Results for “fastq”

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

Takes raw whole-genome sequencing FASTQ files or a pre-existing VCF through variant calling, quality control, and polygenic risk score computation using the PGS Catalog.

Use when working with FASTA or FASTQ files for statistics, filtering, transformation, format conversion, searching, or set operations.

Use when doing lightweight FASTA/FASTQ transformations such as conversion, subsampling, subsequence extraction, trimming, or quick QC with seqtk.

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

Translates raw genomic data into personalized health, longevity, and pharmacogenomic protocols for AI agents.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Use when processing raw FASTQ files for quality control, adapter trimming, length or complexity filtering, polyG tail trimming, or generating QC reports before downstream analysis.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Kit de ferramentas para arquivos genômicos. Leia/escreva alinhamentos SAM/BAM/CRAM, variantes VCF/BCF, sequências FASTA/FASTQ, extraia regiões, calcule cobertura, para pipelines de processamento de dados NGS.

Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.

Use when aligning short reads to a reference genome or indexed sequence database. Suitable for mapping FASTQ/FASTA reads in paired-end or single-end mode to produce SAM output.

Expert guidance for Groq, the LLM inference platform that provides the fastest token generation speeds available, powered by custom LPU (Language Processing Unit) hardware. Helps developers integrate Groq's API for real-time AI applications where latency matters — chatbots, code completion, and streaming responses.

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.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)

Optimizes Active Record queries, implements Turbo Frames and Streams, configures Action Cable, sets up Sidekiq workers, and writes RSpec test suites for Rails 7+ applications.

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

Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

Quantize LLMs to 8/4/3/2/1-bit precision without calibration data, using multiple backends and HuggingFace/vLLM integration.

Use this skill when the user asks about DataQ — FMCSA's data review system at dataqs.fmcsa.dot.gov — for disputing inspection violations, crash records, or other entries that appear in a carrier's CSA / SMS score. Covers Request for Data Review (RDR) process, success rates, common dispute grounds, what evidence to attach, timeline expectations, and how successful disputes reduce BSI (BASIC Severity Indicator) scores. Cite 49 CFR 392.7 and the FMCSA DataQs User Guide.

Systematically QA test a web application and fix bugs found. (gstack)

Systematically QA test a web application and fix bugs found. (gstack)