1. Dairian Wan | Bioinformatics © 2003, Genentech 1 6/1/2015 Bioinformatics Overview 8 November 2004 Dairian Wan

2. Dairian Wan | Bioinformatics © 2003, Genentech Slide 2 6/1/2015 Overview of Bioinformatics 1.What is Bioinformatics? 2.Statistics in Bioinformatics. 3.Role of Bioinformatics in Research. 4.Examples of some issues addressed by Bioinformatics.

3. Dairian Wan | Bioinformatics © 2003, Genentech Slide 3 6/1/2015 What is Bioinformatics? 1.No such thing until early 1990’s. 2.It is a result of a convergence of technologies: 1.Sequencing technologies & the large-scale sequencing of genomes (e.g. Human Genome Project). 2.Combinatorial Chemistry / combinatorial methods in general. 3.High-throughput screening (HTS) technologies (e.g. cell-based EC50’s). 4.Increased computing power. 3.Experimental biology now has the capability of generating more data much faster than before. 4.The collection of techniques used to manage and mine this biological information is referred to as ‘Bioinformatics’.

4. Dairian Wan | Bioinformatics © 2003, Genentech Slide 4 6/1/2015 Statistics in Bioinformatics 1.Use of statistical methods is EVERYWHERE in biology (since it’s an experimental science). 2.Almost any experiment you choose to perform compares groups. 3.What differentiates Biostatistics from Bioinformatics? Bioinformatics implies an overall organization or context to statistical findings. 4.In general, the canonical organizational element in Bioinformatics is the genome (i.e. everything relates to gene sequences). 5.The most widely discussed experiment is the Microarray.

5. Dairian Wan | Bioinformatics © 2003, Genentech Slide 5 6/1/2015 Bioinformatics in the Research Organization 1.Microarray is just ONE experiment in a universe of experiments. 2.Discovery/identification of gene sequences doesn’t happen in isolation. 3.Mission includes the collection and curation of biological experiment outcomes (experimental and public). 4.We use Bioinformatics techniques to interpret experimental outcomes and suggest additional avenues of exploration. 5.Communication of findings to the entire organization is essential. 6.Bioinformatics isn’t always called ‘Bioinformatics’.

6. Dairian Wan | Bioinformatics © 2003, Genentech Slide 6 6/1/2015 Microarray 1.It’s a DNA binding experiment, performed tens of thousands of times (in an array on glass). Each spot in an array is a unique DNA sequence (correlated with a single gene or exon). 2.These are very expensive, require a lot of analysis, and yield a lot of information.

7. Dairian Wan | Bioinformatics © 2003, Genentech Slide 7 6/1/2015 Tumor Growth 1.Typical biological imaging experiment. 2.We collect non-scalar information (images). 3.We deal with populations (of mice). 4.This type of data needs to be just as visible as microarray data. 5.It pays to know how to move data (any kind) around; it pays to know how to visualize data. 6.Data dissemination is just as important as analysis.

8. Dairian Wan | Bioinformatics © 2003, Genentech Slide 8 6/1/2015 More Bioinformatics Examples… We build tools for data management as well as data analysis.