You are an expert in bioinformatics, sequencing technologies, genomics data analysis, and adjacent fields. You are given findings from a MultiQC report, generated by a bioinformatics workflow. MultiQC supports various bioinformatics tools that output QC metrics, and aggregates those metrics into a single report. It outputs a "General Statistics" table with key metrics for each sample across all tools. That table is followed by more detailed sections from specific tools, that can include tables, as well as plots of different types (bar plot, line plot, scatter plot, heatmap, etc.) You are given data from such a report. Your task is to analyse the data, and give 1-2 bullet points of a very short and concise overall summary for the results. Don't waste words: mention only the important QC issues. If there are no issues, just say so. Just print one or two bullet points, nothing else. Please do not add any extra headers to the response. Use markdown to format your reponse for readability. Use directives with pre-defined classes .text-green, .text-red, and .text-yellow to highlight severity, e.g. :span[39.2%]{.text-red}. Highlight any mentioned sample names or sample named prefixes or suffixes with a sample directive, and make sure to use the same color classes for severity, e.g. :sample[A1001.2003]{.text-yellow} or :sample[A1001]{.text-yellow}. Do not put multiple sample names inside one directive. You must use only multiples of 4 spaces to indent nested lists. Two examples of short summaries: - :span[11/13 samples]{.text-green} show consistent metrics within expected ranges. - :sample[A1001.2003]{.text-red} and :sample[A1001.2004]{.text-red} exhibit extremely high percentage of :span[duplicates]{.text-red} (:span[65.54%]{.text-red} and :span[83.14%]{.text-red}, respectively). - All samples show good quality metrics with :span[75.7-77.0%]{.text-green} CpG methylation and :span[76.3-86.0%]{.text-green} alignment rates - :sample[2wk]{.text-yellow} samples show slightly higher duplication (:span[11-15%]{.text-yellow}) compared to :sample[1wk]{.text-green} samples (:span[6-9%]{.text-green})' ---------------------- Tools used in the report: 1. NanoStat Description:

Reports various statistics for long read dataset in FASTQ, BAM, or albacore sequencing summary format (supports NanoPack; NanoPlot, NanoComp).

Links: https://github.com/wdecoster/nanostat/, https://github.com/wdecoster/nanoplot/ ---------------------- ---------------------- Tool: NanoStat Section: Summary Statistics (FASTQ) Title: NanoStat fastq Plot type: violin plot Number of samples: 2 Metrics: Median length - Median read length (bp) Mean length - Mean read length (bp) Read N50 - Read length N50 Median Qual - Median read quality (Phred scale) Mean Qual - Mean read quality (Phred scale) # Reads (K) - Number of reads (thousands) Total Bases (Mb) - Total bases (millions) |Sample Name|Median length|Mean length|Read N50|Median Qual|Mean Qual|# Reads (K)|Total Bases (Mb)| |---|---|---|---|---|---|---|---| |NanoStats|5,216|6,336|7,191|38.80|29.10|5899.47|37377.01| |bc2068|3,778|4,224|4,541|39.80|30.20|3287.62|13887.00| ---------------------- Tool: NanoStat Section: Reads by quality Section description: Read counts categorised by read quality (Phred score). Section help text: Sequencing machines assign each generated read a quality score using the Phred scale. The phred score represents the liklelyhood that a given read contains errors. High quality reads have a high score. Title: NanoStat: Reads by quality Plot type: bar plot Values: # Reads |Sample|Reads >Q30|Reads Q25-30|Reads Q20-25|Reads Q15-20|Reads Q10-15|Reads