SEQC2 Somatic Reference Samples (Tumor/Normal Benchmark Truth Set)

The somatic-variant truth set that anchors cancer-mutation benchmarking, and the ground truth against which ctDNA and MRD assays can validate their calls. Produced by the FDA-led SEQC2 consortium from a paired tumor and normal cell line, the breast cancer line HCC1395 and its matched normal lymphoblastoid line HCC1395BL, it was sequenced across many centers and platforms (Illumina NovaSeq and HiSeq, PacBio Sequel, Ion Torrent, Oxford Nanopore, and 10x Genomics) to build a high-confidence set of somatic mutations with known variant allele frequencies. That known call set is what lets an MRD developer measure detection sensitivity and specificity against truth rather than against another imperfect caller, and the same HCC1395 background is the basis for the contrived ctDNA dilution samples in the companion SEQC2 liquid-biopsy study. The S3 files mounted here are a subset: the high-confidence somatic truth call set (SNV and indel VCFs plus the high-confidence-regions BED). The full 180-run, multi-platform WGS (about 15 TB) remains on the NCBI Sequence Read Archive under accession SRP162370 (Fang et al., Nature Biotechnology, 2021).

Category
Genomics & Bioinformatics
Provider
FDA SEQC2 Consortium (Somatic Mutation Working Group)
License
Public (FDA SEQC2 consortium data, NCBI SRA)
DOI
10.1038/s41587-021-00993-6
Hosted on
Amazon S3, bucket scigantic-mrd-liquid-biopsy/seqc2-reference-truth/, us-east-1
Access
Free and public. No account with the data provider is needed.
Source
https://www.ncbi.nlm.nih.gov/sra/?term=SRP162370
Data structure and loading code

Fields

Runstring (SRR accession)
samplestring (HCC1395 tumor | HCC1395BL normal
Platform / Instrumentstring (NovaSeq | HiSeq | PacBio Sequel
center_namestring (contributing sequencing center)
depth / replicatestring (multiple depths and technical re

Load the data

import os
import pandas as pd
df = pd.read_csv(os.path.join(os.environ['SCIGANTIC_MOUNT_PATH'], 'sample_sheet/SRP162370_runinfo.csv'), nrows=1000)
df.head()

Written for a Scigantic notebook, where /mnt/archive is the mounted dataset.

Analyze this dataset without downloading it

On Scigantic, SEQC2 Somatic Reference Samples (Tumor/Normal Benchmark Truth Set) opens in a hosted JupyterLab notebook with the data already mounted, so you can read files where they are stored instead of copying them first. Each notebook includes an AI assistant that can see the mounted files and write and run analysis code. Open the dataset page to launch a notebook, or browse the full catalog.

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