Utilizing alignment-free methods to enable quantitative gene expression 
analysis of large collections of sequencing data

Mitra, Darvish

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Utilizing alignment-free methods to enable quantitative gene expression analysis of large collections of sequencing data

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Mitra, Darvish
IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;
Fachbereich Mathematik und Informatik der Freien Universität Berlin;

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Citation

Mitra, D. (2023). Utilizing alignment-free methods to enable quantitative gene expression analysis of large collections of sequencing data. PhD Thesis.

Cite as: https://hdl.handle.net/21.11116/0000-000E-710E-4

Abstract

Due to advances in sequencing technologies, the amount of sequencing data is continuously increasing and has reached an amount that calls for new data management methods, to actually utilize the sequencing data. In the last years, a number of different indices haven been developed to simplify the data, thereby reducing the amount of space needed and enabling analysis on large collections of sequencing data. These indices are based on alignment-free methods because in this way the costly step of classical sequence analyses, the alignment, can be omitted.
In this disputation talk, an overview will be given over the different indices that have been developed, namely Reindeer, Gazelle and the Counting DBG.