๐๐ฒ๐ป ๐๐ฝ๐ฝ๐น๐ฒ ๐ช๐ฎ๐๐ฐ๐ต ๐ผ๐บ ๐ท๐ฒ ๐ฒ๐ป๐ธ๐ฒ๐น ๐ฑ๐ฟ๐ฎ๐ด๐ฒ๐ป, ๐ธ๐ฎ๐ป ๐ฑ๐ฎ๐?
Een Apple Watch is bedoeld voor gebruik om je pols. Toch kun je de smartwatch ook op andere manieren dragen, zoals om je enkel. Maar waarom zou je dat doen?
Hemmetti = darn
War deserves a special darn mention.
https://peakd.com/hive-158694/@insaneworks/dragen-collage
#hive #collage #kollaasi #hemmetti #damn #hjemmetmagazine #war #sota #stop #stopwar #dragen
Hemmetti = darn
War deserves a special darn mention.
https://peakd.com/hive-158694/@insaneworks/dragen-collage
#hive #collage #kollaasi #hemmetti #damn #hjemmetmagazine #war #sota #stop #stopwar #dragen
#GATK, #DRAGEN and #DeepVariant are all almost en par - Good news for streamlined diagnostics.
Rapid advances in high-throughput DNA sequencing technologies have enabled the conduct of whole genome sequencing (WGS) studies, and several bioinformatics pipelines have become available. The aim of this study was the comparison of 6 WGS data pre-processing pipelines, involving two mapping and alignment approaches (GATK utilizing BWA-MEM2 2.2.1, and DRAGEN 3.8.4) and three variant calling pipelines (GATK 4.2.4.1, DRAGEN 3.8.4 and DeepVariant 1.1.0). We sequenced one genome in a bottle (GIAB) sample 70 times in different runs, and one GIAB trio in triplicate. The truth set of the GIABs was used for comparison, and performance was assessed by computation time, F1 score, precision, and recall. In the mapping and alignment step, the DRAGEN pipeline was faster than the GATK with BWA-MEM2 pipeline. DRAGEN showed systematically higher F1 score, precision, and recall values than GATK for single nucleotide variations (SNVs) and Indels in simple-to-map, complex-to-map, coding and non-coding regions. In the variant calling step, DRAGEN was fastest. In terms of accuracy, DRAGEN and DeepVariant performed similarly and both superior to GATK, with slight advantages for DRAGEN for Indels and for DeepVariant for SNVs. The DRAGEN pipeline showed the lowest Mendelian inheritance error fraction for the GIAB trios. Mapping and alignment played a key role in variant calling of WGS, with the DRAGEN outperforming GATK.
Rapid advances in high-throughput DNA sequencing technologies have enabled the conduct of whole genome sequencing (WGS) studies, and several bioinformatics pipelines have become available. The aim of this study was the comparison of 6 WGS data pre-processing pipelines, involving two mapping and alignment approaches (GATK utilizing BWA-MEM2 2.2.1, and DRAGEN 3.8.4) and three variant calling pipelines (GATK 4.2.4.1, DRAGEN 3.8.4 and DeepVariant 1.1.0). We sequenced one genome in a bottle (GIAB) sample 70 times in different runs, and one GIAB trio in triplicate. The truth set of the GIABs was used for comparison, and performance was assessed by computation time, F1 score, precision, and recall. In the mapping and alignment step, the DRAGEN pipeline was faster than the GATK with BWA-MEM2 pipeline. DRAGEN showed systematically higher F1 score, precision, and recall values than GATK for single nucleotide variations (SNVs) and Indels in simple-to-map, complex-to-map, coding and non-coding regions. In the variant calling step, DRAGEN was fastest. In terms of accuracy, DRAGEN and DeepVariant performed similarly and both superior to GATK, with slight advantages for DRAGEN for Indels and for DeepVariant for SNVs. The DRAGEN pipeline showed the lowest Mendelian inheritance error fraction for the GIAB trios. Mapping and alignment played a key role in variant calling of WGS, with the DRAGEN outperforming GATK.
Amazing talk by Mike Smoot @[email protected] about #DRAGEN at the #NextflowSummit - @[email protected] / @[email protected] workflows coming to a sequencing machine near you..? ๐ ๐คฉ
๐ฆ๐: https://twitter.com/tallphil/status/1580835620163047424
iCulture.nl
Insaneworks
Insaneworks
Johannes Helmuth
Pierre Lindenbaum
Marcel Ribeiro-Dantas, PhD๐๐