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工具与集成 AI 技能

发现 工具与集成 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。

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显示第 6,937–6,960 个,共 87,268 个

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GitHub

bio-hi-c-analysis-hic-differential

GPTomics

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps.

GitHub

bio-phasing-imputation-genotype-imputation

GPTomics

Impute missing genotypes using reference panels with Beagle or Minimac4. Use when increasing variant density for GWAS, harmonizing data across genotyping platforms, or inferring variants not directly typed in a...

GitHub

bio-read-qc-adapter-trimming

GPTomics

Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contam...

GitHub

bio-long-read-sequencing-clair3-variants

GPTomics

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinic...

GitHub

bio-vcf-statistics

GPTomics

Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.

GitHub

bio-duplicate-handling

GPTomics

Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.

GitHub

bio-gatk-variant-calling

GPTomics

Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use for produc...

GitHub

bio-metagenomics-abundance

GPTomics

Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed fro...

GitHub

bio-de-visualization

GPTomics

Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms.

GitHub

bio-metagenomics-metaphlan

GPTomics

Marker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers. Use when accurate taxonomic profiling is needed and computational resou...

GitHub

bio-genome-assembly-long-read-assembly

GPTomics

De novo genome assembly from Oxford Nanopore or PacBio long reads using Flye and Canu. Produces highly contiguous assemblies suitable for complete bacterial genomes and resolving complex regions.

GitHub

bio-restriction-sites

GPTomics

Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Search with single enzymes, batches of enzymes, or commercially available enzyme sets. Returns cut positions for linear or cir...

GitHub

bio-write-sequences

GPTomics

Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when saving sequences, creating new sequence files, or outputting modified records.

GitHub

bio-workflows-metagenomics-pipeline

GPTomics

End-to-end metagenomics workflow from FASTQ to taxonomic and functional profiles. Covers Kraken2 classification, Bracken abundance estimation, and HUMAnN functional profiling.

GitHub

bio-proteomics-dia-analysis

GPTomics

Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Covers library-free and library-based workflows for deep proteome profiling.

GitHub

bio-crispr-screens-screen-qc

GPTomics

Quality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosin...

GitHub

bio-workflows-imc-pipeline

GPTomics

End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics.

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