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Articles 1 - 18 of 18
Full-Text Articles in Computational Biology
Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., Ruben A Tikidji-Hamburyan, Tarek A El-Ghazawi, Jason W. Triplett
Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., Ruben A Tikidji-Hamburyan, Tarek A El-Ghazawi, Jason W. Triplett
Pediatrics Faculty Publications
The establishment of precise neuronal connectivity during development is critical for sensing the external environment and informing appropriate behavioral responses. In the visual system, many connections are organized topographically, which preserves the spatial order of the visual scene. The superior colliculus (SC) is a midbrain nucleus that integrates visual inputs from the retina and primary visual cortex (V1) to regulate goal-directed eye movements. In the SC, topographically organized inputs from the retina and V1 must be aligned to facilitate integration. Previously, we showed that retinal input instructs the alignment of V1 inputs in the SC in a manner dependent on …
Identification Of Control Targets In Boolean Molecular Network Models Via Computational Algebra, David Murrugarra, Alan Veliz-Cuba, Boris Aguilar, Reinhard Laubenbacher
Identification Of Control Targets In Boolean Molecular Network Models Via Computational Algebra, David Murrugarra, Alan Veliz-Cuba, Boris Aguilar, Reinhard Laubenbacher
Mathematics Faculty Publications
Background: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type …
Rna2dnalign: Nucleotide Resolution Allele Asymmetries Through Quantitative Assessment Of Rna And Dna Paired Sequencing Data., Mercedeh Movassagh, Nawaf Alomran, Prakriti Mudvari, Merve Dede, Cem Dede, Kamran Kowsari, Paula Restrepo, Edmund Cauley, Sonali Bahl, Muzi Li, Wesley Waterhouse, Krasimira Tsaneva-Atanasova, Nathan Edwards, Anelia Horvath
Rna2dnalign: Nucleotide Resolution Allele Asymmetries Through Quantitative Assessment Of Rna And Dna Paired Sequencing Data., Mercedeh Movassagh, Nawaf Alomran, Prakriti Mudvari, Merve Dede, Cem Dede, Kamran Kowsari, Paula Restrepo, Edmund Cauley, Sonali Bahl, Muzi Li, Wesley Waterhouse, Krasimira Tsaneva-Atanasova, Nathan Edwards, Anelia Horvath
Biochemistry and Molecular Medicine Faculty Publications
We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching …
Optimization Of A Genomic Editing System Using Crispr/Cas9-Induced Site-Specific Gene Integration, Jillian L. Mccool Ms., Nick Hum, Gabriela G. Loots
Optimization Of A Genomic Editing System Using Crispr/Cas9-Induced Site-Specific Gene Integration, Jillian L. Mccool Ms., Nick Hum, Gabriela G. Loots
STAR Program Research Presentations
The CRISPR-Cas system is an adaptive immune system found in bacteria which helps protect against the invasion of other microorganisms. This system induces double stranded breaks at precise genomic loci (1) in which repairs are initiated and insertions of a target are completed in the process. This mechanism can be used in eukaryotic cells in combination with sgRNAs (1) as a tool for genome editing. By using this CRISPR-Cas system, in addition to the “safe harbor locus,” ROSAβ26, the incorporation of a target gene into a site that is not susceptible to gene silencing effects can be achieved through few …
Data Development And Analysis Pathways For Marine Mammals And Turtles: Creating A User Interface, Sarina Fernandez, Warren Asfazadour, Eric Archer, Lisa Komoroske
Data Development And Analysis Pathways For Marine Mammals And Turtles: Creating A User Interface, Sarina Fernandez, Warren Asfazadour, Eric Archer, Lisa Komoroske
STAR Program Research Presentations
A major obstacle in genetic research is developing streamlined methods for analyzing large amounts of data. The statistical computer programming language R provides users with the ability to develop packages containing specific functions in order to create more accessible data analysis pipelines. However, writing code in R can still be intimidating to those with little to no coding experience. Fortunately, the R package shiny provides a framework for developing web applications based on R functions. Using shiny, we developed a user-friendly web application containing functions of the R package strataG. The strataG package contains several functions for summarizing genetic data …
Incremental Phylogenetics By Repeated Insertions: An Evolutionary Tree Algorithm, Peter Revesz, Zhiqiang Li
Incremental Phylogenetics By Repeated Insertions: An Evolutionary Tree Algorithm, Peter Revesz, Zhiqiang Li
School of Computing: Faculty Publications
We introduce the idea of constructing hypothetical evolutionary trees using an incremental algorithm that inserts species one-by-one into the current evolutionary tree. The method of incremental phylogenetics by repeated insertions lead to an algorithm that can be used on DNA, RNA and amino acid sequences. According to experimental results on both synthetic and biological data, the new algorithm generates more accurate evolutionary trees than the UPGMA and the Neighbor Joining algorithms.
Ten Simple Rules For Taking Advantage Of Git And Github, Yasset Perez-Riverol, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe Da Veiga Leprevost, Christian Fufezan, Tobias Ternent, Stephen J. Eglen, Daniel S. Katz, Tom J. Pollard, Alexander Konovalov, Robert M. Flight, Kai Blin, Juan Antonio Vizcaíno
Ten Simple Rules For Taking Advantage Of Git And Github, Yasset Perez-Riverol, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe Da Veiga Leprevost, Christian Fufezan, Tobias Ternent, Stephen J. Eglen, Daniel S. Katz, Tom J. Pollard, Alexander Konovalov, Robert M. Flight, Kai Blin, Juan Antonio Vizcaíno
Molecular and Cellular Biochemistry Faculty Publications
No abstract provided.
Machine Learning Meta-Analysis Of Large Metagenomic Datasets: Tools And Biological Insight, Edoardo Pasolli, Duy Tin Truong, Faizan Malik, Levi Waldron, Nicola Segata
Machine Learning Meta-Analysis Of Large Metagenomic Datasets: Tools And Biological Insight, Edoardo Pasolli, Duy Tin Truong, Faizan Malik, Levi Waldron, Nicola Segata
Publications and Research
Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction …
Comparative Genomics, Transcriptomics, And Physiology Distinguish Symbiotic From Free-Living Chlorella Strains, Cristian F. Quispe, Olivia Sonderman, Maya Khasin, Wayne R. Riekhof, James L. Van Etten, Kenneth Nickerson
Comparative Genomics, Transcriptomics, And Physiology Distinguish Symbiotic From Free-Living Chlorella Strains, Cristian F. Quispe, Olivia Sonderman, Maya Khasin, Wayne R. Riekhof, James L. Van Etten, Kenneth Nickerson
Kenneth Nickerson Papers
Most animal–microbe symbiotic interactions must be advantageous to the host and provide nutritional benefits to the endosymbiont. When the host provides nutrients, it can gain the capacity to control the interaction, promote self-growth, and increase its fitness. Chlorella-like green algae engage in symbiotic relationships with certain protozoans, a partnership that significantly impacts the physiology of both organisms. Consequently, it is often challenging to grow axenic Chlorella cultures after isolation from the host because they are nutrient fastidious and often susceptible to virus infection. We hypothesize that the establishment of a symbiotic relationship resulted in natural selection for nutritional and metabolic …
Identification, Characterization, And Life Cycle Of Intein-Associated Homing Endonucleases, Joshua J. Skydel
Identification, Characterization, And Life Cycle Of Intein-Associated Homing Endonucleases, Joshua J. Skydel
Honors Scholar Theses
Inteins are molecular parasites that have been identified in unicellular organisms from the three domains of life. The intein self-excises following translation of the host gene, and therefore incurs a fitness cost for its carrier. The symbiotic state of the intein to its host is dependent on the presence or absence of a homing endonuclease domain, which facilitates horizontal transfer of the molecule. Identification of this domain provides information on the evolutionary history of the intein, as well as patterns of horizontal gene transfer in microbial communities. I have therefore developed Hidden Markov Models (HMMs) to identify homing endonuclease domains …
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
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
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
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
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
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 …
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
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
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 …
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
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% …