chemistry Ph D

David E. Condon, Ph.D.

Renner, SD *****

Email-id : ******@*****.***

Mobile No.: 206-***-****

OrcID: 0000-0002-3755-7078

LinkedIn Profile

Google Scholar

Education

Degree University Major Location Year

Ph.D. University of Rochester Chemistry Rochester, NY 2015 M.S. University of Rochester Chemistry Rochester, NY 2011 B.S. Pennsylvania State University Chemistry State College, PA 2008 Skills

• Genomics & Bioinformatics (9 years experience)

Developed my own pipelines in bisulfite-seq/methyl seq, using machine learning/AI to make polygenic risk scores for Covid using electronic health record (EHR), protein target identification for antimicrobials, and for the global screening array (GSA)

Performed differential expression analysis RNA-Seq in mice, and again at Sanford with cancer patients.

Used Survival GWAS for statistical genetics analysis for Diabetes & CVD, using electronic health record

(EHR) phenotypic data from Sanford BioBank

created polygenic risk score (PRS) for COVID-19, and used previous PRS for prostate cancer

Two years experience with RNA-Seq and Bisulfite sequencing/epigenetics at the University of Pennsyl- vania.

5 years of experience with microarray and whole-genome NGS data at Sanford Health. I have expe- rience using survival genome-wide association studies, imputation, polygenic risk scores, delivering analysis of patient HIPAA-protected data from the electronic medical record (EMR)

experience in leadership while supervising undergrade and graduate students,

Experience with clinical biomarker datasets for prostate cancer

Familiarity with the management, design, and/or development of publicly accessible databases such as NCBI, Saccharomyces Genome Database, PomBase, and FungiDB and data quality assurange

have previously done single-cell sequencing on kidney disease,

experience with bioinformatics programming tools and databases such as Conda, Git, Bioconductor, BLAST, BWA, SAMtools, BedTools, DeepTools, GATK, UCSC Genome Browser, and STAR among others

I also have experience writing three proposals

• Programming (14 years experience)

Python 5 years experience, used for scripting, but matplotlib, numpy, scipy, pandas, scikit-learn, and xgboost

R 5 years experience, used for statistics and plotting

C 3 years experience, used for writing the Defiant, which identifies differentially methylated regions in bisulfite-sequencing data. Used OpenMP for parallel programming

SQL Beginner proficiency

Perl 14 years experience, used for regular scripting and parsing many types of files. Publicly available projects include rotate.xyz

• Artificial Intelligence/Machine Learning I used clinical data to attempt to build predictive models of COVID susceptibility using XGBoost.

• Linux & AWS (15 years experience) 4 years experience with IBM Blue Hive and Blue Gene P and Q supercomputing Linux clusters. 14 years experience with Ubuntu and RedHat Linux. Used AWS for storage of HIPAA-protected data.

• Computational Chemistry (5 years experience). Used AMBER Molecular Dynamics package to simulate LNA CAAU and RNAs CAAU, GACC, AAAA, and UUUU to test molecular dynamics torsion reparametriza- tions and compare with NMR measurements. Have used Gaussian09 and NWChem 6 for calculations with RNAs.

Work Experience

University of Idaho,Research Scientist II; duration: 06/06/2023-01/23/2025

• Developed bioinformatics pipeline to automate drug discovery, originally used for agricultural crops

• Discovered eight new novel anti-fungal drug targets in fungi, manuscript submitted to PLOS One

• Interpreted AlphaFold structures

Sanford Health, Lead Computational Bioinformatics Analyst; 11/27/2017-12/31/2022

• Developed concordance pipeline for Sanford Chip (Global Screening Array), benchmarking with Whole- Genome Sequencing data (WGS)

• Learned Apache Spark (PySpark)

• Developed computational pipeline for clinical application of RNA-Seq

• Used RNA-Seq public TCGA dataset to link different types of cancers

• Learned Survival GWAS for statistical genetics analysis for Diabetes & CVD Project

• Used Machine Learning / artificial intelligence (AI) with XGBoost (gradient boosting library) in Python to study patient data with covid-19 non-interventional health and patient data (electronic health record) re- search. I performed complex data extraction, manipulation, and summarization of large databases to sup- port research activities. I took the lead role in design, testing, validation, analysis, and merging of compli- cated data structures from electronic health records

• Experience with polygenic risk scores in prostate cancer and pre-eclampsia

• Led & managed Imagenetics Journal Club

• Briefly worked with single-cell RNA-Seq and Whole-Exome Sequencing (WES)

• Used patient genetic data to enhance precision medicine, including pharmacogenetics University of Pennsylvania, Postdoctoral Researcher; 1/15/2015-11/15/2017

• Used RNA-Seq for studying placental changes from obesity

• Used Bisulfite-Seq to study epigenetic changes in iron-deficient pregnancy in the rat hippocampus

• learned the R programming language

• Developed Defiant: a program designed to identify and annotate differentially methylated regions from bisulfite-sequencing experiments; using output from epigenetic preprocessing Publications

• Discovery of Novel Targets for Important Human and Plant Fungal Pathogens via Automated Computational Pipeline HitList David E Condon, Brenda K Schroeder, Paul Andrew Rowley, F Marty Ytreberg

• Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus Soo Heon Kwak, Ryan B. Hernandez- Cancela, Daniel A DiCorpo, David E. Condon, et al.

• Stacking in RNA: NMR of four tetramers benchmark molecular dynamics DE Condon, SD Kennedy, BC Mort, R Kierzek, I Yildirim, DH Turner

• The nuclear magnetic resonance of CCCC RNA reveals a right-handed helix, and revised parameters for AMBER force field torsions improve structural predictions from molecular dynamics JD Tubbs, DE Condon, SD Kennedy, M Hauser, PC Bevilacqua,

• Diet-induced obesity alters the maternal metabolome and early placenta transcrip- tome and decreases placenta vascularity in the mouse TJ Stuart, K O’Neill, D Condon, I Sasson, P Sen, Y Xia, RA Simmons

• Optimization of an AMBER force field for the artificial nucleic acid, LNA, and benchmarking with NMR of L

(CAAU) DE Condon, I Yildirim, SD Kennedy, BC Mort, R Kierzek, DH Turner

• Predicting the kinetics of RNA oligonucleotides using Markov state models G Pinamonti, J Zhao, DE Con- don, F Paul, F Noé, DH Turner, G Bussi

• Defiant:(DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus DE Condon, PV Tran, YC Lien, J Schug, MK Georgieff, RA Simmons

• Dysregulation of neuronal genes by fetal-neonatal iron deficiency anemia is associated with altered DNA methylation in the rat hippocampus YC Lien, DE Condon, MK Georgieff, RA Simmons, PV Tran

• In utero bisphenol A exposure is linked with sex specific changes in the transcriptome and methylome of human amniocytes A Bansal, N Robles-Matos, PZ Wang, DE Condon, A Joshi, SE Pinney105 (2), 453-467

• Exposure to gestational diabetes enriches immune-related pathways in the transcriptome and methylome of human amniocytes

• Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets PF Cherukuri, MM Soe, DE Condon, S Bartaria, K Meis, S Gu, FG Frost Awards

• Teas Scholarship for Undergraduate Research, Penn State, Summer 2008

• Moses Passer Memorial Fellowship, 2013-2014

• Sanford Health Quarterly Hero Award 2021

Foreign Languages

• Russian, B2-C1 fluency

• Spanish, 6 years experience 1999-2005

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