Evaluation of Whole Genome Sequencing Data
Evaluation of Whole Genome Sequencing Data
Whole genome sequencing (WGS) can provide comprehensive insights into the genetic makeup of lymphomas. Here we describe a selection of methods for the analysis of WGS data, including alignment, identification of different classes of genomic variants, the identification of driver mutations, and the identification of mutational signatures. We further outline design considerations for WGS studies and provide a variety of quality control measures to detect common quality problems in the data.
Quality Control, ddc:610, Whole Genome Sequencing, Genome, Human, High-Throughput Nucleotide Sequencing, Genomics, Polymorphism, Single Nucleotide, Neoplasms, Mutation, Humans, Exome
Quality Control, ddc:610, Whole Genome Sequencing, Genome, Human, High-Throughput Nucleotide Sequencing, Genomics, Polymorphism, Single Nucleotide, Neoplasms, Mutation, Humans, Exome
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).6 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!