Data Analysis and Next Generation Sequencing: Applications in Microbiology
Friday 30 October 2015
to 17:00 at
Nicolas Innocenti (KTH Royal Institute of Technology)
Next Generation Sequencing (NGS) is a new technology that has revolutionized
the way we study living organisms. Where previously only a few
genes could be studied at a time through targeted direct probing, NGS offers
the possibility to perform measurements for a whole genome at once. The
drawback is that the amount of data generated in the process is large and
extracting useful information from it requires new methods to process and
The main contribution of this thesis is the development of a novel experimental
method coined tagRNA-seq, combining 5’tagRACE, a previously
developed technique, with RNA-sequencing technology. Briefly, tagRNA-seq
makes it possible to identify the 5’ ends of RNAs in bacteria and directly
probe for their type, primary or processed, by ligating short RNA sequences,
the tags, to the beginnings of RNA molecules. We used the method to directly
probe for transcription start and processing sites in two bacterial species, Escherichia
coli and Enterococcus faecalis. It was also used to study polyadenylation
in E. coli, where the ability to identify processed RNA molecules proved
to be useful to separate direct and indirect regulatory effects of this mechanism.
We also demonstrate how data from tagRNA-seq experiments can be
used to increase confidence on the discovery of anti-sense transcripts in bacteria.
A detailed analysis of the data revealed subtle artifacts in the coverage
signal towards 3’ends of genes, that we were able to explain and quantify
based on Kolmogorov’s broken stick model. We also discovered evidences
for circularization of a few RNA transcripts, both in our own data sets and
publicly available data.
Designing the tags used in tagRNA-seq led us to the problem of words
absent from a text. We focus on a particular subset of these, the minimal
absent words (MAWs), and develop a theory providing a complete description
of their size distribution in random text. Genomes from viruses and living
organisms have MAWs a large fraction of which are well modeled by the
theory, but almost always exhibit a behavior different from random texts
in the tail of the distribution. MAWs from this tail are closely related to
sequences present in the genome that preferentially appear in regions with
important regulatory functions.
Finally, and independently from tagRNA-seq, we propose a new approach
to the problem of bacterial community reconstruction in metagenomic, based
on techniques from compressed sensing. We provide a novel algorithm competing
with state-of-the-art techniques in the field.