Global Scale Metabolic Pathway Reconstruction From Environmental Genomes

For more than 3.5 billion years, microorganisms have been the dominant form of life on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput sequencing and mass spectrometry platforms generating multi-omic sequence information (DNA, RNA, protein and metabolites) which contain information about the function and identity of microbial life, have transformed our perception of this microcosmos, illuminating microbial dark matter and conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Despite the power and promise of this new perception, a persistent paucity of scalable software tools to mine, monitor, and interact with environmental sequence information limits knowledge creation and translation. This is especially vexing in a time of climate change when microbial community metabolism offers a virtual blueprint to rebuild our global future in more sustainable ways. EngCyc is the next step in overcoming this challenge.


About EngCyc

EngCyc provides direct and comparative access to virtual blueprints of microbial community metabolism through a portal system on the World Wide Web and support user-defined blueprint construction using MetaPathways 3.0. EngCyc and the associated software enables gene and pathway discovery. Downstream analysis modules provide intuitive and beautiful data exploration options to power knowledge creation and translation.

Figure 1. from: Hanson, Niels W., et al. "Metabolic pathways for the whole community." BMC genomics 15.1 (2014): 1-14



Konwar, Kishori M., et al. "MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information." BMC bioinformatics 14.1 (2013): 1-10.
Hanson, Niels W., et al. "MetaPathways v2. 0: A master-worker model for environmental Pathway/Genome Database construction on grids and clouds." 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology . IEEE, 2014
Konwar, Kishori M., et al. "MetaPathways v2. 5: quantitative functional, taxonomic and usability improvements." Bioinformatics 31.20 (2015): 3345-3347.
Lawson, Christopher E., et al. "Metabolic network analysis reveals microbial community interactions in anammox granules." Nature communications 8.1 (2017): 1-12.
Hawley, Alyse K., et al. "Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients." Nature communications 8.1 (2017): 1-10
Bhattacharjee, Ananda S., et al. "Whole-community metagenomics in two different anammox configurations: process performance and community structure." Environmental science & technology 51.8 (2017): 4317-4327.
Subedi, Gaurav, et al. "Simultaneous selenate reduction and denitrification by a consortium of enriched mine site bacteria." Chemosphere 183 (2017): 536-545.
White III, Richard Allen, et al. "The complete genome and physiological analysis of the eurythermal Firmicute Exiguobacterium chiriqhucha strain RW2 isolated from a freshwater microbialite, widely adaptable to broad thermal, pH, and salinity ranges." Frontiers in microbiology 9 (2019): 3189.
Thompson, Katharine J., et al. "Nutrient acquisition and the metabolic potential of photoferrotrophic Chlorobi." Frontiers in microbiology 8 (2017): 1212
Morgan-Lang, Connor, et al. "TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler." Bioinformatics 36.18 (2020): 4706-4713.
Hahn, Aria S., et al. "A geographically-diverse collection of 418 human gut microbiome pathway genome databases." Scientific data 4.1 (2017): 1-12.
Hanson, Niels W., et al. "Metabolic pathways for the whole community." BMC genomics 15.1 (2014): 1-14


Research Team

Steven Hallam

Principal Investigator & Professor

Steven manages and guides scientific exploration.

Aria Hahn

Technical Lead, PhD

Aria is passionate about the application of high performance data analytics and believes that combined with intelligent statistical and computational technologies, data hold the answers to many burgeoning questions facing multiple industries today. At EngCyc she oversees the technical planning and team.

Kishori Konwar

Bioinformatician, PhD

Kishori M. Konwar is a Computational Biologist at the Broad Institute of MIT and Harvard where he develops distributed algorithms for large-scale biological problems. His past appointments have been at the MIT Computer Science & Artificial Intelligence Lab (CSAIL) and at the Department of Microbiology at the University of British Columbia. He has many interests including computational biology, marketing, engineering, business intelligence, finance, or any fields that are being transformed by the advent of large-scale data.

Ryan McLaughlin

Bioinformatician, PhD

Ryan designs and executes purpose-made software solutions for exploring complex biological systems.

Connor Morgan-Lang

Bioinformatician, Post Doctoral Fellow

Connor develops software for the analysis of microbiome sequence information. He specializes in genome annotation, microbial ecology, and evolution.

Tomer Altman

Senior Bioinformaticist, PhD

Tomer is a Senior Bioinformaticist at Altman Analytics LLC and the Information Architect for the BCB2 Project. He is interested in applying bioinformatics, computer modeling, and machine learning to better understand microbiomes.

Julia Anstett

PhD Student

Julia is responsible for aggregating multi-omics datasets for the EngCyc project, testing software, and editing the MetaPathways manual. She specializes in analysing single-cell and metagenomic datasets.

Aditi Nagaraj Nallan

PhD Student

Aditi is working on building microbial metabolic models using pathway/genome databases for anaerobic and aerobic wastewater treatment processes.

Tony Liu

PhD Student

Tony is exploring methods to increase the completeness of metabolic models by leveraging protein structure prediction.


Contact Us

Prof. Steven Hallam