Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
Browse
3 results
Search Results
Item Algorithms for scalable and efficient population genomics and metagenomics(2022) Javkar, Kiran Gajanan; Pop, Mihai; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Microbes strongly impact human health and the ecosystem of which they are a part. Rapid improvements and decreasing costs in sequencing technologies have revolutionized the field of genomics and enabled important insights into microbial genome biology and microbiomes. However, new tools and approaches are needed to facilitate the efficient analysis of large sets of genomes and to associate genomic features with phenotypic characteristics better. Here, we built and utilized several tools for large-scale whole-genome analysis for different microbial characteristics, such as antimicrobial resistance and pathogenicity, that are important for human health. Chapters 2 and 3 demonstrate the needs and challenges of population genomics in associating antimicrobial resistance with genomic features. Our results highlight important limitations of reference database-driven analysis for genotype-phenotype association studies and demonstrate the utility of whole-genome population genomics in uncovering novel genomic factors associated with antimicrobial resistance. Chapter 4 describes PRAWNS, a fast and scalable bioinformatics tool that generates compact pan-genomic features. Existing approaches are unable to meet the needs of large-scale whole-genome analyses, either due to scalability limitations or the inability of the genomic features generated to support a thorough whole-genome assessment. We demonstrate that PRAWNS scales to thousands of genomes and provides a concise collection of genomic features which support the downstream analyses. In Chapter 5, we assess whether the combination of long and short-read sequencing can expedite the accurate reconstruction of a pathogen genome from a microbial community. We describe the challenges for pathogen detection in current foodborne illness outbreak monitoring. Our results show that the recovery of a pathogen genome can be accelerated using a combination of long and short-read sequencing after limited culturing of the microbial community. We evaluated several popular genome assembly approaches and identified areas for improvement. In Chapter 6, we describe SIMILE, a fast and scalable bioinformatics tool that enables the detection of genomic regions shared between several assembled metagenomes. In metagenomics, microbial communities are sequenced directly without culturing. Although metagenomics has furthered our understanding of the microbiome, comparing metagenomic samples is extremely difficult. We describe the need and challenges in comparing several metagenomic samples and present an approach that facilitates large-scale metagenomic comparisons.Item GENOME ASSEMBLY AND VARIANT DETECTION USING EMERGING SEQUENCING TECHNOLOGIES AND GRAPH BASED METHODS(2018) Ghurye, Jay; Pop, Mihai; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The increased availability of genomic data and the increased ease and lower costs of DNA sequencing have revolutionized biomedical research. One of the critical steps in most bioinformatics analyses is the assembly of the genome sequence of an organism using the data generated from the sequencing machines. Despite the long length of sequences generated by third-generation sequencing technologies (tens of thousands of basepairs), the automated reconstruction of entire genomes continues to be a formidable computational task. Although long read technologies help in resolving highly repetitive regions, the contigs generated from long read assembly do not always span a complete chromosome or even an arm of the chromosome. Recently, new genomic technologies have been developed that can ''bridge" across repeats or other genomic regions that are difficult to sequence or assemble and improve genome assemblies by ''scaffolding" together large segments of the genome. The problem of scaffolding is vital in the context of both single genome assembly of large eukaryotic genomes and in metagenomics where the goal is to assemble multiple bacterial genomes in a sample simultaneously. First, we describe SALSA2, a method we developed to use interaction frequency between any two loci in the genome obtained using Hi-C technology to scaffold fragmented eukaryotic genome assemblies into chromosomes. SALSA2 can be used with either short or long read assembly to generate highly contiguous and accurate chromosome level assemblies. Hi-C data are known to introduce small inversion errors in the assembly, so we included the assembly graph in the scaffolding process and used the sequence overlap information to correct the orientation errors. Next, we present our contributions to metagenomics. We developed a scaffolding and variant detection method MetaCarvel for metagenomic datasets. Several factors such as the presence of inter-genomic repeats, coverage ambiguities, and polymorphic regions in the genomes complicate the task of scaffolding metagenomes. Variant detection is also tricky in metagenomes because the different genomes within these complex samples are not known beforehand. We showed that MetaCarvel was able to generate accurate scaffolds and find genome-wide variations de novo in metagenomic datasets. Finally, we present EDIT, a tool for clustering millions of DNA sequence fragments originating from the highly conserved 16s rRNA gene in bacteria. We extended classical Four Russians' speed up to banded sequence alignment and showed that our method clusters highly similar sequences efficiently. This method can also be used to remove duplicates or near duplicate sequences from a dataset. With the increasing data being generated in different genomic and metagenomic studies using emerging sequencing technologies, our software tools and algorithms are well timed with the need of the community.Item High Performance Computing for DNA Sequence Alignment and Assembly(2010) Schatz, Michael Christopher; Salzberg, Steven L; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent advances in DNA sequencing technology have dramatically increased the scale and scope of DNA sequencing. These data are used for a wide variety of important biological analyzes, including genome sequencing, comparative genomics, transcriptome analysis, and personalized medicine but are complicated by the volume and complexity of the data involved. Given the massive size of these datasets, computational biology must draw on the advances of high performance computing. Two fundamental computations in computational biology are read alignment and genome assembly. Read alignment maps short DNA sequences to a reference genome to discover conserved and polymorphic regions of the genome. Genome assembly computes the sequence of a genome from many short DNA sequences. Both computations benefit from recent advances in high performance computing to efficiently process the huge datasets involved, including using highly parallel graphics processing units (GPUs) as high performance desktop processors, and using the MapReduce framework coupled with cloud computing to parallelize computation to large compute grids. This dissertation demonstrates how these technologies can be used to accelerate these computations by orders of magnitude, and have the potential to make otherwise infeasible computations practical.