Design e Frontend

Splice-aware RNA-seq aligner producing sorted BAM and splice junction tables. Builds genome index, runs two-pass alignment for better junctions. Outputs sorted BAM, junctions (SJ.out.tab), stats (Log.final.out), optional gene counts. Use Salmon for fast pseudoalignment; STAR when a BAM is needed for variant calling, IGV, or ENCODE pipelines.

Nature figure preparation: resolution (300+ DPI), formats (AI/EPS/TIFF), RGB color, Helvetica/Arial fonts, lowercase panel labels, image integrity requirements.

Decision framework for manual marker-based, automated (CellTypist), and reference-based (popV) cell type annotation in scRNA-seq. Three-tier strategy: Tier 1 manual markers, Tier 2 CellTypist, Tier 3 popV ensemble transfer. Use when planning or troubleshooting annotation.

Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.

Construa, leia, valide e modifique modelos de redes biológicas SBML usando a API Python libSBML, com suporte para SBML Níveis 1-3, reações, espécies, regras e extensão FBC. Interopera com COBRApy, Tellurium/RoadRunner e COPASI, sendo ideal para a construção programática de modelos metabólicos/de sinalização baseados em ODE ou restrições.

Inferir e visualizar a comunicação intercelular a partir de dados de scRNA-seq usando CellChat (R) ou liana (Python). O fluxo de trabalho envolve a construção do CellChat, análise de pares ligante-receptor, expressão gênica, probabilidades de comunicação, sinalização de vias e centralidade da rede, com visualizações e comparações entre condições para dados humanos e de camundongos.

MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.

Guide to interpreting BUSCO completeness statuses: why Duplicated BUSCOs count as complete, parsing output files, computing/comparing completeness across proteomes/genomes, common counting mistakes. Use when running BUSCO QC, comparing assemblies, or reporting completeness. See also: prokka-genome-annotation for annotation workflows feeding BUSCO.

Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.

NEJM figure preparation: resolution (300-1200 DPI), editable vector formats (AI/EPS/SVG), in-house medical illustration policy, and strict image integrity requirements.

Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.

Análise baseada em restrições (COBRA) de modelos metabólicos em escala genômica, incluindo FBA, FVA, knockouts, amostragem de fluxo, envelopes de produção, preenchimento de lacunas e otimização de meios. É utilizada para design de linhagens, identificação de genes essenciais e análise de fluxo, complementando a modelagem cinética com tellurium e a visualização com Escher.