The Bioinformatics Unit

Scientific innovation over the past twenty years, driven to a great extent by the Human Genome Project, has produced unprecedented biological research capabilities. Most notably, scientists can now conduct large-scale, comprehensive explorations of biological content—be it sequencing whole genomes (~3 billion base pairs in a human cell nucleus), profiling a cell’s protein population (~20,000 variants in a human cell), screening tens of thousands of potential drugs, and so on. The need to efficiently and accurately analyze the reams of intricate information that modern, large-scale basic, preclinical, and clinical studies generate has led to an unprecedented growth in the need for bioinformatics analysis.

Bioinformatics denotes the study of biological and medical data using computational tools. This merger of biology and computing has become a major driving force in biological investigation worldwide and is set to be a corner stone of research in  the life sciences in the years to come.

The Vision

The growing need for bioinformatic analysis has lead to the emergence of the ‘Analysis Bottleneck’. The size and complexity of the new datasets obtained by the ever advancing experimental techniques present a very big challenge for analysis. The field now requires extensive computing resources and  big storage arrays as well as advanced software tools and specific know-how. The goal of the bioinformatics unit of the INCPM is to provide access to those resources to a large community of scientists, lowering the entry level requirements as to enable faster and more advanced analysis by a larger group of people.

Our Goals

The biological scientific community community is a diverse network of researchers with very different levels of interest and knowledge in the different aspects of the biological questions and of the analysis process. This means that there is no “one size fits all” solution that can be utilized by the entire community. Further to that, the spectrum of questions tackled and experimental results requiring analysis varies tremendously from one research field to another. Therefor, any computational solution can hope to tackle “out of the box”, only strongly common elements of the analysis process.
In accordance with that understanding, the INCPM Bioinformatics unit will provide support for computational analysis on several levels:

• Analysis as a service – Continuing on  a well established tradition, the unit continues to provide analysis of data, working closely with researchers on exciting, out of the ordinary questions. Our analysis team will utilize state of the art analysis tools and infrastructure built within the unit and tools publicly available.
• Graphical interface – For mainstream analysis, the bioinformatics unit will provide collaborators who wish to take charge of the analytical pipeline with a graphical tool enabling easy assembly of pre-defined analysis boxes to define an analysis pipeline in a user friendly manner.
• Programmatic environment – For advanced users and to support our own analysis team, we are creating an environment supporting the building of analysis pipeline through programming based on the open source tool “R”. Components encapsulating standard analysis steps enable easy and fast analysis while the programmatic interface enables total freedom in logic implementation. The environment is being built as an open source project, enabling bioinformatics units and computational researchers around the world to utilize it and contribute components to it.
• Physical – As the most basic layer, we will be providing the computing power and environment required for analysis. High performance cluster and storage array hosting various analysis and visualization tools will be made available to all collaborating scientists.

Our Team

The INCPM bioinformatics unit is combining a well established team of analysis professionals with a start-up team for analysis systems development. We are currently looking to expand our development team and looking for candidates who are interested in finding the common elements among cutting edge computational biology questions and the best way to provide a high throughput solution to a high throughput problem. If you feel this is the job for you, feel free to send your information to: ramij at weizmann.ac.il.