Computational Biology Commons^™

Using Hadoop To Identify False Positives In Bacterial Strain Typing From Dna Fingerprints, Colin C. Adams

Computer Science and Software Engineering

Pyroprinting is a novel technique used by the Department of Biological Sciences to obtain “fingerprints” from the DNA of E. coli isolates in order to categorize them into strains. To determine the number of false positives that occur in the pyroprinting process, isolates with the same pyroprints needed to be sequenced to see if their underlying alleles match. If they do match, this shows they are indeed the same strain and are a true positive. If the alleles don’t match, they are different strains and are a false positive. To do this 100 isolates with nucleotide identifiers were sequenced. Over …

Phylogenetic Analysis Of Human Cytomegalovirus Pus27 And Pus28: Ascertaining An Independent Or Linked Evolutionary History, Jessica A. Scarborough

Undergraduate Honors Theses

Human cytomegalovirus (HCMV) is a widespread pathogen that is particularly skilled at evading immune detection and defense mechanisms, largely due to extensive co-evolution with its host’s immune system. One aspect of this co-evolution involves the acquisition of four virally encoded GPCR chemokine receptor homologs, products of the US27, US28, UL33 and UL78 genes. G protein-coupled receptors (GPCR) are the largest family of cell surface proteins, found in organisms from yeast to humans. In this research, phylogenetic analysis was used to investigate the origins of the US27 and US28 genes, which are adjacent in the viral genome. The results indicate that …

Investigating Metastatic Lineage In Colorectal Cancer By Single Cell Dna Sequencing, Marco Leung

Dissertations and Theses (Open Access)

Metastasis is the primary cause of human cancer deaths. Patients with metastatic colorectal cancer (mCRC) show only an 11% 5-year survival rate, compared to those without local or distant metastases (92% 5-year survival rate). Understanding the CRC tumor evolution may provide valuable insights on how to improve treatment in patients with mCRC. However, the genomic basis of metastasis has been difficult to study, in part due to the extensive intratumor heterogeneity at both the primary and metastatic tumor sites, and the low frequency of subclones with metastatic potential. Previous studies have applied conventional bulk next-generation sequencing (NGS) methods, which have …

Accurate Mutation Annotation And Functional Prediction Enhance The Applicability Of -Omics Data In Precision Medicine, Tenghui Chen

Dissertations and Theses (Open Access)

Clinical sequencing has been recognized as an effective approach for enhancing the accuracy and efficiency of cancer patient management and therefore achieve the goals of personalized therapy. However, the accuracy of large scale sequencing data in clinics has been constrained by many different aspects, such as clinical detection, annotation and interpretation of the variants that are observed in clinical sequencing data. In my Ph.D thesis work, I mainly investigated how to comprehensively and efficiently apply high dimensional -omics data to enhance the capability of precision cancer medicine. Following this motivation, my dissertation has been focused on two important topics in …

Detecting Gene-Gene Interactions Using A Permutation-Based Random Forest Method, Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore

Dartmouth Scholarship

Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.

Fastpop: A Rapid Principal Component Derived Method To Infer Intercontinental Ancestry Using Genetic Data, Yafang Li, Jinyoung Byun, Guoshuai Cai, Xiangjun Xiao, Younghun Han, Olivier Cornelis, James E. Dinulos, Joe Dennis, Douglas Easton, Ivan Gorlov, Michael F. Seldin, Christopher I. Amos

Dartmouth Scholarship

Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the …

Phagephisher: A Pipeline For The Discovery Of Covert Viral Sequences In Complex Genomic Datasets, Thomas Hatzopoulos, Siobhan C. Watkins, Catherine Putonti

Bioinformatics Faculty Publications

Obtaining meaningful viral information from large sequencing datasets presents unique challenges distinct from prokaryotic and eukaryotic sequencing efforts. The difficulties surrounding this issue can be ascribed in part to the genomic plasticity of viruses themselves as well as the scarcity of existing information in genomic databases. The open-source software PhagePhisher (http://www.putonti-lab.com/phagephisher) has been designed as a simple pipeline to extract relevant information from complex and mixed datasets, and will improve the examination of bacteriophages, viruses, and virally related sequences, in a range of environments. Key aspects of the software include speed and ease of use; PhagePhisher can be used with …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret

UW Biostatistics Working Paper Series

We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the …

Resolving Gnetum Evolutionary History, Angela Mcfadden

All Master's Theses

Gnetum are non-flowering seed plants of the tropics, indigenous to South America, Africa, and Asia. This group of about 40 species is fascinating to botanists because it shares distinctive morphological characteristics with flowering plants, such as broad leaves, woody stems, and flower-like strobili. There are still questions surrounding the relationships within the genus of Gnetum. With that in mind, I focused my work on generating phylogenetic hypotheses, using two molecular data sets: a concatenation of over 60 different chloroplast genes (66,815 base pairs), and the whole chloroplast genome (128,772 base pairs). This allowed me to compare the two phylogenies …

System Genetic Analysis Of Mechanisms Underlying Excessive Alcohol Consumption, Maren L. Smith

Theses and Dissertations

Increased alcohol consumption over time is one of the characteristic symptoms of Alcohol Use Disorder (AUD). The molecular mechanisms underlying this escalation in intake is still the subject of study. However, the mesocortical and mesolimbic dopamine pathways, and the extended amygdala, because of their involvement in reward and reinforcement are believed to play key roles in these behavioral changes. Multiple gene expression studies have shown that alcohol affects the expression of thousands of genes in the brain. The studies discussed in this document use the systems biology technique of co-expression network analysis to attempt to find

patterns within genome-wide expression …

A Pipeline For Creation Of Genome-Scale Metabolic Reconstructions, Shaun W. Norris

Theses and Dissertations

The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the …

Characterization Of Somatically-Eliminated Genes During Development Of The Sea Lamprey (Petromyzon Marinus), Stephanie A. Bryant

Theses and Dissertations--Biology

The sea lamprey (Petromyzon marinus) undergoes programmed genome rearrangements (PGRs) during early development that facilitate the elimination of ~20% of the genome from the somatic cell lineage, resulting in distinct somatic and germline genomes. To improve our understanding of the evolutionary/developmental logic of PGR, we generated computational predictions to identify candidate germline-specific genes within a transcriptomic dataset derived from adult germline and the embryonic stages encompassing PGR. Validation studies identified 44 germline-specific genes and characterized patterns of transcription and DNA loss during early embryogenesis. Expression analyses reveal that several of these genes are differentially expressed during early embryogenesis …

Genomic Prediction Of Gene Bank Wheat Landraces, José Crossa, Diego Jarquin, Jorge Franco, Paulino Pérez-Rodríguez, Juan Burgueño, Carolina Saint-Pierre, Prashant Vikram, Carolina Sansaloni, Cesar Petroli, Denis Akdemir, Clay Sneller, Matthew Reynolds, Maria Tattaris, Thomas Payne, Carlos Guzman, Roberto J. Peña, Peter Wenzl, Sukhwinder Singh

Department of Agronomy and Horticulture: Faculty Publications

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% …

Ten Simple Rules For Digital Data Storage, E. M. Hart, P. Barmby, D. Lebauer, F. Michonneau, S. Mount, P. Mulrooney, T. Poisot, K. H. Woo, Naupaka B. Zimmerman, J. W. Hollister

Biology Faculty Publications

No abstract provided.

Deep Models For Brain Em Image Segmentation: Novel Insights And Improved Performance, Ahmed Fakhry, Hanchuan Peng, Shuiwang Ji

Computer Science Faculty Publications

Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.

Results: In this work, we proposed a novel design of DNNs for …

Finding Function In The Unknown, Kelly Boyd, Emma Highland, Amanda Misch, Amber Hu, Sushma Reddy, Catherine Putonti

Bioinformatics Faculty Publications

Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies.

Leveraging Global Gene Expression Patterns To Predict Expression Of Unmeasured Genes, James Rudd, René A. Zelaya, Eugene Demidenko, Ellen L. Goode, Casey S. Greene S. Greene, Jennifer A. Doherty

Dartmouth Scholarship

BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Identifying Gene-Gene Interactions That Are Highly Associated With Body Mass Index Using Quantitative Multifactor Dimensionality Reduction (Qmdr), Rishika De, Shefali S. Verma, Fotios Drenos, Emily R. Holzinger

Dartmouth Scholarship

Despite heritability estimates of 40–70% for obesity, less than 2% of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Using genotypic data from 18,686 individuals across five study cohorts – ARIC, CARDIA, FHS, CHS, MESA – we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in …

The Importance Of Physicochemical Characteristics And Nonlinear Classifiers In Determining Hiv-1 Protease Specificity, Timmy Manning, Paul Walsh

Department of Biological Sciences Publications

This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear …