nodalpoint.org - Research - Comments

books do help

mndoci — Mon, 05 Feb 2007 01:05:12 -0500

I've been on the management side of things for a few years, without any formal training, and books have definitely been a great asset. Where they've helped me, apart from some theoretical knowledge, is to put certain decisions and actions into perspective. Have I missed not having formal training? Maybe in a couple of areas, but mostly, not at all.

My Blog: http://mndoci.com

training

Neil — Sun, 04 Feb 2007 23:28:42 -0500

"Let me tell you about scientific management"
--The Birmingham School of Business School, The Fall

It is strange that very few of us receive formal training in these areas. I've often been advised that completing a course or formal qualification in project management is A Good Thing, particularly if you're looking to move out of academia.

[mesh]ups

Stewb — Mon, 15 May 2006 07:55:31 -0400

It's got a "humans" MeSH term - and humans are "mammals". Power of ontologies, yadda yadda.

Not that it makes the paper any more relevant - but that's why it's not in the abstract. :)

Where are the mammals?

ecacofonix — Fri, 12 May 2006 13:49:11 -0400

I adhere to what Google and Yahoo say about search engine technology being in its infancy. But what I'm a bit intrigued about it is, I couldn't find the word "mammal" in the original abstract ( not trusting my powers of observation, I used the "find" option on the browser)...so I wonder, if search engines are in their infancy or whether they are not yet born???

Vic, Castor Oil Online

Going further...

mako — Wed, 25 Jan 2006 03:00:37 -0500

Thinking beyond pnots.

Well...

chris — Sat, 21 Jan 2006 14:51:33 -0500

Ideally, a postdoc/series of postdocs is to give you further research-only years and training before you get bogged down in middle level management and/or serious teaching commitments. The assumption here is that everyone is eventually going to become a group leader - which obviously doesn't have to be true any more. The alternatives I've seen in some places are "senior scientist" positions. They don't come with tenure, and they don't pay as well as higher professorships, but they aren't post-docs, either. SS's are independent researchers affiliated with one/several groups, but they don't want the hassle of grant writing, teaching, management, etc. Not a perfect solution by any means, but better than eternal post-doc syndrome.

I disagree about whether bioinformaticians require PhD/Postdoc training. It's a question of what you do: service/support provision doesn't require the amount of training that independent project management does.

How depressing

Neil — Wed, 18 Jan 2006 22:49:25 -0500

My heart sinks when I read posts like this one, because (a) that's my life, that is and (b) I'm compelled to comment and so to examine myself in the process.

I know lots of people who've done postdocs to age 40 or more. I've been a postdoc for 8 years, technically 4 contracts and have just signed up for at least another 3 years. So yes, it is quite "normal".

Tenure track is a more common notion in the USA than anywhere else, in my experience. I suppose we all imagine we might have a "permanent" job one day, but most of us don't think that far ahead when we start out. Perhaps because undergraduate and postgraduate training take so long in the USA there's a feeling that people who've committed so much time are likely to stay in the system for ever. Perhaps they just have a stronger sense that they deserve permanent positions after a certain time than the rest of us.

Bioinformatics/computational biology have changed many aspects of biological research and I think the job market is one of those aspects. Put simply, people like us are difficult to employ because we don't fit into the traditional academic niches. It's generally accepted that a tenured academic will run their own group. But what sort of group does a computational biologist, who isn't tied down to any one biological system, run? Many departments see a purely computational group as a waste of space. So most people who do computational biology are associated with a wet lab and can define a few specific areas of biology in which they are interested.

Furthermore, when it comes to employment, there is a distinct difference between bioinformatics and computational biology. "Bioinformatics skills" are not viewed as research skills by funding agencies or people who can offer you tenure. I've been on several grants in which my importance as a bioinformatician has been emphasised and have got the distinct impression that those skills are viewed merely as basic computer literacy, akin to being able to use Microsoft Office and are not sufficient grounds for recognition as a research scientist. On the other hand, "computational biology" implies an ability to perform novel research through algorithm development, complex mathematical analyses and so on.

Tenured or permanent jobs do come up - at the rate of about one a year in my experience. As with any job you don't just walk into it because you've done your time - you have to prove that you're the best candidate. In academia that means a track record - plenty of good, 1st author publications and success in obtaining funding. Again, it's hard for a "bioinformatician" to get good first author papers, because they tend to work in teams and their contribution, whilst essential, is rarely the main focus of the work. So to get those papers you need to be in a research-intensive computationally-focused environment.

Lastly, postdocs (including myself) are often poorly informed about alternatives to academia. Once you've been in the system past a certain point it becomes hard to remember what the world outside is like.

pseudoknots etc

IanHolmes — Sun, 08 Jan 2006 03:09:06 -0500

A major trend in both genomic and structural biology is the emergence of minor parameters for RNA tertiary interactions. It would be amazing to see this emerge in a purely probabilistic model (which I believe it necessarily must, eventually) - a framework that is extensible mathematically. The overwhelming variety of non-specific interactions is of course a supreme challenge.

you mean like stochastic tree-adjoining grammars?

here are a few more links

a chisel and a stone:

mako — Thu, 05 Jan 2006 20:55:37 -0500

My comments to this blog are presumably solicited. As for the topic of this thread, my own world (research wise) continues to shrink towards a singularity out of which I do hope a Ph.D. will emerge. So bear with my increasingly pedantic arguments.

The host of pols that engage in translesion synthesis fascinates me at the moment. There has been a few papers recently which relate the interest in this biological puzzle.

I agree with the intent to reduce the complexity of computational biology operations. A wise man once told me that there is a linear time solution to nearly every algorithmic problem. It would be nice to see people spending more time on cleverness than brute force attacks.

Personally I would die a happy man if the misuse of quotations in sentences and posted placards was rigorously attacked. You know "what" I mean.

Finally, I hope for and fear the years of Google ahead. Most of the time I simply ruse my own ineptitude at not being able to build a supremely useful tool that Google would drool over and buy from me. I look forward to the net being pushed (by Google, and ruthlessly at that!) towards a model closer to its' initial goals (information accessibility). I gleefully wait for the final riddance of old money communication and media giant conglomerations. Yet I fear the eventual and inevitable misuse of gTechnologies by the companies which now run our countries.

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My 2c

chris — Thu, 05 Jan 2006 14:19:54 -0500

Here's my no-particular-order wish list for this year. Most of it, I am afraid, is pretty unachievable. My dreams...

Linux/OSS use in everyday research settings increases, perhaps even with a smidgeon of support?
More clever experiments take advantage of high-throughput technologies, rather than brute force approaches. Come on people, let's put some finesse back into science!
Quantitative genetics re-emerges, in the molecular setting.
The realisation sinks in that single experiments won't answer fundamental biological questions.
Ditto that biology is pretty damned complicated, not just a toy physics problem.
An end to anti-evolution pro-fundie religious nonsense.
A total ban on one-chip microarray studies, and public execution of all authors claiming 2-fold significance/including array pics/classifying cancer samples in articles. It's either been done before or doesn't work. Get over it.
An integrated pdf/citation platform, preferably with automated updating capabilities and hubmed plugins. Oh, and would someone trawl through the entire literature and fix those damned pdf metadata?
More scientists writing for Wikipedia.
More literature added value through blogging (think Faculty of 1000, in opensource mode).
Free beer for computational biologists.
Everything Neil said :-)

The year ahead ...

PedroBeltrao — Thu, 05 Jan 2006 13:48:32 -0500

Two areas where I think computers will be helping to do some interesting things next year are cellular networks and neurobiology.

I am also frustrated about how useless the advances in studying protein interaction networks has been to our understanding of how the cell actually works. In this respect I think next year we will continue to see more interaction data but hopefully put into better use. We need more coverage and in more species to understand how robust are complexes inside the cell to interaction re-wiring over evolutionary time for example. So far projects are aiming at species that are 800My to 1By apart and are impossible almost to compare.
To understand (be able to simulate and predict) the behaviour of a cell maybe we could start by understanding simpler interaction networks like a bacteria. Binary interaction information is not enough. We need concentrations, Kds, localization, etc (most of this things can be predicted).

I know very little about neurobiology (even less than about cellular networks :) but one thing that still bothers me about neurobiology studies is the missing information in between molecular knowledge and behaviour/cognition/memory/learning. Computers are already helping to "read" arrays of probes and to understand how the firing of multiple neurons code for thoughts of mechanical action. Remember the monkey thought controlling the robot ? Computers can also help to make the connection between how a change at the molecular level or a drug changes the firing of neurons in live animals and therefore change behaviour.

my hopes

Neil — Thu, 05 Jan 2006 05:57:50 -0500

A few things that I'd like to see - will no doubt be adding to this:

Agreed standards for storage and annotation of draft genome data
Less focus on minor improvements to algorithms/methods and more focus on biological discovery - especially in the journal Bioinformatics
A readable piece of literature about the semantic web
More maths, stats and computer science in all undergrad biology courses from day one
Better communication between bench biologists and computational biologists - preferably through improved computer literacy on the part of the former
All universities enforcing a ban on Microsoft products for key servers, tearing up software licensing agreements and migrating en masse to Linux and open source
Some rationalisation for microbial genome projects other than "lots of data is good"
Harsher criticism and assessment of environmental sequencing and metagenome projects
A decent, free open-source alternative to EndNote
Some understanding by funding agencies of how to assess non-traditional interdisciplinary proposals, i.e. those that don't revolve around "single topics" such as a particular organism or some spurious promise of medical breakthrough
A free open-source batch queue system that's better than PBS or SGE
Widespread adoption of open access by biology journals
Journal websites not so full of Shockwave Flash ads that my CPU overheats
Unique author IDs at PubMed

Semantic Web, XML and integration for LifeSci

agbiotec — Tue, 03 Jan 2006 14:39:27 -0500

Having done over the last couple of years my share of Bioinformatics with web browsers, also some grinding with BioPerl and BioJava, I am hoping to see more XML-integrated Life Sciences for 2006,,,

XML + RDF for storing our data and being able to manipulate them with ease; and integration of disparate data sources.

Web services for on-the-fly assembly of data analysis pipelines, on-demand Bioinformatics computing, being able to up-scale and get away from the tedious HTML interface of most data sources out there,,, more science and less technical stuff.

But I think there is a lot more to go until we get there. And will keep going until the labs that create genomics - proteomics data, stop putting them in a custom mySQL database that hide behind an HTML interface,,, they have adopted the large-dataset producing techs like chips, mass-spec etc. ... Why they don't also adopt some semantic web or XML technologies to offer easily consumed data to the rest of the world ???

Ooh...

Stewb — Tue, 03 Jan 2006 13:00:08 -0500

Two things that I think'll be hot (in my homo sapiens centric view):

Human structural genomics - in the chromatin architecture sense - finally getting some high-throughput (sort of) data, from ENCODE if nothing else (oh yeah, ENCODE is next year's HapMap).
High recall, high precision regulatory element prediction should happen sometime in the next year. Then we'll just be stuck with the same problem as we have with genes i.e. we know where they are, but not what do they do...

My hopes are that people will stop writing about protein interaction networks until they get some new datasets and that a community builds around some open source LIMS so that we finally have a fully featured one that is continually supported and patched, etc etc.

That's my two pence, anyway.

writing style

chris — Fri, 23 Dec 2005 13:25:37 -0500

I'm a big fan of these sorts of articles. The best writing usually tells a story: a continuous narrative for some reason resonates deeply with the human mind and allows us to absorb information and place things into perspective. It's an old trick, going back to the legends and parables of the ancient worlds of Gilgamesh and Homer.

One of the things we often miss when reading/writing a scientific paper is this narrative: the introduction should at least set the scene, but in truth so many scientists are so poor at writing that their papers become garbled and abstruse, even for other experts in their field.

Maybe we should all try to tell stories in our next papers?