Careers in Bioinformatics
Bioinformatics/Biostatistics Jobs In Leading Organizations
Currently a large number of companies with varying technical backgrounds are involved in biomarkers and a massive growth of these markets are estimated from 2008 to 2018 in drug discovery, clinical trials, and bioinformatics. Your aspiration to become a part of Biostatistics would definitely come true with hard work, intensive study, intelligence and analytical synthesis at various levels in Oncology Biomarker Group.
As a key member of the Bioinformatics biostatistics group your scope of involvement will be broad, like mentoring junior Biomarker Analysts, follow timeliness and adequate quality of all deliverables for assigned trials and provide expertise in statistics, data mining and bioinformatics and their applications to clinical trials.
The number of people looking forward for a good career as a biostaticians is increasing vastly; this is because of the awareness that the job opportunities are good with best pay scale. Choosing such stream is really worthy and helps to fly high in the career with in a short span of time. The opportunities for biostaticians are very good all around the world, one can get their desired job without any risk just by applying their efforts in a proper and planned way. Wish you success in all your endeavors.
Bioinformatics/Biostatistics Jobs In Leading Organizations
Andrew Dalke's Slides for Introduction to Python
Andrew has a blog, and a company with a presence on the web (where the slides are hosted), and he's on Stack Overflow as dalke.
Thanks, Andrew.
Andrew Dalke's Slides for Introduction to Python
What Is the HapMap?
This is an article that recently appeared on The International HapMap project, http://www.hapmap.org/.
The HapMap is a catalog of common genetic variants that occur in human beings. It describes what these variants are, where they occur in our DNA, and how they are distributed among people within populations and among populations in different parts of the world. The International HapMap Project is not using the information in the HapMap to establish connections between particular genetic variants and diseases. Rather, the Project is designed to provide information that other researchers can use to link genetic variants to the risk for specific illnesses, which will lead to new methods of preventing, diagnosing, and treating disease.
Figure 1: When DNA sequences on a part of chromosome 7 from two random individuals are compared, two single nucleotide polymorphisms (SNPs) occur in about 2,200 nucleotides.
The DNA in our cells contains long chains of four chemical building blocks -- adenine, thymine, cytosine, and guanine, abbreviated A, T, C, and G. More than 6 billion of these chemical bases, strung together in 23 pairs of chromosomes, exist in a human cell. (Seehttp://www.dnaftb.org/dnaftb/ for basic information about genetics.) These genetic sequences contain information that influences our physical traits, our likelihood of suffering from disease, and the responses of our bodies to substances that we encounter in the environment.
The genetic sequences of different people are remarkably similar. When the chromosomes of two humans are compared, their DNA sequences can be identical for hundreds of bases. But at about one in every 1,200 bases, on average, the sequences will differ (Figure 1). One person might have an A at that location, while another person has a G, or a person might have extra bases at a given location or a missing segment of DNA. Each distinct "spelling" of a chromosomal region is called an allele, and a collection of alleles in a person's chromosomes is known as a genotype.
Differences in individual bases are by far the most common type of genetic variation. These genetic differences are known as single nucleotide polymorphisms, or SNPs (pronounced "snips"). By identifying most of the approximately 10 million SNPs estimated to occur commonly in the human genome, the International HapMap Project is identifying the basis for a large fraction of the genetic diversity in the human species.
For geneticists, SNPs act as markers to locate genes in DNA sequences. Say that a spelling change in a gene increases the risk of suffering from high blood pressure, but researchers do not know where in our chromosomes that gene is located. They could compare the SNPs in people who have high blood pressure with the SNPs of people who do not. If a particular SNP is more common among people with hypertension, that SNP could be used as a pointer to locate and identify the gene involved in the disease.
However, testing all of the 10 million common SNPs in a person's chromosomes would be extremely expensive. The development of the HapMap will enable geneticists to take advantage of how SNPs and other genetic variants are organized on chromosomes. Genetic variants that are near each other tend to be inherited together. For example, all of the people who have an A rather than a G at a particular location in a chromosome can have identical genetic variants at other SNPs in the chromosomal region surrounding the A. These regions of linked variants are known as haplotypes (Figure 2).
In many parts of our chromosomes, just a handful of haplotypes are found in humans. [See The Origins of Haplotypes.] In a given population, 55 percent of people may have one version of a haplotype, 30 percent may have another, 8 percent may have a third, and the rest may have a variety of less common haplotypes. The International HapMap Project is identifying these common haplotypes in four populations from different parts of the world. It also is identifying "tag" SNPs that uniquely identify these haplotypes. By testing an individual's tag SNPs (a process known as genotyping), researchers will be able to identify the collection of haplotypes in a person's DNA. The number of tag SNPs that contain most of the information about the patterns of genetic variation is estimated to be about 300,000 to 600,000, which is far fewer than the 10 million common SNPs.
Once the information on tag SNPs from the HapMap is available, researchers will be able to use them to locate genes involved in medically important traits. Consider the researcher trying to find genetic variants associated with high blood pressure. Instead of determining the identity of all SNPs in a person's DNA, the researcher would genotype a much smaller number of tag SNPs to determine the collection of haplotypes present in each subject. The researcher could focus on specific candidate genes that may be associated with a disease, or even look across the entire genome to find chromosomal regions that may be associated with a disease. If people with high blood pressure tend to share a particular haplotype, variants contributing to the disease might be somewhere within or near that haplotype.
What Is the HapMap?
1st International Conference on Bioinformatics and Systems Biology @ Annamalai University, Tamil Nadu
The Annamalai University is a unitary, teaching and residential university.The Department of Zoology, Anna University with sponsership from ICMR, DSt, DBT ir organizing a 3-day International Conference on Bioinformatics and Systems Biology in the month of February 2010.
Scope of the Conference |
Themes of the Conference
Papers are solicited on, but not limited to, the following topics:
• Agricultural bioinformatics
• Bioinformatics databases & Bioinformatics of diseases
• Chemoinformatics
• Computational genomics and proteomics
• DNA Assembly, clustering and mapping
• DNA Bar coding and Bioinformatics
• Drug discovery & Data mining, Database ontologies
• Gene expression and microarrays, Gene identification
• Hidden Markov models
• Immunoinformatics of diseases, Molecular sequence analysis
• Neuroinformatics
• Phylogeny reconstruction algorithms
• Protein structure prediction and modelling
• Parallel algorithms for biological analysis , Simulation and system dynamics
Bioinformatics and Systems Biology 2010 invites high-quality original full papers on any topic related to Bioinformatics and System Biology. The submitted papers must have not been published or under the consideration for publication in any other journal or conference with formal proceedings. All accepted papers will have to be presented by one on the authors at the conference.
The online submission system will be open from December 2, 2009 to January 20, 2010.
Registration
All the participants and their accompanying members are requested to register by paying the necessary registration fees. Participants are requested to fill the enclosed registration form (Photo copy may be used) and mail it to the Organizing Secretary along with the copies of abstract and registration fees payable by DD from a Nationalized bank in favour of “The Registrar, Annamalai University” Payable at Annamalainagar (or) Chidambaram.
Accomodation
Limited accommodation will be arranged by the organizers in the University guest house / Hostel etc. (First come first serve basis).
Important Dates
Paper submission open December 2, 2009
Paper submission deadline January 20, 2010
Paper acceptance decision January 28, 2010
Poster submission open December 30, 2009
Poster submission deadline January 28, 2010
Poster acceptance decision January 30, 2010
Registration open December 2, 2009
Early-bird registration upto January 30, 2010
Conference Feb 19 - 21, 2010
Organizing Secretary
Dr.SABESAN, Ph.D.
Prof. & Wing Head, Dept. of Zoology, D.D.E.
Annamalai University,Annamalainagar-608 002, Tamil Nadu,India
E-mail : sabesan1956@gmail.com, sabesan_muthukumar@yahoo.com
sabesan_in@redifmail.com, Mobile : 09994932234
1st International Conference on Bioinformatics and Systems Biology @ Annamalai University, Tamil Nadu
Your own Bioinformatics workstation using VigyaanCD
VigyaanCD at
http://www.vigyaancd.org/
has a nice collection of bioinformatics software and is worth downloading. It will boot directly from the CD and needs very little linux expertise as the X-windows system is almost like windows.
Your own Bioinformatics workstation using VigyaanCD
Lowering Pharma firewalls: Just for Bioinformatics or Chemoinformatics also
Pre-competitive informatics initiatives
A very interesting opinion piece appeared in September issue of Nature Reviews Drug Discovery discussing the importance of pre-competitive informatics initiatives in drug discovery. Article suggest that many companies are already beginning to embrace this idea, and that for some companies
This was a very timely review in the wake of several initiatives such a Innovative Medicines Initiative (IMI), EBI industry programme, Pistoia Alliance and many others. The idea of lowering industry firewalls caught more attention after the announcement of Sage Bionetworks, a non-profit medical research organization established this year with initiatives of Merck duo Eric Schadt and Stephen Friend. A similar kind of effortOpen Source Drug Discovery(OSDD) was launched by CSIR, India earlier this year with a initial investment of US $38 million. OSDD consortium is trying to implement open source model for Drug Discovery and public-private partnership is one of the major focus of this initiative. Exciting isn't it? But wait there is a twist in story, there are no definitive answers for what type of data is pre-competitive and what is not? The definition of of pre-competitive is fluidic and it depends on several factors, one of them is whether data belongs to biology or chemistry. Article suggests that any data and tools used by biologists should be under consideration for pre-competitive sharing but those used by chemist should remain the competitive or proprietary (which is very much according to current trends). I could not find any rational reason behind this argument except the fact that there is overwhelming amount of public data in biology domain and day by day companies and institution are finding it hard to manage, integrate and use them for drug discovery. I will go further and suggest that much of these initiatives serves no benefits unless otherwise the data and tools belonging to chemistry domain is also considered as pre-competitive. Ironically much of the data and tools released by pharmaceutical companies under these initiatives are yet to proof their importance. For instance much hyped Life Science Grid released by Eli Lilly (which went open source in year 2008) failed to attract even an average user base. Lilly released only the biology side of the grid which includes a selected group of non proprietary plug-ins, including those for Gene Browser, NCBI Entrez, and Gene Ontology. Forgive me but there are already better tools for the biology in public domain. In my opinion unhindered access to data and tool is prerequisite for the success of the pre-competitive landscape which require more active contributions from the industry participants. Currently the systems is evolving and for now something is better than nothing.
Apart from the issues related to definition of pre-competitive boundaries there are several other bottlenecks, for instance the who will fund the long-term maintainability of such an infrastructure, those remain unresolved .
Lowering Pharma firewalls: Just for Bioinformatics or Chemoinformatics also
CIRCOS - VISUALIZING THE GENOME, AMONG OTHER THINGS




Circos is designed for visualizing genomic data such as alignments, conservation, and generalized 2D data, such as line, scatter, heatmap and histogram plots. Circos is very flexible — you can use it to visualize any kind of data, not just genomics. Circos has been used to visualize customer flow in the auto industry, volume of courier shipments, database schemas, and presidential debates.
The creation of Circos was motivated by a need to visualize intra- and inter-chromosomal relationships within one or more genomes, or between any two or more sets of objects with a corresponding distance scale. Circos is similar to chromowheel and, to a lesser extent, genopix.
Circos uses a circular composition of ideograms to mitigate the fact that some data, like combinations of intra- and inter-chromosomal relationships (alignments, duplications, assembly paired-ends, etc) are very difficult to organize when the underlying ideograms (or contigs) are arranged as lines. In many cases, it is impossible to keep the relationship lines from crossing other structures and this deteriorates the effectiveness of the graphic.
Specific features are included to help viewing data on the genome. The genome is a large structure with localized regions of interest, frequently separated by large oceans of uninteresting sequence. To help visualize data in this context, Circos can create images with variable axis scaling, permitting local magnification of genomic regions to be controlled without cropping. Scale smoothing ensures that the magnification level changes smoothly. In combination with axis breaks and custom ideogram order, the final image can be easily tuned to offer the clearest illustration of your data.
All aspects of the output image are tunable, making Circos a flexible and extensible tool for the generation of publication-quality, circularly composited renditions of genomic data and related annotations.
Circos is written in Perl and produces bitmap (PNG) and vector (SVG) images using plain text configuration and input files.
CIRCOS - VISUALIZING THE GENOME, AMONG OTHER THINGS
Evloving computing definitions and realms
Melanie Swan has written an interesting piece about Expanding notion of Computing on her blog Broader Perspective. In response to the question how computing definitions and realms are evolving, she writes
the traditional linear Von Neumann model is being extended with new materials, 3D architectures, molecular electronics and solar transistors. Novel computing models are being investigated such as quantum computing, parallel architectures, cloud computing, liquid computing and the cell broadband architecture like that used in the IBM Roadrunner supercomputer. Biological computing models and biology as a substrate are also under exploration with 3D DNA nanotechnology, DNA computing, biosensors, synthetic biology, cellular colonies and bacterial intelligence, and the discovery of novel computing paradigms existing in biology such as the topological equations by which ciliate DNA is encrypted.

Evloving computing definitions and realms
Backup and fault tolerance in systems biology: Striking similarity with Cloud computing
[T]he overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets.
In order to understand the systems biology of robustness provided by redundant TFs and their role in broader cellular context authors explored dependence of findings on the TFs' homology relationships and shared protein interaction network. They observed that TFs with the most similar paralogs had no overlap between their binding and knockout data, while protein interaction networks provide physical support for knockout effects.
Further Gitter describes importance of his research as,
It's extremely rare in nature that a cell would lose both a master gene and its backup, so for the most part cells are very robust machines. We now have reason to think of cells as robust computational devices, employing redundancy in the same way that enables large computing systems, such as Amazon, to keep operating despite the fact that servers routinely fail
Backup and fault tolerance in systems biology: Striking similarity with Cloud computing
BioPAX or SBML?
Katherine Mejia asked this question on twitter, well just now I want to keep it short,
- Both BioPAX (Level 3) and SBML (Level 2) can encode signaling pathways, metabolic pathways and regulatory pathways, although SBML can represent finer details.
- If your aim is simulation and prediction then SBML seems to be reasonable choice.
- SBML has better software and API support than BioPAX, no doubt this one is major advantage with SBML.
- Similarly BioPAX is rich standard when it comes to encode the interactions such as genetics, molecular or non-molecular.
Visual comparison of SBML, BioPAX, CellML and PSIBioPAX or SBML?
Bioinformatics and Systems Biology: Multidisciplinary scientists versus Interdisciplinary scientist
Multidisciplinarity is the act of joining together two or more disciplines without integration. Each discipline yields discipline specific results while any integration would be left to a third party observer. An example of multidisciplinarity would be a panel presentation on the many facets of the AIDS pandemic (medicine, politics, epidemiology) in which each section is given as a stand-alone presentation.
A multidisciplinary community or project is made up of people from different disciplines and professions who are engaged in working together as equal stakeholders in addressing a common challenge. The key question is how well can the challenge be decomposed into nearly separable subparts, and then addressed via the distributed knowledge in the community or project team. The lack of shared vocabulary between people and communication overhead is an additional challenge in these communities and projects. However, if similar challenges of a particular type need to be repeatedly addressed, and each challenge can be properly decomposed, a multidisciplinary community can be exceptionally efficient and effective. A multidisciplinary person is a person with degrees from two or more academic disciplines, so one person can take the place of two or more people in a multidisciplinary community or project team. Over time, multidisciplinary work does not typically lead to an increase nor a decrease in the number of academic disciplines.
"Interdisciplinarity" in referring to an approach to organizing intellectual inquiry is an evolving field, and stable, consensus definitions are not yet established for some subordinate or closely related fields.
An interdisciplinary community or project is made up of people from multiple disciplines and professions who are engaged in creating and applying new knowledge as they work together as equal stakeholders in addressing a common challenge. The key question is what new knowledge (of an academic discipline nature), which is outside the existing disciplines, is required to address the challenge. Aspects of the challenge cannot be addressed easily with existing distributed knowledge, and new knowledge becomes a primary subgoal of addressing the common challenge. The nature of the challenge, either its scale or complexity, requires that many people have interactional expertise to improve their efficiency working across multiple disciplines as well as within the new interdisciplinary area. An interdisciplinarary person is a person with degrees from one or more academic disciplines with additional interactional expertise in one or more additional academic disciplines, and new knowledge that is claimed by more than one discipline. Over time, interdisciplinary work can lead to an increase or a decrease in the number of academic disciplines.
Bioinformatics and Systems Biology: Multidisciplinary scientists versus Interdisciplinary scientist



