Espindola Camacho Andres

Dr. Espindola also contributed to developing protocols that enhance pathogen availability in sequencing libraries, leading to increased sensitivity in detection using Target Specific Reverse Primer Pools (TASPERT). With vast experience in analyzing genomic, transcriptomic, and metagenomic big data from both long and short-read sequencing platforms.

Dr. Espindola is currently creating computational models to predict crop productivity using the soil microbiome. He teaches Bioinformatics for Agricultural Biosecurity at both graduate and undergraduate levels and has mentored more than 30 students, including graduate and undergraduate as well as postdocs. His research program aims to develop fast protocols that can detect and predict microbial presence in sequencing samples using the latest bioinformatic methods, including data mining techniques, statistical and simulation modeling, and machine learning.

andres.espindola@okstate.edu

Education / Professional Prep / Appointments

Prep/Appointments:

Ph.D. Plant Pathology;

M.S. Entomology and Plant Pathology;

B.S. Biotechnology

Graduate Bioinformatics Certificate

Specialty Skills and Knowledge

Current Research

Bioinformatics and machine learning for pathogen detection

Soil microbiome studies related to crop productivity,

Design, and optimization of molecular biology

pipelines with emphasis on advanced protocols for high-throughput sequencing in pathogen

detection.

Bioinformatics,

Molecular biology

Machine learning

Microbial ecology for microbial detection

Data analysis

Visualization

Lab Website

https://aespindola.com

Key Publications

Dang, T., Wang, H., Espindola, A. S., Habiger, J., Vidalakis, G., & Cardwell, K. (2023).

Development and statistical validation of E-probe diagnostic nucleic acid analysis (EDNA) assays

for the detection of citrus pathogens from raw high-throughput sequencing data.

PhytoFrontiers, 3(1), 113–123.

Narayanan, S., Espindola, A. S., Malayer, J., Cardwell, K., & Ramachandran, A. (2023).

Development and evaluation of Microbe Finder (MiFi)®: a novel in silico diagnostic platform for

pathogen detection from metagenomic data. Journal of Medical Microbiology, 72(6).

https://doi.org/10.1099/jmm.0.001720

Bocsanczy, A. M., Espíndola, A. S., Cardwell, K., & Norman, D. (2023). Development and

validation of e-probes with MiFi® system for detection of Ralstonia solanacearum species

complex in blueberries. PhytoFrontiers TM . https://doi.org/10.1094/PHYTOFR-04-22-0043-FI

Espindola, A. S., Cardwell, K., Martin, F. N., Hoyt, P. R., Marek, S. M., Schneider, W., & Garzon, C.

D. (2022). A Step Towards Validation of High-Throughput Sequencing for the Identification of

Plant Pathogenic Oomycetes. Phytopathology, 112(9), 1859–1866.

Espindola, A.S**., Daniela Sempertegui-Bayas., Danny Bravo-Padilla., Viviana Freire-Zapata.,

Francisco Ochoa-Corona and Kitty Cardwell. 2021. TASPERT: Target-specific reverse transcript

pools to improve HTS plant virus diagnostics. Viruses, 13(7), 1223.

Espindola, A. S**., and Cardwell, K. F. 2021. Microbe Finder (MiFi®): Implementation of an

Interactive Pathogen Detection Tool in Metagenomic Sequence Data. Plants, 10(2), 250.