Results for “fasta”

Build, deploy, evaluate, debug, and measure latency for a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection and VLM classification.

Build production-grade Azure Cosmos DB NoSQL services with Python/FastAPI, including client setup with dual authentication, service layer CRUD operations, partition key strategies, parameterized queries, and TDD patterns.

Use when simulating paired-end short reads from a reference FASTA for testing, benchmarking, or pipeline validation

Run replication, robustness, and specification-sensitivity workflows for Stata projects. Use when a researcher wants to reproduce a result, rerun a pipeline, compare specifications, audit a do-file sequence, or check whether a claim is stable.

Runs a fast quality gate that detects the project stack, performs static analysis, checks cross-layer consistency, and fixes issues between pipeline phases.

Guides development of Fastify Node.js backend servers and REST APIs using TypeScript or JavaScript, covering routes, plugins, validation, error handling, authentication, testing, performance, logging, deployment, and more.

Apply design, canon, and modules to every page in the inventory to produce a deployable static HTML site. Per-page, incremental, and idempotent.

Orchestrates end-to-end software development from scaffolding to deployment, covering frontend, backend, database, testing, and CI/CD workflows.

Configures Azure Monitor Application Insights with OpenTelemetry auto-instrumentation for Python applications in one line.

Perform high-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames, including overlap, nearest, merge, coverage, complement, subtract, and reading/writing BED, VCF, BAM, GFF, FASTA, and FASTQ formats with streaming and cloud-native support.

Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its structure/content/quality before deciding what analysis to run. Covers tabular (.csv .tsv .xlsx .parquet), arrays (.npy .npz .hdf5 .h5 .mat .fits), sequence/genomics (.fasta .fastq .sam .bam .vcf .bed .gff .gtf .h5ad), microscopy (.tif .nd2 .czi .lif .ims .dcm .nii), spectroscopy/MS (.mzML .mzXML .mgf .fid .jdx), chemistry (.pdb .cif .mol .sdf .xyz .gro), and proteomics/metabolomics (.pepXML .mzid .mzTab). For zero-shot forecasting of a series use alterlab-timesfm; to create/configure a chunked cloud array store use alterlab-zarr. Part of the AlterLab Academic Skills suite.