A new method for indexing genomes using on-disk suffix trees

We propose a new method to build persistent suffix trees for indexing the genomic data. Our algorithm DiGeST (Disk-Based Genomic Suffix Tree) improves significantly over previous work in reducing the random access to the in-put string and performing only two passes over disk data. DiGeST is based on the two-phase multi-way merge sort paradigm using a concise binary representation of the DNA alphabet. Furthermore, our method scales to larger genomic data than managed before

Computational functional annotation of crop genomics using hierarchical orthologous groups

Improving agronomically important traits, such as yield, is important in order to meet the ever growing demands of increased crop production. Knowledge of the genes that have an effect on a given trait can be used to enhance genomic selection by prediction of biologically interesting loci. Candidate genes that are strongly linked to a desired trait can then be targeted by transformation or genome editing. This application of prioritisation of genetic material can accelerate crop improvement. However, the application of this is currently limited due to the lack of accurate annotations and methods to integrate experimental data with evolutionary relationships. Hierarchical orthologous groups (HOGs) provide nested groups of genes that enable the comparison of highly diverged and similar species in a consistent manner. Over 2,250 species are included in the OMA project, resulting in over 600,000 HOGs. This thesis provides the required methodology and a tool to exploit this rich source of information, in the HOGPROP algorithm. The potential of this is then demonstrated in mining crop genome data, from metabolic QTL studies and utilising Gene Ontology (GO) annotations as well as ChEBI terms (Chemical Entities of Biological Interest) in order to prioritise candidate causal genes. Gauging the performance of the tool is also important. When considering GO annotations, the CAFA series of community experiments has provided the most extensive benchmarking to-date. However, this has not fully taken into account the incomplete knowledge of protein function – the open world assumption (OWA). This will require extra negative annotations, for which one such source has been identified based on expertly curated gene phylogenies. These negative annotations are then utilised in the proposed, OWA-compliant, improved framework for benchmarking. The results show that current benchmarks tend to focus on the general terms, which means that conclusions are not merely uninformative, but misleading

Practical methods for constructing suffix trees

Sequence datasets are ubiquitous in modern life-science applications, and querying sequences is a common and critical operation in many of these applications. The suffix tree is a versatile data structure that can be used to evaluate a wide variety of queries on sequence datasets, including evaluating exact and approximate string matches, and finding repeat patterns. However, methods for constructing suffix trees are often very time-consuming, especially for suffix trees that are large and do not fit in the available main memory. Even when the suffix tree fits in memory, it turns out that the processor cache behavior of theoretically optimal suffix tree construction methods is poor, resulting in poor performance. Currently, there are a large number of algorithms for constructing suffix trees, but the practical tradeoffs in using these algorithms for different scenarios are not well characterized.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47869/1/778_2005_Article_154.pd

Declarative Querying For Biological Sequences.

Life science research labs today manage increasing volumes of sequence data. Much of the data management and querying today is accomplished procedurally using Perl, Python, or Java programs that integrate data from different sources and query tools. The dangers of this procedural approach are well known to the database community-- a) severe limitations on the ability to rapidly express queries and b) inefficient query plans due to the lack of sophisticated optimization tools. This situation is likely to get worse with advances in high-throughput technologies that make it easier to quickly produce vast amounts of sequence data. The need for a declarative and efficient system to manage and query biological sequence data is urgent. To address this need, we designed the Periscope/SQ system. Periscope/SQ extends current relational systems to enable sophisticated queries on sequence data and can optimize and execute these queries efficiently. This thesis describes the problems that need to be solved to make it possible to build the Periscope/SQ system. First, we describe the algebraic framework which forms the backbone of Periscope/SQ. Second, we describe algorithms to construct large scale suffix tree indexes for efficiently answering sequence queries. Third, we describe techniques for selectivity estimation and optimization in the context of queries over biological sequences. Next, we demonstrate how some of the techniques developed for Periscope/SQ can be applied to produce a powerful mining algorithm that we call FLAME. Finally, we describe GeneFinder, a biological application built on top of Periscope/SQ. GeneFinder is currently being used to predict the targets of transcription factors. Today, genomic and proteomic sequences are the most abundantly available source of high-quality biological data. By making it possible to declaratively and efficiently query vast amount of sequence data, Periscope/SQ opens the door to vast improvements in the pace of bioinformatics research.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/55670/2/tatas_1.pd

LC an effective classification based association rule mining algorithm

Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider

Scalable String and Suffix Sorting: Algorithms, Techniques, and Tools

This dissertation focuses on two fundamental sorting problems: string sorting and suffix sorting. The first part considers parallel string sorting on shared-memory multi-core machines, the second part external memory suffix sorting using the induced sorting principle, and the third part distributed external memory suffix sorting with a new distributed algorithmic big data framework named Thrill.Comment: 396 pages, dissertation, Karlsruher Instituts f\"ur Technologie (2018). arXiv admin note: text overlap with arXiv:1101.3448 by other author