Skip to main content Startseite Ersteller biotender-max awesome-bio-agent-skills bio-workflows-outbreak-pipeline
bio-workflows-outbreak-pipeline End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains.
Zur Installation springen Skills Marktplatz Entdecken und erkunden Sie KI-Skills, die von der Community erstellt wurden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Prompt-Details anzeigen Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill bio-workflows-outbreak-pipelineDer Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.
Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.
ZIP herunterladen Herunterladen... Mehr aus diesem Repository Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow it.
bio-comparative-genomics-ortholog-inference Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.
eqtl-catalogue-region-fetch Fetch a region of cis-eQTL summary statistics from EBI eQTL Catalogue v7+
via tabix-on-FTP. Use when an agent needs eQTL beta / SE / p-value for
every variant in a window around a gene's TSS for one specific dataset
(study × tissue × quantification method). Input: dataset_id, chromosome,
start, end, optional molecular_trait_id. Output: harmonised TSV slice.
Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name bio-workflows-outbreak-pipeline description End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains. tool_type mixed primary_tool mlst workflow true depends_on ["epidemiological-genomics/pathogen-typing","epidemiological-genomics/amr-surveillance","epidemiological-genomics/phylodynamics","epidemiological-genomics/transmission-inference","epidemiological-genomics/variant-surveillance"] qc_checkpoints [{"after_typing":"Valid ST assigned, cgMLST distance matrix computed"},{"after_amr":"AMR genes identified with >90% identity"},{"after_phylodynamics":"Root-to-tip R2 >0.5, clock rate plausible"},{"after_transmission":"Transmission pairs consistent with epi data"}]
Version Compatibility
Reference examples tested with: AMRFinderPlus 3.12+, BioPython 1.83+, IQ-TREE 2.2+, Nextclade 3.3+, TreeTime 0.11+, matplotlib 3.8+, mlst 2.23+, pandas 2.2+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Outbreak Pipeline
"Characterize a pathogen outbreak from my isolate sequences" → Orchestrate MLST typing, SNP phylogeny, TreeTime time-scaled tree construction, TransPhylo transmission inference, AMR profiling, and variant surveillance for genomic epidemiology.
Complete workflow for genomic epidemiology: from pathogen isolates to transmission networks and outbreak characterization.
Workflow Overview
Pathogen Isolate Genomes (FASTA/FASTQ)
|
v
+---------+---------+
| |
v v
[1a. MLST Typing] [1b. AMR Detection] <-- Parallel execution
| |
+--------+----------+
|
v
[2. Core Genome Alignment] --> snippy / ParSNP
|
v
[3. Phylodynamics] --> TreeTime / BEAST2
|
v
[4. Transmission Inference] --> TransPhylo
|
v
Transmission Network + R0 Estimates + Timeline
Prerequisites
conda install -c bioconda mlst abricate snippy iqtree fasttree
pip install treetime transphylo biopython pandas matplotlib
Rscript -e "install.packages('TransPhylo')"
Primary Path: Bacterial Outbreak Investigation
Step 1a: MLST Typing (Parallel)
#!/bin/bash
ISOLATES="isolate1.fasta isolate2.fasta isolate3.fasta"
OUTDIR="outbreak_results"
mkdir -p ${OUTDIR} /{mlst,amr,alignment,phylo,transmission}
echo "=== MLST Typing ==="
for
in
$ISOLATES
do
basename
$fasta
$fasta
${OUTDIR}
${sample}
done
cat
${OUTDIR}
${OUTDIR}
echo
"MLST complete: ${OUTDIR} /mlst/all_mlst.tsv"
Step 1b: AMR Detection (Parallel) echo "=== AMR Detection ==="
for fasta in $ISOLATES ; do
sample=$(basename $fasta .fasta)
abricate --db ncbi $fasta > ${OUTDIR} /amr/${sample} .amr.tsv
done
abricate --summary ${OUTDIR} /amr/*.amr.tsv > ${OUTDIR} /amr/amr_summary.tsv
echo "AMR summary: ${OUTDIR} /amr/amr_summary.tsv"
Step 2: Core Genome Alignment echo "=== Core Genome Alignment ==="
REFERENCE="reference.gbk"
for fasta in $ISOLATES ; do
sample=$(basename $fasta .fasta)
snippy --outdir ${OUTDIR} /alignment/snippy_${sample} \
--ref $REFERENCE \
--ctgs $fasta \
--cpus 8
done
snippy-core --ref $REFERENCE ${OUTDIR} /alignment/snippy_*
mv core.* ${OUTDIR} /alignment/
echo "Core alignment: ${OUTDIR} /alignment/core.aln"
Step 3: Phylodynamics with TreeTime import subprocess
from Bio import Phylo, AlignIO
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
outdir = Path('outbreak_results' )
subprocess.run([
'iqtree2' , '-s' , str (outdir / 'alignment/core.aln' ),
'-m' , 'GTR+G' , '-B' , '1000' , '-bnni' , '-T' , 'AUTO' ,
'--prefix' , str (outdir / 'phylo/outbreak' )
], check=True )
metadata = pd.DataFrame({
'name' : ['isolate1' , 'isolate2' , 'isolate3' , 'isolate4' , 'isolate5' ],
'date' : ['2024-01-15' , '2024-01-22' , '2024-02-01' , '2024-02-10' , '2024-02-15' ]
})
metadata.to_csv(outdir / 'phylo/metadata.tsv' , sep='\t' , index=False )
subprocess.run([
'treetime' ,
'--tree' , str (outdir / 'phylo/outbreak.treefile' ),
'--aln' , str (outdir / 'alignment/core.aln' ),
'--dates' , str (outdir / 'phylo/metadata.tsv' ),
'--outdir' , str (outdir / 'phylo/treetime_output' ),
'--coalescent' , 'skyline' ,
'--clock-filter' , '3'
], check=True )
print ('TreeTime output:' , outdir / 'phylo/treetime_output' )
Step 4: Transmission Inference with TransPhylo library( TransPhylo)
library( ape)
tree <- read.nexus( "outbreak_results/phylo/treetime_output/timetree.nexus" )
dateT <- 2024.2
w_shape <- 2
w_scale <- 7/ 365
res <- inferTTree( tree, dateT = dateT,
w.shape = w_shape, w.scale = w_scale,
mcmcIterations = 10000 ,
startNeg = 1 , startPi = 0.5 )
ttree <- extractTTree( res)
medTTree <- medTTree( res)
pdf( "outbreak_results/transmission/transmission_tree.pdf" , width= 10 , height= 8 )
plotTTree( medTTree)
dev.off( )
wiw <- computeMatWIW( res)
write.csv( wiw, "outbreak_results/transmission/who_infected_whom.csv" )
R0 <- getOffspringMulti( res)
cat( "R0 estimate:" , mean( R0) , "(95% CI:" , quantile( R0, 0.025 ) , "-" , quantile( R0, 0.975 ) , ")\n" )
Python Alternative: TransPhylo via rpy2 import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
import pandas as pd
from pathlib import Path
pandas2ri.activate()
transphylo = importr('TransPhylo' )
ape = importr('ape' )
outdir = Path('outbreak_results' )
tree = ape.read_nexus(str (outdir / 'phylo/treetime_output/timetree.nexus' ))
date_t = 2024.2
w_shape = 2
w_scale = 7 /365
res = transphylo.inferTTree(tree, dateT=date_t, w_shape=w_shape, w_scale=w_scale,
mcmcIterations=10000 , startNeg=1 , startPi=0.5 )
med_tree = transphylo.medTTree(res)
ro.r(f'''
pdf("{outdir} /transmission/transmission_tree.pdf", width=10, height=8)
plotTTree(medTTree({res} ))
dev.off()
''' )
print (f'Transmission tree saved to {outdir} /transmission/' )
Visualization: Outbreak Timeline import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
metadata = pd.read_csv('outbreak_results/phylo/metadata.tsv' , sep='\t' )
metadata['date' ] = pd.to_datetime(metadata['date' ])
mlst = pd.read_csv('outbreak_results/mlst/all_mlst.tsv' , sep='\t' , header=None ,
names=['file' , 'scheme' , 'ST' ] + [f'locus{i} ' for i in range (7 )])
mlst['sample' ] = mlst['file' ].apply(lambda x: x.split('/' )[-1 ].replace('.fasta' , '' ))
amr = pd.read_csv('outbreak_results/amr/amr_summary.tsv' , sep='\t' )
combined = metadata.merge(mlst[['sample' , 'ST' ]], left_on='name' , right_on='sample' )
fig, ax = plt.subplots(figsize=(12 , 6 ))
colors = {'ST11' : 'red' , 'ST258' : 'blue' , 'ST307' : 'green' }
for st in combined['ST' ].unique():
subset = combined[combined['ST' ] == st]
ax.scatter(subset['date' ], [1 ]*len (subset), label=f'ST{st} ' ,
s=100 , c=colors.get(f'ST{st} ' , 'gray' ), alpha=0.7 )
ax.set_xlabel('Date' )
ax.set_ylabel('' )
ax.set_title('Outbreak Timeline by Sequence Type' )
ax.legend()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d' ))
plt.xticks(rotation=45 )
plt.tight_layout()
plt.savefig('outbreak_results/outbreak_timeline.pdf' )
Parameter Recommendations Step Parameter Value Rationale snippy --mincov 10 Minimum coverage for variant call IQ-TREE -m GTR+G General time-reversible model TreeTime --clock-filter 3 Remove temporal outliers >3 IQR TransPhylo w.shape, w.scale 2, 7/365 Generation time ~7 days for many bacteria TransPhylo mcmcIterations 10000+ Ensure convergence
Troubleshooting Issue Likely Cause Solution No MLST match Novel ST or poor assembly Check assembly quality, submit novel ST Poor temporal signal Insufficient sampling, recombination Remove recombination with Gubbins, check dates TreeTime clock-filter removes many Wrong root, contamination Re-root tree, check sample quality TransPhylo non-convergence Wrong generation time Adjust w.shape/w.scale, increase iterations Missing AMR genes Database mismatch Try multiple databases (ncbi, card, resfinder)
Output Files File Description mlst/all_mlst.tsvSequence types for all isolates amr/amr_summary.tsvAMR gene presence/absence matrix alignment/core.alnCore genome SNP alignment phylo/outbreak.treefileML phylogenetic tree phylo/treetime_output/Dated tree and molecular clock transmission/transmission_tree.pdfInferred transmission network transmission/who_infected_whom.csvTransmission probability matrix
Related Skills
database-access/sra-data - Download outbreak FASTQ from SRA / ENA
database-access/ncbi-datasets-cli - Bulk-pull pathogen reference genomes (e.g. datasets download virus)
epidemiological-genomics/pathogen-typing - MLST and cgMLST details
epidemiological-genomics/amr-surveillance - AMRFinderPlus, ResFinder
epidemiological-genomics/phylodynamics - TreeTime, BEAST2 parameters
epidemiological-genomics/transmission-inference - TransPhylo configuration
epidemiological-genomics/variant-surveillance - Nextclade for viral outbreaks
phylogenetics/modern-tree-inference - IQ-TREE2 model selection
