CRESSENT

A modular bioinformatics toolkit for annotation and analysis of CRESS DNA viruses.

Overview

CRESSENT is an open-source, modular bioinformatics toolkit for the discovery, annotation, and ecological analysis of circular Rep-encoding single-stranded (CRESS) DNA viruses from metagenomic data. It combines sequence annotation, motif detection, recombination analysis, and phylogenetics in one reproducible, installable package.

Scientific or practical problem

CRESS DNA viruses (ssDNA viruses) are abundant and ecologically important across environments, but consistent, reproducible annotation of their genomes — origin-of-replication motifs, stem-loops, iterons, recombination signals — has lacked a dedicated, standardized toolkit. Researchers were largely relying on ad hoc scripts.

My role

Lead developer. I designed and implemented CRESSENT end to end: core Python and R modules, the command-line interface, the analysis modules described below, the test suite, documentation, packaging, and distribution. I also led the associated manuscript.

Dataset or data type

Metagenome-assembled CRESS DNA virus genomes and reference sequences (public/community datasets used for benchmarking).

Methods and technologies

• Python and R implementation, packaged as a command-line tool
• Motif analysis (origin-of-replication motifs)
• Recombination analysis
• Stem-loop detection
• Iteron analysis
• Sequence logo generation
• Automated test suite and continuous integration
• Packaging and distribution via PyPI and Bioconda
• Benchmarking against existing methods

Workflow diagram

flowchart LR
  A[CRESS virus genomes] --> B[Motif & structural analysis]
  B --> C[Recombination detection]
  C --> D[Annotated genome + sequence logos]
  D --> E[Ecological / comparative analysis]

Major results or outputs

An installable, tested, documented toolkit released publicly on PyPI and Bioconda, described in a peer-reviewed methods paper: Pavan, R.R., Sullivan, M.B. and Tisza, M. (2026). CRESSENT: a bioinformatics toolkit to explore and improve ssDNA virus annotation. Microbial Genomics, 12:001632.

Challenges and decisions

Balancing biological accuracy (motif/structural detection in short, variable ssDNA genomes) against usability for non-specialist users — this shaped the decision to provide both a scriptable API and a simple CLI, and to invest early in automated testing so annotation logic changes couldn’t silently break results.

Publications

Pavan, R.R., Sullivan, M.B. and Tisza, M., 2026. CRESSENT: a bioinformatics toolkit to explore and improve ssDNA virus annotation. Microbial Genomics, 12:001632. https://doi.org/10.1099/mgen.0.001632

Code and documentation

• Repository: github.com/ricrocha82/cressent
• Package: PyPI and Bioconda (add direct package links when confirmed)

Collaborators

Developed in collaboration with the Sullivan Lab (The Ohio State University) and colleagues; manuscript co-authored with M.B. Sullivan and M. Tisza.

Current status

Published and actively maintained.

Limitations or confidentiality note

None — this is fully public, published work.