I am pursuing Masters in Bioinformatics (just started). Did my graduation in Botany, Attended some workshops and a few internships in bioinformatics.
I have a few questions, so please help me out kindly. [^_^]
•
written
by
bloodnaousky •
Hey,
1 I do not know, I never heard of it. I think a good github profile with documented tools/algorithms will have more value in 'other countries.'
2 To answer this question you have to first decide what to work on. What are you interested in ? Some languages do some tasks easier than others and vice versa. But even then it doesn't mean that you only need to learn one language. Often you will need combination of some.
3 For the love of whatever you believe in or do not believe in, do yourself a favor and do not ask this question to yourself. Here is the answer I gave to a question with similar context a couple of weeks ago.
4 Again, if you are just starting out, I think this should not be a priority. In groups that are not well known, you mostly depend on your own skills, but if you are successful, you can use it to your advantage and grow strong foundation there. In groups that are well known, you may get a couple of nice papers, ride the wave etc. but it can also spell your doom. If you want an "anti-fragile" career (in the sense that Nassim Taleb uses the word), focus on what you like first.
5 There are a lot of opportunities just like in any other field of science. But one has to 'see' the opportunity first. To see, you need to tinker with stuff that you love to do and gain experience.
6 I'm sorry to sound salty, but with the current state of academia it's all the same to me.
7 Foremost, have concrete foundation in mathematics, it will help a lot. If you know your stuff well and can create maintainable work without depending on anyone, they will recruit you. If I were you, I would stay away from wet lab, it was an awful lot of repetitive stuff : it will be painful. At least it was painful for me.
8 This question redirects to my answer at 3 which redirects to that link.
I know these answers are salty and I kind of understand the situation/competition you are in which ultimately pushed to you to ask these questions. The system is not perfect, I do not think anyone disagrees with that. But I do not think we can undo a mistake (out of our control) by doing another mistake (which we could have controlled).
Best of luck in your life,
I would stay away from wet lab, it was an awful lot of repetitive stuff
Yeah, cause data analysis is never repetitive...
Hi Harold,
The problem lies in the detail here. There are repetitive parts in every task out there. If we are going to try proving a point by looking at edge cases we can claim a lot stuff.
My take on this topic is that there is a problem at the level of wet lab and that is it is not repetitive only because it is repetitive but it is repetitive because experiments often has to be repeated so many times due to reagents, samples etc. This in turn boils down to the following phenomena: "For a bioinformatician, in which field he has more control, wet lab part or the computational part?" This is important because it will determine how much he needs to suffer before he can achieve his goal.
I will give example from my self. I work on data visualizations for about 10 years. I usually draw something on A4 paper and try to prototype it in the computer. Usually it goes well + I learn a lot of stuff. There are pipelines out there for analysis and etc. But what happens when they don't work or I don't like them? I write them myself. It is exactly this part where wet lab differs. During my PhD I remember I had to do some functional studies for a candidate gene. I had to do A LOT of western blots, trying get a nice staining. Each time, it would fail and I would have to change the antibody again and repeat. You want to purify your own anti body? Good luck! I observed similar things in all of my friends who at time were in pursuit of wet lab/computational research.
Out there, there are a lot of people caught in a similar loop and at the end of their 4 year PhD, they finally get the title "Doctor of Philosophy" without doing too much thinking into what they have done. This is not anyone's fault do not misunderstand. The system is structured like that. But if we do not address the elephant in the room, it will only get worse.
There are people who love the technical aspect of the wet lab. And I love them. We cannot progress without them. And maybe they are even more crucial to the advancement of science. But if this fellow is oriented towards bioinformatics (which he seems to be from the questions) why would I relay him to a field where he will have less degrees of freedom?
Or maybe I am wrong who knows...
@Ibrahim, my statement was meant as a joke; not trying to offend. There are plenty of reasons to avoid bench work if it's not your interest. I just found the 'repetitive' comment funny - perhaps b/c I just ran the same variant-calling pipeline on 30 different samples.
There are plenty of bioinformaticians who have labs to do bench work. In fact some deliberately start wet labs so they can collect specific kind of experimental data that may not be otherwise available. If one is capable of working in both domains, one may actually have an advantage.
All good points. I'll add that it is better to get good foundations that will help you learn by yourself latter and allow you to keep growing with your jobs. Programming languages are not important, they're just tools to do a task. How you use them is what matters. Many programming languages give you libraries implementing algorithms you may need but what's important is to understand the assumptions and the parameters that these algorithms rely on. Anybody can write an R script to do PCA but how many of those who do actually understand the output and can interpret it in the context they're working in. Certifications may be important in some industries and clinical branches of academia but I've never seen them considered seriously. In my experience, in academia, what matters most are demonstrable achievements and good recommendations from people you've actually worked with/for. As to the international scene, the only degree that is truly international is the PhD. A PhD also makes it easier to get job visas (a PhD holder is considered a highly-skilled worker in countries that have this visa category). Given the current state of employment in academia (mostly one or two years contracts, low pay), I suggest going to industry after a PhD, at least you'll be making (a little bit) more money if not having more stability. But then in industry you don't always need a PhD. Wet lab is good to have as an experience as it can give you some insight into how real data is produced and may facilitate communication with data producers but it takes time to acquire a level that will be considered seriously to make a difference.
Thankyou so much to all of you for guiding me through the process had a very hard time going through the mathematical part and the coding part since i was a botany student. @ibrahim non of that salty at all. I am almost through the process and by the end upcoming may-June I'll be fully prepared to finally step into the industry.
Does anyone know about benefits of BINC certification exam ( held by Pondichery University, India) ??
From BINC FAQs
"While anyone with a Bachelors degree in Science, Medicine, Engineering etc. is eligible to appear in the BINC examination and receive a certificate, the fellowships are given only to those who have a Masters degree and are enrolled in the Ph.D. programme in Bioinformatics in any recognized institute/university in India. The fellowships are awarded to eligible candidates strictly on the basis of the merit list in the BINC examination."
If you want to go for PhD in India, you get a fellowship almost equivalent to CSIR-NET. You become a certified-bioinformatician; though a certificate cannot judge your knowledge upto the mark(that comes with experience). I am not discouraging you; everyone doing master's in India should at least try one to take this test. It will be an added advantage.
If you don't qualify for BINC, it does not mean that the doors are closed. I have a lot of friends in India and abroad pursuing PhD and they did not cleared this exam. By the way, top 10 candidates receive a prize money of INR10k (just another motivation).
What is its value in other countries?
Scientific people ever where judge you by your knowledge; though national exams adds credibility to your resume.
Please guide me on which computer language to prefer more ?
As mentioned by Ibrahim, a combination is indeed required. But some suggestions from my side will be
Which branch is most trending at this time over the globe !
Instead, you should ask, which stuff you like the most !
Please suggest a few good Companies/labs to apply for internships further.
Again, that depends on your interest. You can look out on LinkedIn or Biotechnika(in india only) and other online forums for open internship positions. But before that, identify your interests.
Are their any business possible prospects in this field ?
Yes. there are. Again, a lot of experience required. A lot of start ups coming up with mundane jobs; very few of them actually know what they are doing.
Which is better B.Tech in Bioinformatics or M.Sc in Bioinformatics ?
You can have a job even with B.tech, but at some point, you may be asked to have atleast master's. With jobs requiring only Masters, a B.Tech degree could be considered if your skill set match the requirements. If I was hiring a person, I will first judge his/her knowledge.
What extra work needs to be done to make my recruitment possible .i.e wet lab techniques etc ?
Biology, mathematics and statistics. Unfortunately, a lot of people tend to give more weightage to technical skills and just forget about the underlying biology that is essentially required to interpret meaningful results as a bioinformatician. Applying a statistical method is easy, but the important question is "which" method to apply and why.
There are plenty of bioinformaticians who have labs to do bench work. In fact some deliberately start wet labs so they can collect specific kind of experimental data tha may not be otherwise available. If one is capable of working in both domains, one may actually have an advantage.
•
written
by
masterseodotid •
Hi masterseodotid,
There is no need to leave links to your website randomly in a post. I have removed the link. If you do this repeatedly your account will be suspended.
Cheers,
Wouter
Ibrahim and Vijay gave long and detailed answers, so I will just fill small gaps:
- Please guide me on which computer language to prefer more ?
Here you have to walk a line between best languages and most adopted languages - not always the same. For developing pipelines and parsing files, Python, Perl and shell scripting; for statistical analyses, R/Bioconductor and Python; for developing high-performing algorithms, C/C++ and Java.
- Which branch is most trending at this time over the globe !
Trends change so fast, you have to keep up, mainly by following blogs, tweets (argh), forums and pre-print servers - published papers are always lagging at least a bit, sometimes they are oddly outdated when finally published.
- Please suggest a few good Companies/labs to apply for internships further.
A cheap international internship (but unpaid) is to contribute to open source bioinformatics projects. Find one with active development, receptive owners, follow the contributing guidelines and find tasks adequate to your skill level.
Log in to answer this question.
Have you checked past threads that have asked similar questions? In fact you had asked a similar question a few months back: Career in Bioinformatics
As with a lot of these question you are going to get answers that may be valid today but you won't be in the job market for another couple of years until you finish your M.Sc. so you will need to keep that in mind.
Sorry, its been 2.5 years since i last replied to this post 😅
Thank you so much sir for your valuable points, it all helped through the way.
update