A Novel Tree Structure for Pattern Matching in Biological Sequences

This dissertation proposes a novel tree structure, Error Tree (ET), to more efficiently solve the Approximate Pattern Matching problem, a fundamental problem in bioinformatics and information retrieval. The problem involves different matching measures such as the Hamming distance, edit distance, and wildcard matching. The input is usually a text of length n over a fixed alphabet of size Σ, a pattern P of length m, and an integer k. The output is those subsequences in the text that are at a distance ≤ k from P by Hamming distance, edit distance, or wildcard matching. An immediate application of the approximate pattern matching is the Planted Motif Search, an important problem in many biological applications such as finding promoters, enhancers, locus control regions, transcription factors, etc. The (l, d)-Planted Motif Search is defined as the following: Given n sequences over an alphabet of size Σ, each of length m, and two integers l and d, find a motif M of length l, where in each sequence there is at least an l-mer (substring of length l) at a Hamming distance of ≤ d from M. Based on the ET structure, our algorithm ET-Motif solves this problem efficiently in time and space. The thesis also discusses how the ET structure may add efficiency when it comes to Genome Assembly and DNA Sequence Compression. Current high-throughput sequencing technologies generate millions or billions of short reads (100-1000 bases) that are sequenced from a genome of millions or billions bases long. The De novo Genome Assembly problem is to assemble the original genome as long and accurate as possible. Although high quality assemblies can be obtained by assembling multiple paired-end libraries with both short and long insert sizes, the latter is costly to generate. Moreover, the recent GAGE-B study showed that a remarkably good assembly quality can be obtained for bacterial genomes by state-of-the-art assemblers run on a single short-insert library with a very high coverage. This thesis introduces a novel Hierarchical Genome Assembly (HGA) method that takes further advantage of such high coverage by independently assembling disjoint subsets of reads, combining assemblies of the subsets, and finally re-assembling the combined contigs along with the original reads. We empirically evaluate this methodology for eight leading assemblers using seven GAGE-B bacterial datasets consisting of 100bp Illumina HiSeq and 250bp Illumina MiSeq reads with coverage ranging from 100x-∼200x. The results show that HGA leads to a significant improvement in the quality of the assembly for all evaluated assemblers and datasets. Still, the problem involves a major step which is overlapping the ends of the reads together and allowing few mismatches (i.e. the approximate matching problem). This requires computing the overlaps between the ends of all-against-all reads. The computation of such overlaps when allowing mismatches is intensive. The ET structure may further speed up this step. Lastly, due to the significant amount of DNA data generated by the Next- Generation-Sequencing machines, there is an increasing need to compress such data to reduce the storage space and transmission time. The Huffman encoding that incorporates DNA sequence characteristics proves to better compress DNA data. Different implementations of Huffman trees, centering on the selection of frequent repeats, are introduced in this thesis. Experimental results demonstrate improvement on the compression ratios for five genomes with lengths ranging from 5Mbp to 50Mbp, compared with the use of a standard Huffman tree algorithm. Hence, the thesis suggests an improvement on all DNA sequence compression algorithms that employ the conventional Huffman encoding. Moreover, approximate repeats can be compressed and further improve the results by encoding the Hamming or edit distance between these repeats. However, computing such distances requires additional costs in both time and space. These costs can be reduced by using the ET structure

Automated linear motif discovery from protein interaction network

Master'sMASTER OF SCIENC

Seed-driven Learning of Position Probability Matrices from Large Sequence Sets

Peer reviewe

Survey of Deoxyribonucleic Acid Motif Finding Algorithms

An important task in biology is to identify binding sites in DNA for transcription factors. These binding sites are short DNA segments which are called motifs. Given a set of DNA sequences, the motif finding problem is to detect overrepresented motifs that are good candidates for being transcription factor binding sites. The current study is a survey of motif finding algorithms. The study shows that a sensible approach to detect motif is to search for statistically overrepresented motifs in the promoter region of a set of co-regulated genes. The weak point of the available motif finding algorithms is that they tend to be sensitive to the noise, i.e., the presence of upstream sequences in data set that do not contain the motif. We conclude that instead of relying on a single motif finding tool, biologists should use a few complementary tools and pursue the top few predicted motifs of each.Computer Science Departmen

gene regulatory element prediction with bayesian networks

Ph.DDOCTOR OF PHILOSOPH

Combinatorial and Probabilistic Approaches to Motif Recognition

Short substrings of genomic data that are responsible for biological processes, such as gene expression, are referred to as motifs. Motifs with the same function may not entirely match, due to mutation events at a few of the motif positions. Allowing for non-exact occurrences significantly complicates their discovery. Given a number of DNA strings, the motif recognition problem is the task of detecting motif instances in every given sequence without knowledge of the position of the instances or the pattern shared by these substrings. We describe a novel approach to motif recognition, and provide theoretical and experimental results that demonstrate its efficiency and accuracy. Our algorithm, MCL-WMR, builds an edge-weighted graph model of the given motif recognition problem and uses a graph clustering algorithm to quickly determine important subgraphs that need to be searched further for valid motifs. By considering a weighted graph model, we narrow the search dramatically to smaller problems that can be solved with significantly less computation. The Closest String problem is a subproblem of motif recognition, and it is NP-hard. We give a linear-time algorithm for a restricted version of the Closest String problem, and an efficient polynomial-time heuristic that solves the general problem with high probability. We initiate the study of the smoothed complexity of the Closest String problem, which in turn explains our empirical results that demonstrate the great capability of our probabilistic heuristic. Important to this analysis is the introduction of a perturbation model of the Closest String instances within which we provide a probabilistic analysis of our algorithm. The smoothed analysis suggests reasons why a well-known fixed parameter tractable algorithm solves Closest String instances extremely efficiently in practice. Although the Closest String model is robust to the oversampling of strings in the input, it is severely affected by the existence of outliers. We propose a refined model, the Closest String with Outliers problem, to overcome this limitation. A systematic parameterized complexity analysis accompanies the introduction of this problem, providing a surprising insight into the sensitivity of this problem to slightly different parameterizations. Through the application of probabilistic and combinatorial insights into the Closest String problem, we develop sMCL-WMR, a program that is much faster than its predecessor MCL-WMR. We apply and adapt sMCL-WMR and MCL-WMR to analyze the promoter regions of the canola seed-coat. Our results identify important regions of the canola genome that are responsible for specific biological activities. This knowledge may be used in the long-term aim of developing crop varieties with specific biological characteristics, such as being disease-resistant