Genome-wide selection of unique and valid oligonucleotides

Functional genomics methods are used to investigate the huge amount of information contained in genomes. Numerous experimental methods rely on the use of oligo- or polynucleotides. Nucleotide strand hybridization forms the underlying principle for these methods. For all these techniques, the probes should be unique for analyzed genes. In addition to being unique for the studied genes, the probes should fulfill a large number of criteria to be usable and valid. The criteria include for example, avoidance of self-annealing, suitable melting temperature and nucleotide composition. We developed a method for searching unique and valid oligonucleotides or probes for genes so that there is not even a similar (approximate) occurrence in any other location of the whole genome. By using probe size 25, we analyzed 17 complete genomes representing a wide range of both prokaryotic and eukaryotic organisms. More than 92% of all the genes in the investigated genomes contained valid oligonucleotides. Extensive statistical tests were performed to characterize the properties of unique and valid oligonucleotides. Unique and valid oligonucleotides were relatively evenly distributed in genes except for the beginning and end, which were somewhat overrepresented. The flanking regions in eukaryotes were clearly underrepresented among suitable oligonucleotides. In addition to distributions within genes, the effects on codon and amino acid usage were also studied

RazerS - Fast Read Mapping with Sensitivity Control

Second-generation sequencing technologies deliver DNA sequence data at unprecedented high throughput. Common to most biological applications is a mapping of the reads to an almost identical or highly similar reference genome. Due to the large amounts of data, efficient algorithms and implementations are crucial for this task. We present an efficient read mapping tool called RazerS. It allows the user to align sequencing reads of arbitrary length using either the Hamming distance or the edit distance. Our tool can work either lossless or with a user-defined loss rate at higher speeds. Given the loss rate, we present an approach that guarantees not to lose more reads than specified. This enables the user to adapt to the problem at hand and provides a seamless tradeoff between sensitivity and running time

Bioinformatic approaches for genome finishing

Husemann P, Tauch A. Bioinformatic approaches for genome finishing. Bielefeld: Universitätsbibliothek Bielefeld; 2011

New Algorithms for EST clustering

Philosophiae Doctor - PhDExpressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology. There are a few systems, designed for this purpose and a few more are currently under development. These systems are reviewed in the "Literature and software review". Different strategies of supervised and unsupervised clustering are discussed, as well as sequence comparison techniques, such as based on alignment or oligonucleotide compositions. Analysis of potential bottlenecks and estimation of computation complexity of EST clustering is done in Chapter 2. This chapter also states the goals for the research and justifies the need for new algorithm that has to be fast, but still sensitive to relatively short (40 bp) regions of local similarity. A new sequence comparison algorithm is developed and described in Chapter 3. This algorithm has a linear computation complexity and sufficient sensitivity to detect short regions of local similarity between nucleotide sequences. The algorithm utilizes an asymmetric approach, when one of the compared sequences is presented in a form of oligonucleotide table, while the second sequence is in standard, linear form. A short window is moved along the linear sequence and all overlapping oligonucleotides of a constant length in the frame are compared for the oligonucleotide table. The result of comparison of two sequences is a single figure, which can be compared to a threshold. For each measure of sequence similarity a probability of false positive and false negative can be estimated. The algorithm was set up and implemented to recognize matching ESTs with overlapping regions of 40bp with 95% identity, which is better than resolution ability of contemporary EST clustering tools This algorithm was used as a sequence comparison engine for two EST clustering programs, described in Chapter 4. These programs implement two different strategies: stringent and loose clustering. Both are tested on small, but realistic benchmark data sets and show the results, similar to one of the best existing clustering programs, 02_cluster, but with a significant advantage in speed and sensitivity to small overlapping regions of ESTs. On three different CPUs the new algorithm run at least two times faster, leaving less singletons and producing bigger clusters. With parallel optimization this algorithm is capable of clustering millions of ESTs on relatively inexpensive computers. The loose clustering variant is a highly portable application, relying on third-party software for cluster assembly. It was built to the same specifications as 02_ cluster and can be immediately included into the STACKPack package for EST clustering. The stringent clustering program produces already assembled clusters and can apprehend alternatively processed variants during the clustering process

Novel computational techniques for mapping and classifying Next-Generation Sequencing data

Since their emergence around 2006, Next-Generation Sequencing technologies have been revolutionizing biological and medical research. Quickly obtaining an extensive amount of short or long reads of DNA sequence from almost any biological sample enables detecting genomic variants, revealing the composition of species in a metagenome, deciphering cancer biology, decoding the evolution of living or extinct species, or understanding human migration patterns and human history in general. The pace at which the throughput of sequencing technologies is increasing surpasses the growth of storage and computer capacities, which creates new computational challenges in NGS data processing. In this thesis, we present novel computational techniques for read mapping and taxonomic classification. With more than a hundred of published mappers, read mapping might be considered fully solved. However, the vast majority of mappers follow the same paradigm and only little attention has been paid to non-standard mapping approaches. Here, we propound the so-called dynamic mapping that we show to significantly improve the resulting alignments compared to traditional mapping approaches. Dynamic mapping is based on exploiting the information from previously computed alignments, helping to improve the mapping of subsequent reads. We provide the first comprehensive overview of this method and demonstrate its qualities using Dynamic Mapping Simulator, a pipeline that compares various dynamic mapping scenarios to static mapping and iterative referencing. An important component of a dynamic mapper is an online consensus caller, i.e., a program collecting alignment statistics and guiding updates of the reference in the online fashion. We provide Ococo, the first online consensus caller that implements a smart statistics for individual genomic positions using compact bit counters. Beyond its application to dynamic mapping, Ococo can be employed as an online SNP caller in various analysis pipelines, enabling SNP calling from a stream without saving the alignments on disk. Metagenomic classification of NGS reads is another major topic studied in the thesis. Having a database with thousands of reference genomes placed on a taxonomic tree, the task is to rapidly assign a huge amount of NGS reads to tree nodes, and possibly estimate the relative abundance of involved species. In this thesis, we propose improved computational techniques for this task. In a series of experiments, we show that spaced seeds consistently improve the classification accuracy. We provide Seed-Kraken, a spaced seed extension of Kraken, the most popular classifier at present. Furthermore, we suggest ProPhyle, a new indexing strategy based on a BWT-index, obtaining a much smaller and more informative index compared to Kraken. We provide a modified version of BWA that improves the BWT-index for a quick k-mer look-up

Computational Tools for a Novel Transcriptional Profiling Method

In this thesis we provide computational tools for the planning of VTT-TRAC experiments. VTT-TRAC is a novel method for measuring expression levels of genes. Monitoring gene expression by measuring the amounts of transcribed mRNAs (transcriptional profiling) has become an important experimental method in molecular biology. This has been due to rapid advance in the high-throughput measurement technology. Methods like microarrays are capable of measuring thousands of expression levels in one experiment. However, new methods that are fast and reliable are still needed. In addition to scientific research, such methods have important applications for example in medical diagnostics and bioprocess control. High-throughput technologies require automatic tools for planning of experiments. In VTT-TRAC a special fragment of DNA called a probe has to be selected for each profiled gene. In addition, the method allows multiplexing i.e. profiling a large number of genes together as a group called

Bioinformatics for personal genomics: development and application of bioinformatic procedures for the analysis of genomic data

In the last decade, the huge decreasing of sequencing cost due to the development of high-throughput technologies completely changed the way for approaching the genetic problems. In particular, whole exome and whole genome sequencing are contributing to the extraordinary progress in the study of human variants opening up new perspectives in personalized medicine. Being a relatively new and fast developing field, appropriate tools and specialized knowledge are required for an efficient data production and analysis. In line with the times, in 2014, the University of Padua funded the BioInfoGen Strategic Project with the goal of developing technology and expertise in bioinformatics and molecular biology applied to personal genomics. The aim of my PhD was to contribute to this challenge by implementing a series of innovative tools and by applying them for investigating and possibly solving the case studies included into the project. I firstly developed an automated pipeline for dealing with Illumina data, able to sequentially perform each step necessary for passing from raw reads to somatic or germline variant detection. The system performance has been tested by means of internal controls and by its application on a cohort of patients affected by gastric cancer, obtaining interesting results. Once variants are called, they have to be annotated in order to define their properties such as the position at transcript and protein level, the impact on protein sequence, the pathogenicity and more. As most of the publicly available annotators were affected by systematic errors causing a low consistency in the final annotation, I implemented VarPred, a new tool for variant annotation, which guarantees the best accuracy (>99%) compared to the state-of-the-art programs, showing also good processing times. To make easy the use of VarPred, I equipped it with an intuitive web interface, that allows not only a graphical result evaluation, but also a simple filtration strategy. Furthermore, for a valuable user-driven prioritization of human genetic variations, I developed QueryOR, a web platform suitable for searching among known candidate genes as well as for finding novel gene-disease associations. QueryOR combines several innovative features that make it comprehensive, flexible and easy to use. The prioritization is achieved by a global positive selection process that promotes the emergence of the most reliable variants, rather than filtering out those not satisfying the applied criteria. QueryOR has been used to analyze the two case studies framed within the BioInfoGen project. In particular, it allowed to detect causative variants in patients affected by lysosomal storage diseases, highlighting also the efficacy of the designed sequencing panel. On the other hand, QueryOR simplified the recognition of LRP2 gene as possible candidate to explain such subjects with a Dent disease-like phenotype, but with no mutation in the previously identified disease-associated genes, CLCN5 and OCRL. As final corollary, an extensive analysis over recurrent exome variants was performed, showing that their origin can be mainly explained by inaccuracies in the reference genome, including misassembled regions and uncorrected bases, rather than by platform specific errors

ACCURATE ALIGNMENT OF SEQUENCING READS FROM VARIOUS GENOMIC ORIGINS

Ph.DDOCTOR OF PHILOSOPH

Cheminformatics Tools to Explore the Chemical Space of Peptides and Natural Products

Cheminformatics facilitates the analysis, storage, and collection of large quantities of chemical data, such as molecular structures and molecules' properties and biological activity, and it has revolutionized medicinal chemistry for small molecules. However, its application to larger molecules is still underrepresented. This thesis work attempts to fill this gap and extend the cheminformatics approach towards large molecules and peptides. This thesis is divided into two parts. The first part presents the implementation and application of two new molecular descriptors: macromolecule extended atom pair fingerprint (MXFP) and MinHashed atom pair fingerprint of radius 2 (MAP4). MXFP is an atom pair fingerprint suitable for large molecules, and here, it is used to explore the chemical space of non-Lipinski molecules within the widely used PubChem and ChEMBL databases. MAP4 is a MinHashed hybrid of substructure and atom pair fingerprints suitable for encoding small and large molecules. MAP4 is first benchmarked against commonly used atom pairs and substructure fingerprints, and then it is used to investigate the chemical space of microbial and plants natural products with the aid of machine learning and chemical space mapping. The second part of the thesis focuses on peptides, and it is introduced by a review chapter on approaches to discover novel peptide structures and describing the known peptide chemical space. Then, a genetic algorithm that uses MXFP in its fitness function is described and challenged to generate peptide analogs of peptidic or non-peptidic queries. Finally, supervised and unsupervised machine learning is used to generate novel antimicrobial and non-hemolytic peptide sequences