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    RPE1 v1.1 vs CHM13 vs HG002 β€” status and next steps

    I attempted to build side-by-side tables and Plotly figures comparing assembly metrics (assembly size, contig/ scaffold N50, contig count, gap count, QV / consensus accuracy, % completeness / BUSCOs, number of chromosomes/chr-level scaffolds, and number of unresolved centromere/rDNA gaps) for RPE1v1.1, CHM13 (T2T), and HG002. However, the packet of research documents you provided does not contain the numeric assembly metrics for RPE1v1.1, CHM13, or HG002 needed to populate accurate tables and graphs.

    To avoid inventing or guessing values, I need to fetch the primary assembly QC reports (or run reproducible assembly-evaluation commands) for these three assemblies. I can either: (A) retrieve and cite canonical publications / assembly release notes that report these metrics, or (B) run a bioinformatics agent that will download assembly FASTA files and compute uniform metrics (assembly length, contig N50, scaffold N50, contig count, gap count, QV via Merqury or yak, and BUSCO completeness) so the comparison is computed consistently.

    Why I cannot safely produce numeric comparison right now

    • Neither RPE1v1.1 numeric metrics nor authoritative release notes for it are present in the provided research list, so numeric values would be unsupported by the supplied data.
    • CHM13 (Telomere-to-Telomere assembly) and HG002 (the GIAB Ashkenazi reference) have multiple versions, and metrics differ by release β€” to compare fairly we must choose explicit versions and sources (paper, GenBank/GCA accessions, or assembly portal) and cite them.
    • Assemblies are commonly reported with different metrics (contig N50 vs scaffold N50, or QV measured by different methods). To avoid apples-vs-oranges comparisons I prefer computing all metrics from the same raw FASTA files using the same pipeline; that requires fetching the assemblies.

    Two safe, reproducible options I recommend

    1. Fetch canonical assembly reports / papers and extract published metrics (I will cite each source inline). This is fast and appropriate if you want published QC numbers.
    2. Run an AI Bioinformatics agent to download the exact FASTA files for RPE1v1.1, CHM13 and HG002 and compute all metrics uniformly (contig N50, scaffold N50, contig count, gap count, total length, QV via Merqury/k-mer based, BUSCO). This produces directly comparable results and a reproducible table + Plotly graphs; I strongly recommend this for rigorous comparisons.

    What the agent will produce (deliverables)

    • One reproducible table with uniform metrics for RPE1v1.1, CHM13 (specific release), and HG002 (specific release) with citations and links to the underlying assembly FASTA/GCA/GenBank accession(s).
    • At least two Plotly charts: (1) bar chart of assembly total length / contig count / gap count; (2) log-scale N50 comparison and QV/BUSCO completeness stacked view. Each figure will be labeled, with axes and units.
    • Detailed methods block describing exactly which FASTA accessions were used and exactly how metrics were computed (software and versions), plus provenance links so the process is reproducible and auditable.

    Why I recommend running the agent

    Published assembly numbers can vary by release and by how QV and completeness were computed. Running the AI Scientist agent produces uniform, reproducible metrics calculated the same way across all three assemblies and includes direct links to the exact assembly files used.

    Note on evidence & transparency

    I will include inline, machine-readable citations to each assembly release/paper and to the computed QC outputs. If you prefer I can instead extract only published metrics from the T2T and GIAB papers and any RPE1 assembly release notes (if available) β€” tell me which you prefer.

    Quick question before I proceed

    Would you like me to (A) fetch published metrics from canonical papers/releases, or (B) run the AI Scientist Analysis to compute uniform metrics directly from assembly FASTAs? Click the button below to run the agent now.



    Feedback:    

    Updated: March 13, 2026

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     Analysis Wizard



    Downloading the three assembly FASTA files and computing contig/scaffold N50, total length, contig count, gap count, QV (k-mer based), and BUSCO completeness, producing CSV tables and Plotly-ready JSON outputs.



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    Create Tables: Compare RPE1v1.1 assembly metrics to CHM13 and HG002 Science Art

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