Bio Tools

biotender-max/bio-tools

Biology research tools reference. Always available inside agent containers.

4k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

132

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill bio-tools

What comes with it

9 085 bytes besides the instruction

templates/pymol_render_template.py

templates/qc_summary_plot_template.py

templates/volcano_plot_template.py

The instruction itself

15 sections, as written by the author

Bio Tools Reference

You are running inside a BioClaw container with the following biology tools pre-installed.

Layout: Runnable plot/PyMOL scripts live under templates/ (synced to /home/node/.claude/skills/bio-tools/templates/).

Quick Reference

Sequence Search

# Nucleotide BLAST
blastn -query input.fa -subject ref.fa -outfmt 6 -evalue 1e-5

# Protein BLAST
blastp -query protein.fa -subject ref_protein.fa -outfmt 6

# Translate then search
blastx -query nucleotide.fa -subject protein_db.fa -outfmt 6

Read Alignment

# Index reference
bwa index reference.fa

# Align short reads
bwa mem reference.fa reads_R1.fq reads_R2.fq > aligned.sam

# Long reads
minimap2 -a reference.fa long_reads.fq > aligned.sam

# SAM to sorted BAM
samtools view -bS aligned.sam | samtools sort -o sorted.bam
samtools index sorted.bam

Quality Control

# FastQC report
fastqc reads.fq -o qc_output/

# FASTA/FASTQ stats
seqtk comp reads.fq | head
seqtk size reads.fq

Genome Arithmetic

# Intersect two BED files
bedtools intersect -a regions.bed -b features.bed

# Coverage
bedtools coverage -a regions.bed -b aligned.bam

# Get FASTA from BED regions
bedtools getfasta -fi reference.fa -bed regions.bed

Python Quick Recipes

# Read FASTA/FASTQ
from Bio import SeqIO
for record in SeqIO.parse("input.fa", "fasta"):
    print(record.id, len(record.seq))

# Fetch from NCBI
from Bio import Entrez
Entrez.email = "[email protected]"
handle = Entrez.efetch(db="nucleotide", id="NM_000546", rettype="fasta")
record = SeqIO.read(handle, "fasta")

# Differential expression
from pydeseq2 import DeseqDataSet, DeseqStats
dds = DeseqDataSet(counts=count_matrix, metadata=metadata, design="~condition")
dds.deseq2()
stat_res = DeseqStats(dds, contrast=["condition", "treated", "untreated"])
stat_res.summary()

# Single-cell RNA-seq
import scanpy as sc
adata = sc.read_h5ad("data.h5ad")
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.tl.pca(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)

# Molecular structures
from rdkit import Chem
from rdkit.Chem import Descriptors
mol = Chem.MolFromSmiles("CC(=O)OC1=CC=CC=C1C(=O)O")  # Aspirin
print(f"MW: {Descriptors.MolWt(mol):.1f}")
print(f"LogP: {Descriptors.MolLogP(mol):.2f}")

Important Notes

For remote BLAST against NCBI, use Bio.Blast.NCBIWWW.qblast() — this sends the query over the network
For large files, prefer streaming with SeqIO.parse() over SeqIO.read()
Plots: Save to /workspace/group/plot.png with dpi=150, bbox_inches="tight". For publication-ready figures, use cnsplots or pyGenomeTracks (see below).
Write output files to /workspace/group/ so the user can access them
Versioning: When re-running analysis, save to output/YYYY-MM-DD/ to avoid overwriting; update _latest.md with paths to newest outputs

Reusable Figure Templates

Prefer these built-in scripts when creating common BioClaw figures, instead of writing one-off plotting code from scratch.

Volcano Plot Template

Path:

/home/node/.claude/skills/bio-tools/templates/volcano_plot_template.py

Example:

python /home/node/.claude/skills/bio-tools/templates/volcano_plot_template.py \
  --input /workspace/group/counts.csv \
  --output /workspace/group/volcano_plot.png \
  --title "Differential Expression Volcano Plot"

Expected columns by default: gene, log2FC, pvalue

QC Summary Plot Template

Path:

/home/node/.claude/skills/bio-tools/templates/qc_summary_plot_template.py

Example:

python /home/node/.claude/skills/bio-tools/templates/qc_summary_plot_template.py \
  --input /workspace/group/qc_metrics.csv \
  --output /workspace/group/qc_summary.png \
  --title "Sequencing QC Summary"

Expected sample column by default: sample

Useful metric columns: total_reads, q30_pct, gc_pct, duplication_pct

PyMOL Render Template

Path:

/home/node/.claude/skills/bio-tools/templates/pymol_render_template.py

Examples:

python /home/node/.claude/skills/bio-tools/templates/pymol_render_template.py \
  --input 1M17 \
  --output /workspace/group/1m17_render.png \
  --highlight-selection "resn AQ4"

python /home/node/.claude/skills/bio-tools/templates/pymol_render_template.py \
  --input /workspace/group/structure.pdb \
  --output /workspace/group/structure_render.png \
  --style cartoon

Inline Plot Snippets (Heatmap, PCA, Bar)

When the built-in scripts don't fit, use these patterns. Save to /workspace/group/<name>.png.

Heatmap (rows=genes, columns=samples):

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("/workspace/group/expression.csv", index_col=0)
sns.heatmap(np.log1p(df).iloc[:50], cmap='RdBu_r', center=0)
plt.savefig("/workspace/group/heatmap.png", dpi=150, bbox_inches="tight")

PCA scatter (columns: PC1, PC2, condition):

import pandas as pd
import matplotlib.pyplot as plt
coords = pd.read_csv("/workspace/group/pca_coords.csv")
for c in coords['condition'].unique():
    sub = coords[coords['condition'] == c]
    plt.scatter(sub['PC1'], sub['PC2'], label=c)
plt.legend()
plt.savefig("/workspace/group/pca.png", dpi=150, bbox_inches="tight")

Bar plot (columns: gene, count):

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("/workspace/group/top_genes.csv").head(20).sort_values('count', ascending=True)
plt.barh(df['gene'], df['count'])
plt.savefig("/workspace/group/barplot.png", dpi=150, bbox_inches="tight")

Publication-Ready Plots (cnsplots)

cnsplots provides Cell/Nature/Science journal-style figures. Use for volcano, bar, box, violin, heatmap, etc.

import cnsplots as cns
import pandas as pd
import numpy as np

# Volcano plot (columns: gene, log2FC, pvalue or padj)
df = pd.read_csv("/workspace/group/counts.csv")
df["-log10(p)"] = -np.log10(df["pvalue"].clip(lower=1e-300))  # or use padj
cns.figure(height=200, width=200)
cns.volcanoplot(data=df, x="log2FC", y="-log10(p)", symbol="gene")
cns.savefig("/workspace/group/volcano_cns.png")

# Boxplot with Mann-Whitney test
cns.figure(150, 150)
cns.boxplot(data=df, x="group", y="value", pairs="all")
cns.savefig("/workspace/group/boxplot.png")

# Heatmap from AnnData (single-cell)
import scanpy as sc
adata = sc.read_h5ad("/workspace/group/data.h5ad")
cns.figure(200, 200)
cns.heatmapplot(adata, row_cluster=True, col_cluster=True, cmap="bwr")
cns.savefig("/workspace/group/heatmap_cns.png")

See cnsplots docs for more: violin, scatter, survival, ROC, GSEA, etc.

Genome Browser Tracks (pyGenomeTracks)

pyGenomeTracks plots genome browser tracks (BED, BigWig, GTF, etc.). BEDTools must be installed (already in container).

# 1. Create config from your files
make_tracks_file --trackFiles /workspace/group/peaks.bed /workspace/group/coverage.bw -o /workspace/group/tracks.ini

# 2. Plot a region (chr:start-end)
pyGenomeTracks --tracks /workspace/group/tracks.ini --region chr1:1000000-4000000 -o /workspace/group/genome_tracks.png --dpi 150

Supported file types: .bed, .bw (bigwig), .gtf, .gff, .arcs, .links. Edit tracks.ini to adjust track colors, heights, titles.

How to use it

Copy the folder

Take biotender-max/bio-tools from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.