Results for “rna”

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Use when converting ViennaRNA-style sequence-plus-dot-bracket records on stdin into RNA connectivity-table output.

MaxFuse parameter tuning for datasets with large protein:RNA cell ratios (>100:1)

Use when searching DNA or RNA queries against nucleotide sequence databases with HMMER's nucleotide homology search engine.

Use when aligning RNA-seq reads to a reference genome using graph-based indexing for fast and sensitive spliced alignment.

Use when aligning RNA-seq reads to a reference genome with junction detection, including exon-exon junctions and gene fusions.

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.

Use when converting RNA connectivity-table (`.ct`) files into extended FASTA with dot-bracket structures, optionally removing pseudoknots or modified bases.

Use when aligning long FASTQ reads to a reference genome with Subread's long-read aligner, optionally in RNA-seq mode.

Use when aligning spliced RNA-seq reads to a reference genome, generating genome indices, or performing splice-aware alignment for transcriptome analysis.

In patients with primary aldosteronism receiving mineralocorticoid receptor antagonist therapy, the guideline recommends titrating the MRA dose upward to raise renin when blood pressure remains uncontrolled and renin is suppressed. Consider this step when hypertension is not at goal despite MRA therapy and plasma renin activity (or direct renin concentration) is low.

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

Use when simulating stochastic folding kinetics of single-stranded nucleic acids, computing first passage times between structures, or analyzing RNA/DNA folding trajectories.

李娜（网球 / 体育）认知与表达框架（压缩蒸馏）：个性球员叙事、怼媒体金句、职业化独立 触发：法网、直率采访 等。非替本人编造

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

Recommends mineralocorticoid receptor antagonists for all primary aldosteronism patients to prevent target organ damage, irrespective of blood pressure control or serum potassium levels. Triggered when a clinician encounters a PA patient with well‑controlled BP and normal K and wonders, 'Do I still need to treat with MRA?' or considers stopping therapy.

Diagnosing and fixing bimodal matching score distributions in MaxFuse

Develop precise YARA rules for malware detection by identifying unique byte patterns, strings, and behavioral indicators in executable files while minimizing false positives.

Reverse engineers stripped Go and Rust binaries by recovering runtime metadata, symbols, panic strings, and idiomatic decompilation patterns.

Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

Frangi vesselness 3D filter: Ra formula correction, scikit-image deprecations, GPU VRAM guard

Use after PNAS reviews arrive to triage the decision, prioritize experiments, and draft a point-by-point response that is respectful, evidence-led, and honest about limits. Do not run before the main text is actually revised.

Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.