Results for “biological-pathway”

Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Covers over-representation analysis (ORA), Gene Set Enrichment Analysis (GSEA), and single-sample scoring using gseapy, g:Profiler, and Enrichr libraries.

Manipulate biological sequences, parse FASTA/GenBank/PDB files, access NCBI databases, run BLAST searches, and perform phylogenetics using the Biopython library.

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).

Processes and analyzes multiple physiological signals (ECG, respiration, EDA, EMG, PPG, EOG) together using NeuroKit2, including cross-signal features like RSA and event-related analysis.

Assesses genome, transcriptome, and protein completeness with BUSCO v6, automatically resolving the correct lineage from an organism description and generating reproducible reports.

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

Provides reference documentation and code patterns for Biopython, covering sequence handling, alignments, NCBI database access, BLAST, protein structures, phylogenetics, and other bioinformatics tasks.

Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.

Query 40+ bioinformatics services (UniProt, KEGG, ChEMBL, Reactome) with a unified Python interface for cross-database analysis, identifier mapping, and sequence analysis.

Document end-to-end business processes in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.

Build and deploy bioinformatics workflows as serverless pipelines on the Latch platform using Python decorators, cloud data management, and GPU support.

Authenticate, browse pipelines, samples, and projects, upload data, launch pipeline executions, and check run status on any Flow.bio instance via CLI.