Multiple Anchor Staged Local Sequence Alignment Algorithm - MASAA.

Technology advancements have helped biologists gather massive amount of biological data including genomic sequences of various species today. Sequence alignment techniques play a central role in investigating the adaptive significance of organism traits and revealing evolutionary relations among organisms by comparing these biological data. This thesis presents an algorithm to perform pairwise local sequence alignment. Recent pairwise local sequence alignment algorithms are either slow and sensitive or fast and less sensitive. Our algorithm is faster and at the same time sensitive. The algorithm employs suffix tree data structure to accurately identify long common subsequences in the two given sequences quickly. Regions of high similarity are again identified between segments of long subsequences already found. Several measures are taken into consideration to design the algorithm, such that the output is biologically meaningful. Data sets are carefully chosen and the output is compared with a well known algorithm, BLASTZ. Experiments conducted demonstrate that our algorithm performs better than BLASTZ in computation time, while either preserving or exceeding the accuracy of alignments at times.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b160049

Chromosome Descrambling Order Analysis in ciliates

Ciliates are a type of unicellular eukaryotic organism that has two types of nuclei within each cell; one is called the macronucleus (MAC) and the other is known as the micronucleus (MIC). During mating, ciliates exchange their MIC, destroy their own MAC, and create a new MAC from the genetic material of their new MIC. The process of developing a new MAC from the exchanged new MIC is known as gene assembly in ciliates, and it consists of a massive amount of DNA excision from the micronucleus, and the rearrangement of the rest of the DNA sequences. During the gene assembly process, the DNA segments that get eliminated are known as internal eliminated segments (IESs), and the remaining DNA segments that are rearranged in an order that is correct for creating proteins, are called macronuclear destined segments (MDSs). A topic of interest is to predict the correct order to descramble a gene or chromosomal segment. A prediction can be made based on the principle of parsimony, whereby the smallest sequence of operations is likely close to the actual number of operations that occurred. Interestingly, the order of MDSs in the newly assembled 22,354 Oxytricha trifallax MIC chromosome fragments provides evidence that multiple parallel recombinations occur, where the structure of the chromosomes allows for interleaving between two sections of the developing macronuclear chromosome in a manner that can be captured with a common string operation called the shuffle operation (the shuffle operation on two strings results in a new string by weaving together the first two, while preserving the order within each string). Thus, we studied four similar systems involving applications of shuffle to see how the minimum number of operations needed to assemble differs between the types. Two algorithms for each of the first two systems have been implemented that are both shown to be optimal. And, for the third and fourth systems, four and two heuristic algorithms, respectively, have been implemented. The results from these algorithms revealed that, in most cases, the third system gives the minimum number of applications of shuffle to descramble, but whether the best implemented algorithm for the third system is optimal or not remains an open question. The best implemented algorithm for the third system showed that 96.63% of the scrambled micronuclear chromosome fragments of Oxytricha trifallax can be descrambled by only 1 or 2 applications of shuffle. This small number of steps lends theoretical evidence that some structural component is enforcing an alignment of segments in a shuffle-like fashion, and then parallel recombination is taking place to enable MDS rearrangement and IES elimination. Another problem of interest is to classify segments of the MIC into MDSs and IESs; this is the second topic of the thesis, and is a matter of determining the right "class label", i.e. MDS or IES, on each nucleotide. Thus, training data of labelled input sequences was used with hidden Markov models (HMMs), which is a well-known supervised machine learning classification algorithm. HMMs of first-, second-, third-, fourth-, and fifth-order have been implemented. The accuracy of the classification was verified through 10-fold cross validation. Results from this work show that an HMM is more likely to fail to accurately classify micronuclear chromosomes without having some additional knowledge

METHODS FOR HIGH-THROUGHPUT COMPARATIVE GENOMICS AND DISTRIBUTED SEQUENCE ANALYSIS

High-throughput sequencing has accelerated applications of genomics throughout the world. The increased production and decentralization of sequencing has also created bottlenecks in computational analysis. In this dissertation, I provide novel computational methods to improve analysis throughput in three areas: whole genome multiple alignment, pan-genome annotation, and bioinformatics workflows. To aid in the study of populations, tools are needed that can quickly compare multiple genome sequences, millions of nucleotides in length. I present a new multiple alignment tool for whole genomes, named Mugsy, that implements a novel method for identifying syntenic regions. Mugsy is computationally efficient, does not require a reference genome, and is robust in identifying a rich complement of genetic variation including duplications, rearrangements, and large-scale gain and loss of sequence in mixtures of draft and completed genome data. Mugsy is evaluated on the alignment of several dozen bacterial chromosomes on a single computer and was the fastest program evaluated for the alignment of assembled human chromosome sequences from four individuals. A distributed version of the algorithm is also described and provides increased processing throughput using multiple CPUs. Numerous individual genomes are sequenced to study diversity, evolution and classify pan-genomes. Pan-genome annotations contain inconsistencies and errors that hinder comparative analysis, even within a single species. I introduce a new tool, Mugsy-Annotator, that identifies orthologs and anomalous gene structure across a pan-genome using whole genome multiple alignments. Identified anomalies include inconsistently located translation initiation sites and disrupted genes due to draft genome sequencing or pseudogenes. An evaluation of pan-genomes indicates that such anomalies are common and alternative annotations suggested by the tool can improve annotation consistency and quality. Finally, I describe the Cloud Virtual Resource, CloVR, a desktop application for automated sequence analysis that improves usability and accessibility of bioinformatics software and cloud computing resources. CloVR is installed on a personal computer as a virtual machine and requires minimal installation, addressing challenges in deploying bioinformatics workflows. CloVR also seamlessly accesses remote cloud computing resources for improved processing throughput. In a case study, I demonstrate the portability and scalability of CloVR and evaluate the costs and resources for microbial sequence analysis

Accelerated Profile HMM Searches

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches

Improvements in the Accuracy of Pairwise Genomic Alignment

Pairwise sequence alignment is a fundamental problem in bioinformatics with wide applicability. This thesis presents three new algorithms for this well-studied problem. First, we present a new algorithm, RDA, which aligns sequences in small segments, rather than by individual bases. Then, we present two algorithms for aligning long genomic sequences: CAPE, a pairwise global aligner, and FEAST, a pairwise local aligner. RDA produces interesting alignments that can be substantially different in structure than traditional alignments. It is also better than traditional alignment at the task of homology detection. However, its main negative is a very slow run time. Further, although it produces alignments with different structure, it is not clear if the differences have a practical value in genomic research. Our main success comes from our local aligner, FEAST. We describe two main improvements: a new more descriptive model of evolution, and a new local extension algorithm that considers all possible evolutionary histories rather than only the most likely. Our new model of evolution provides for improved alignment accuracy, and substantially improved parameter training. In particular, we produce a new parameter set for aligning human and mouse sequences that properly describes regions of weak similarity and regions of strong similarity. The second result is our new extension algorithm. Depending on heuristic settings, our new algorithm can provide for more sensitivity than existing extension algorithms, more specificity, or a combination of the two. By comparing to CAPE, our global aligner, we find that the sensitivity increase provided by our local extension algorithm is so substantial that it outperforms CAPE on sequence with 0.9 or more expected substitutions per site. CAPE itself gives improved sensitivity for sequence with 0.7 or more expected substitutions per site, but at a great run time cost. FEAST and our local extension algorithm improves on this too, the run time is only slightly slower than existing local alignment algorithms and asymptotically the same