MicroMosaic Elizabeth Deyett, PhD

Case Study

Building Scalable Microbial Genome Analysis Infrastructure

Achieving ~90% reduction in genome analysis time through cloud-native, end-to-end bioinformatics architecture

Context
Environmental biotech building a microbe-first R&D and biobanking program
Pillars
Data, Bioinformatics & Analysis, Data Strategy, Governance & Leadership

Achieving ~90% reduction in genome analysis time through cloud‑native, end‑to‑end bioinformatics architecture

Executive Summary

An early‑stage environmental biotechnology company set out to build a microbe‑first research and biobanking program spanning bacteria and fungi, multiple sequencing technologies, and long‑term strain discovery for bioremediation. With no scientific, data, or computational infrastructure beyond basic enterprise tools and no internal expertise beyond a single scientist, the risk of fragmented, slow, and irreproducible analysis was high. I designed and implemented a cloud‑native, fully automated bioinformatics platform that transformed genome analysis from a manual bottleneck into scalable scientific infrastructure—reducing analysis time by ~90%, enabling the parallel analysis of 100 fungal genomes in a single day, and establishing a durable foundation linking physical biobanks to genomic intelligence.

Context

An early-stage environmental biotechnology company was preparing to build a microbe-first R&D program focused on bioremediation. The long-term vision included metagenomics, whole genome sequencing (WGS), amplicon sequencing, and comparative genomics across both bacterial and fungal organisms. The goal was not only to identify and characterize microbes, but to build a living biobank that tightly linked physical freezer inventory (live isolates) with digital genomic intelligence—so strains with promising functional traits could be efficiently rediscovered, compared, and deployed over time.

At the time, there was no existing data or bioinformatics infrastructure. There was no established workflow, no compute environment, no database standards, and no in-house expertise beyond my role. I was effectively the entire data team, supported by a single part-time contract bioinformatician working asynchronously in a different time zone. Upper management provided no technical guidance or operational support; this system needed to be designed, built, and maintained end-to-end from scratch.

Recognizing that downstream science would fail without upstream standardization, we made a strategic decision to invest early in reproducible, scalable bioinformatics infrastructure before experimental throughput increased.

Problem

Without automation and standardization, microbial genome analysis would have been:

  • Slow and manual – tool-by-tool execution, one genome at a time
  • Error-prone and inconsistent – ad hoc parameters, undocumented steps
  • Impossible to scale – fungal genomes, metagenomes, and long-read data dramatically increase complexity
  • Difficult to reuse – results scattered across folders, not database- or LIMS-ready

In practical terms, analyzing a single genome without a robust pipeline typically takes half a day to a full day of hands-on work. At the scale the company envisioned (dozens to hundreds of genomes across organisms and sequencing technologies), this approach would have required multiple full-time bioinformaticians and still failed to deliver consistent, reproducible outputs.

There was also a larger strategic risk: without standardized identifiers, reports, and metadata, the company would be unable to reliably connect genomic results back to physical isolates in freezers—undermining the entire concept of a functional microbial biobank.

What I Built

I designed and implemented a modular, cloud-native bioinformatics platform centered around four production-grade Nextflow pipelines, capable of handling bacterial and fungal data across multiple sequencing modalities.

Core Architecture

  • Workflow engine: Nextflow (chosen for reproducibility, community support, and rapid development)
  • Compute: AWS Batch for dynamic resource allocation, horizontal/vertical scaling, and cost efficiency
  • Storage: Amazon S3 with standardized folder structures for long-term data integrity and downstream databasing
  • Version control: GitHub for collaborative development and traceability
  • Environments: Conda + Docker to lock tool versions and ensure reproducibility
  • Networking: Full AWS VPC setup, routing, and permissions configured from scratch

Pipelines Built

  1. Whole Genome Sequencing (WGS)

    • Supports bacteria and fungi
    • Handles Illumina short reads, Nanopore long reads, PacBio long reads, and hybrid assemblies

    • Steps:

      • Read QC and cleaning
      • Assembly (technology-aware)
      • Assembly QC
      • Gene calling (Prokka, Prodigal, Augustus)
      • Functional annotation (CAZy, antiSMASH, EggNOG, InterPro, Barrnap, tRNAscan)

  2. Metagenomics

    • Assembly and binning
    • Assembly QC
    • Gene calling and annotation
    • Parallel generation of two complementary HTML reports: (1) an amplicon-like community-level report derived from metagenomic reads, and (2) a MAGs-focused report analogous to the WGS report, designed to summarize, compare, and interpret multiple metagenome-assembled genomes within a single analysis

  3. Amplicon Sequencing

    • DADA2-based workflow wrapped in Nextflow
    • Alpha and beta diversity
    • Taxonomic classification
    • Community composition visualizations

  4. Comparative Genomics

    • Pulls assemblies and annotations directly from S3
    • Works across WGS and metagenome-derived bins

    • Performs:

      • ANI calculations
      • Phylogenetic tree construction
      • MultiQC summaries across strains
      • Annotation-level comparisons

Design Highlights

  • Species-aware logic for fungal vs bacterial differences in QC, taxonomy, gene calling, databases, and resource allocation
  • Config-driven parameter tuning with command-line overrides
  • Fully modular design enabling reuse and extension
  • One-line execution for end users using standardized input files capturing essential sample metadata (e.g., file locations, sample names, organism type), ensuring consistent, auditable, and scalable analysis

Reporting & Data Integration

Each pipeline generated interactive HTML reports with:

  • Circos genome plots
  • Assembly QC statistics
  • Taxonomic classification (reported across multiple classifiers for cross-tool agreement)
  • Annotation summaries with searchable tables
  • Embedded explanatory language describing what each section means

Reports were designed for wet-lab scientists, managers, and external collaborators.

All outputs were integrated into a LIMS/ELN system that also housed a digital freezer inventory. Standardized naming conventions and shared identifiers ensured that physical isolates, genomic data, and analytical results lived in the same system—making future strain selection and comparison trivial from a data architecture standpoint.

Outcome

  • ~90% reduction in analysis time compared to manual execution
  • 100 fungal genomes analyzed in a single day using parallelization and optimized resource management
  • Near hands-off operation once launched

  • Analysis costs on the order of:

    • ~$15 per bacterial genome
    • ~$40 per fungal genome (depending on configuration and sequencing depth)

  • Successfully run not only by me, but by:

    • A collaborating bioinformatician
    • Multiple trained users, including undergraduate researchers and a bench scientist

The system was validated using reference genomes prior to experimental samples, and taxonomic classifications were cross-checked across 2–3 independent tools to ensure accuracy and consistency.

This infrastructure supported grant timelines, was showcased at three conferences, and formed the basis of an invited talk at the Seventh International Symposium on Bioremediation and Environmental Biotechnology.

Why It Mattered

This work transformed genome analysis from a fragile, manual bottleneck into durable scientific infrastructure. It demonstrated that:

  • Small teams can operate at enterprise-scale throughput with the right systems
  • Early investment in standardization prevents years of downstream technical debt
  • Bioinformatics pipelines are not just analytical tools, but organizational memory

Although organizational follow-through ultimately limited downstream scientific impact, the platform itself proved that microbial discovery at scale is achievable, affordable, and reproducible when designed intentionally.

For me personally, building this system reduced burnout, enabled focus on higher-level scientific questions, and reaffirmed the value of systems-level thinking, careful architecture, and clear communication—principles that now define my freelance and consulting work through MicroMosaic.

Tech Stack at a Glance

Workflow & Reproducibility Nextflow · GitHub · Conda · Docker

Cloud & Infrastructure AWS Batch · Amazon EC2 · Amazon S3 · AWS VPC/IAM

Sequencing Modalities Supported Illumina (short‑read) · Nanopore (long‑read) · PacBio (long‑read) · Hybrid assemblies

Core Bioinformatics (Representative Tools) Read QC: FastQC · fastp · Trimmomatic Assembly: SPAdes · metaSPAdes · Flye · Unicycler Gene Calling: Prokka · Prodigal · Augustus Annotation: eggNOG · InterPro · antiSMASH · CAZy · Barrnap · tRNAscan Taxonomy: Sourmash · Centrifuge · GTDB (used in complementary combinations to assess and report cross‑tool agreement)

Reporting & Visualization HTML/Markdown reports · Interactive Plotly‑based visualizations · Searchable tables

Data Integration LIMS / ELN systems · SQL databases (Amazon Redshift) · Standardized metadata & naming conventions

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