Documentation

Clovers — Protein-Coding Gene Recognition System v1.1.0

Overview

Clovers (Clustering ORFs Via Ensemble Refinement Strategy) is a high-performance ab initio gene finder that revisits and reformulates the geometric model-based paradigm pioneered by the ZCURVE family with a novel technical route. Clovers delivers state-of-the-art performance, enables the de novo discovery of overlapping genes that are difficult for conventional tools to detect, and runs faster with a reasonable memory footprint.

Input Formats

Clovers accepts the following file formats. Compressed variants (gzip) are also supported.

FASTA
.fa .fasta .fna .ffn
GenBank
.gb .gbk .gbff
EMBL
.embl

Output Formats

  • GFF3 — General Feature Format version 3. Widely supported by genome browsers such as IGV and JBrowse. Contains gene coordinates, scores, and feature attributes.
  • BED — Browser Extensible Data format. Tab-delimited track format (chromosome, start, end, name, score, strand) for genome browsers such as the UCSC Genome Browser.
  • GenBank (gbk) — Standard GenBank flat file format with annotations.
  • GenBank (full) (gbk-full) — Extended GenBank format with complete feature details, including original sequence and all attributes. Compatible with NCBI submission pipelines.

Parameters

Input

FieldDescription
Upload File Upload a genome file in FASTA (.fa, .fasta, .fna, .ffn), GenBank (.gb, .gbk), or EMBL (.embl) format. Gzip compressed files are also supported.
Paste FASTA Sequences Paste a FASTA-formatted nucleotide sequence directly into the textarea. Mutually exclusive with file upload — providing one will disable the other.
Example Loads a built-in example genome for quick testing, pre-populated from static/fa/example.fa.

Output Settings

ParameterDefaultDescription
Output format GFF3 Format of the prediction result. Options: GFF3 (General Feature Format, recommended), BED (Browser Extensible Data), GenBank (gbk), and GenBank (full) (gbk-full, with complete feature details for NCBI submission).
Translation table auto Genetic code used for translating DNA to protein. Options: Auto — automatic detection (default), 1 (Standard), 4 (Mycoplasma), 11 (Bacterial, Archaeal), 15 (Mitochondrial/Protozoan), 16 (Mitochondrial/Vertebrate), 25 (Candidate Division SR1).

Prediction Parameters

ParameterDefaultDescription
Score threshold 0.5 SVM score cutoff for gene prediction. Higher values increase precision (fewer false positives), lower values increase recall (more predictions). Range: 0–1.
Min gene length (bp) 90 bp Minimum length of a putative gene in base pairs. Shorter values may detect small ORFs but increase false positives. Minimum allowed: 30 bp.
Procedure single Prediction strategy (-p). single (default) for isolated single genomes; meta for metagenomic assemblies.
Default topology Linear Topology assumed for sequences without topology annotation (plain FASTA). linear (default) treats every sequence as linear; choose circular (-c) for complete genomes and plasmids.

Training Files (optional)

FieldDescription
SVM training file Upload a custom SVM model file to override the built-in model. Leave empty to use the default.
RBS training file Upload a custom RBS training file to override the built-in RBS model. Leave empty to use the default.

Additional Outputs

OptionDescription
Protein translations (.faa) When enabled, a .faa file containing the translated protein sequences will be generated alongside the main prediction result.
Nucleotide sequences (.fna) When enabled, a .fna file containing the nucleotide sequences of predicted genes will be generated alongside the main prediction result.
ORF-flower map visualization When enabled (default), an ORF-flower map visualization of the predicted genes is generated from the Clovers debug output and shown on the result page.

Command Line Usage

Clovers can be used directly from the command line for batch processing and pipeline integration:

clovers -i input.fasta -o output.gff -f gff -c -g 11 -l 90 -s 0.499

For a complete list of options:

clovers --help

Additional command-line flags include BLAST+ integration (-b, -e, -C), frameshift correction (-F), and GOL-Reporter overlap analysis (-O, -A, -D).

Citation

If Clovers contributes to your research, please cite:

Zhang Z, You Z, Lin Y*, Gao F*. High-performance ab initio gene prediction with Clovers.

Contact

For questions, bug reports, or collaboration inquiries:

Yan Lin
ylin@tju.edu.cn
Co-author & Corresponding author
Feng Gao
fgao@tju.edu.cn
Co-author & Corresponding author