V ponedeljek, 21. maja, bosta v Veliki predavalnici na Famnitu  (Glagoljaška 8, Koper) ob

14. in 15. uri bioinformatski predavanji

dr. Epameinondasa Fritzilasa in dr. Paula Medvedeva

o analizi genoma.

 

Vljudno vabljeni!

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14:00-15:00

Dr. Epameinondas Fritzilas, Illumina Cambridge, Computational Biology Group, United Kingdom

An introduction to Illumina's sequencing technology and the challenges of re-sequencing applications

The so-called "next-generation sequencing" technologies play a key role in modern molecular biology: they open many possibilities for new discoveries and, at the same time, bring biology closer to the quantitative sciences. In this tutorial talk, we will first give an introduction to Illumina's sequencing-by-synthesis technology that makes it possible to generate large volumes of sequence data in a cost-effective way. Then, we will turn our attention towards an important application of high-throughput sequencing: the detection of differences between a sequenced sample genome and a pre-assembled reference genome. We will sketch the necessary steps to achieve that goal and highlight the technological and algorithmic challenges that make this task non-trivial.

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15:00-16:00

Dr. Paul Medvedev, University of California, San Diego

Algorithms for reconstructing genomes using high-throughput sequencing

Whole-genome shotgun sequencing is an experimental technique used for obtaining information about a genome's sequence, whereby it is broken up into millions of short segments (called reads) whose sequence is then determined. Recent technological advances hold the potential for tremendous bio-medical discovery; however, the challenges posed by the novelty and sheer quantity of the data are increasingly computational. A long-standing problem is how to infer the genomic sequence of an unknown species from its reads, called genome assembly. On the other hand, even within the same species the genomes of two individuals differ, and the problem of detecting such variation has received a lot of attention in the last few years.

In this talk, we will describe algorithms for assembling genomes, discovering structural variants, and correcting errors in the reads. Our methods are based on genome graphs, which capture the structure of a genome even when its sequence is not fully known (as with the case of sequencing data). We show how traditional genome graph models can be extended to capture matepair information  (pairs of reads at a known distance apart), which is crucial for improving the quality of assembly. We also show how genome graphs can be used for detecting structural variation through a method called CNVer which uses a reduction to the bidirected network-flow problem.

No prior knowledge of biology or advanced mathematics is required.