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2nd Generation Sequencing

Overview

New generation sequencing technologies like Illumina Solexa and 454 Life Sciences create vast amounts of short sequencing reads that are mapped onto a reference genome or assembled to build new genomic contigs. As the usual output size of such a method is around several millions, bioinformatic analyses are an important ingredient to any such experiment. So far there have been interesting applications of 2nd generation sequencing in genomics and we are interested to contribute new methods for solving problems that arise from next generation sequencing data, see a recent review of Pop and Salzberg. For example ChIP-seq, which will eventually substitute the ChIP-chip approach [2], leads to millions of reads that identify regions bound by proteins of interest, e.g. transcription factors. We report about the projects in our department that involve the analysis of different types of sequencing data. Page sections:
people
literature
contact

Digital gene expression profiling with RNA-Seq

Microarrays are the state-of-the-art to measure gene expression, because they allow simultaneous measurement of a complete genome in one experiment. In this project, we have utilizied Illumina technology to sequence the poly adenylated fraction (RNA-Seq) of two cell lines. The short reads obtained can be used to infer gene expression levels, similar to gene microarrays, but avoiding biases introduced by hybridization methodologies. Using RNA-Seq we detect 25% more genes than microarrays. Moreover, we are able to find new transcription units and can also report splice variants of the transcripts expressed in the cell. This project is a collaboration with Marie-Laure Yaspo's Group at the Lehrach department . This work is published online at Science Express reported about by Genome Web News.

Resequencing for disease gene identification

Due to the low costs of new sequencing technologies, resequencing of small target regions in disease patients can help elucidating the cause of genetic disorders. As a special case, patients with balanced translocations have been investigated and the disease chromosomes with the translocations have been sequenced using Illumina Solexa technology in Wei Chen's lab from the Ropers department . We contributed a novel algorithm to predict chromosomal breakpoints in patients with balanced translocations from short read data. Because of the high sequencing depths, the predictions where highly accurate and sufficiently close to find the exact chromosomal breakpoint positions by PCR amplification. The analysis of three patients lead to the identification of three new candidate genes for mental retardation (Chen et.al. [4]).

Algorithms for short read handling

We are investigating different strategies for short read assembly and matching using state-of-the-art string indices in cooperation with the group Algorithmic Bioinformatics lead by Prof. Knut Reinert at the Free University in Berlin.

People

Martin Vingron martin.vingron@molgen.mpg.de
Anne-Kathrin Emde emde@molgen.mpg.de
Stefan Haas stefan.haas@molgen.mpg.de
Hugues Richard hugues.richard@molgen.mpg.de
Christian Rödelsperger christian.roedelsperger@molgen.mpg.de
Marcel H. Schulz marcel.schulz@molgen.mpg.de

Literature


Contact

Prof. Dr. Martin Vingron

Gene Regulation Group
Department Computational Molecular Biology
Max Planck Institute for Molecular Genetics
Ihnestrasse 73
14195 Berlin, Germany
Tel: +49-30-8413-1150
FAX: +49-30-8413-1152





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