Biometry and Biosystems Informatics


Dr. Lingling An

Dr. Jeong-Yool Yoon

Biometry and biosystems Informatics deals with health issues, mostly pathogenic diseases, found among humans, animals, and the environment (air, water, and food). Emerging infectious diseases, including avian and swine influenza, SARS, tuberculosis, Shiga-toxin producing E. coli, etc., are closely associated with animals, food, and the environment. Solutions to infectious diseases need to be investigated at the level of ecological systems. The ABE department has a strong tradition of addressing these issues, utilizing smartphone- and cloud-based diagnostics, big data analysis, genomic/proteomic identification of pathogens/diseases, lab-on-a-chip biosensors, and nanotechnology-based sensing and therapeutics.

Our students are heavily involved in rearching this area.  [More here] 


Research Areas

Vegetation Index & Phenology 

Dr. Kamel Didan

Using global remote sensing from NASA satellites, and a variety of other platforms and sources, Dr. Kamel Didan’s VIP Lab analyzes vegetation in a global context then extrapolates to the regional levels.  One of the key ABE’s missions is the development of tools that assist in managing resources and aid with decision making, especially when factors such as climate change or drought, as they relate to vegetation, must be accounted for. This allows scientists, engineers, and experts to investigate the interconnectivity of vegetation, food availability, water scarcity, and climate change.

With this wealth of information at our finger tips, we can understand and forecast the slow acting long term changes in the natural ecosystem and calculate the effects it will have on field agriculture crop cultivation – whether deviations in yield as a result of changing climate or the long term impacts of drought on production capacity.


The challenge?  Regular capture of global data about vegetation is a complex and teamwork endeavor particularly when cloud cover disrupts the quality of this data – considering clouds cover +60% of the Earth surface daily.  ABE’s VIP Lab develops software to fill in these gaps, using Terabytes of historical data and complex algorithms to paint a complete and consistent picture of global vegetation distribution in the past, present, and even  the short-term future.

Vegetation Index & Phenology (VIP) Lab


Handheld devices for nucleic acid analysis - Dr. Jeong-Yeol Yoon

Biosensors Lab ( is currently developing handheld, field-ready or point-of-care PCR and LAMP devices for analyzing nucleic acids from myriads of human, animal, food, and environmental samples. Smartphone (or Arduino/Raspberry Pi with CMOS camera) is being used as an optical sensing device as well as data processing/storage device. Unlike the other real-time quantification methods for PCR and LAMP, we are the first in utilizing interfacial effects to drastically reduce the analysis time and improve the sensitivity.

Image: Handheld PCR device where the smartphone monitors the interfacial effects. Science Advances 2015, 1(8): e1400061 (

Biosensensors Lab Website

The Wound Microbiome - Dr. Bonnie Hurwitz

Microbes play a key role in host physiology, health and disease. Yet, microbial community interactions, over space and time, and with environmental stress are less well understood. The largest roadblocks in reaching an ecosystem-level understanding of host-microbe-environment interaction is the difficulty in interconnecting large-scale datasets from varied sources. In collaboration with Dr. David Armstrong of the UA College of Medicine, our lab is creating an interoperable graph database resources to support large-scale data analysis in microbiome-related research, which extends Bio4J to include KEGG pathway data, and interconnecting disparate biological graph databases towards functional metagenomic analyses. These development efforts will be applied to research on elucidating phage-encoded processes related to bacterial colonization in chronic diabetic foot ulcers. 

Hurwitz Lab Website

Big Data Analytics for Viral Ecology - Dr. Bonnie Hurwitz

"Big data" is pervasive in biology and can be used to discover new insights into interconnected biological processes. Despite innovations in sequencing technology, bottlenecks still exist in analyzing these massive and highly contextualized datasets.  Analysis requires the harmonization and integration of multiple biological datasets such as genes, protein function, pathways and environmental or host-related factors. 

Here we perform massive comparative metagenomic sequence analysis using the Hadoop big data architecture, and interconnect these data with biological annotations stored in a scalable Neo4J graph database. We demonstrate the utility of our toolkit using a large-scale viral metagenomics dataset from the TARA Oceans Expedition. This work represents a first step in storing, comparing, and querying massive metagenomic datasets using scalable big data architectures toward understanding viruses and their impact on host-processes in the ocean.

Hurwitz Lab Website

Discovery and Translation of Arid-Land Microbiomes (TRANS-AM) - Dr. Bonnie Hurwitz

Among the most accessible and important microbiomes are those associated with plants – the bacteria, fungi, archaea, protists, and viruses that occur in plant tissues, on plant surfaces, and in soil.  Wide-spread evidence points to the importance of plant microbiomes in influencing plant productivity, protecting plants against environmental stresses, and transferring phenotypic traits — such as drought-, salinity-, or heat tolerance – rapidly and reliably from one host plant to another.

Recent advances in microbial ecology highlight the global uniqueness of Arizona’s plant-microbiome resources, and leaps forward in technology now permit the rapid discovery and application of these resources in innovative ways.  As a diverse group of faculty in the College of Agriculture and Life Science at the University of Arizona we share common interests in discovering and translating arid-land plant microbiomes. Our collaboration brings together microbiology, genomics, biotechnology, crop science, nutritional science, bioengineering, plant ecology, and bioinformatics.

Hurwitz Lab Website

Pacific Ocean Virome - Dr. Bonnie Hurwitz

Bacteria and their viruses are fundamental drivers of many ecosystem processes. While databases and resources for studying function in uncultured bacterial communities are relatively advanced, fewer exist for their viral counterparts. The majority of viral sequences are functionally ‘unknown’ making viruses a virtually untapped resource of physiological information.

We provide a community resource that organizes this unknown sequence  into protein clusters using 32 viral metagenomes from four regions in the Pacific Ocean. These protein clusters more than double currently available viral protein clusters and provide a framework from which to draw on for future metadata-enabled functional inquiries of the vast viral unknown.

View in iMicrobe or use the PCPipe App in the CyVerse Discovery Environment to create your own protein clusters.

The Pacific Ocean Virome (POV): A Marine Viral Metagenomic Dataset and Associated Protein Clusters for Quantitative Viral Ecology

Hurwitz Lab Website

iMicrobe Data Commons - Dr. Bonnie Hurwitz

The iMicrobe project is supported by The Gordon and Betty Moore Foundation’s Marine Microbiology Initiative to promote the reuse and discovery of large-scale datasets together with a common ecosystem of bioinformatics tools, data storage and compute resources in CyVerse Cyberinfrastructure.  We're making genomic, metagenomic, transcriptomic, and metatranscriptomic data accessible via the iMicrobe Data Commons.

Our project team organizes data in an accessible data commons in CyVerse Cyberinfrastructure, curates and standardized ontologies for metadata and attach these metadata tags to the data using CyVerse metadata capabilities, and develops training courses to enable microbial ecologists to access these data resources

Hurwitz Lab Website

Functional Repertoire of the Gut Microbiome in a Mouse Colon Cancer Model Dr. Bonnie Hurwitz

Mutations in Transforming Growth Factor-β (TGFβ) account for 30% of the known mutations associated with human colorectal cancer (CRC) 5–7. Yet the role of genes in this pathway are not clear. CRC is only induced in mice with both a genetic predisposition and specific microbes in the gut. 

To better understand the crosstalk between gut epithelial cells we use metagenomic datasets from mouse models with/without mutations in TGFβ signaling genes and in the presence/absence of known colonic inflammatory factors.  We interlink these large-scale -omics datasets with functional annotations and metabolic pathways to better understand the role of microbes in colon cancer. This work is in collaboration with Dr. Tom Doetschman of the UA College of Medicine.

Hurwitz Lab Website

Taxonomic assignment analysis for metagenomic samples based on next generation sequencing data - Dr. Lingling An

Metagenomics is the study of multiple genomes from multiple species in communities obtained by direct sequencing from an environment, without the need for culturing them. Based on the sequence data from a metagenomic sample the basic questions will be addressed include “what species or genomes are there?”, “what are their relative abundance?”, and “how are multiple metagenomes different?” Metagenomic experimental process is shown as below. The question Q1 and Q3 in the picture will be addressed and answered in this project.

Statistical Bioinformatics Group Website

Statistical methods for functional metagenomic analysis with applications in biological threat detection - Dr. Lingling An

High-throughput next generation sequencing technologies provide a powerful way to detect biological threats from metagenomic samples taken directly from the environment without prior knowledge of sample composition. In the analysis of metatranscriptomic data sets, we can examine and compare the active gene functions and pathways in the environmental or host-associated metagenomic samples with the presence or absence of biological threat agents (organisms or viruses). This is accomplished by identifying which genes are active in a sample and characterizing which functional patterns are associated with the presence of biothreat agents. Moreover, functional analysis of metagenomes can explore how functional diversity of microbial communities correlate with important biological factors of interest including the presence of a particular threat organism and its virulence level. In this research we propose to build rigorous statistical models and rapid computational algorithms to detect biological threats based on metatranscriptomic sequencing data, i.e., we are targeting the Q2 & Q3 in the above graph.

Statistical Bioinformatics Group Website

Benchmark risk assessment in food safety - Dr. Lingling An

The focus of this project is development and study of new statistical methods for use in food safety/microbial risk assessment. Application is directed to settings where a microbial pathogen is measured on food processing equipment or food contact surfaces. Of interest is calculation of benchmark or other safe, low pathogen levels in order to manage risk of contamination of the food being processed in such environments. The resulting guidelines will improve risk management by commercial food establishments when dealing with potential microbial contamination of processing environments, and provide data for science-based risk assessment of food processing environments by regulatory agencies.

Statistical Bioinformatics Group Website