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To gain further insight into the issue of tumor heterogeneity, we multi-sampled a tumor and submitted to methylation analysis each of the tumor fragments containing malignant cells.
SNV and genotype concordance improved when multi sample methods were applied.
Multi sample variant identification was done on the CyEnce cluster of Iowa State University, which includes 288 nodes with 128Gb RAM each.
Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2%99.2%2%) across software.
The total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants.
Local realignment was conducted using the Genome Analysis Toolkit (GATK) [ 37] and SNPs were extracted from the reads alignment file using UnifiedGenotyper, based on multi sample calling.
To compare time required for single and multi sample variant detection, the time required for several single sample runs was summed and compared to multi sample runs for the same number of samples.
Surprisingly, Cheng et al. [ 18] found slightly better sensitivity in single sample results of SAM and UG compared to multi sample results in a population-based sample of 96 Southeast Asian Malays with deep whole genome sequence information.
Surprisingly, the total number of InDels identified using single sample variant detection methods was also higher for PL (+4%) and SAM (+34%) than when multi sample methods were used.
Pipeline commands are available in Additional file 5. Multi sample variant detection was performed with the same three software applications above as well as with the GATK HC (version 2.7-4-g6f46d11 [ 6, 7]) using default settings.
For UG, Liu et al. [ 26] observed an increase in sensitivity of around 1%, whereas our results showed a slightly more pronounced improvement of NRS when multi sample methods were applied (4%).
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