Report: 7th one Day Workshop on the Significance of Bioinformatics in Life Sciences

Hah...
We boarded local vans in the scorching heat of May and after a dose of dust and good deal of sweating, reached CUST.
Faculty and students greeted us-nice and polite as always...guided us to registration desks and we collected our stuff for the workshop..
Workshop commenced an hour late but just a minute after we reached, Not that we were chief guests because we were late ourselves and the chief guest arrived just after we set foot on the campus...:P
 Dr. Tanwani gave a very good, informative talk... I had performed a precheck of all the speakers profiles and publications but despite having just two publications, his knowledge and ethics and practical work was amazing.. Another proof that practical people publish less (Just Like Dr. Faisal Farid of CECOS, Peshawar)... Ok not to digress from topic...
He is a goldmine of tissue image data in different cancers and encourages collaboration so if you are interested, just set a meet up with him at PIMS or drop him an email. He will be coming to CUST again for a seminar soon so one could meet him up there as well. Very sweet, optimistic, helpful and God and humanity loving doctor.
After that Dr. Faraz of NUST opened new avenues of thinking---with his multimillion dollar presentation (gave me two ideas for a future publications so thank you so much Dr. Sahib :P)
Image processing seemed to dominate the workshop agenda and another exciting presentation about digital pathology by Dr. Adnan Mujahid. Proud to see Pakistanis making good progress in this field and bagging prizes and recognition at international scale. Thumbs up Dr. Sahib!

This was followed by Dr. Waseem Haider's exciting amazing NGS talk. So impressed about his self help of bioinformatics learning and knowledge delivery. Revised some old concepts and gained some new... Strongly recommended for learning stuff in NGS (through his future workshops which he holds time to time in COMSATS). I was always unhappy that his workshops were too expensive but respect increased manyfold when I learnt that money earned from his teaching and training workshops is spent on the less privileged people of the society. This made me really proud of him...:)
 Missed LUMS professor Dr Safee Ullah's presence.. Dr. Shaukat Iqbal grabbed the audience's attention with his little puns and stunts. Very wonderful way of introducing microarrays...
Forgot to mention that we had lunch and tea-though not up to the mark but we were there to learn and not for food so it did not matter much..:)
Second session was not that good but provided some food for brain.
Again took public transport for coming back and reached home dead tored at around 7:00 pm---Overall, Saturday put to good use...

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Publising hypothesis...

While reading "In theory" by Sydney Brenner, the nobel laureate at http://www.sciencedirect.com/science/article/pii/S0960982297700952?np=y, I felt swooned that I am not wrong in practicing theoretical biology most of the time.
According to Animesh Ray of Keck Graduate Institute, publishing hypotheses used to be the norm in the past. The first Watson and Crick paper was little more than a hypothesis (The first sentence of that paper was: "We wish to suggest a structure for the salt of deoxyribose nucleic acid (D.N.A.)."). So was the Corey-Pauling alpha-helix paper. "one-gene, one-enzyme" paper by Beadle and Tatum was a hypothesis. The first DNA coding paper by Gamow was a hypothesis. Although not formally published, Crick's famed tRNA paper, which introduced the "wobble-hypothesis" for the third anticodon position, which allowed deciphering of the genetic code, was a hypothesis, was widely circulated among the practitioners. The central dogma paper by Crick was a hypothesis. The so-called French-flag model of morphogen gradient in developmental biology was a hypothesis by Lewis Wolpert. Crick wrote a paper in Nature in early 1970s on the probable physical size limit of morphogens, which was entirely a hypothesis (no morphogen was yet identified). The proposal that eukaryotic chromosome ends must have a special structure (specifically, a hair-pin, which some 15 years later was discovered as telomeres) was a hypothesis advanced by Jim Watson in the late 1960s in Nature. The famous Bohr's paper on atomic theory was strictly speaking a hypothesis (consistent with past data), the general theory of relativity was a hypothesis (proved a few years later by observing the bending of light past the sun during a complete solar eclipse).
Schroedinger's equation paper was a hypothesis (it can't be derived). Plank's famous paper that introduced quantization of energy was a hypothesis. I could go on and on. In recent times, journal editors and reviewers have generally and unintentionally conspired together to not publish hypothesis without validation, because of impact factor considerations--a hypothesis without validation is hard to evaluate, and so it is risky for a journal to publish because it might be proved wrong and then the article would not be cited further. For example, in the early-to-mid seventies, there was a paper published in Nature entitled "A quantum mechanical muscle model" (or something very close to that language), which proposed that actin and myosin molecules generate force through a quantum mechanical resonance process, which turned out to be untestable (not incorrect, mind you), and was hardly cited (the untimely death by suicide of the author due to depression might have contributed to the paper not much cited, however). Unfortunately, as Carl Popper has shown, hypotheses that are proven wrong are the most useful hypotheses for the progress of science. So the current trend arguably might impede progress. Remember -- the idea that the earth was round was once also thought-of as radical.

On the other end, journals consider results incomplete if we do not use the statistical operation (which are hypothetical as well) for instance check out all the recent uproar about P value nonsense in journals like Nature and Science.. Secondly, every paper ends with hypothesis that is taken as a lead for further research.

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5 papers got rejected this week-sigh!

Journals are increasingly becoming skeptical of computational based studies (targetted at molecular mechanism inference or cure finding of deadly diseases), citing need of experimental proof and that computational based studies alone add to the clutter of literature. If skeptical now, why were not the algorithms and tools generated for such analyses subjected to more critical analysis in the first place?
After the publication of Editorial by NEJM, shunning work on secondary data, it has furthur added to the woes of computational biologists. It just seems that monopoly and power play of the few minds who have now shifted their focus from theory to experiment. We all know that experiments are the ultimate truth but computation assisted the preliminary findings.
It just seems like the gameplay of stock brokers, who based on corollories and derivatives of financial terms, tempt and loot innocent people. Ultimately, the big fish devours the small in the game of rise and fall of the share prices. Similar is the case of Academia, a slight bit of unethical or wrong thing gets noticed and highlighted  for junior researchers, resulting in btter consequences, while seniors do whatever they please and get away with it, wrapped in scarves woven from innocent laypeople's respect for supreme intellectuals and brainy devils.

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Problems of collaborative research projects

In case of multi-organization and uni based projects, funding organizations can be and are too often are a force for hindering interdisciplinary efforts . They think you are trying to confuse them and things go pretty bad and review process blocks multidisciplinary work. But if by crossing all the mountains of such things, you get funding approved or go for computational projects with university approval or personal resources (for mini projects in free time) and somehow drag relevant people into the project and start it, ton of other problems pop up.

Collaboration decreases competition we will have and multi-disciplinary research always creates more exciting stuff but it is not with out its cost and in present times. Cost mostly outweigh the benefits. In my case, it certainly did.

I have a long list of the problems I encountered during my on-line project collaborations, both in and cross-country.

One major problem is allocating authorship credit in a large collaborative research.
Once you start working together, after a little input, other person stops putting in which means you can't kick that person out and can't keep him still in. It is also not always the case that multiple authors means that a collaboration has taken case.
Correspondence is also a major issue, senior would like to have that even though wants the formatting and heck of submission to be done by the junior.
Some are such late responders and cause unnecessary delays and one can't continue to submit revisions etc without approval of all authors.
In some cases, horrific things happen like they take your data and soon after that design a relevant or corollary type thing of your project and leave this hanging mid-air and start working on the new project as independent investigators.
If they come to know that your are collaborating because you are not an expert in that thing, exploitation mounts up.

To overcome such problems, interested people should read 'A brief guide to research collaboration for the young scholar' published by Elsevier at 
https://www.elsevier.com/connect/a-brief-guide-to-research-collaboration-for-the-young-scholar

Following is the guideline framework proposed by leading scientists to avoid problems in collaborative work. It could be modified to suit your own needs.

Co-author agreement for a scientific project

Overall goals and vision

We have agreed to collaborate in scientific research and publish our results in a scientific paper (or perhaps several papers). We agree to the following guidelines as we work together toward this goal. This agreement ends after the paper is published, data are archived, and media inquires conclude.

We enter into this agreement voluntarily, and we can leave the agreement voluntarily as described below.

Who will do what?

Team members are expected to make contributions as specified during the formation of the Team. These responsibilities might include carrying out research, contributing specimens and data, analyzing data, and writing the paper.

The paper will be led by an acknowledged Project Leader (often the principal investigator, research group leader, or graduate supervisor). The Leader will facilitate decision-making and communication among the Team. The Leader may be the person who started the project, who invited members to join the Team, who is expected to do the most work on the project, or who is head of a research group. In some cases, particularly for small groups, the Project will be managed through collective decisions or some other method.

Once the Team is formed, any decisions on adding new co-authors or Team members should be made by consensus rather than individual decisions.

Data for this project belong to the Team for the purposes of this paper. Data will be managed by the people who generate them and shared as needed for analysis. Upon publication, the data will likely be deposited into a permanent, publicly accessible archive, such as DataONE, and in accordance with journal policies. The data will be credited to the people who created them and will be linked to this paper through the metadata.

Authorship, credit, and responsibility

Authorship will be limited to those who have contributed substantially to the paper. If a member of the Team does not contribute substantially as initially agreed, that person will be removed as a co-author, as determined by the Leader in consultation with the Team.

The Leader will determine the order of authorship for the co-authors based on contributions to the project. In practice the Leader is often be the first, last or corresponding author.

If appropriate to the journal, the acknowledgements of the paper will describe each co-author's specific contributions. The contributions of other collaborators who are not co-authors will also be described in the acknowledgements.

All co-authors share some degree of responsibility for the entire paper as an accurate, verifiable research report. Co-authors are responsible for the accuracy of their contributions, but may have only limited responsibility for other results.

All co-authors must give their permission for publication prior to submission of each version of the paper.

All co-authors can give presentations of this paper after publication, using material in the paper and dataset, providing they reference the paper and their co-authors. Ideally, they will also notify the co-authors of these presentations beforehand.

All co-authors can respond to media inquiries relating to this paper. Press releases should include the names and contact information of all co-authors. Team members should acknowledge the contributions of other co-authors during interviews and encourage reporters to contact them.

Contingencies and communication

The Leader will manage Team communication by organizing regular communications, such as email updates or phone calls. The default might be one communication per month, with more frequent communications when necessary.

All Team members agree to reply to emails and phone call concerning the project, especially drafts of the paper, within a reasonable period of time, such as within one week.

All Team members agree to notify the rest of the team prior to sharing the manuscript with people outside the Team. Team members will be given a chance to comment prior to sharing.

No Team member can block publication of the paper except because of concerns related to scientific soundness — e.g., the data collection, analyses and presentation were done incorrectly. Concerns related to policy, management, or scientific implications are not grounds for a co-author to block publication. If a majority of Team members believe the paper should be published based on sound science, the paper will move forward. Every reasonable effort should be made by the Leader and others to reach a consensus on moving forward with a publication.

Team members may voluntarily remove themselves from the project, and from co-authorship, at any point if they no longer have time for the project or they disagree with some aspect of the project or paper. If a Team member voluntarily leaves the project or is asked to leave the Team because they are opposed to the paper being published, the Team members and Leader will need to discuss with the dissenting member if his/her contributions can still be used, and perhaps described in the Acknowledgements, or will have to be removed from the paper.

Team members are free to develop their own collaborations and directions using the ideas and data in the paper, once it is published. Team members should make every reasonable effort to inform each other when starting new collaborations and spin-off projects that result from this paper. In practice, the Team members may continue to work together on follow-up projects, but this needs to be discussed among the group, and should not be assumed.

Conflict of interest

All Team members will disclose to the Team any real or perceived conflicts of interest related to this project and paper.

All Team members will disclose to the Team whether they or any close family members or associates will benefit financially from this project and paper.

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Till 2015-Some unresolved questions in genomics research?

Here are opinions of some experts:

1. Michael Wendell from Washington University in St. Louis says "A very broad goal of "genomics" and its companion "bioinformatics" is to transition us beyond the purely empirical state of knowing gene (and pathway) lists, their basic functions and interactions, etc. to a state where we understand the (almost certainly non-linear) set of control laws that govern the cellular and larger-scale biological machinery of an organism. As Eric Lander said many times regarding the first human genome sequence: it's a list of parts. At the moment, one might say that genomics is now figuring how they fit together and what types of problems "broken parts" can cause. Ultimately, we need to know how these parts *work as a system* (at all scales), so that predictive models can be constructed. As a corollary, I think genomics will see an increasing influx of workers who specialize in these types of problems, especially from the engineering and mathematical realms."

2. Alex Ignatov of Research Center of Phytoengineering says most important stuff to consider is:
1) What is the mechanism of genome dynamics
2) What is the mechanism of information transfer from gene to phenotypic trait?
3) Is there any general rules ant purposes or evolution? "

3. Mark M. Abitol of Necker University Hospital and INSERM CENTER UMR says "The most immediate questions to solve seem to me the following ones :
1) Identifying and clarifying the different families of Non Coding RNAs and the role of each non Coding RNA
2) There are many  genes encoding small peptides apparently and it is likely that most of them have not yet be identified. For Those identified, the role of these peptides are still unknown in many instances
3) the links between energy expenditure and the occurence of biological events or processes are in most instances unknown
4) The energetic cost of alternative splicing is still unknown and the same holds true for translation
5) Energy homeostasis is still an enormous conundrum without a crystal clear overwiew and modelization
6) The dynamics of genomes remains largely a mystery
7) DNA DAMAGE RESPONSE and DNA repair remain a huge fields of  mysteries even though a lot of work has provided many answers
8) The RNA world remains largely unexplored
9) The mathematic modelization of biological pathways and of life is in its infancy."

4. Miguel Andarde of Johannes Gutenberg-Universität Mainz says "The 3D structure of genomes, their dynamic changes and their control."

5. Y. Tom Yang from First Dimension Biosciences (Suzhou) LTD says "Seeking "The Main Program"

I like to watch kids playing an orchestra. "An orchestra is a large instrumental ensemble that contains sections of string, brass, woodwind, and percussion instruments." There is a single person, you may or may not notice his existence. Yet, his hand moves, with a baton or not, drive the entire orchestra to make the fantastic music we enjoy. Is a "life" like an orchestra? Are there many instruments that work seamlessly together? If yes, who is "The Conductor"?

Here is another comparison:

To program with modern languages (C++, Java, Python, etc), we would have basic modules (i.e. classes), and objects for these classes; and then these modules would be linked together by a "Main" program. The "Main Program" usually takes some input arguments, and then call upon these objects to achieve a function. The Main operates essentially in the time domain and its working logic is determined by the status reported back from those objects. "The Main Program" is the system integrator and decision maker. It is part of the system, but in the mean time it is kind of beyond the system.

In genetic programs (i.e. all life forms), the architect should be similar to concerts and to computer programming. For simplicity, I will just use the case of programming. The genes are "classes". Each transcription of a gene is an "instance" of the "class", hence, an "object". The collection of genes are the working parts. They are the classes for programming. That said, how can we make the genes to work together? By comparing it to programming, we know we need a drive, i.e. "the Main Program".

Where "The Main Program" is located in the genome? How "the Main" was written? Nobody knows yet. Presumably it is a COMMON part that is within every genome for all life forms, being it be bacteria, eukaryotes, fungi, plants, but excluding virus. It should be mostly composed of genes in the class of "Transcription Factors", as it needs to call upon many genes, and to conduct a symphony.

So if we study some model systems, from the time of "fertilization" in diploid genomes, or from the time of budding in a yeast, the "Main" is kicked off. Through an evolutionary study of the pathways, we should be able to pinpoint to this Main. It should contain some massive genes (large in size) which acts as integrators of the system states, ie. the switch board protein, which then makes a decision and turns on a group of other genes through cascades.

Let's we join hand to find this "The Main Program". It is the "Conductor of Life". From there, the genetic logic will become clear. "

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