SBM Lab
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Can Flux Balance Analysis Predict Antibiotic Synergy? 107,296 Simulations Say No — and What to Do Instead
A companion to two ESKAPE preprints: standard LP-based flux balance analysis structurally cannot detect synergy between essential gene pairs (107,296 simulations across 3 pathogens, zero synergy found). The fix is to stop using FBA as a synergy calculator and use it as a feature generator — partial-inhibition simulations plus ML to curate drug targets, fully open-source.
Can an LLM Run an RNA-seq Analysis on Its Own? Building ARIA, a Decision-Aware Transcriptome Framework
A companion to our preprint: reproducible pipelines run RNA-seq steps reliably, but the decisions between steps still need an expert. ARIA puts an LLM in that reasoning seat across 8 decision points — and on 4 public datasets it recovered paired designs, technical covariates, and known biology, with cross-method agreement r > 0.99.
A MOGONET-Style Multi-Omics Biomarker Pipeline: Why a Near-Random Graph Net Still Earns Its Place
Honest engineering write-up of a MOGONET-style multi-omics consensus biomarker pipeline. On a small synthetic cohort the graph network scores near-random in leak-free cross-validation (AUC 0.53) — yet the 5-evidence consensus puts known markers in 9 of its top 10. Here is the architecture, the real results, and why a weak single model is still a useful voter.
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FragPipe vs MaxQuant — 2026 Real Speed Benchmark (Same Data, Same Output Quality)
Detailed 2026 speed comparison: FragPipe (MSFragger engine) vs MaxQuant on identical DDA datasets, identical FASTA, identical FDR settings. Where FragPipe's 10-50× speedup comes from (fragment ion indexing), where it matches MaxQuant in protein ID counts, and the specific cases where MaxQuant is still better.
limma vs DEqMS for Proteomics — When to Use Which (n=3 to n=20+ Comparison)
Both limma and DEqMS provide moderated t-statistics for proteomics differential expression. limma was built for microarrays; DEqMS adds a proteomics-specific variance prior tied to peptide count. This guide compares them on small-n (n=3) and larger (n=10, n=20) proteomics designs with real recommendations on when DEqMS's extra step is worth it.
Imputing Missing Values in Proteomics — knn vs minDet vs MNAR — What Actually Works
Proteomics datasets are full of NAs, and how you handle them can flip your DEP list. This guide compares the four real-world options — leave NAs alone, k-Nearest Neighbors (knn), minimum detected (minDet), and explicit MNAR (Missing Not At Random) imputation — with practical recommendations for DIA-NN and MaxQuant outputs.
BioMart Pig ↔ Mouse 1:1 Ortholog Mapping for Cross-Species Proteomics (R + Python Tutorial)
Step-by-step tutorial: download the Ensembl BioMart Release 111 Sus scrofa ↔ Mus musculus 1:1 ortholog table and join it to your per-species protein quantification in R (biomaRt) and Python (pandas). Why gene-symbol matching is fragile, how to handle one-to-many and many-to-many orthologs, and how to validate your mapping against UniProt.
Why You Must NOT Merge Species FASTA Databases in Cross-Species Proteomics (Shared Peptide Problem)
In cross-species LC-MS/MS proteomics (Porcine ECM vs Mouse Matrigel, human vs mouse cells, host-pathogen, etc.) merging species FASTA into one search database breaks protein quantification. This is the shared-peptide ambiguity problem. Here's why it happens, how to set up species-separated searches in MaxQuant and DIA-NN, and how to handle peptides that genuinely match both species.
Reanalyzing PRIDE PXD023694 — Matrigel Nuclear Contaminants (EWSR1, RUVBL2) You'll Find in Real Data
A practical reanalysis of PRIDE dataset PXD023694 (Cho lab cross-species ECM) — what proteins actually come out of mouse Matrigel vs porcine ECM, the basement membrane signature (LAMB1/NID1/LAMC1/HSPG2) that replicates Park et al. 2026, and the contamination patterns (Matrigel nuclear proteins EWSR1/RUVBL2, porcine smooth muscle remnants TPM4/MYL9) that simulations miss but real data shows.