Computational Biology Commons^™

From Fair To Cure: Guidelines For Computational Models Of Biological Systems, Herbert M. Sauro, Eran Agmon, Michael L. Blinov, John H. Gennari, Joseph L. Hellerstein, Adel Heydarabadipour, Bartholomew E. Jardine, Elebeoba May, David P. Nickerson, Lucian P. Smith, Gary D. Bader, Frank T. Bergmann, Patrick M. Boyle, Andreas Dräger, James R. Faeder, Song Feng, Juliana Freire, Fabian Fröhlich, James A. Glazier, Thomas E. Gorochowski, Tomas Helikar, Henning Hermjakob, Stefan Hoops, Peter Hunter, Princess I. Imoukhuede, Sarah M. Keating, Matthias König, Reinhard Laubenbacher, Leslie M. Loew, Carlos F. Lopez, William W. Lytton, Rahuman S. Malik-Sheriff, Andrew Mcculloch, Pedro Mendes, Lealem Mulugeta, Chris J. Myers, Jerry G. Myers, Anna Niarakis, David D. Van Niekerk, Brett G. Olivier, Alexander A. Patrie, Ellen M. Quardokus, Nicole Radde, Johann M. Rohwer, Sven Sahle, James C. Schaff, Falk Schreiber, T. J. Sego, Janis Shin, Jacky L. Snoep, Rajanikanth Vadigepalli, H. Steven Wiley, Dagmar Waltemath, Ion I. Moraru

Computational Medicine Center Faculty Papers

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model …

Quantile Index Predictors Using R Package Hyper.Gam, Tingting Zhan, Misung Yi, Inna Chervoneva

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

MOTIVATION: Evaluation of single-cell protein expression from immunohistochemistry images is used increasingly in biomedical research. Many proteins are used solely for phenotyping cells in the tumor microenvironment. Other proteins with meaningfully quantitative expression levels provide so-called functional protein biomarkers. There is still a limited number of methods and software tools available for utilizing the entire distributions of single-cell expression levels.

RESULTS: We present the R package hyper.gam, providing a supervised learning framework for deriving biomarkers based on single-cell distribution quantiles. The single-cell data are first converted into sample quantile functions, which are then used as predictors in scalar-on-function regression models …

Computational Approaches To Understand Chemoresistance & Tumor Evolution Using Longitudinal Clinical Data And Lineage Tracing, Sahil Seth

Dissertations and Theses (Open Access)

Tumors are highly heterogeneous and dynamic, continually adapting and evolving in response to their microenvironment as well as external perturbations. Multi-region (spatial) and single cell sequencing has enabled us to anatomize the heterogeneity further and provide evidence of its association with chemo and drug resistance. To investigate this further we took two different approaches to understand the chemo-resistance, and functional heterogeneity in Triple negative breast cancer (TNBC) and Pancreatic ductal carcinoma in situ (PDAC) from an evolutionary perspective.

The first approach was to leverage tumor profiling from an ongoing randomized clinical trial in triple-negative breast cancer (ARTEMIS) to assess mechanisms …

Rare Coding Variants In 35 Genes Associate With Circulating Lipid Levels-A Multi-Ancestry Analysis Of 170,000 Exomes, George Hindy, Peter Dornbos, Mark D Chaffin, Dajiang J Liu, Minxian Wang, Margaret Sunitha Selvaraj, David Zhang, Joseph Park, Carlos A Aguilar-Salinas, Lucinda Antonacci-Fulton, Diego Ardissino, Donna K Arnett, Stella Aslibekyan, Gil Atzmon, Christie M Ballantyne, Francisco Barajas-Olmos, Nir Barzilai, Lewis C Becker, Lawrence F Bielak, Joshua C Bis, John Blangero, Eric Boerwinkle, Lori L Bonnycastle, Erwin Bottinger, Donald W Bowden, Matthew J Bown, Jennifer A Brody, Jai G Broome, Noël P Burtt, Brian E Cade, Federico Centeno-Cruz, Edmund Chan, Yi-Cheng Chang, Yii-Der I Chen, Ching-Yu Cheng, Won Jung Choi, Rajiv Chowdhury, Cecilia Contreras-Cubas, Emilio J Córdova, Adolfo Correa, L Adrienne Cupples, Joanne E Curran, John Danesh, Paul S De Vries, Ralph A Defronzo, Harsha Doddapaneni, Ravindranath Duggirala, Susan K Dutcher, Patrick T Ellinor, Leslie S Emery, Jose C Florez, Myriam Fornage, Barry I Freedman, Valentin Fuster, Ma Eugenia Garay-Sevilla, Humberto García-Ortiz, Soren Germer, Richard A Gibbs, Christian Gieger, Benjamin Glaser, Clicerio Gonzalez, Maria Elena Gonzalez-Villalpando, Mariaelisa Graff, Sarah E Graham, Niels Grarup, Leif C Groop, Xiuqing Guo, Namrata Gupta, Sohee Han, Craig L Hanis, Torben Hansen, Jiang He, Nancy L Heard-Costa, Yi-Jen Hung, Mi Yeong Hwang, Marguerite R Irvin, Sergio Islas-Andrade, Gail P Jarvik, Hyun Min Kang, Sharon L R Kardia, Tanika Kelly, Eimear E Kenny, Alyna T Khan, Bong-Jo Kim, Ryan W Kim, Young Jin Kim, Heikki A Koistinen, Charles Kooperberg, Johanna Kuusisto, Soo Heon Kwak, Markku Laakso, Leslie A Lange, Jiwon Lee, Juyoung Lee, Seonwook Lee, Donna M Lehman, Rozenn N Lemaitre, Allan Linneberg, Jianjun Liu, Ruth J F Loos, Steven A Lubitz, Valeriya Lyssenko, Ronald C W Ma, Lisa Warsinger Martin, Angélica Martínez-Hernández, Rasika A Mathias, Stephen T Mcgarvey, Ruth Mcpherson, James B Meigs, Thomas Meitinger, Olle Melander, Elvia Mendoza-Caamal, Ginger A Metcalf, Xuenan Mi, Karen L Mohlke, May E Montasser, Jee-Young Moon, Hortensia Moreno-Macías, Alanna C Morrison, Donna M Muzny, Sarah C Nelson, Peter M Nilsson, Jeffrey R O'Connell, Marju Orho-Melander, Lorena Orozco, Colin N A Palmer, Nicholette D Palmer, Cheol Joo Park, Kyong Soo Park, Oluf Pedersen, Juan M Peralta, Patricia A Peyser, Wendy S Post, Michael Preuss, Bruce M Psaty, Qibin Qi, D C Rao, Susan Redline, Alexander P Reiner, Cristina Revilla-Monsalve, Stephen S Rich, Nilesh Samani, Heribert Schunkert, Claudia Schurmann, Daekwan Seo, Jeong-Sun Seo, Xueling Sim, Rob Sladek, Kerrin S Small, Wing Yee So, Adrienne M Stilp, E Shyong Tai, Claudia H T Tam, Kent D Taylor, Yik Ying Teo, Farook Thameem, Brian Tomlinson, Michael Y Tsai, Tiinamaija Tuomi, Jaakko Tuomilehto, Teresa Tusié-Luna, Miriam S Udler, Rob M Van Dam, Ramachandran S Vasan, Karine A Viaud Martinez, Fei Fei Wang, Xuzhi Wang, Hugh Watkins, Daniel E Weeks, James G Wilson, Daniel R Witte, Tien-Yin Wong, Lisa R Yanek, Amp-T2d-Genes, Myocardial Infarction Genetics Consortium, Nhlbi Trans-Omics For Precision Medicine (Topmed) Consortium, Nhlbi Topmed Lipids Working Group, Sekar Kathiresan, Daniel J Rader, Jerome I Rotter, Michael Boehnke, Mark I Mccarthy, Cristen J Willer, Pradeep Natarajan, Jason A Flannick, Amit V Khera, Gina M Peloso

Faculty, Staff and Student Publications

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, …

Machine Learning Applications For Drug Repurposing, Hansaim Lim

Dissertations, Theses, and Capstone Projects

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …

On The Distribution Of Genetic Variation In Ecological Communities, Isaac Overcast

Dissertations, Theses, and Capstone Projects

Biodiversity in ecological communities is structured hierarchically across spatial and temporal scales. Many open questions remain as to how this structure accumulates. For example, what are the relative contributions of dispersal versus in situ speciation? Or, how important are stochastic drift versus deterministic processes? Up to this point, these questions have been investigated by isolated disciplines (e.g. macroecology, comparative phylogeography, macroevolution) using tools and data that tend to focus on only one axis of community scale data (e.g. phylogenies, relative abundances, and/or trait information). Yet we know that there are feedbacks among processes that respond on short, medium, and long …

Incorporating Pathway Information Into Feature Selection Towards Better Performed Gene Signatures, Suyan Tian, Chi Wang, Bing Wang

Biostatistics Faculty Publications

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, …

Functional Analysis Of Synthetic Gene Circuits Controlling A Protein Pump In Yeast, Junchen Diao

Dissertations and Theses (Open Access)

Synthetic biology aims to build biological devices to understand living systems and explore new applications. Synthetic gene circuits such as genetic switches, oscillators and logic gates are at the core of many synthetic biology applications. These gene circuits often include a sensor/regulator protein capable to detect small molecules and then transduce them into a regulatory signal to generate measurable output. Similar signal transduction networks are also abundant in nature. However, in many natural and engineered scenarios, the output also affects the regulator/sensor protein. How such interactions between the regulator/sensor and the output affect synthetic gene circuit function has not been …

A Quick Guide For Building A Successful Bioinformatics Community., Aidan Budd, Manuel Corpas, Michelle D Brazas, Jonathan C Fuller, Jeremy Goecks, Nicola J Mulder, Magali Michaut, B F Francis Ouellette, Aleksandra Pawlik, Niklas Blomberg

Computational Biology Institute

"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding …

Identifying Potential Cancer Driver Genes By Genomic Data Integration., Yong Chen, Jingjing Hao, Wei Jiang, Tong He, Xuegong Zhang, Tao Jiang, Rui Jiang

College of Science & Mathematics Departmental Research

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis …

Pathoscope: Species Identification And Strain Attribution With Unassembled Sequencing Data., Owen E Francis, Matthew Bendall, Solaiappan Manimaran, Changjin Hong, Nathan L Clement, Eduardo Castro-Nallar, Quinn Snell, G Bruce Schaalje, Mark J Clement, Keith A Crandall, W Evan Johnson

Computational Biology Institute

Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence …

Cross-Ontology Multi-Level Association Rule Mining In The Gene Ontology., Prashanti Manda, Seval Ozkan, Hui Wang, Fiona M. Mccarthy, Susan M. Bridges

BCoE Publications

The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by …