Perform quality control on genomic sequencing data with FastQC and fastp.
npx skills add https://github.com/TianGzlab/OmicsClaw --skill genomics-qc
Please help me install this Agent Skill. Skill: genomics-qc Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/genomics/genomics-qc Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill genomics-qc
This Skill addresses the critical need for quality control in genomic sequencing data, preventing issues caused by low-quality reads, adapters, or incorrect base calls that can corrupt downstream analyses.
Run quality control on the provided fastq file 'sample_R1.fastq.gz' and save the results to the 'qc_output' directory.
High-intent search queries and answers about installing and using this skill.
To perform quality control on FASTQ data, this Skill assesses Phred quality scores, GC and N content, and read length distribution. It also trims adapters and low-quality bases, ultimately generating detailed MultiQC summaries and trimmed FASTQ files for accurate downstream analysis.
You need to trim adapter sequences from genomic reads before aligning them to a reference genome. This Skill removes adapter contamination and low-quality base calls, preventing issues that could corrupt downstream alignment results and ensuring accurate genomic data analysis.
Yes, this genomic QC workflow requires Python 3.11+ and the numpy and pandas libraries. These dependencies are essential for processing sequencing data, calculating base call metrics, and generating the MultiQC reports outputted to your specified directory.
The best way to generate a MultiQC report is to run this Skill on your raw FASTQ files. It wraps FastQC and fastp to automatically evaluate Phred scores and adapter contamination, producing a comprehensive MultiQC summary alongside trimmed reads.
Genome alignment can fail or produce inaccurate results due to low-quality reads, incorrect base calls, or adapter contamination in your FASTQ files. Running this Skill identifies and removes these problematic sequences, ensuring data integrity for successful alignment.
biomaps-infraRead, manipulate, and write genomic data files via a Python interface.
TianGzlabAlign FASTQ reads to reference genomes with BWA-MEM, Bowtie2, and Minimap2.
ClawBioAutomate QC, trimming, alignment, and BAM processing for sequencing data.
pradyumnasagarFilters and trims Phred quality scores in FASTQ reads using Biopython and NumPy.
