In this study, we performed systematic comparative analysis of seven widely-used SNV-calling methods, including SAMtools, the GATK Best Practices pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, on both simulated and real single-cell RNA-seq datasets. We generated SMART-seq2 data for 70 CD45- single cells, which were derived from two colorectal cancer patients (P0411 and P0413). The average sequencing depths of these cells were 1.4 million reads per cell. We also generated tumor and adjacent normal bulk WES data, as well as tumor bulk RNA-seq data for these patients.

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Dataset

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Dataset ID	Technology	Samples
EGAD00001005373	Illumina HiSeq 4000	75

Dataset Description

In this study, we performed systematic comparative analysis of seven widely-used SNV-calling methods, including SAMtools, the GATK Best Practices pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, on both simulated and real single-cell RNA-seq datasets.
We generated SMART-seq2 data for 70 CD45- single cells, which were derived from two colorectal cancer patients (P0411 and P0413). The average sequencing depths of these cells were 1.4 million reads per cell. We also generated tumor and adjacent normal bulk WES data, as well as tumor bulk RNA-seq data for these patients.

Label	Code	Version
obsolete research use only	DUO:0000014	2019-01-07
publication required	DUO:0000019	2019-01-07
user specific restriction	DUO:0000026	2019-01-07
institution specific restriction	DUO:0000028	2019-01-07
health or medical or biomedical research	DUO:0000006	2019-01-07

Who controls access to this dataset

For each dataset that requires controlled access, there is a corresponding Data Access Committee (DAC) who determine access permissions. Access to actual data files is not managed by the EGA. If you need to request access to this data set, please contact:

Peking University BIOPIC Data Access Committee (PUBDAC).The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any academic research institutions complying with the laws and bioethic regulation policies of China will be approved.
Contact person: Xianwen Ren
Email: renxwise [at] pku [dot] edu [dot] cn
More details: EGAC00001000551

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