CPE Genomics in Hospital Reservoirs

Explain how the major carbapenemase families differ in mechanism, genetic context, and epidemiological significance.

Identify the roles of insertion sequences, transposons, integrons, and plasmids in carbapenemase-gene mobilisation.

Explain why sinks, drains, toilets, and showers can select for and sustain CPE populations and resistance plasmids.

Distinguish clonal strain transmission from horizontal transfer of a resistance plasmid.

Formulate testable genomic hypotheses linking damp-environment reservoirs, colonisation, and patient infection.

Define the core objects used in bacterial genomics: genome, chromosome, plasmid, sequencing read, reference, contig, assembly, and annotation.

Compare short and long sequencing reads in terms of length, error profile, coverage, and their roles in bacterial genome reconstruction.

Distinguish read quality control, de novo assembly, read mapping, polishing, annotation, and comparative genomics by the question each operation answers.

Recognise the information stored in FASTQ, FASTA, SAM/BAM, GFF, and VCF files and identify which tools consume and produce each format.

Create reproducible genomic software environments with Conda or Mamba and export their specifications.

Verify file integrity, isolate identity, and short-read pairing before analysis.

Explain Phred scores, base quality, adapters, GC content, duplication, read length, and paired-end reads before interpreting a short-read QC report.

Explain what FastQC and MultiQC examine, then run their commands and interpret their inputs, outputs, flags, and limitations.

Decide whether short reads require trimming by connecting QC warnings to their likely effects on downstream analyses.

Explain what fastp changes, then run it and compare the before-and-after read-quality evidence.

Define long-read yield, read-length N50, read quality, genome size, and nominal coverage before running and interpreting NanoPlot.

Explain how contamination or mixed cultures affect reads and assemblies before applying taxonomic and k-mer screening.

Convert read-QC evidence into a beginner-readable pass, caution, or fail decision with a recorded reason.

Explain how an assembler converts overlapping read information into contigs and why repeats and sequencing errors create ambiguity.

Explain the purpose, inputs, outputs, and main assumptions of Shovill or SPAdes before running a short-read assembly command.

Explain why Autocycler requires multiple mostly complete long-read assemblies before generating them with different assemblers and read subsets.

Explain Autocycler compression, clustering, trimming, resolution, and combination in plain language before running the consensus workflow.

Interpret Autocycler cluster metrics and graphs using a step-by-step checklist for automatic acceptance or manual review.

Explain how Polypolish uses aligned short reads to correct a long-read consensus before running and validating the polishing command.

Explain reference mapping, alignments, mapping quality, CIGAR strings, primary and secondary alignments, depth, breadth, and spanning reads before using minimap2 and samtools.

Explain what QUAST and CheckM2 measure before running them and deciding whether an assembly is suitable for downstream analysis.

Explain genome-based species assignment and average nucleotide identity before running GTDB-Tk and skani or FastANI for Enterobacter and Citrobacter.

Resolve disagreements among submitted species labels, genome-based taxonomy, and contamination evidence.

Explain what bacterial genome annotation adds to an assembly before running Bakta and inspecting each main output file.

Explain alleles, loci, sequence types, and typing schemes before running command-line MLST.

Explain how AMR databases match genomic sequences to curated genes before running AMRFinderPlus or ResFinder and interpreting uncertain matches.

Confirm carbapenemase gene alleles by inspecting read coverage and local assembly quality.

Classify carbapenemase genes as plasmid-borne, chromosomally integrated, or unresolved using convergent genomic evidence.

Explain SNPs, core genomes, recombination, phylogenetic trees, branch support, and pairwise genomic distance as measures of isolate relatedness.

Choose among core-SNP, core-genome, and gene-content approaches for a defined relatedness question.

Explain the difference between reference-free core-genome alignment and reference-based core-SNP calling before running Panaroo or Snippy.

Explain why recombination can distort clonal relationships before running and interpreting Gubbins.

Explain what a maximum-likelihood phylogeny represents before running IQ-TREE and interpreting its model and branch-support outputs.

Explain what a SNP-distance matrix represents before running snp-dists and checking the resulting table.

Evaluate genomic-distance distributions before choosing operational cluster criteria.

Define candidate isolate clusters by combining genomic relatedness with epidemiological context rather than relying on a universal cutoff.

Explain how hospital, ward, room, outlet, patient, niche, and date variables can represent spatial and temporal relationships without implying transmission.

Explain the question answered by a sampling-effort plot before building one with R and ggplot2 commands.

Explain how metadata are matched to tree tips before annotating a phylogeny with ggtree commands.

Explain how an epidemiological timeline is constructed before generating aligned environmental, colonisation, and infection timelines in R.

Evaluate whether observed time and location overlaps make proposed reservoir-to-patient links plausible.

Explain nodes, edges, weights, and network layouts before building a genomic-spatiotemporal network with igraph.

Identify sampling bias, patient-movement gaps, and unsampled reservoirs that could confound transmission narratives.

Rank cross-niche candidates for hybrid-genome investigation using transparent genomic and epidemiological criteria.

Explain plasmid modular organisation and the limitations of replicon-based plasmid classification.

Explain the evidence MOB-recon uses to classify plasmid and chromosomal sequences before running and checking its commands.

Distinguish autonomous plasmids from chromosomal integrations and unresolved repeat-linked contigs.

Explain replicons, relaxases, mating systems, oriT sites, and predicted mobility before running MOB-typer and PlasmidFinder.

Explain insertion sequences, transposons, and integrons before running ISEScan, IntegronFinder, and curated searches around carbapenemase genes.

Diagnose plasmid reconstruction ambiguity caused by repeats, cointegrates, or multiple related plasmids.

Build a curated collection that separates complete plasmids, high-confidence partial plasmids, and unresolved sequences.

Explain sequence sketches and Mash distance before running Mash to compare and cluster plasmids.

Explain protein families and presence-and-absence matrices before using MMseqs2 to compare plasmid gene content.

Explain gene order and synteny before running clinker to compare carbapenemase plasmids.

Explain the tracks, rings, identity scales, and reference choices in circular plasmid figures before using pyGenomeViz or BRIG.

Explain structural variants and whole-sequence alignment before using minimap2 or nucmer to compare related plasmids.

Explain reference-mapping breadth, depth, multimapping, and shared-sequence ambiguity before using BWA-MEM2 and samtools to screen for plasmid support.

Distinguish evidence for the same plasmid, a related plasmid backbone, and a shared carbapenemase cassette.

Test whether host-genome and plasmid relationships support clonal spread, plasmid spread, or both.

Identify near-identical plasmids occurring across Enterobacter and Citrobacter lineages, species, or sequence types.

Compare carbapenemase cassette conservation when plasmid backbones differ.

Assess the temporal and spatial opportunity for each proposed cross-niche plasmid-transfer event.

Evaluate whether an event is more consistent with environment-to-patient transfer, patient-to-environment transfer, or an unsampled common source.

Explain why genomic similarity alone rarely proves transfer direction or direct transmission.

Assign transparent confidence grades to proposed horizontal-transfer events.

Explain how host-genome, plasmid, species, niche, time, and location evidence fit together before constructing an HGT evidence table in R.

Define evidence-based gene categories related to adhesion, biofilm formation, metal tolerance, disinfectant tolerance, and stress response.

Explain sequence homology, identity, coverage, curated databases, and false-positive matches before screening for biofilm and persistence genes with BLAST or DIAMOND.

Distinguish core lineage-associated biofilm genes from accessory or mobile genes.

Determine whether selected persistence genes are chromosomal, plasmid-borne, or located on other mobile elements.

Explain bacterial population structure and confounding before using pyseer to test whether genes are associated with environmental niches.

Interpret genomic detection of a biofilm-associated gene as functional potential rather than proof of phenotype.

Integrate biofilm and stress-tolerance findings into cautious hypotheses of damp-environment persistence.

Create an isolate-level audit table connecting read QC, assembly QC, taxonomic assignment, typing, and plasmid confidence.

Perform sensitivity analyses for genomic-distance, plasmid-similarity, and mapping thresholds.

Triangulate host-genome, plasmid, spatial, temporal, and niche evidence in selected case studies.

Design a coherent figure set combining cohort summaries, phylogenies, timelines, networks, and plasmid comparisons.

Write reproducible methods for the short-read, hybrid-assembly, population-genomic, and plasmid analyses.

Report results using language calibrated to the strength and directionality of the evidence.

Explain the effects of incomplete sampling, assembly uncertainty, and metadata limitations on the study conclusions.

Explain samples, rules, inputs, outputs, dependencies, and workflow graphs before implementing a small pipeline with Snakemake or Nextflow.