prokka-genome-annotation
Prokka Genome Annotation
Overview
Prokka is a command-line pipeline for rapid annotation of prokaryotic genomes (bacteria, archaea, and viruses). It uses a tiered search strategy: protein-coding genes (CDS) are predicted with Prodigal and searched first against a genus-specific database, then RefSeq proteins, then Pfam/TIGRFAMs HMMs. Non-coding RNA genes (rRNA, tRNA, tmRNA) are identified with Barrnap, Aragorn, and Infernal. Prokka processes a single FASTA assembly in minutes and outputs a comprehensive annotation in GFF3, GenBank, FASTA, and tabular formats.
When to Use
- Annotating a newly assembled bacterial or archaeal genome from Illumina, PacBio, or Nanopore assemblies
- Getting functional protein annotations (CDS with product names, EC numbers, GO terms) from a draft or complete genome
- Preparing annotation files for downstream comparative genomics (Roary pan-genome, OrthoFinder)
- Annotating viral or phage genomes when kingdom-specific databases are important
- Performing metagenome-assembled genome (MAG) annotation with the
--metagenomeflag - Parsing annotated outputs in Python with BioPython for downstream sequence or feature analysis
- Use PGAP (NCBI Prokaryotic Genome Annotation Pipeline) instead when the goal is NCBI GenBank submission with standards compliance
- Use Bakta instead for faster annotation with built-in NCBI-compatible outputs and a more regularly updated database
Prerequisites
More from jaechang-hits/sciagent-skills
scientific-brainstorming
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
12snakemake-workflow-engine
Python-based workflow management system for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake automatically determines the execution order and parallelism. Supports local, SLURM, LSF, AWS, and Google Cloud execution via profiles; per-rule conda/Singularity environments. Use for bioinformatics NGS pipelines, ML training workflows, and any multi-step file-processing analysis. Use Nextflow instead for Groovy-based dataflow pipelines or when nf-core ecosystem integration is required.
11esm-protein-language-model
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and protein embeddings. Use when designing novel proteins, extracting sequence representations for downstream ML, or predicting structure from sequence. Local GPU or EvolutionaryScale Forge cloud API. For traditional structure prediction use AlphaFold; for small-molecule cheminformatics use RDKit.
11biopython-sequence-analysis
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
11shap-model-explainability
>-
11archs4-database
Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
11