Bioinformatics Network APBioNet. Thanks to Sentausa.
In the UK The Bioinformatics Resource (part of the BBSRC's CCP11 project) project maintains (among many other resources) lists of (mainly) British Masters and PhDs in bioinformatics. If you have any suggestions or updates please contact me with them. You can publicize your course and offer a public service at the same time.
Rhodes University, Grahamstown, South Africa offers an MSc. in Bioinformatics and Computational Molecular Biology. Thanks to Natalie Twine.
Cathal Seoighe wrote a while back about the South African National Bioinformatics Institute (SANBI). Ruediger Braeuning has since written to point out that bioinformatics training in South Africa has been radically reorganized. He says:
"A new institute, the National Bioinformatics Nework (NBN), has been created. We have nodes at Universities all over the country (UWC, UCT, SUN, RU, UKZN, UP, WITS). Our main tasks are to:
- develop capacity in Bioinformatics
- perform world-class research
- support local Biotechnology initiatives
"We do offer courses on various topics in Bioinformatics ranging in length from 3 days to several weeks. We also train Bioinformaticists on MSc, PhD and post doc level. Undergraduate programs are currently being developed. Bursaries are available. For more information visit our Website."
South African National Bioinformatics Institute (SANBI) Honours Bioinformatics Course at the University of the Western Cape. Next year the same institute will be offering a Master's in bioinformatics---thanks to Cathal Seoighe.
If you know of any other bioinformatics courses on the African continent please feel free to mail me about them.
According to Pablo Nehab-Hess the Laboratório Nacional de Computação Científica (LNCC), Brazil and the Universidade Federal do Rio de Janeiro (UFRJ) recently created a joint Bioinformatics MSc programme, through the Genetics Department of UFRJ and the Department of Applied Computational Mathematics of LNCC.
Thanks to Jordan Patterson for the information that the University of Alberta offers four-year Biology or Computer Science degrees with a specialization in bioinformatics. The Faculty of Computer Science there offers Master's and PhD training in bioinformatics.
Benjamin Horsman wrote to tell me that Simon Fraser University and the University of British Columbia are collaborating on a new Bioinformatics training program with the British Columbia Cancer Agency. The program offers post-graduate diploma, Master's, and PhD training in Bioinformatics. Now Simon Fraser University also offers a joint major programme in Molecular Biology and Biochemistry (MBB) and Computer Science in Bioinformatics. Thanks to Brittany Nielsen for the info.
Thanks to Momchil Georgiev for the information that the University of California at San Diego offers a Bioinformatics graduate programme and to Dana Brehm that there is now a new bachelor's program, to quote her:
"[This is an] undergraduate, interdisciplinary program for undergraduates leading to a B.S. degree. The new Bioinformatics major is offered by the Division of Biology, and the departments of Chemistry/Biochemistry, Computer Science and Engineering, and Bioengineering. A student may choose to major in Bioinformatics in any one of the four departments or division. The Division of Biology currently offers two Bioinformatics courses, and with the advent of the cross-disicplinary major, even more courses are going to be taught 2002-03 and 2003-04."
David Delong wrote to me to point out that the College of Natural and Agricultural Sciences at the University of California, Riverside is developing a "Center in Genomics and Bioinformatics" which will offer a PhD curriculum in genomics and bioinformatics from academic year 2001-2002 onwards.
Catherine Velazquez says that The University of California, Santa Cruz offers a new undergraduate BS course in bioinformatics. They have a Frequently Asked Questions. Now they also offer an MS/PhD in Bioinformatics. Thanks to Kevin Karplus for the update.
Javier Rojas Balderrama emailed me to point out thatYale University offers a Bioinformatics and Computational Biology track as part of its combined Biological and Biomedical Sciences graduate programme.
According to Eric VanWieren Georgia State University offers a Master's and PhD in Computer Science with a focus on bioinformatics. The university's Bachelor of Science in Computer Science also offers a "Fundamentals of Bioinformatics" course.
The University of Illinois at Chicago offers graduate programmes covering Bioengineering Bioinformatics through its Bioengineering department as well as an undergraduate course track. Thanks to Amit Sabnis.
Iowa State University offers an Interdisciplinary Ph.D. Program in Bioinformatics and Computational Biology (BCB).
Tim Young wrote to say that Johns Hopkins University in Maryland offers an MS in Bioinformatics through the Zanvyl Krieger School of Arts and Sciences Advanced Academic Programs and Whiting School of Engineering Engineering and Applied Science Programs for Professionals. They are also offering a Bioinfomatics concentration with their MS in Biotechnology program.
Thanks to Anu Haniharan for drawing my attention to mixing up the Minnesota and New Jersey paragraphs.
The message also states that the University of Medicine and Dentistry New Jersey (UMDNJ) offers a programme in biomedical informatics.
Thanks to Anu Haniharan for drawing my attention to mixing up the Minnesota and New Jersey paragraphs.
The University at Buffalo has been involved in establishing a "Center of Excellence in Bioinformatics". It used to a range of courses in bioinformatics and related subjects, but all the course links seem to be dead now. Thanks to Jeff Ligas for the original notification.
Cornell and Rockefeller Universities, together with the Sloan-Kettering Research Institute offer a "Tri-institutional program in Computational Biology and Medicine". Thanks to Brant Inman.
Since September 2003 Farmingdale State University of New York has offered a unique baccalaureate Bioscience curriculum including bioinformatics as one of its concentrations. Thanks to Charles Adair for this information.
According to Maureen Downey, the College of Staten Island, part of the City University of New York also offers a challenging program in bioinformatics.
If you know of any other bioinformatics courses on the American continent please feel free to mail me about them.
Andrew Johnson writes: "There is a relatively new Biomedical Informatics program in Ohio. (I'm entering the program in a few months). Though the department stands alone, it is in the College of Medicine at the Ohio State Medical Center. Entrance is offered through a new Integrated Biomedical Sciences Graduate Program.".
The University of Pennsylvania offers some of the best known and longest established bioinformatics programmes at Batchelor's, Master's and PhD levels. Thanks to Louis Licamele for pointing out my oversight (I just assumed I'd already listed them!) He also points out that Georgetown University is planning bioinformatics courses too.
Tom Andrews, a student on the course, has written to me to tell me that Texas A&M University at Corpus Christi is currently offering a BS computer science degree in bioinformatics.
Jeremy Read told me that St. Edward's University in Austin offers a B.S. in Bioinformatics.
The Keck Center for Computational Biology---a joint venture of Baylor College of Medicine; University of Houston; Rice University; University of Texas Health Science Center, Houston; M.D. Anderson Cancer Center; and University of Texas Medical Branch, Galveston---offers undergraduate (not 2003) and graduate level training in Computational Biology.
The Virginia Polytechnic Institute and State University's Bioinformatics Institute offers graduate options in Bioinformatics. Thanks to William S. Preissner for correcting this entry.
Niranjan Swaroop Sharma wrote to tell me about the Bioinformatics Institute of India which is offering a whole range of bioinformatics programmes and qualifications in both regular and distance learning formats. I would have reported on this earlier, but have not been able to view the site in Mozilla. I finally viewed the site using Konqueror today (24Jul03). Perhaps some tinkering with the ASP code is needed there...
Vaibhav Sinha wrote to tell me that the Institute of Bioinformatics and Applied Biotechnology (IBAB) in Bangalore is offering bioinformatics courses.
Thanks to Surjeet Singh for drawing my attention to the Indian Institute of Information Technology-Allahabd which runs a Master of Technology (M. Tech Bioinformatics) degree.
According to Rahul Agrawal, the Indian Institute of Technology Delhi, New Delhi provides courses in Biochemical Engineering and Biotechnology. He adds that another branch of the Institute, IIT Kharagpur also provides various courses in this area.
Risabh Bhandari writes to say:
"The recently rechristened CBT (Center for Biochemical Technology) [link dead 13Nov02] which is a CSIR Lab [in New] Delhi has started a PG Diploma in Bioinformatics in association with Informatics institute. The course covers a large area in the field with [its] primary focus on computational and programming concepts. The course is 6 months in duration, [and] conducted at the national Head office of [the] Informatics institute."
Uma Paresmeswaran wrote to say that SASTRA, which is based near Trichy, Tamil Nadu, will be offering a B.Tech.Programme in Bioinformatics from 2003/2004, the first institute in India offering this course at the undergraduate level?
There is, according to Aditi Arur, an MSc distance education program in Bioinformatics, offered by Sikkim Manipal University India.
Sugandha Singhal wrote to mention the undergraduate and graduate programmes in bioinformatics at Vellore Institute of Technology in Tamil Nadu and the undergraduate programme in bioinformatics at Amity Institute, NOIDA.
Dr Amir Feisal Merican wrote to say that the Institute of Biological Sciences, Faculty of Sciences, University of Malaya, Kuala Lumpur, is offering a BSc (Bioinformatics) undergraduate degree programme. Yam confirmed this that this degree has been taught for 3 years.
Alfred Simbun suggested three more Malaysian universities offering bioinformatics degrees: Universiti Industri Selangor (UNISEL), Kolej Universiti Teknologi & Pengurusan Malaysia (KUTPM) and Universiti Kebangsaan Malaysia (UKM)
Kebangsaan University, Malaysia (UKM) will start to offer a Bachelor's Degree in Bioinformatics to its next intake, in July, 2003.
Thanks to Abdul Hameed for pointing out that two universities in Pakistan---COMSATS Institute of Technology and the Mohammad Ali Jinnah University---will be offer four-year Bachelor of Sciences degrees in bioinformatics from September 2003.
Lam Ah Wah wrote to tell me that the Nanyang Technological University (NTU) starts a BioInformatics undergraduate and part-time post-graduate MSc course in Jul 2002. Be warned: their Web site has hideous frame/window based "portal" which breaks half a dozen rules of good interface design. Chua Hian Koon managed to find a better link, and I browsed from there to the syllabus here.
If you know of any other bioinformatics courses is Asia please feel free to mail me about them.
You can obtain a Graduate Certificate in Bioinformatics from Curtin University of Technology in Western Australia.
There are (according to H L View) PhD, MPhil and Honours programmes in bioinformatics (plus a bioinformatics minor) available at Murdoch University's Centre for Bioinformatics and Biological Computing.
Rachel Oh said that is possible to study a near-bioinformatics programme at QUT (Queensland University of Technology): the B. Sci (biotech maj.) & IT (in software engineering & data comms) IF29. A copy of the course is available by searching their Website.
According to Jonathan Watts, "Queensland University of Technology in Brisbane QLD offers a Bachelor of Applied Science Innovation, with a major in Bioinformatics" from 2004.
If you know of any other bioinformatics courses is Australasia please feel free to mail me about them.
A consortium including nearly all the French-speaking universities of Belgium (Bruxelles, Liège, Louvain, Mons, Namur and Gembloux) is offering the "Inter-University DEA/DES (Master) in Bioinformatics".Bioinformatics Centre at The University of Copenhagen offers a two-year masters program in bioinformatics. Thanks to Thomas Litman.
Syddansk Universitet (The University of Southern Denmark) offers both BSc- and MSc- level Bioinformatik / Experimental Bioinformatics. Thanks to Fiona Nielsen for the updated link---"Center for Experimental Bioinformatics".
The Finnish Graduate School in Computational Biology, Bioinformatics, and Biometry or "ComBi" is a joint venture of the University of Helsinki (English), the University of Turku (English) and the University of Tampere (English).
Fabio Pardi writes that the Université Paris VII offers a DEA en Analyse de Génomes et Modélisation Moléculaire. Thanks to Brant Inman again for this link to the course. Isabelle da Piedade kindly provided this list of Master's and PhD programmes in France:
- Mastere Sciences, Mention: Bioinformatique, Biochimie Structurale et G�nomique (BBSG) at Aix-Marseille University
- Master's/PhD bioinformatique at Universite de Rennes
- Master's/PhD Analyse de G�nomes et Mod�lisation Mol�culaire Universit� Paris 7 Jussieu
- Master's/PhD "Application des Math�matiques et de l'Informatique � la Biologie" at Universit� d'Evry
- Master's Bioinformatique at Universit� Paul Sabatier Toulouse
- Master's/PhD bioinformatique de Bioinformatique et Genomique at Universit� Versailles St Quentin
- Master's Pro Bioinformatique, Universite de Clermont-Ferrand
- Master's Bioinformatique at Universite de Lille
- Master's Bioinformatique at Universite Montpellier II
Thanks to Amelie Stein for several of these entries.
The Technische Fachhochschule Berlin (University of Applied Science) offers an MSc in Bioinformatics and the Freie Universität Berlin (Free University) offers both an MSc. and a BSc. in Bioinformatics. Thank you to Sebastian Kurscheid for this information. Alexandra Reitelmann wrote to say that Bonn-Aachen International Center for Information Technology (B-IT) is offering a new English-language Master's programme in Life Science Informatics. The B-IT is a joint venture between the University of Bonn, the RWTH Aachen University, the University of Applied Sciences Bonn Rhein-Sieg, and Fraunhofer Institutszentrum Birlinghoven Castle (IZB).
The Institut für Informatik at Johann Wolfgang Goethe-Universität Frankfurt am Main offers a programme in Bioinformatik.
You can do a PhD in bioinformatics in the Department of Computational Molecular Biology at the Max Planck Institute for Molecular Genetics. Thanks to Martin Okrslar---and to Pooja Jain for the correction to my broken link.National University of Ireland Maynooth set up a four-year Batchelor's course in Computational Biology and Bioinformatics two years ago.
Tel Aviv University offers a BSc. in Bioinformatics. Thanks to Racheli Zakarin for the link.
The famous Weizmann Institute in Rehovot teaches an MSc. called "Multidisciplinary Program in Computational Biology and Bioinformatics". This PDF document has more information. Gad Abraham, who told me about this, points out that "all studies there are conducted in English and that there are no tuition fees"
The Centre for Molecular and Biomolecular Informatics (CMBI) at the University of Nijmegen offers a Master's degree in bioinformatics. This is a one or two year course leading to a degree with the formal title of "Master in Life Sciences", but the subtitle "Bioinformatics".
Francisco Rocha wrote to say that Escola Superior de Biotecnologia (ESB) teaches a bioinformatics programme [follow the link labelled "Bioinformática"] in both Lisbon and Oporto. The teaching institution is the Universidade Católica do Porto.
Bjorn Olsson writes that, as well as a 4-year Master's Degree in Bioinformatics, the University of Skövde offers a number of short courses and allow computer science master's students to include bioinformatics in their degree. There is more information here.
Daniel Nilsson drew my attention to the MSc in Bioinformatics Engineering in Uppsala. Thanks to Erik Kanders for correcting the link.
The Stockholm Bioinformatics Centre, Stockholm University, offers PhD-level shorter courses in bioinformatics subjects.
The School of Mathematical and Computing Sciences at Chalmers offers undergraduate and Master's programmes in bioinformatics. Thanks to Samuel Hargestam.
Fabio Pardi wrote that the Swiss Institute of Bioinformatics offered a Master's degree (DEA). It was a collaboration between the Swiss Institute of Bioinformatics and three faculties of the Universities of Geneva and Lausanne. According to Javier Rojas Balderrama this programme is now closed.
In 2002 I prepared a review of bioinformatics education in the UK for the journal Briefings in Bioinformatics. The article ends with a detailed listing of all current and some future undergraduate and graduate courses in bioinformatics the UK as of September 2002, along with links. You can read a preprint here.
Birkbeck College is a British centre with a proud tradition in educating working and/or mature students to the highest academic standards.
In October 2004, Cardiff University started two different courses: Bioinformatics or Genetic Epidemiology and Bioinformatics either full-time or part-time and at MSc/PG Cert or Diploma level. Thanks to Ian Brewis, who pointed out that Cardiff's programme is distinguished by offering students a stronger thread of genetic epidemiology for those students interested in this.
In April 2002 City University's Bioinformatics group moved to the University of Glasgow Department of Computer Science. . Thanks to Will Bachelor for alerting me to the existence of this group. City still offers MScs in Pharmaceutical Information Management and Health Informatics
Edinburgh University, offers an MSc./Diploma in Quantitative Genetics and Genome Analysis and an MRes (MSc./Diploma by Research) in Life Sciences in which you can specialize in Quantitative trait analysis and genomics .
In November 2004, Fiona Croll alerted me to Herriot-Watt University's Bioinformatics (IT) MSc jointly taught by the university's School of Mathematical and Computer Sciences and its School of Life Sciences.
On 20Jan03 UKeU, the UK government-backed company set up to provide online degrees from UK universities to students worldwide, announced a new Master's level programme in Bioinformatics from the Universities of Leeds and Manchester. (Thanks again to Jo Wixon for this.)
Newcastle University's MRes in Bioinformatics began in September 2003.
Thank you to David Parkinson (no relation to Helen, above) for pointing out to me that for the past two years Sheffield Hallam University has offered an MSc/PGDip in Bioinformatics at its Graduate School in Science, Engineering and Technology.
University College London (UCL) offers a final year undergraduate course: "Bioinformatics:Genes, Proteins and Computers".
Together with Harrow School of Computer Science, The University of Westminster, a new university in London, offers an MSc. in Bioinformatics as both a full- and part-time course. Again this is aimed primarily at graduates of the biological sciences. York University's Department of Biology offers Masters courses and PhDs in both computational biology and biomolecular science.
If you know of any other bioinformatics courses in Europe please feel free to mail me about them.
Many visitors to the FAQ ask about bioinformatics distance learning. Eventually I will try to gather together all those courses on this list that can be taken remotely---if I ever have the time. Unfortunately I don't at the moment. All I can suggest is that you examine the courses yourself through the links provided in the FAQ. Many can be taken over the Net or offer components that can be studied at a distance. (And, if you do compile such a list for yourself, do please email it to me and I will post it here for the benefit of our users with, as usual, a full credit for your efforts.)
If you are thinking of studying at a UK institution you might want to search through the pre-print of my review of UK bioinformatics education for the word "distance". At the moment I think the courses at Birkbeck, Exeter and Oxford offer either full or part distance learning options.
- I am a newbie and I want to do bioinformatics.
- I am a biologist and I want to do bioinformatics.
- I am a computer scientist and I want to do bioinformatics.
- More general advice
- Where can I find bioinformatics jobs?
If you want to get involved in bioinformatics, now is an exciting time, but (certainly for less senior practitioners) it looks as though demand for bioinformaticians is currently falling, partly for general economic reasons, partly, perhaps, because drugs companies in particular have been disappointed with the pay-off from their investment in the field.
This section is opinionated; there are people in the field, both computer scientists and biologists, who I would love to provoke (or convert). If you are a newcomer, and especially if you come from one of bioinformatics component pure disciplines, I hope my ranted warnings will help you to avoid the mistakes of your predecessors---and I write as one of the mistaken. David S. Roos put it well in his review in the journal Science:
"Lack of familiarity with the intellectual questions that motivate each side can also lead to misunderstandings. For example, writing a computer program that assembles overlapping expressed sequence tags (EST) sequences may be of great importance to the biologist without breaking any new ground in computer science. Similarly, proving that it is impossible to determine a globally optimal phylogenetic tree under certain conditions may constitute a significant finding in computer science, while being of little practical use to the biologist."
Please read the education section above for information about some of the places you can currently study bioinformatics. Please do not direct questions about eligibility, course quality or admissions policy to me, but to ask the individual institutions directly.
If you are a high school student / sixth former, think about taking an interdisciplinary computational biology or bioinformatics bachelor's degree of the sort offered at, for example, Manchester University in the UK or UPenn in the States. Don't worry if you can't find a place on such a course or there isn't one nearby; perhaps the best way to approach this subject is from two sides. Do a bachelor's degree in one area while taking a healthy interest in the other---or (if you can afford to) complement a first degree in one part of the discipline with a second degree in the second.
If you already have a degree in a biological discipline there are similar Master's courses---both interdisciplinary (e.g. Birkbeck's in London) and conversion type courses---for biologists or others to learn computer science, for example.
If you are currently doing a computer science or biology PhD, try to take advantage of the opportunity to take courses in the "other" discipline.
To a biologist I would say: take as many real computing courses as you can. It's important not just to learn a programming language, but also to learn the discipline of computing; to structure and document your work in a rigorous way. What courses you take might be directed by the kind of work you are interested in doing when you graduate---whether you see yourself supporting bioinformatics applications or building them. For the former you need all-round familiarity with the programs themselves and the hardware and software needed to run them---plus your existing understanding of biology. For the latter you need to learn a structured programming language and the principles of good program design---plus the ability to talk to and understand biologists.
Courses biologists might consider taking:
Of all the computing courses available it is most important that you have a proper introduction to the UNIX operating system(s). Most current bioinformatics software (especially the free stuff) runs on "open" platforms like Linux and the Web. The UNIX philosophy is elegant, powerful, and frustrating. Master it and you will save a lot of time.
Learn some maths. Basic statistics, logic/set theory and a little calculus would be my recommendation. Many practising biologists have little or no grasp of elementary concepts like statistical significance, permutations and combinations and the principles of good experimental design. Logic will come in handy at the very least if you want to query databases in an intelligent way.
If you're interested in development, learn a real programming language: Pascal, C(++), Java or Fortran.
Perl and HTML are the stuff that holds the Web together. A grasp of these is essential for a lot of the Web/database work being done by many bioinformaticians at the moment.
Good old BASIC can be very useful as an introduction to programming or as a tool in its own right, but none of these latter languages is built to crunch numbers and tackle real world biological problems---which isn't to say people don't try...
One thing that I will emphasise repeatedly in this section is the simple value of doing some "proper" biological laboratory science. I have sat through many talks during which a bioinformatics "scientist" describes in great detail how his---it's usually "his"---application of a trendy mathematical tool offers a supposed insight into a (sometimes supposed) biological problem. Nine times out of ten I know that this method will never be so much as sneezed on by a practising biologist.
Quantitative scientists sometimes talk about their interest in studying some aspect of "God's mind". Biologists, in contrast, are interested in "Mother Nature". You might meditate on God in the hope of some revelation, but to understand Nature you have to meet her in the flesh. You are as likely to be useful to biologists working in isolation at the keyboard as you are to conceive with your clothes on. Desk-bound bioinformaticians have written code that has turned out to be popular with biologists, but almost always because they have collaborated with biologists.
Courses quantitative scientists might consider taking:
- Molecular biology
"MoBi" was the bioinformatics of its day; desperately fashionable, the province of new, higher-paid practitioners and considered with slight suspicion by more traditional biologists. It was once a great achievement to sequence a modest stretch of DNA, now it's a job for robots. Today the technology of molecular biology is very well established. Scientists can buy kits to perform the sort of genetic manipulations that would make your parents' jaws drop. Some of the kits are so simple your small children could use them (with a modest amount of training and supervision).
Despite the profusion of commercial kits, there is still a requirement for real skill in molecular biology and the general level of scientific understanding required to be a good biological scientist---rather than just completing a practical class---doesn't come easy. Living matter, the stuff you have to work with is unpredictable and responds slowly---except when it's dying. Even supposedly fast-growing bacteria can take a long time to yield up their secrets.
Now, fashions in biomedical research are shifting from molecular biology back to cell biology and protein biochemistry, but it's well worth offering yourself up as a volunteer for some vacation work in a molecular biology lab. The term is now more often used to refer to the technological tools provided by MoBi to biology in general, rather than to fundamental research in the field itself. Those tools are common to a vast array of different kinds of research, from archaeology to zoology.
- Protein (bio)chemistry
Protein (bio)chemistry is experiencing a revival. Proteins are still more delicate and fussy than nucleic acids. The same advice that applies to molecular biology applies to protein biochemistry. That stuff bioinformatics people refer to as "wet lab science" is much harder than it looks.
You might find it more difficult to get access to a good protein lab than a good molecular biology lab and do protein science with real wizards, but the very least you can do is read about the theoretical aspects of the subject.
For insights into the principles of proteins structure, try, for example, Carl Branden and John Tooze's "Introduction to Protein Structure" [Garland ISBN 0-8153-2305-0]. Physicists in particular might find the lack of general unifying principles in this area overwhelming. Unfortunately there's no substitute for acquiring a "feel" from the subject by examining a lot of examples. Still the most critical stages in the successful prediction of protein structure from sequence are those requiring human intervention.
Thomas E. Creighton has been responsible for a range of standard texts on protein chemistry. If you are working in a protein lab you are likely to come across his "Protein Function : A Practical Approach" [ISBN 019963615X] and the rather more expensive and theoretical "Proteins : Structures and Molecular Properties" [ISBN 071677030X]
- Evolutionary biology
It's a worn quote, but worth repeating:
"The mechanisms that bring evolution about certainly need study and clarification. There are no alternatives to evolution as history that can withstand critical examination. Yet we are constantly learning new and important facts about evolutionary mechanisms. Nothing in biology makes sense except in the light of evolution."
Theodosius Dobzhansky in "American Biology Teacher" vol.35
Darwin's theory is one of the simplest and most misunderstood in science. Start with a good layperson's introduction, Richard Dawkin's "The Selfish Gene" (and remember: it's a metaphor, stupid) or Steve Jones' paraphrasing of Darwin's original "The Origin of the Species" "Almost Like a Whale". All biologists agree on the underlying principles, but they are nearly ready to kill one another over the details. After reading a decent book on evolutionary biology you should have at least a handful of good questions. Now you are ready to take a class in the subject. Take your questions with you. You'll probably start an argument---or a fight.
These damned biologists are making me use Word instead of LaTeX to write up---what can I do?
Use the software
Get access to an installation of EMBOSS and/or Staden and get someone to lead you through the tools available. RasMol is a simple, but powerful and elegant molecular imaging program which can teach you a great deal about biological macromolecules; try a tutorial. Get out on the Web and do some productive surfing for a change :-) . The best starting point is the Human Genome Mapping Project Resource Centre's "GenomeWeb". There's so much stuff out there -- and most of it is free to academics.
Start here at Bioinformatics.Org's Job Announcements Homepage...
Then move on to the appointments / careers sections of the the major scientific journals, or, better, search their Web jobs pages with "bioinformatics":
Appropriately for a Web-dependent discipline, there are a variety of specialist commercial Web sites which carry bioinformatics jobs:
There are also a number of companies actively recruiting in the area. Here are a few:
- How can I find a sequence?
- How can I align two sequences?
- How can I predict the function of a gene (product)?
- How can I predict the structure of a sequence?
- How can I write up?
This section includes some simple rules-of-thumb to apply when performing common bioinformatics tasks. I try to give a reference to a more detailed source of guidance where I know of one.
The most common task in bioinformatics must be the acquisition of some bioinformatics data on which to operate. Usually this in the form of a nucleic acid or protein sequence, stored as characters in the appropriate alphabet together with a header of related information: for example some kind of unique identifying number the species from which the original biological substrate was obtained, the names of any authors who published the sequence and so on.
You may have already generated your own sequence data experimentally. In this case you are likely to want to find sequences which are identical or similar (and therefore possibly related) to yours. The task is then one of similarity search.
A paradoxical problem generated by the success of the bioinformatics revolution is the increasing difficulty of navigating the huge amount of data available. Once you could print out most of the existing sequence databases onto paper and cram them into a single binder. Now a search for "actin" alone will pull out hundreds and hundreds of sequences. The key to find what you want is to develop your own discriminatory skills rather than rely on computers to figure out what it is you're really after.
Make sure you are clear about your aim first. If you are looking for a sequence for a specific scientific purpose then you might be best to start with a relevant human-generated publication. For example, you have cloned a gene which is part of a well-characterised biochemical pathway and you want to find other sequences of the same functional gene product in other species (orthologues) Entrez PubMed is your friend.
PubMed is a huge and very comprehensive database of the biomedical scientific literature., created by the U.S. National Library of Medicine (NLM). Entrez PubMed is another indispensable resource of the U.S. National Centre for Biotechnology Information (NCBI). Both are part of the U.S. Department of Health and Human Services National Institutes of Health
Swiss-Prot is curated by human beings.
Use SRS at the RFCGR
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Use Boolean logic
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This section will be expanded---and there will be a more basic and detailed explanation for novice searchers, but, in the meantime, here are the top tips cribbed from the excellent paper by Hugh B. Nicholas Jr., David W Deerfield II and Alexander J. Ropelewski in BioTechniques.
- Use a local favourite program on the Web server of your choice.
- Use at least two and preferably three similarity tables.
- If using Smith-Waterman or FASTA algorithms ensure that the gap opening penalty is high enough.
- If the initial search finds no or insufficient matches repeat it with a highly diverged matrix and/or with a Smith-Waterman-based server.
- If this doesn't work try switching from a PAM matrix to a BLOSUM matrix.
Hugh, David and Alexander again on when not to use the default search parameters provided by a server.
- ...when the homologues you are looking for to match your query are highly diverged.
- ...when the query or matches are short.
- ...when you are only interested in a specific (in the sense of "species") subset of database matches with a particular evolutionary relationship to your sequence of interest---a relationship not implied by the default settings.
This section will also be expanded for newbies, until then, here are Hugh, David and Alexander's tips for alignment:
- Use an appropriately divergent matrix (I'll be adding a table soon to explain this).
- Reduce your gap penalty relative to that you used for your database search.
- Use the MaxSegs/Waterman-Eggert version of the dynamic programming algorithm to provide the best local alignment and also to search for repeats.
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You could start with anyone of these excellent guides (listed strictly in alphabetical order):
- Rob Russell's Guide to Structure Prediction (version 2)
- András Fiser and Andrej Šali's Comparative protein structure modeling
- Gert Vriend's Professional gambling
Here's Peter J. Steinbach's "Introduction to Macromolecular Simulation"
- What is an alignment?
- What is a DNA array?
- What is a homologue?
- What is an ontology?
- What is a scoring matrix?
Here I attempt to define some common terms in bioinformatics. I have tried to balance clarity, brevity and rigour. Let me know if I let one of these priorities over-ride the others.
When two symbolic representations of DNA or protein sequences are arranged next to one another so that their most similar elements are juxtaposed they are said to be aligned. Many bioinformatics tasks depend upon successful alignments. Alignments are conventionally shown as a traces.
In a symbolic sequence each base or residue monomer in each sequence is represented by a letter. The convention is to print the single-letter codes for the constituent monomers in order in a fixed font (from the N-most to C-most end of the protein sequence in question or from 5' to 3' of a nucleic acid molecule). This is based on the assumption that the combined monomers evenly spaced along the single dimension of the molecule's primary structure. From now on I shall refer to an alignment of two protein sequences.
Every element in a trace is either a match or a gap. Where a residue in one of two aligned sequences is identical to its counterpart in the other the corresponding amino-acid letter codes in the two sequences are vertically aligned in the trace: a match. When a residue in one sequence seems to have been deleted since the assumed divergence of the sequence from its counterpart, its "absence" is labelled by a dash in the derived sequence. When a residue appears to have been inserted to produce a longer sequence a dash appears opposite in the unaugmented sequence. Since these dashes represent "gaps" in one or other sequence, the action of inserting such spacers is known as gapping.
A deletion in one sequence is symmetric with an insertion in the other. When one sequence is gapped relative to another a deletion in sequence a can be seen as an insertion in sequence b. Indeed, the two types of mutation are referred to together as indels. If we imagine that at some point one of the sequences was identical to its primitive homologue, then a trace can represent the three ways divergence could occur (at that point).
Biological interpretation of an alignment
A trace can represent a substitution:
A trace can represent a deletion:
A trace can represent a insertion:
For obvious reasons I do not represent a silent mutation.
Traces may represent recent genetic changes which obscure older changes. Here I have only represented point mutations for simplicity. Actual mutations often insert or delete several residues.
Thanks to Bioinformatics.Org member Ravi Jain for the following answer, which I present verbatim.
DNA microarrays consist of thousands of immobilized DNA sequences present on a miniaturized surface the size of a business card or less. Arrays are used to analyze a sample for the presence of gene variations or mutations (genotyping), or for patterns of gene expression, performing the equivalent of ca. 5 000 to 10 000 individual "test tube" experiments in approximately two days of time.
Robotic technology is employed in the preparation of most arrays. The DNA sequences are bound to a surface such as a nylon membrane or glass slide at precisely defined locations on a grid. Using an alternate method, some arrays are produced using laser lithographic processes and are referred to as biochips or gene chips. The composition of DNA on the arrays is of two general types:
- Oligonucleotides or DNA fragments (approximately 20-25 nucleotide bases). These arrays are frequently used in genotyping experiments. The sequences of alternate gene forms may be included for detection of mutations or normal variants (polymorphisms).
- Complete or partial cDNA (approximately 500-5 000 nucleotide bases). These arrays are generally used for relative gene expression analysis of two or more samples; however, oligonucleotide-based arrays may also be used for these studies.
DNA samples are prepared from the cells or tissues of interest. For genotyping analysis, the sample is genomic DNA. For expression analysis, the sample is cDNA, DNA copies of RNA. The DNA samples are tagged with a radioactive or fluorescent label and applied to the array. Single stranded DNA will bind to a complementary strand of DNA. At positions on the array where the immobilized DNA recognizes a complementary DNA in the sample, binding or hybridization occurs. The labeled sample DNA marks the exact positions on the array where binding occurs, allowing automatic detection. The output consists of a list of hybridization events, indicating the presence or the relative abundance of specific DNA sequences that are present in the sample.
What is a homologue?
"Homology" is a much-misused term and existed in biology long before the notion of protein sequences. Strictly homology cannot be qualified; it is not correct to state that two proteins are "30% homologous" with each other, for example. If we could look back far enough in the evolutionary histories of any two molecules under comparison, we would be guaranteed to find a common ancestor eventually, but this is not true homology. An example of this would be the relationship between two variants of a single ancestral enzyme resulting from a gene duplication event. As a rule-of-thumb, true homology should be assigned only when the feature which leads us to suspect a relationship between molecules is one we consider likely to have derived from the molecules' common ancestor. To quote Page and Holmes [Molecular Evolution: A Phylogenetic Approac, Roderick D. M. Page and Edward C. Holmes; Blackwell Scientific; ISBN 0865428891]:
"The classic molecular example is the parallel evolution of amino acid sequences in the lysozyme enzyme in leaf-eating langur monkeys and in cows. Both animals have independently evolved foregut fermentation using bacteria, and in both cases lysozyme has been recruited to degrade these bacteria. Therefore, langur and cow lysozymes are homologous as genes; however, as digestive enzymes they are not homologous because this functionality was not present in the ancestral lysozyme"Although sequence determines structure, it is possible for two proteins to have very different sequences and functions and share a common fold. In fact, most gene products with similar three-dimensional structures are insufficiently similar at the sequence level for true homology or analogy (non-homologous similarity) to be distinguished.
Biology is changing from being a descriptive to an analytical science. Accurate and consistent descriptions are, however, vital to analysis. The idea of ontologies has been co-opted from philosophy and artificial intelligence to partition bioinformatic knowledge in a way which can be reliably navigated by computers.
This preprint of a review by Ele Holloway of the European Bioinformatics Institute gives a more detailed insight into the varied approaches to ontologies in bioinformatics by covering a recent meeting on the subject. The final version appears in Comparative and Functional Genomics.
The following explanation was edited from a contribution by Amelie Stein.
The aim of a sequence alignment, is to match "the most similar elements" of two sequences. This similarity must be evaluated somehow. For example, consider the following two alignments:
They seem quite similar: both contain one "indel" and one substitution, just at different positions. However, if we think of the letters as amino acid residues rather than elements of strings, alignment (a) is the better one, because isoleucine (I) and leucine (L) are similar sidechains, while tryptophan (W) has a very different structure. This is a physico-chemical measure; we might prefer these days to say that leucine simply substitutes for isoleucine more frequently---without giving an underlying "reason" for this observation.
However we explain it, it is much more likely that a mutation changed I into L and that W was lost, as in (a), than that W changed into L and I was lost. We would expect that a change from I to L would not affect the function as much as a mutation from W to L---but this deserves its own topic.
To quantify the similarity achieved by an alignment, scoring matrices are used: they contain a value for each possible substitution, and the alignment score is the sum of the matrix's entries for each aligned amino acid pair. For gaps (indels), a special gap score is necessary---a very simple one is just to add a constant penalty score for each indel. The optimal alignment is the one which maximizes the alignment score.
PAM matrices are a common family of score matrices. PAM stands for Percent Accepted Mutations, where "accepted" means that the mutation has been adopted by the sequence in question. Thus, using the PAM 250 scoring matrix means that about 250 mutations per 100 amino acids may have happened, while with PAM 10 only 10 mutations per 100 amino acids are assumed, so that only very similar sequences will reach useful alignment scores.
PAM matrices contain positive and negative values: if the alignment score is greater than zero, the sequences are considered to be related (they are similar with respect to the used scoring matrix), if the score is negative, it is assumed that they are not related. "Relationship" here may refer to evolution as well as functionality of the proteins, and of course the choice of the matrix affects the result, so one has to make an assumption on the similarity of the sequences in order to receive a useful result: rather distant sequences won't produce a good alignment using PAM 10, and the optimal aligment of two very similar sequences with PAM 500 may be less useful than that with PAM 50.
Finally, it should be noted that only some scoring matrices use similarity to evaluate alignments, but others use distance, so the be careful interpreting the results!
After this brief and necessarily superficial overview, you might want to read some more about scoring matrices.
Thanks to the following people for questions:
- Jonathan Després
- Salma B. Rafi
- Amelie Stein
- Michael Wentzel
Thanks to the following people for corrections, links and sources:
- Anuradha Acharya
- Charles Adair
- Rahul Agrawal
- Ken Allen
- Tom Andrews
- M Antro
- Aditi Arur
- Paulo Almeida
- Jeff Ames
- Jim Auer
- Will Bachelor
- Justin Baker
- Javier Rojas Balderrama
- Nigel Barber
- Risabh Bhandari
- Ruediger Braeuning
- Ian A Brewis
- Pierre Bushel
- Debra Burhans
- Andrea Cabibbo
- Chua Hian Koon
- Betty Cheng
- Leonard Crane
- Fiona Croll
- Paul Curley
- David Delong
- Maureen Downey
- Steffen Durinck
- Lynda Ellis
- Rafiu Fakunle
- Pedro Fernandes
- Matthew Foster
- Momchil Georgiev
- Sebastien Gerega
- Jesmminder Gill
- Georges Grinstein
- Mike Goodrich
- Brandon H.
- Maximilian Haeussler
- Abdul Hameed
- Anu Haniharan
- Samuel Hargestam
- Clare Hayes
- H. L. Hiew
- Ele Holloway
- Matt Hope
- Benjamin Horsman
- Brant Inman
- Pooja Jain
- Andrew Johnson
- Bulat K
- Tobias Kailich
- Erik Kanders
- Kevin Karplus
- Beatrice Kilel
- Gerd Klaassen
- David Klemitz
- Peter Kublik
- Sebastian Kurscheid
- Dominic Lau
- Raymond Lau
- Darren Lee
- Wentian Li
- Louis Licamele
- Jeff Ligas
- Olga Likhodi
- Thomas Litman
- Steve Masticola
- Matt at ColorBasePair.com
- James McInerney
- Conor Meehan
- Junaid A. Mehta
- Lisa Mullan
- David Murphy
- Feisal Merican
- Markus Montigel
- Dr. Nagesh
- Pablo Nehab-Hess
- Alex O'Neill
- Brittany Nielsen
- Fiona Nielsen
- Daniel Nilsson
- Rachel Oh
- Martin Okrslar
- Bjorn Olsson
- Uma Parameswaran
- Fabio Pardi
- David Parkinson
- Helen Parkinson
- Rama Penta
- Isabelle de Piedade
- Jean-Etienne Poirrier
- William S. Preissner
- Antony Quinn
- Jeremy Read
- G. Deepak Reddy
- Alexandra Reitelmann
- Judith Risse
- Francisco Rocha
- John Rowland
- Vishal Rupani
- Amit Sabnis
- Manuel Schmidt
- Cathal Seoighe
- Niranjan Swaroop Sharma
- Richard Sheehan
- Nihar Sheth
- Bolanle Shoge
- Alfred Simbun
- Sugandha Singhal
- Vaibhav Sinha
- Amelie Stein
- Jennifer Steinbachs
- Mattias Thorslund
- James Thompson
- Natalie Twine
- Eric VanWieren
- Catherine Velazquez
- Lam Ah Wah
- Jonathan Watts
- Kathy Wiederin
- Linda Wilson
- Tim Young
- Zuthur Yew
- Tim Young
- Racheli Zakarin
- Hussein Zedan
- Humberto Ortiz Zuazaga
- Michael Zuker
Thanks to the following people for suggesting answers:
- Jeff Bizzaro
- Paul Boardman
- Ravi Jain
- Alex Kasman
- Sangeeta Sawant
- Fredj Tekaia
- Jo Wixon
This resource is maintained by and © Damian Counsell, UK Medical Research Council Rosalind Franklin Centre for Genomic Research (the RFCGR) 1998-2004. It is made available under a modified version of the Open Publication Licence.
The FAQ has also been mirrored, without credit or any attempt to link to the Open Content Licence, at the so-called "National Bioinformatics Institute". If you are thinking of handing over money for their "certification" you can draw your own conclusions about their standing from this fact.
The first version of this Bioinformatics FAQ was prepared when I was responsible for bioinformatics in the Section for Cell and Molecular Biology at the Institute of Cancer Research (the ICR) in London.
I am now a Bioinformatics Specialist at the Rosalind Franklin Centre for Genomics Research, part of the Proteomics Group and am supported by the Medical Research Council. This page does not represent their views, but I will happily read your criticisms. Although I may act on your advice I take no responsibility for anything that might happen if you browse here.