ReddRadar
Agent skill
bio-proteomics-quantification
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-proteomics-quantification
SKILL.md
Version Compatibility
Reference examples tested with: MSnbase 2.28+, numpy 1.26+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Protein Quantification
"Quantify proteins from my mass spec data" → Extract protein abundances from MS data using label-free (LFQ, spectral counting), isobaric labeling (TMT, iTRAQ), or metabolic labeling (SILAC) approaches.
- R:
MSstats::dataProcess()for feature-to-protein summarization - Python:
pandasfor MaxLFQ-style normalization and ratio calculation - R:
MSnbasefor isobaric tag reporter ion extraction
Label-Free Quantification (LFQ)
Intensity-Based (MaxLFQ Algorithm)
import pandas as pd
import numpy as np
def maxlfq_normalize(intensities):
'''Simplified MaxLFQ normalization'''
log_int = np.log2(intensities.replace(0, np.nan))
# Median centering per sample
sample_medians = log_int.median(axis=0)
global_median = sample_medians.median()
normalized = log_int - sample_medians + global_median
return normalized
Spectral Counting
def spectral_count_normalize(counts, total_spectra):
'''Normalized spectral abundance factor (NSAF)'''
# Divide by protein length, then by total
nsaf = counts / total_spectra
return nsaf / nsaf.sum()
TMT/iTRAQ Quantification
library(MSnbase)
# Load reporter ion data
tmt_data <- readMSnSet('tmt_data.txt')
# Normalize with reference channel
tmt_normalized <- normalize(tmt_data, method = 'center.median')
# Summarize to protein level
protein_data <- combineFeatures(tmt_normalized, groupBy = fData(tmt_data)$protein,
fun = 'median')
Python TMT Processing
def extract_tmt_intensities(spectrum, reporter_mz, tolerance=0.003):
'''Extract TMT reporter ion intensities'''
mz, intensity = spectrum.get_peaks()
tmt_intensities = {}
for channel, target_mz in reporter_mz.items():
mask = np.abs(mz - target_mz) < tolerance
if mask.any():
tmt_intensities[channel] = intensity[mask].max()
else:
tmt_intensities[channel] = 0
return tmt_intensities
TMT_10PLEX = {'126': 126.127726, '127N': 127.124761, '127C': 127.131081,
'128N': 128.128116, '128C': 128.134436, '129N': 129.131471,
'129C': 129.137790, '130N': 130.134825, '130C': 130.141145,
'131': 131.138180}
SILAC Quantification
def calculate_silac_ratio(heavy_intensity, light_intensity):
'''Calculate SILAC H/L ratio'''
if light_intensity > 0 and heavy_intensity > 0:
return np.log2(heavy_intensity / light_intensity)
return np.nan
# Typical mass shifts
SILAC_SHIFTS = {
'Arg10': 10.008269, # 13C6 15N4 Arginine
'Lys8': 8.014199, # 13C6 15N2 Lysine
'Arg6': 6.020129, # 13C6 Arginine
'Lys6': 6.020129 # 13C6 Lysine
}
MSstats Workflow (R)
Goal: Convert MaxQuant output into normalized protein-level abundance estimates using MSstats feature-to-protein summarization.
Approach: Reformat MaxQuant evidence and proteinGroups files into MSstats input format, then apply median equalization normalization with Tukey's median polish for protein-level summarization.
library(MSstats)
# Prepare input from MaxQuant
maxquant_input <- MaxQtoMSstatsFormat(
evidence = read.table('evidence.txt', sep = '\t', header = TRUE),
proteinGroups = read.table('proteinGroups.txt', sep = '\t', header = TRUE),
annotation = read.csv('annotation.csv')
)
# Process and normalize
processed <- dataProcess(maxquant_input, normalization = 'equalizeMedians',
summaryMethod = 'TMP', censoredInt = 'NA')
# Protein-level summary
protein_summary <- quantification(processed)
Related Skills
- data-import - Load MS data before quantification
- differential-abundance - Statistical testing after quantification
- expression-matrix/counts-ingest - Similar matrix handling
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
FreedomIntelligence/OpenClaw-Medical-Skills
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
FreedomIntelligence/OpenClaw-Medical-Skills
chemist-analyst
Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-alignment-io
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
FreedomIntelligence/OpenClaw-Medical-Skills
sleep-analyzer
分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。
FreedomIntelligence/OpenClaw-Medical-Skills
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-hi-c-analysis-matrix-operations
Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.
Didn't find tool you were looking for?