Compressive algorithms for search and storage in biological data

Disparate biological datasets often exhibit similar well-defined structure; efficient algorithms can be designed to exploit this structure. In this doctoral thesis, we present a framework for similarity search based on entropy and fractal dimension; here, we prove that a clustered search algorithm scales in time with metric entropy number of covering hyperspheres-if the fractal dimension is low. Using these ideas, entropy-scaling versions of standard bioinformatics search tools can be designed, including for small-molecule, metagenomics, and protein structure search. This 'compressive acceleration' approach taking advantage of redundancy and sparsity in biological data can be leveraged also for next-generation sequencing (NGS) read mapping. By pairing together a clustered grouping over similar reads and a homology table for similarities in the human genome, our CORA framework can accelerate all-mapping by several orders of magnitude. Additionally, we also present work on filtering empirical base-calling quality scores from Next Generation Sequencing data. By using the sparsity of k-mers of sufficient length in the human genome and imposing a human prior through the use of frequent k-mers in a large corpus of human DNA reads, we are able to quickly discard over 90% of the information found in those quality scores while retaining or even improving downstream variant-calling accuracy. This filtering step allows for fast lossy compression of quality scores.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mathematics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Yu, Yun William

Advisor dc:contributor.advisor

• Bonnie Berger.

• Mathematics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/112879

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/112879