scholarly journals A streamlined solution for processing, elucidating and quality control of cyclobutane pyrimidine dimer sequencing data

2021 ◽  
Author(s):  
Quanhu Sheng ◽  
Hui Yu ◽  
Mingrui Duan ◽  
Scott Ness ◽  
Jiapeng He ◽  
...  
Keyword(s):  
Quality Control ◽  
Pyrimidine Dimer ◽  
Cyclobutane Pyrimidine Dimer ◽  
Sequencing Data

Multiple Electronic and Structural Factors Control Cyclobutane Pyrimidine Dimer and 6-4 Thymine-Thymine Photodimerization in a DNA Duplex

2017 ◽  
Vol 23 (60) ◽  
pp. 15177-15188 ◽  
Author(s):  
Irene Conti ◽  
Lara Martínez-Fernández ◽  
Luciana Esposito ◽  
Siegfried Hofinger ◽  
Artur Nenov ◽  
...  
Keyword(s):  
Pyrimidine Dimer ◽  
Structural Factors ◽  
Cyclobutane Pyrimidine Dimer ◽  
Dna Duplex

Blockage of RNA polymerase II at a cyclobutane pyrimidine dimer and 6–4 photoproduct

2004 ◽  
Vol 320 (4) ◽  
pp. 1133-1138 ◽  
Author(s):  
Joan Seah Mei Kwei ◽  
Isao Kuraoka ◽  
Katsuyoshi Horibata ◽  
Manabu Ubukata ◽  
Eiry Kobatake ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Pyrimidine Dimer ◽  
Cyclobutane Pyrimidine Dimer

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

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