ArticleScientific reports2015
Evaluating information content of SNPs for sample-tagging in re-sequencing projects.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Development and validation of a minimal SNP genotyping panel for the differentiation of Cannabis sativa cultivars.BMC genomics · 2025Article
- Screening a new set of microhaplotypes in exonic regions for sample identity testing and paternity testing during whole exome sequencing analysis.International journal of legal medicine · 2025Article
- Development of a coding SNP panel for tracking the origin of whole-exome sequencing samples.BMC genomics · 2024Article
- A pipeline for sample tagging of whole genome bisulfite sequencing data using genotypes of whole genome sequencing.BMC genomics · 2023Article
- Mining whole genome sequence data to efficiently attribute individuals to source populations.Scientific reports · 2020Article
- Robust in-silico identification of Cancer Cell Lines based on RNA and targeted DNA sequencing data.Scientific reports · 2019Article
- Article
- Sample tracking in microbiome community profiling assays using synthetic 16S rRNA gene spike-in controls.Scientific reports · 2018Article
- Detection of Cross-Sample Contamination in Multiple Myeloma Samples and Sequencing Data.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Robust in-silico identification of cancer cell lines based on next generation sequencing.Oncotarget · 2017Article
- A SNP panel and online tool for checking genotype concordance through comparing QR codes.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sample-tagging is designed for identification of accidental sample mix-up, which is a major issue in re-sequencing studies. In this work, we develop a model to measure the information content of SNPs, so that we can optimize a panel of SNPs that approach the maximal information for discrimination. The analysis shows that as low as 60 optimized SNPs can differentiate the individuals in a population as large as the present world, and only 30 optimized SNPs are in practice sufficient in labeling up to 100 thousand individuals. In the simulated populations of 100 thousand individuals, the average Hamming distances, generated by the optimized set of 30 SNPs are larger than 18, and the duality frequency, is lower than 1 in 10 thousand. This strategy of sample discrimination is proved robust in large sample size and different datasets. The optimized sets of SNPs are designed for Whole Exome Sequencing, and a program is provided for SNP selection, allowing for customized SNP numbers and interested genes. The sample-tagging plan based on this framework will improve re-sequencing projects in terms of reliability and cost-effectiveness.
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