SBM Lab
시스템 생물학 & 바이오인포매틱스 블로그. 프로테오믹스, 바이오마커, 건강과학에 대한 연구 기반 콘텐츠를 제공합니다.
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FragPipe vs MaxQuant, Measured: Same Six Runs, Same Known Answer (9 Minutes vs 62)
I ran FragPipe 24 (MSFragger 4.4.1 + IonQuant) and MaxQuant 2.8.1 on the same six Orbitrap runs of a three-species benchmark with known ratios. FragPipe finished in 8.9 minutes against 61.5, quantified 1.6–2.7× more proteins, and was more accurate on the proteins both tools quantified. What that does and does not mean, and the Linux setup traps.
MaxQuant 2.8 proteinGroups.txt, Scored Against a Known Answer: LFQ vs iBAQ vs Intensity, and What Match-Between-Runs Adds
I ran MaxQuant 2.8.1 on six public Orbitrap runs where human, yeast and E. coli were mixed at known ratios, then scored every intensity column against the right answer. LFQ was far more accurate than raw Intensity but quantified fewer proteins, iBAQ ratios were identical to Intensity, MBR made already-quantified proteins more accurate, and the old Reverse filter silently returns zero rows in 2.8.
After DIA-NN 2.x: From report.parquet and pg_matrix to a Result You Can Publish (210 Public Runs, R)
I ran DIA-NN 2.3.2 on a public 210-run SWATH dataset and worked through the downstream half: which output file to start from, what the protein matrix already filters, what match-between-runs added, how much is missing, and why 210 runs were really n = 70.
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Zero Proteins Passed FDR: How to Read and Report a Null Result (DIA-NN Reanalysis, 70 Placentas)
A real proteomics reanalysis where no protein survived FDR in any comparison. What the p-value histogram shows, what the confidence intervals rule out, the smallest effect the design could detect, and how the same data produce 37 false hits if technical replicates are counted as samples.
A MaxQuant Run Took Me 10+ Hours on a Windows PC — the Checks I Now Do Before Pressing Start
On an ordinary Windows PC, one MaxQuant run took over 10 hours. At that price every wrong setting costs a day. These are the mistakes I made — iBAQ/LFQ left off, FASTA, match-between-runs, unfiltered proteinGroups — and the checks that now come first.
It Almost Worked: Four Ways a Multi-Omics Pipeline Nearly Reported a False Success
A consensus biomarker pipeline on renal cell carcinoma nearly replicated its own results by counting the same 110 patients twice. Then the replication guard turned out to pass when nothing had replicated. A record of four failures that each looked exactly like success.
The Number That Survived a Full Day of Review: What Two Agents Couldn't See
Two AI agents with deliberately different access caught six errors in one working day. A seventh survived both reviews — a p-value with no source anywhere in the run record. The six were caught across a gap; the seventh arrived as prose.
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.