NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks is a research paper published in Genome biology (2021). On theSindex it has a DataRank of 0. It has been cited 94 times.
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National Institute of General Medical Sciences
Grant: GM132713
NIGMS NIH HHS
Grant: R01 GM132713
National Institutes of Health
Grant: 5R01GM132713-04
Detection and annotation of structural variants from long-read sequencing
FWCI
8.14
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks
Additional file 1 of NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks
Additional file 4 of NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks
Additional file 4 of NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks
Additional file 1 of Species-specific basecallers improve actual accuracy of nanopore sequencing in plants
Additional file 1 of Species-specific basecallers improve actual accuracy of nanopore sequencing in plants
Additional file 1 of Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data
Additional file 1 of Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data
Additional file 2 of Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data
Additional file 2 of Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data
Additional file 1 of A computational framework for improving genetic variants identification from 5,061 sheep sequencing data
Additional file 1 of A computational framework for improving genetic variants identification from 5,061 sheep sequencing data
Additional file 14 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 14 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 11 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 12 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 13 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 12 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 10 of Next-generation fungal identification using target enrichment and Nanopore sequencing
Additional file 11 of Next-generation fungal identification using target enrichment and Nanopore sequencing