Science bundle v1.0.0

CRISPR TP53 Guide Design: Receipts

Every claim in the TP53 guide-design episode bound to its source paper, figure, and verification verdict — the receipts behind the video.

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Source Paper

Improved prediction of CRISPR-Cas9 on-target activity with a deep learning model

DOI: 10.1038/s41467-021-23576-0 License Retraction: Verified

Publish Gates

✓ Schema valid ✓ Manifest SHA-256 ✓ All claims verified ✓ AI disclosure reviewed ✓ Figure rights cleared ✓ Human approved

Claims & Receipts (2)

figure C001

TP53 loss-of-function mutations confer resistance to standard chemotherapy in approximately 50% of solid tumors profiled in TCGA pan-cancer cohorts.

Verified · grade A

Evidence

Source DOI 10.1016/j.cell.2018.03.039 2018

Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers
Figure Figure 3B

Frequency of TP53 alterations across 33 TCGA tumor types, ordered by descending prevalence.
Script span 84s - 102s

"Across the pan-cancer cohort, TP53 loss shows up in about half of solid tumors -- this is the single most common driver event in human cancer."

Verifier note: DOI resolved, figure locator confirmed against cached PDF, script_span quote matches transcript verbatim.

figure C002

PAM-proximal seed-region mismatches in sgRNA design reduce on-target Cas9 cleavage efficiency by more than an order of magnitude.

Verified · grade A

Evidence

Source DOI 10.1038/nbt.2647 2013

DNA targeting specificity of RNA-guided Cas9 nucleases
Figure Figure 2A

Seed-region mismatch tolerance across 100+ sgRNA variants, normalized to perfect-match cleavage.
Script span 311s - 329s

"If you put your mismatch in the seed -- the 10-12 nt right next to the PAM -- you lose more than 10x of your on-target activity."

Verifier note: Effect size cross-checked against Doench 2014 supplementary table.

Provenance Manifest

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