Table 2 Benchmark datasets generated from SEQC-II consortium efforts and potential application in SV detection
From: Towards accurate and reliable resolution of structural variants for clinical diagnosis
Working group | Reference samples | Benchmark data | Potential benefit for SV detection | Link |
|---|---|---|---|---|
Tumor-normal sample: HCC1395BL as normal and HCC1395 as tumor | Fresh DNA: WGS - HiSeq, NovaSeq, 10X Genomics, and PacBio) WES - Hiseq and Ion Torrent AmpliSeq - MiSeq Microarray -AffyChip CytoScan HD | • Somatic SV benchmark establishment • Low allelic frequency (LOF) somatic SV detection in liquid biopsy or FFPE samples • Deep learning-based somatic SV detection • Reproducibility and repeatability assessment of somatic SV detection based on multiple sample and design | All raw data (FASTQ files): NCBI’s SRA database (SRP162370) VCF and source code: ftp://ftp-trace.ncbi.nlm.nih.gov/seqc/ftp/release/Somatic_Mutation_WG/ BAM files: Seven Bridges’ s Cancer Genomics Cloud (CGC) platform and license is needed. | |
FFPE/mixed DNA: WGS/WES: Hiseq | ||||
Fresh cells: scCNV: 10X Genomics | scCNV data: SRA repository under accession code no. PRJNA504037. Source code: https://github.com/oxwang/fda_scRNA-seq and https://codeocean.com/capsule/0497386 or https://doi.org/10.24433/CO.1559060.v1. | |||
Sample A: ten cancer cell line mixture Sample B: a normal male cell line (Agilent OneSeq Human Reference DNA, PN 5190–8848) Spike in samples: 5% AcroMetrix spikes-ins + Sample B | 8 pan-cancer gene panels: WES: HiSeq, NovaSeq, Ion Torrent, Nanopore, Stranded RNAseq WGS: 10X Genomics Microarrays: SNP array and aCGH | • Reproducibility and repeatability assessment of actionable somatic SV assessment • Benefit of gene fusion detection by integrating DNAseq and RNAseq | FASTQ or BAM: BioProject PRJNA677997 - https://www.ncbi.nlm.nih.gov/bioproject/PRJNA677997. | |
Chinese Quartet samples (B-lymphocyte cell line and blood samples) | WGS: Hiseq, NovaSeq, illumina X10, PacBio Microarrays: SNP array | • Influential factors on reproducibility assessment for germline SV detection • Germline SV detection concordance between B-lymphocyte cell line and blood samples • Deep learning-based somatic SV detection • Cross check the best practice of germline SV detection with NIST efforts | Raw data: BioProject PRJNA723125 (HapMap samples) https://www.ncbi.nlm.nih.gov/bioproject/PRJNA723125/ and NODE OEP001896 (Chinese Quartet Samples) https://www.biosino.org/node/project/detail/OEP001896 Source code: https://github.com/justwalking2017/SEQC_WG3_Script | |
HapMap samples (HG001) | ||||
HapMAP Ashkenazi Trio | WGS: HiSeq, BGISEQ, MGISEQ, NovaSeq WES: Ion proton and Ion S5 | Raw data: BioProject PRJNA646948 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA646948), within accessions SRR12898279–SRR12898354 Source code:https://www.github.com/jfoox/abrfngs2 | ||
Bacterial genomes (ATCC MSA-3001) | Miseq, Ion PGM, Ion S5, MinION, Flongle, and GenapSys |