Results for “transcriptome”

Transcribe audio to text using ElevenLabs Scribe v2, supporting 90+ languages, speaker diarization, and word-level timestamps.

Analyze RNA velocity in single-cell RNA-seq data with scVelo, estimating cell state transitions from unspliced/spliced mRNA dynamics, inferring trajectory directions, computing latent time, and identifying driver genes.

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores, drug sensitivity data, and gene effect profiles to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets.

Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.

Validates inputs, generates samplesheets and launch scripts, and optionally executes nf-core/pacsomatic matched tumor-normal workflows from BAM files, supporting local runs and scheduler submission (LSF/Slurm/PBS/SGE).

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.

Process and analyze mass spectrometry data with the Matchms Python library, including importing spectra, filtering peaks, calculating similarity scores, and building reproducible analytical workflows.

Predicts gene and transcript structure from a DNA sequence using the hosted Genomic Intelligence API, producing a report and JSON output.

Read, write, and analyze genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

Query the Cancer Dependency Map (DepMap) for CRISPR gene dependency scores, drug sensitivity data, and gene effect profiles to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.