총 67개의 글
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
An honest, end-to-end guide to using AI across the full DIA-NN proteomics pipeline — from report.tsv quantification and downstream statistics through figure interpretation, Discussion writing, and a manuscript draft. Includes real prompts, where AI fails (with caught examples), verification checklists, and 2026 journal disclosure requirements.
A working record of reproducing the Park et al. 2026 cross-species extracellular matrix proteomics workflow (Porcine esophagus EEM vs Mouse Matrigel) — including three simulation iteration failures (circular logic, pseudocount artifact, gene-symbol vs Ensembl BioMart ortholog mapping) and independent PRIDE PXD023694 validation. Practical lessons for MaxQuant + riBAQ + DEP analysis.
Porcine 식도 EEM과 Mouse Matrigel을 비교하는 cross-species 프로테오믹스 분석 — Park et al. 2026 방법론을 재현하던 중 v1 시뮬레이션의 순환 논리, v2의 pseudocount artifact, BioMart 1:1 ortholog 검증 단계까지의 실제 워크플로 기록. PRIDE PXD023694 실데이터로 독립 검증한 결과까지.
프로테오믹스를 처음 시작하는 연구자와 실무자를 위한 LC-MS/MS 워크플로 완전 가이드. 샘플 준비(FASP/S-Trap), LC 그라디언트, DDA vs DIA, TMT vs LFQ 정량 비교, MaxQuant/FragPipe/DIA-NN 파이프라인 선택, 통계 분석 함정까지 2026년 표준 워크플로 정리.
Comprehensive STRING database tutorial covering web interface, R STRINGdb package, confidence scoring, evidence channels, network visualization, and integration with Cytoscape and downstream functional analysis.
End-to-end practical guide to scRNA-seq analysis using Seurat and Scanpy. Covers QC, normalization, batch correction, clustering, marker gene identification, cell type annotation, and trajectory analysis with code examples.
In-depth comparison of the three standard RNA-seq differential expression tools: DESeq2, edgeR, and limma-voom. Covers statistical models, when to use each, performance benchmarks, and a complete decision framework with R code examples.
Complete guide to TF activity inference using DoRothEA, viper, SCENIC, and ChEA3. Learn why TF expression alone is misleading, how to integrate PPI Hub + Pathway + TF results into validated biomarker candidates, and the REMARK/TRIPOD reporting standards.
Comprehensive guide to Gene Ontology and Pathway enrichment analysis. Compare clusterProfiler, topGO, g:Profiler, fgsea, and learn to avoid the background gene set trap that invalidates most analyses. Includes ORA vs GSEA decision framework.
Complete practical guide to building Protein-Protein Interaction networks and identifying Hub proteins. Compare STRING, BioGRID, IntAct databases, learn centrality metrics (Degree, Betweenness, MCC), and avoid common analysis pitfalls.
DoRothEA, viper, SCENIC, ChEA3로 TF 활성을 추론하고, PPI/Pathway/TF 결과를 통합해 바이오마커 후보를 발굴하는 실전 워크플로.
clusterProfiler, topGO, fgsea, MSigDB로 GO와 Pathway Enrichment를 수행하는 실전 워크플로. ORA vs GSEA 선택 기준과 background 함정.
STRING부터 Cytoscape, R igraph까지 — DEG 리스트를 받았을 때 가장 먼저 그려야 할 것. centrality 지표 비교와 흔한 함정.
Complete biomarker discovery and validation workflow — biomarker discovery methods (proteomics, metabolomics, genomics), study design, statistical pitfalls, regulatory pathway (FDA/CE-IVD/MFDS), and validation steps that separate publishable findings from clinically useful tests. Includes REMARK/TRIPOD checklists and real failure case studies.
Step-by-step DIA-NN tutorial covering installation, spectral library generation, parameter optimization, and downstream analysis. Common errors and practical tips from two to three years of using DIA-NN — corrected in October 2026 against a real DIA-NN 2.3.2 run.
After 8 months running the same samples through both MaxQuant and DIA-NN, here are the real results: speed, protein IDs, quantification quality, memory usage, and which tool wins for which experiment type.
A frank look at where proteomics biomarker discovery stands in 2026 — the genuine advances from AI/ML integration, the persistent failure modes that have plagued the field for 20 years, and the real-world workflows that are starting to produce clinically translatable results.
Should you learn R or Python for bioinformatics? An honest, workflow-based comparison covering RNA-seq, proteomics, machine learning, and visualization. Real code examples, benchmark data, and a clear recommendation based on your goals.
End-to-end RNA-seq analysis tutorial covering quality control, alignment, quantification, differential expression with DESeq2, pathway analysis, and visualization. All code included, all common errors addressed.
글로벌 제약회사부터 국내 바이오벤처까지, 실제 면접에서 나온 질문들과 합격자들의 답변 전략을 완전 공개. 기술면접, 인성면접, 케이스 스터디까지 모든 면접 유형별 준비법을 담았습니다.
R 에러부터 실험 실패까지, 바이오 연구에서 자주 만나는 문제들의 완벽한 해결법을 정리했습니다. 8년간의 실무 경험으로 쌓은 트러블슈팅 노하우와 예방법까지 모든 것을 공개합니다.
DIA-NN, MaxQuant, Perseus부터 최신 AI 도구까지, 실제 사용해본 바이오인포매틱스 소프트웨어들의 성능과 비용을 완전 비교 분석. 예산별 최적 선택 가이드와 무료 대안까지 모든 것을 담았습니다.
바이오 전공자의 현실적인 취업 전략과 고연봉 달성 방법을 실제 데이터와 합격 후기를 바탕으로 완전 정리. 제약회사, 바이오벤처, 연구소별 준비 전략과 면접 노하우까지 모든 것을 담았습니다.
프로테오믹스 분석의 혁신 도구 DIA-NN의 설치부터 결과 해석까지 모든 것을 다룹니다. MaxQuant 대비 장점과 실제 사용 경험을 바탕으로 한 완전 가이드를 제공합니다.
논문 투고용 Figure를 R과 Python으로 만들면서 겪은 시행착오. 저널 요구사항 맞추기, DPI, 폰트, 색상 선택까지 실전 팁을 정리합니다.
Comprehensive guide to missing value imputation in label-free quantitative proteomics. Covers MCAR vs MNAR, MinProb, KNN, BPCA, and minimum/2 methods with R and Python code. Real benchmark results included.
Step-by-step guide to differential expression analysis in proteomics using R. Covers limma, Welch's t-test, Mann-Whitney, ANOVA with Tukey HSD, BH correction, and volcano plots. With real code and statistical diagnostics.
NGS 데이터 전처리에서 FastQC 리포트만 보고 넘어가면 놓치는 것들. 실제 분석에서 겪은 전처리 함정과 해결법을 공유합니다.
DE 분석 결과를 LLM이 해석하여 리포트를 자동 생성하려 했다. Ollama qwen3:30b의 환각 — 없는 단백질 생성, 수치 날조, 가짜 논문 인용. anti-hallucination 프롬프트와 템플릿 기반 렌더링으로 환각 0%를 달성한 과정.
BioAI Market — 웹 기반 프로테오믹스 분석 플랫폼의 기술적 구현기. QC부터 DE, Pathway 분석까지 파이프라인 설계, ANOVA 벡터화로 215초→5초 최적화, limma 통합, 멀티그룹 UI 구현 경험을 공유한다.
바이오인포매틱스 분야에서 실제로 취업하고 일하면서 느낀, 진짜 필요한 스킬과 과대평가된 스킬을 솔직하게 정리합니다.
Explore clinical proteomics applications in diagnostics, drug development, and precision medicine. Learn how protein analysis is transforming patient care in 2026.
In-depth review of DIA-NN proteomics software. Learn about its features, performance benchmarks, library-free analysis, and how to use it for DIA mass spectrometry data.
Complete MaxQuant tutorial for beginners. Learn how to set up, configure, and run MaxQuant for proteomics data analysis with step-by-step instructions and tips.
Learn how biomarker discovery works from start to finish. This guide covers the complete pipeline from sample collection to clinical validation using proteomics and multi-omics.
Complete guide to single-cell proteomics in 2026. Learn about SCoPE-MS, plexDIA, mass cytometry, and how single-cell protein analysis is transforming biology.
Explore how machine learning is revolutionizing drug discovery. From target identification to clinical trials, learn how AI accelerates pharmaceutical development in 2026.
Understand the key differences between proteomics and genomics. Learn how these complementary fields study genes vs proteins and why both matter for modern biology.
Step-by-step guide to analyzing mass spectrometry data for proteomics. Learn about raw data processing, database searching, quantification, and statistical analysis.
Discover the best bioinformatics tools in 2026 for genomics, proteomics, and systems biology. From Python libraries to cloud platforms, find the right tools for your research.
Learn what proteomics is in simple terms. Discover how scientists study proteins, why it matters for medicine, and how mass spectrometry powers modern proteomics research.
Bioconductor에 패키지가 2000개 넘는데, 도대체 뭘 써야 하나? 직접 써보면서 정리한 현실적인 패키지 선택 기준을 공유합니다.
RNA-seq 분석을 R로 처음 시도하면서 겪었던 시행착오와 삽질의 기록. FASTQ부터 DESeq2까지, 아무도 안 알려주는 실전 팁을 정리했습니다.
Why Bioinformatics Databases Matter Bioinformatics databases are the backbone of modern biological research. They store, organize, and provide access to the vast more
The Intersection of Machine Learning and Systems Biology Systems biology aims to understand biological systems as integrated wholes. Machine learning provides the more
Understanding Data Acquisition in Mass Spectrometry In bottom-up proteomics, peptides eluting from a liquid chromatography column enter the mass spectrometer, which must more
The Biomarker Discovery Challenge Biomarkers — measurable indicators of biological states or conditions — are fundamental to modern medicine. They guide diagnosis, more
Why R for Genomic Data Analysis? R has established itself as the dominant programming language for genomic data analysis. The Bioconductor project more
Introduction to Network Biology Biological systems function through intricate networks of molecular interactions. Proteins rarely act alone — they form complexes, signal more
What is Proteomics? Proteomics is the large-scale study of proteins — their structures, functions, interactions, and modifications. While the genome provides the more
The Drug Discovery Crisis Developing a new drug takes an average of 10-15 years and costs over $2.6 billion. The success rate more
Why Multi-Omics Integration Matters Modern biological research generates data across multiple molecular layers — genomics, transcriptomics, proteomics, metabolomics, and epigenomics. Each omics more
What is Systems Biology? Systems biology is an interdisciplinary field that focuses on understanding biological systems as integrated and interacting networks of more