Jitendra Narayan
Tag cloud
Our Sponsors
Five unique traits of effective computational biologist
Bioinformatics research is driven by large set of software, scripts, and tools to analyse gigantic biological data. Being a great biological programmer or bioinformatician involves more than writing code that works. The biological programmers who rise to the top ranks of their profession are not only good programmer but also expert in biological stuff. Moreover, In order to be a good and effective biological programmer, you need to possess a combination of traits that allow your computational as well as biological skill, experience, and knowledge to produce working code. There are some technically skilled biological programmers who will never be effective because they lack the other important traits needed. Here are top five traits that are necessary to become a great biological programmer.
1. Learn and get updated
Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.
In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.
2. Not a merely coder!!!
I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.
According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.
3. Be Social with biologist
The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.
I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.
4. Do not search, research for answers
Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.
Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.
5. Love and defend your research
You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.
You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.
Thanks to all of you for reading.
Comments
Fantabalous!!
Corpas M, Fatumo S, Schneider R. (2012) How not to be a bioinformatician. Source Code Biol Med. 7(1):3. [link]
Wilson G, Aruliah DA, Brown CT, Chue Hong NP, Davis M, Guy RT, Haddock SHD, Huff K, Mitchell IM, Plumbley M, Waugh B, White EP, WIlson P (2013) Best Practices for Scientific Computing. arXivhttp://arxiv.org/abs/1210.0530 [link]
Sandve GK, Nekrutenko A, Taylor J, Hovig E (2013) Ten Simple Rules for Reproducible Computational Research. PLoS Comput Biol9(10): e1003285. [link]
Bourne PE (2011) Ten Simple Rules for Getting Ahead as a Computational Biologist in Academia. PLoS Comput Biol7(1): e1002001. [link]
Oshlack A (2013) A 10-step guide to party conversation for bioinformaticians. Genome Biology14:104 [link]
Via A, De Las Rivas J, Attwood TK, Landsman D, Brazas MD, et al. (2011) Ten Simple Rules for Developing a Short Bioinformatics Training Course. PLoS Comput Biol7(10): e1002245. [link]
Via A, Blicher T, Bongcam-Rudloff E, Brazas MD, Brooksbank C, Budd A, De Las
Rivas J, Dreyer J, Fernandes PL, van Gelder C, Jacob J, Jimenez RC, Loveland J,
Moran F, Mulder N, Nyrönen T, Rother K, Schneider MV, Attwood TK. (2013) Best practices
in bioinformatics training for life scientists. Brief Bioinform.14(5):528-37. [link]
Slonim DK, Yanai I (2009) Getting Started in Gene Expression Microarray Analysis. PLoS Comput Biol5(10): e1000543. [link]
Logan DW, Sandal M, Gardner PP, Manske M, Bateman A (2010) Ten Simple Rules for Editing Wikipedia. PLoS Comput Biol6(9): e1000941. [link]
Prlić A, Procter JB (2012) Ten Simple Rules for the Open Development of Scientific Software. PLoS Comput Biol8(12): e1002802 [link]
Bernard V, Michaut M (2013) Explain Bioinformatics to Your Grandmother! PLoS Comput Biol9(10): e1003305. [link]
Noble WS (2009) A Quick Guide to Organizing Computational Biology Projects. PLoS Comput Biol5(7): e1000424. [link]
Kallioniemi O, Wessels L, Valencia A. (2011) On the organization of bioinformatics core services in biology-based research institutes. Bioinformatics. 27(10):1345. [link]
Ouzounis CA, Valencia A. (2003) Early bioinformatics: the birth of a discipline–a personal view. Bioinformatics. 19(17):2176-90. [link]
Valencia A. (2002) Bioinformatics: biology by other means. Bioinformatics. 18(12):1551-2. [link]
Huttenhower C, Hofmann O (2010) A Quick Guide to Large-Scale Genomic Data Mining.PLoS Comput Biol6(5): e1000779. [link]
Dudley JT, Butte AJ (2009) A Quick Guide for Developing Effective Bioinformatics Programming Skills. PLoS Comput Biol5(12): e1000589. [link]
Bassi S (2007) A Primer on Python for Life Science Researchers. PLoS Comput Biol3(11): e199. [link]
Searls DB (2012) An Online Bioinformatics Curriculum. PLoS Comput Biol8(9): e1002632 [link]
Giddings MC (2008) On the Process of Becoming a Great Scientist. PLoS Comput Biol4(2): e33. [link]
Altschul S, Demchak B, Durbin R, Gentleman R, Krzywinski M, Li H, Nekrutenko A, Robinson J, Rasband W, Taylor J, Trapnell C. (2013) The anatomy of successful computational biology software. Nat Biotechnol.31(10):894-7.
Nat. Biotech Editorial (2013) In need of an upgrade. Nat Biotechnol.31(10):857.
Li JW, Bolser D, Manske M, Giorgi FM, Vyahhi N, Usadel B, Clavijo BJ, Chan TF, Wong N, Zerbino D, Schneider MV. (2013) The NGS WikiBook: a dynamic collaborative online training effort with long-term sustainability. Brief Bioinform. 14(5):548-55.
Source: http://biomickwatson.wordpress.com/
Recently published Nature commentary on "So you want to be a computational biologist?" by Nick Loman & Mick Watson give advice when starting out on computational projects. http://www.nature.com/nbt/journal/v31/n11/full/nbt.2740.html