Doctor of Philosophy (PhD), Computational Biology and Bioinformatics08/12-02/18
San Diego State University with Claremont Graduate University

Bachelor of Science (BS), Computer Science08/05-05/11
San Diego State University

Edwards Bioinformatics Lab 
San Diego State University
5500 Campanile Drive, San Diego, CA 92182
Mentor: Professor Robert A. Edwards

Bioinformatics Scientist II03/18-Present
Progenity, R&D

• Highlights
  • Led development and design of single-molecule detection assay analytical software, processing daily experiments as an integral piece for rapid results during early stage product development
  • Performed genome wide discovery for differentially methylated DNA loci as potential biomarkers for NASH liver disease in acquired clinically diagnosed NASH samples
  • Developed probabilistic classifier to distinguish between levels of NASH liver disease severity and fibrosis measures in bisulfite sequence samples using methylation biomarkers, achieving proof-of-concept performance targets
  • Led development and design of paired-end shallow whole genome sequencing NIPT analytical software (aneuploidies, microdeletions, and fetal fraction prediction); optimized algorithm parameters achieving MVP and TPP targets
  • Implemented key pre-processing and QC components for the protein biomarker-based Preeclampsia diagnostic rule-out test analytical software, achieving software performance validation at each product milestone
  • Established the in-house microbiome 16S analysis pipeline as lead bioinformatics scientist for microbiome analyses
  • Created and managed R&D automated pipelines across several projects (NASH, Preeclampsia, NIPT, single-molecule detection platform), ensuring daily experiments underwent quality control, pre-processing, and primary analysis within 1-day turnaround times
• List of notable skills, tools, and activities
  • Early product development
    • Disease biomarker discovery
    • Proof-of-concept predictive modeling
    • Data characterization
    • R&D-centric software development
    • Technical reports and presentations
    • Project risk assessment
  • Data types
    • NGS: targeted, whole genome, bisulfite
    • 16S rRNA sequencing
    • Fluorescent small molecule imaging
    • ELISA protein concentrations
    • Patient characteristics and covariates (categorical and quantitative)
  • Data analysis and modeling
    • Ad-hoc data analysis
    • Data management, organization, and documentation
    • AWS data storage and management
    • Summary statistics and visualizations
    • Hypothesis testing
    • Predictive modeling and classification
    • Modeling performance reports and visualizations
  • Software development
    • Git/GitHub version tagging, code review, pair-programming
    • Unit/regression testing in CI environments
    • Sphinx code documentation and user documentation
    • Data processing parallelization using high performance computing (HPC) SLURM scheduler
    • Poetry packaging manager
    • Docker containerization
    • RMarkdown and Jupyter Notebooks
  • Programming languages, libraries, and visualization tools
    • Python: SciPy, NumPy, pandas, scikit-image, scikit-learn, statsmodels, seaborn, plotnine, matplotlib
    • R: ggplot2, caret, glmnet, bioconductor, dada2, phyloseq
    • Bash, shell, Perl

Graduate Research Assistant07/12-03/18
Robert Edwards Bioinformatics Lab, San Diego State University

• Led production of PyFBA, a genome-scale metabolic model builder using phenotypic-based assays, to identify novel metabolic pathways and transporters within bacterial genomes
• Developed PMAnalyzer to profile bacterial phenotypes across thousands of growth curves from monoculture and microbial community assays using a logistic growth model fit to absorbance data
• Participated in design and development of a full NGS metagenome and metatranscriptome computational pipeline used for emergency Cystic Fibrosis patient sputum sequencing data

Bioinformatics Scientist and Software Engineer09/11-07/12
Life Technologies, Ion Torrent R&D

• Developed a Django web-interface search engine for hundreds of internal sequencing results produced by all Ion Torrent R&D across the country, increasing ease of access and accepted as an integral tool for comparative studies throughout the company
• Developed a variety of C, Python, and Perl programs for customer facing Torrent Suite plugins. Utilized JIRA software tracking and version control for all products
• Supported scientists with custom pipelines to analyze sequence quality and mapping statistics for deficiencies in sequencing protocols and the Ion Torrent AmpliSeq primer design algorithm, including GC bias, primer-strand preferences, targeted sequencing efficacy

Bioinformatics and Molecular Biology Internship05/11-08/11
Life Technologies, Ion Torrent R&D and SOLiD Collaborations

• Created and presented analyses measuring the quality of sequencing experiments testing various sequencing polymerase mutants for longer reads and higher Phred quality scores
• Led and supervised sequencing and mapping runs that produced variant calls, mapping statistics, and mapping quality summaries for the SOLiD Collaborations team
• Assisted team members with sequencing runs, prepared and initialized PGM sequencing instruments, and resolved server-related issues to improve the flow of experiments

Undergraduate Research Assistant05/09-05/11
Robert Edwards Bioinformatics Lab, San Diego State University

• Developed several Java programs interfacing with a web service API for the SEED genome annotation database
• Constructed a search hash-algorithm in Java to accurately identify DNA sequence similarity between multiple sequence files
• Co-developed a front-end Android app producing figures and statistics for the Real Time Metagenomics server-side annotation tool
• Developed a social network web application to access and distribute metagenomics data while leveraging the network’s API and infrastructure to connect with colleagues and collaborators

1. Cuevas, D.A. (2018). Bridging the genomic gaps: genome-scale metabolic network tools for bioinformatics analyses. Dissertation. ProQuest Dissertations Publishing. 10749152.Link


2. Cuevas, D.A. and Edwards, R.A. (2018). Growth Score: a single metric to define growth in 96-well phenotype assays. PeerJ 6:e4681. DOI: 10.7717/peerj.4681.


3. Cuevas, D.A. and Edwards, R.A. (2017). PMAnalyzer: a new web interface for bacterial growth curve analysis. Bioinformatics 33(12), 1905-1906. DOI: 10.1093/bioinformatics/btx084.


4. Cuevas, D.A., Edirisinghe, J., Henry, C.S., Overbeek, R., O’Connell, T.G., and Edwards, R.A. (2016). From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. Systems Microbiology, 907. DOI: 10.3389/fmicb.2016.00907.


5. Cuevas, D.A., Garza, D., Sanchez, S.E., Rostron, J., Henry, C.S., Vonstein, V., Overbeek, R.A., Segall, A., Rohwer, F., Dinsdale, E.A., and Edwards, R.A. (2014). Elucidating genomic gaps using phenotypic profiles. F1000Research 3, 210. DOI: 10.12688/f1000research.5140.2.

1. Boling, L., Cuevas, D.A., Grasis, J.A., Kang, H.S., Knowles, B., Levi, K., Maughan, H., McNair, K., Rojas, M.I., Sanchez, S.E., Smurthwaite, C., and Rohwer, F. (2020). Dietary Prophage Inducers and Antimicrobials: Towards Landscaping the Human Gut Microbiome. Gut Microbes, 11:4, 721-734. DOI: 10.1080/19490976.2019.1701353.


2. Kuang, E., Marney, M., Cuevas, D.A., Edwards, R.A., and Forsberg, E.M. (2020). Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics. Metabolomics, 10(4):156. DOI: 10.3390/metabo10040156.


3. Edwards, R.A., Vega, A.A., Norman, H.M., et al. (2019). Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nature Microbiology, 4, 1727–1736. DOI: 10.1038/s41564-019-0494-6.


4. Cobián Güemes, A.G., Lim Y.W., Quinn, R.A., Conrad, D.J., Benler, S., Maughan, H., Edwards, R.A., Brettin, T., Cantú, V.A., Cuevas, D.A., Hamidi, R., Dorrestein, P., and Rohwer, F. (2019). Cystic Fibrosis Rapid Response: Translating Multi-omics Data into Clinically Relevant Information, mBio, 10:e00431-19. DOI: 10.1128/mBio.00431-19.


5. Silveira, C.B., Coutinho, F.H., Cobián-Güemes, A.G., Cantu, V.A., Cuevas, D.A., Knowles, B., Cavalcanti, G.S., Soares, A.C., Silva, G.G.Z., Doane, M., Francini-Filho, R., Thompson, C., Dinsdale, E., Edwards, R., Gregoracci, G., Rohwer, F., and Thompson, F. (2018). Phage-mediated transfer of bacterial virulence genes in coral reefs. (In submission).


6. Silva, G.G.Z., Haggerty, J.M., Cuevas, D.A., Doane, M., Dinsdale, E.A., Dutilh, B.E., and Edwards, R.A. (2018). Ecological Implications of Metagenomics Data Analysis. (In submission).


7. Sczyrba, A., Hofmann, P., Belmann, P., et al. (2017). Critical Assessment of Metagenome Interpretation − a benchmark of computational metagenomics software. Nature Methods 14(11), 1063-1071. DOI: 10.1038/nmeth.4458.


8. Kang, H.S., McNair, K., Cuevas, D., Bailey, B., Segall, A.M., and Edwards, R.A. (2017). Prophage genomics reveals patterns in phage genome organization and replication. bioRxiv, 114819. DOI: 10.1101/114819.


9. Sanchez, S.E., Cuevas, D.A., Rostron, J.E., Liang, T.Y., Pivaroff, C.G., Haynes, M.R., Nulton, J., Felts, B., Bailey, B.A., Salamon, P., Edwards, R.A., Burgin, A.B., Segall, A.M., and Rohwer, F. (2015). Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins. Journal of Visualized Experiments, e52854. DOI: 10.3791/52854.


10. Lim, Y.W., Cuevas, D.A., Silva, G.G.Z., Aguinaldo, K., Dinsdale, E.A., Haas, A.F., Hatay, M., Sanchez, S.E., Wegley-Kelly, L., Dutilh, B.E., Harkins, T.T., Lee, C.C., Tom, W., Sandin, S.A., Smith, J.E., Zgliczynski, B., Vermeij, M.J.A., Rohwer, F., and Edwards, R.A. (2014). Sequencing at sea: challenges and experiences in Ion Torrent PGM sequencing during the 2013 Southern Line Islands Research Expedition. PeerJ 2, e520. DOI: 10.7717/peerj.520.


11. Dutilh, B.E., Thompson, C.C., Vicente, A.C., Marin, M.A., Lee, C., Silva, G.G., Schmieder, R., Andrade, B.G., Chimetto, L., Cuevas, D., Garza, D.R., Okeke, I.N., Aboderin, A.O., Spangler, J., Ross, T., Dinsdale, E.A., Thompson, F.L., Harkins, T.T., and Edwards, R.A. (2014). Comparative genomics of 274 Vibrio cholerae genomes reveals mobile functions structuring three niche dimensions. BMC Genomics 15, 654. DOI:10.1186/1471-2164-15-654.


12. Silva, G.G.Z., Cuevas, D.A., Dutilh, B.E., and Edwards, R.A. (2014). FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares. PeerJ 2, e425. DOI: 10.7717/peerj.425.


13. Disz, T., Akhter, S., Cuevas, D., Olson, R., Overbeek, R., Vonstein, V., Stevens, R., and Edwards, R.A. (2010). Accessing the SEED Genome Databases via Web Services API: Tools for Programmers. BMC Bioinformatics 11, 319. DOI:10.1186/1471-2105-11-319.

14th Annual SDSU Applied Computational Science and Engineering Student Support Poster Award04/17
Tioga Research, Inc.

ARCS Foundation Scholar, Two Years09/15-05/17
ARCS San Diego Chapter

2014 GEN TEN Award06/14
Genetic Engineering and Biotechnology News Publisher

Graduate S-STEM Scholarship, Two Years01/14-01/16
National Science Foundation via SDSU Computational Science Research Center

11th Annual Rocky Mountain Bioinformatics Conference Poster Prize12/13
F1000Research

Statistical Biomedical Informatics Scholarship, Two Semesters08/12-05/13
Biomedical Informatics Research Center, San Diego State University

NIH New Interdisciplinary Workforce Fellow07/09-07/10
Computational Science Research Center, San Diego State University

Dean's Honors List, Six Semesters08/08-05/11
San Diego State University

Sigma Xi Scientific Society - Full memberSince 05/16
Society for Industrial and Applied MathematicsSince 10/15
San Diego State University Science Alumni NetworkSince 05/15
San Diego State University Biomedical Technology Student Association - WebmasterSince 09/12

PLoS One

Unraveling metabolism network models of kelp-associated bacteria03/18
(Talk) San Diego State University Student Research Symposium, San Diego, CA

Metabolic model and data-driven exploration linking genotypes to phenotypes07/17
(T) SDSU Computational Science Research Center Colloquium, San Diego, CA

Metabolic model and data-driven exploration linking genotypes to phenotypes04/17
(Poster) SDSU Applied Computational Science and Engineering Student Support, San Diego, CA

Metabolic model and data-driven exploration linking genotypes to phenotypes04/17
(P) ARCS Scientist of the Year Award Dinner, San Diego, CA

Metabolic model and data-driven exploration linking genotypes to phenotypes03/17
(P) San Diego State University Student Research Symposium, San Diego, CA

From DNA to FBA: how to build your own genome-scale metabolic model04/16
(P) SDSU Applied Computational Science and Engineering Student Support, San Diego, CA

From DNA to FBA: how to build your own genome-scale metabolic model04/16
(P) ARCS Scientist of the Year Award Dinner, San Diego, CA

From DNA to FBA: how to build your own genome-scale metabolic model03/16
(T) San Diego State University Student Research Symposium, San Diego, CA

Phenotyping Diverse Bacteria for Metabolic Network Reconstruction 06/15
(P) General Meeting of the American Society for Microbiology, New Orleans, LA

Phenotyping Diverse Bacteria for Metabolic Network Reconstruction 04/15
(P) SDSU Applied Computational Science and Engineering Student Support, San Diego, CA

Phenotyping Diverse Bacteria for Metabolic Network Reconstruction 03/15
(T) San Diego State University Student Research Symposium, San Diego, CA

High-Throughput Phenotype Profiling for Bacterial Flux-Balance Model Optimization 06/14
(T) Biotechnology Industry Organization International Convention, San Diego, CA

High-Throughput Phenotype Profiling for Bacterial Flux-Balance Model Optimization 05/14
(P) General Meeting of the American Society for Microbiology, Boston, MA

Metabolic model optimization using high-throughput phenotypic assays 04/14
(T) Diponegoro University, Semarang, Indonesia

High-Throughput Phenotype Profiling for Bacterial Flux-Balance Model Optimization 03/14
(T) San Diego State University Student Research Symposium, San Diego, CA

High-Throughput Phenotype Profiling for Bacterial Flux-Balance Model Optimization 01/14
(P) Southern California Systems Biology Conference, Irvine, CA

High-Throughput Phenotype Profiling for Bacterial Flux-Balance Model Optimization 12/13
(T & P) Rocky Mountain Bioinformatics Conference, Snowmass, CO

Measuring Growth Quality of Bacteria Using Phenotypic Microarrays 05/13
(P) San Diego Microbiology Group All-day Meeting, San Diego, CA

Characterizing Phenotypic Properties of Unexplored Viral Genes 03/13
(T) San Diego State University Student Research Symposium, San Diego, CA

Compiling Genome Metadata for Comparative Analysis 12/12
(T) Rocky Mountain Bioinformatics Conference, Snowmass, CO

Rapid Sequence Searching Using Hashing 03/11
(T) San Diego State University Student Research Symposium, San Diego, CA

Real-Time Metagenomics 05/10
(P) General Meeting of the American Society for Microbiology, San Diego, CA

Real-Time Metagenomics 05/10
(P) San Diego Microbiology Group All-day Meeting, San Diego, CA

Real-Time Metagenomics 03/10
(P) SDSU Applied Computational Science and Engineering Student Support, San Diego, CA

Real-Time Metagenomics 03/10
(T) San Diego State University Student Research Symposium, San Diego, CA

Guest lecture at San Diego State University. Course: BIOL549 - Microbial Genetics and Physiology11/16
Flux-balance analysis using genome-scale metabolic models

Guest speaker at the ARCS General Meeting, Rancho Sante Fe, CA06/16
Metabolic modeling in the era of genomics

Summer workshop at San Diego State University05/16 - 08/16
Introduction to Python programming for microbiology scientists