Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Approximate String Matching: Improving Data Structures and Algorithms

Pockrandt, C. M. (2019). Approximate String Matching: Improving Data Structures and Algorithms. PhD Thesis. doi:10.17169/refubium-2185.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Hochschulschrift

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Pockrandt, Christopher Maximilian1, 2, Autor                 
Reinert, Knut3, Gutachter                 
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              
2Fachbereich Mathematik und Informatik der Freien Universität Berlin, ou_persistent22              
3Efficient Algorithms for Omics Data (Knut Reinert), Max Planck Fellow Group, Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385698              

Inhalt

einblenden:
ausblenden:
Schlagwörter: approximate; fm index; string matching; search; epr dictionaries; BWT
 Zusammenfassung: This thesis addresses important algorithms and data structures used in sequence analysis for applications such as read mapping. First, we give an overview on state-of-the-art FM indices and present the latest improvements. In particular, we will introduce a recently published FM index based on a new data structure: EPR dictionaries. This rank data structures allows search steps in constant time for unidirectional and bidirectional FM indices. To our knowledge this is the first and only constant-time implementation of a bidirectional FM index at the time of writing. We show that its running time is not only optimal in theory, but currently also outperforms all available FM index implementations in practice.

Second, we cover approximate string matching in bidirectional indices. To improve the running time and make higher error rates suitable for index-based searches, we introduce an integer linear program for finding optimal search strategies. We show that it is significantly faster than other search strategies in indices and cover additional improvements such as hybrid approaches of index-based searches with in-text verification, i.e., at some point the partially matched string is located and verified directly in the text.

Finally, we present a yet unpublished algorithm for fast computation of the mappability of genomic sequences. Mappability is a measure for the uniqueness of a genome by counting how often each $k$-mer of the sequence occurs with a certain error threshold in the genome itself. We suggest two applications of mappability with prototype implementations: First, a read mapper incorporating the mappability information to improve the running time when mapping reads that match highly repetitive regions, and second, we use the mappability information to identify phylogenetic markers in a set of similar strains of the same species by the example of E. coli. Unique regions allow identifying and distinguishing even highly similar strains using unassembled sequencing data.

The findings in this thesis can speed up many applications in bioinformatics as we demonstrate for read mapping and computation of mappability, and give suggestions for further research in this field.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 20192019-04-15
 Publikationsstatus: Online veröffentlicht
 Seiten: 175 S.
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Art des Abschluß: Doktorarbeit

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle

einblenden: