Adam Thrash

Bioinformatics Research Associate

Institute for Genomics, Biocomputing & Biotechnology

Mississippi State University, Starkville, MS

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Curriculum Vitae

Publications

2020

Thrash A, Hoffmann F, Perkins A. Toward a more holistic method of genome assembly assessment. BMC Bioinformatics 2020;21:249. doi:10.1186/s12859-020-3382-4.

Tekedar HC, Arick MA, Hsu C-Y, Thrash A, Blom J, Lawrence ML, et al. Identification of Antimicrobial Resistance Determinants in Aeromonas veronii Strain MS-17-88 Recovered From Channel Catfish (Ictalurus punctatus). Front Cell Infect Microbiol 2020;10:348. doi:10.3389/fcimb.2020.00348.

Thrash A, Warburton ML. A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information. JoVE (Journal of Visualized Experiments) 2020:e61268. doi:10.3791/61268.

Thrash A, Tang JD, DeOrnellis M, Peterson DG, Warburton ML. PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets. Plants. 2020;9:58. doi:10.3390/plants9010058

2019

Rycroft TE, Foran CM, Thrash A, Cegan JC, Zollinger R, Linkov I, et al. AOPERA: A proposed methodology and inventory of effective tools to link chemicals to adverse outcome pathways. ALTEX - Alternatives to animal experimentation. 2019. doi:10.14573/altex.1906201.

Feugang JM, Thrash A, Liao SF, Ryan PL, Mustapha PA, Willard ST. Exploring potential biomarkers for boar sperm cryopreservation using RNA-sequencing technology. Theriogenology. 2019;137:135.

Thrash A, Arick II MA, Barbato RA, Jones RM, Douglas TA, Esdale J, et al. Keanu: a novel visualization tool to explore biodiversity in metagenomes. BMC Bioinformatics. 2019;20. doi:10.1186/s12859-019-2629-4.

Li H, Thrash A, Tang JD, He L, Yan J, Warburton ML. Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis. The Plant Journal. 2019. doi:10.1111/tpj.14282.

Grover CE, Arick II MA, Thrash A, Conover JL, Sanders WS, Peterson DG, et al. Insights into the Evolution of the New World Diploid Cottons ( Gossypium , Subgenus Houzingenia ) Based on Genome Sequencing. Genome Biology and Evolution. 2019;11:53–71. doi:10.1093/gbe/evy256

2018

Warburton ML, Womack ED, Tang JD, Thrash A, Smith JS, Xu W, et al. Genome-Wide Association and Metabolic Pathway Analysis of Corn Earworm Resistance in Maize. The Plant Genome. 2018;11:0.

Thrash A, Arick II M, Peterson DG. Quack: A quality assurance tool for high throughput sequence data. Analytical Biochemistry. 2018;548:38–43. doi:10.1016/j.ab.2018.01.028

2017

Grover CE, Arick II MA, Conover JL, Thrash A, Hu G, Sanders WS, et al. Comparative Genomics of an Unusual Biogeographic Disjunction in the Cotton Tribe (Gossypieae) Yields Insights into Genome Downsizing. Genome Biology and Evolution. 2017;9:3328–44.

2015

Rice J, Dees K, Thrash A, Perkins A. Investigating genome similarity through cross mapping percentage. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics - BCB ’15. Atlanta, Georgia: ACM Press; 2015. p. 543–4. doi:10.1145/2808719.2811456.

Presentations

2019

Vinas N, Thrash A, Arick II MA, Jones R, Douglas T, Perkins E. Keanu: A Novel Visualization Tool to Explore Biodiversity in Metagenomes. Presented at ABRF 2019. J Biomol Tech. 2019;30 Suppl:S24.

Feugang JM, Thrash A, Liao SF, Ryan PL, Mustapha PA, Willard ST. Exploring potential biomarkers for boar sperm cryopreservation using RNA-sequencing technology. Presented at the IX International Conference of Boar Semen Preservation.

2018

Thrash A. Keanu: An Interactive Tool for Exploring Sample Content. Presented 15th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Starkville, MS).

Thrash A, Arick II M, Peterson DG. Quack: A Tool for Rapid FASTQ Quality Assurance. Poster, 15th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Starkville, MS).

Thrash A, Hoffmann F, Perkins A. A Machine Learning Approach to Genome Assessment. Poster, 15th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Starkville, MS).

2017

Thrash A, Showmaker K, Peterson DG. SoyLENT: Visualizing Soybean Pedigrees. Poster, 14th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Little Rock, AR)

2016

Thrash A, Perkins A. Performance of Probabilistically Assigning Sequence Reads in Metatranscriptomic Studies Using Gene Expression. Poster, 13th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Memphis, TN)

Thrash A. Update on the MCBIOS Timber Rattlesnake Genome Project. Presented 13th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Memphis, TN)

2015

Rice J, Dees K, Thrash A, Perkins A. Investigating genome similarity through cross mapping percentage. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics - BCB ’15. Atlanta, Georgia: ACM Press; 2015. p. 543–4. doi:10.1145/2808719.2811456.

Thrash A, Perkins A. Accurately Assigning Sequence Reads in Metatranscriptomic Studies. Poster, 12th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Little Rock, AR)

Thrash A. Metagenomics and Mississippi. Poster, Research Day @ the Capitol (Jackson, MS)

2014

Thrash A, Perkins A. Probabilistic Assignment Of High Throughput Sequence Data In Metatranscriptomic Studies. Poster, 11th Annual Conference of the Midsouth Computational Biology & Bioinformatics Society (Stillwater, OK)

Thrash A. Metagenomics and Mississippi. Poster, Research Day @ the Capitol (Jackson, MS)

Programs

PAST

PAST is a collaboration with the USDA-ARS. My collaborator was recently awarded a USDA Innovation Award to add new features and expand PAST's compatibility to other types of GWAS data.


Abstract

In recent years, a bioinformatics method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and successfully used to find significant pathways and mechanisms explaining phenotypic traits of interest in plants. However, the many scripts implementing this method were not straightforward to use, had to be customized for each project, required user supervision, and took more than 24 hours to process data.

PAST (Pathway Association Study Tool), a new implementation of this method, has been developed to address these concerns. PAST has been implemented as a package for the R language. Two user-interfaces are provided; PAST can be run by loading the package in R and calling its methods, or by using an R Shiny guided user interface. In testing, PAST completed analyses in approximately half an hour to one hour by processing data in parallel and produced the same results as the previously developed method. PAST has many user-specified options for maximum customization.

To promote a powerful new pathway analysis methodology that interprets GWAS data to find biological mechanisms associated with traits of interest, we developed a more accessible, efficient, and user-friendly tool. These attributes make PAST accessible to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotypes.

PAST is available on GitHub, Bioconductor, and at MaizeGDB.

Keanu

Keanu is a collaboration with the U.S. Army ERDC.


Abstract

One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier.

Keanu, a tool for exploring sequence content, helps a user to understand the presence and abundance of organisms in a sample by analyzing alignments against a database that contains taxonomy data and displaying them in an interactive web page. The content of a sample can be presented either as a collapsible tree, with node size indicating abundance, or as a bilevel partition graph, with arc size indicating abundance. Here, we illustrate how Keanu works by exploring shotgun metagenomics data from a sample collected from a bluff that contained paleosols and a krotovina in an alpine site in Ft. Greely, Alaska.

Keanu provides a simple means by which researchers can explore and visualize species present in sequence data generated from complex communities and environments.

Keanu is freely available at GitHub.

Quack

The quality of data generated by high-throughput DNA sequencing tools must be rapidly assessed in order to determine how useful the data may be in making biological discoveries; higher quality data leads to more confident results and conclusions. Due to the ever-increasing size of data sets and the importance of rapid quality assessment, tools that analyze sequencing data should quickly produce easily interpretable graphics. Quack addresses these issues by generating information-dense visualizations from FASTQ files at a speed far surpassing other publicly available quality assurance tools in a manner independent of sequencing technology.

Quack is freely available at GitHub.

Experience

Research Associate II

From: April 2019 - Present

At: Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Starkville, MS

Build relationships with collaborators and develop software solutions to their problems; assist clients and collaborators with achieving their research goals by providing data analysis and methods descriptions.

  • Analyze client/collaborator data with current bioinformatics software and pipelines
  • Develop custom reports to better visualize and explain complex data
  • Develop scripts to customize analyses
  • Develop software to meet needs of collaborators
  • Draft manuscripts and assist with manuscript production
  • Train student workers as needed

Research Associate I

From: August 2015 - April 2019

At: Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Starkville, MS

Primarily assist clients and collaborators with achieving their research goals by providing data analysis and methods descriptions.

  • Analyze client/collaborator data with current bioinformatics software and pipelines
  • Develop scripts to customize analyses
  • Develop software to meet needs of collaborators
  • Assist with manuscript production
  • Assist with training student workers as needed

Graduate Research Assistant

From: May 2014 - May 2015

At: Department of Computer Science and Engineering, Mississippi State University, Starkville, MS

Researched whether sequences that mapped to both a host and pathogen could be assigned probabilistically

  • Use bioinformatics tools, such as Bowtie2, samtools
  • Created posters for presentation at conferences
  • Assisted undergraduates with other parts of the project

Undergraduate Research Assistant

From: November 2012 - May 2014

At: Department of Computer Science and Engineering, Mississippi State University, Starkville, MS

Researched whether sequences that mapped to both a host and pathogen could be assigned probabilistically

  • Use bioinformatics tools, such as Bowtie2, samtools
  • Created posters for presentation at conferences

Education

PhD, Computer Science

From: May 2016 - August 2019

At: Department of Computer Science and Engineering, Mississippi State University, Starkville, MS

Dissertation

A machine learning approach to genome assessment

An exploration of current methods of genome assembly assessement, where these methods fall short, and how machine learning can be used to model expert knowledge for genome assessment

Highlighted Coursework

  • Machine Learning, Fall 2017
  • Visual Data Analysis with R
  • Data Information and Visualization
  • Directed Individual Study of Genome Assembly

MS, Computer Science

From: May 2014 - May 2016

At: Department of Computer Science and Engineering, Mississippi State University, Starkville, MS

Highlighted Coursework and Achievements

  • Essentials of Molecular Genetics
  • Genomes and Genomics
  • High Throughput Sequence Analysis

Computational Biology Certificate

This certificate requires students to take a combination of five computer science and biological sciences courses to build their understanding of how to combine the two disciplines.

BS, Software Engineering, Summa Cum Laude

From: August 2010 - May 2014

At: Department of Computer Science and Engineering, Mississippi State University, Starkville, MS