scholarly journals Detection of FLT3 Internal Tandem Duplication in Targeted, Short-Read-Length, Next-Generation Sequencing Data

2013 ◽  
Vol 15 (1) ◽  
pp. 81-93 ◽  
Author(s):  
David H. Spencer ◽  
Haley J. Abel ◽  
Christina M. Lockwood ◽  
Jacqueline E. Payton ◽  
Philippe Szankasi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Tandem Duplication ◽  
Read Length ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Internal Tandem Duplication ◽  
Short Read ◽  
Flt3 Internal Tandem Duplication ◽  
Generation Sequencing ◽  
Short Read Length

PERHAPS: Paired-End short Reads-based HAPlotyping from next-generation Sequencing data

2020 ◽  
Author(s):  
Jie Huang ◽  
Stefano Pallotti ◽  
Qianling Zhou ◽  
Marcus Kleber ◽  
Xiaomeng Xin ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Snp Array ◽  
Simple Approach ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Short Read ◽  
Array Data ◽  
Short Reads ◽  
Generation Sequencing

Abstract The identification of rare haplotypes may greatly expand our knowledge in the genetic architecture of both complex and monogenic traits. To this aim, we developed PERHAPS (Paired-End short Reads-based HAPlotyping from next-generation Sequencing data), a new and simple approach to directly call haplotypes from short-read, paired-end Next Generation Sequencing (NGS) data. To benchmark this method, we considered the APOE classic polymorphism (*1/*2/*3/*4), since it represents one of the best examples of functional polymorphism arising from the haplotype combination of two Single Nucleotide Polymorphisms (SNPs). We leveraged the big Whole Exome Sequencing (WES) and SNP-array data obtained from the multi-ethnic UK BioBank (UKBB, N=48,855). By applying PERHAPS, based on piecing together the paired-end reads according to their FASTQ-labels, we extracted the haplotype data, along with their frequencies and the individual diplotype. Concordance rates between WES directly called diplotypes and the ones generated through statistical pre-phasing and imputation of SNP-array data are extremely high (>99%), either when stratifying the sample by SNP-array genotyping batch or self-reported ethnic group. Hardy-Weinberg Equilibrium tests and the comparison of obtained haplotype frequencies with the ones available from the 1000 Genome Project further supported the reliability of PERHAPS. Notably, we were able to determine the existence of the rare APOE*1 haplotype in two unrelated African subjects from UKBB, supporting its presence at appreciable frequency (approximatively 0.5%) in the African Yoruba population. Despite acknowledging some technical shortcomings, PERHAPS represents a novel and simple approach that will partly overcome the limitations in direct haplotype calling from short read-based sequencing.

scholarly journals Detection of somatic structural variants from short-read next-generation sequencing data

2019 ◽  
Author(s):  
Tingting Gong ◽  
Vanessa M Hayes ◽  
Eva KF Chan
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Genomics ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Factors Affecting ◽  
Ngs Data ◽  
Generation Sequencing

AbstractSomatic structural variants (SVs) play a significant role in cancer development and evolution, but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS, and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of eight commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the eight SV callers examined in this paper. As the importance of large structural variants become increasingly recognised in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection and guidance on selecting an appropriate SV caller.

scholarly journals Detection of somatic structural variants from short-read next-generation sequencing data

2020 ◽  
Author(s):  
Tingting Gong ◽  
Vanessa M Hayes ◽  
Eva K F Chan
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Genomics ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Factors Affecting ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Somatic structural variants (SVs), which are variants that typically impact >50 nucleotides, play a significant role in cancer development and evolution but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of seven commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the seven SV callers examined in this paper. As the importance of large SVs become increasingly recognized in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection that should be considered when choosing SV callers.

scholarly journals Generation of high-resolutiona prioriY-chromosome phylogenies using “next-generation” sequencing data

2013 ◽  
Author(s):  
Gregory R Magoon ◽  
Raymond H Banks ◽  
Christian Rottensteiner ◽  
Bonnie E Schrack ◽  
Vincent O Tilroe ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Phylogenetic Tree ◽  
Y Chromosome ◽  
A Priori ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Short Read ◽  
Tree Generation ◽  
Generation Sequencing

An approach for generating high-resolutiona priorimaximum parsimony Y-chromosome (“chrY”) phylogenies based on SNP and small INDEL variant data from massively-parallel short-read (“next-generation”) sequencing data is described; the tree-generation methodology produces annotations localizing mutations to individual branches of the tree, along with indications of mutation placement uncertainty in cases for which "no-calls" (through lack of mapped reads or otherwise) at particular sites precludes precise phylogenetic placement of mutations. The approach leverages careful variant site filtering and a novel iterative reweighting procedure to generate high-accuracy trees while considering variants in regions of chrY that had previously been excluded from analyses based on short-read sequencing data. It is argued that the proposed approach is also superior to previous region-based filtering approaches in that it adapts to the quality of the underlying data and will automatically allow the scope of sites considered to expand as the underlying data quality improves (e.g. through longer read lengths). Key related issues, including calling of genotypes for the hemizygous chrY, reliability of variant results, read mismappings and "heterozygous" genotype calls, and the mutational stability of different variants are discussed and taken into account. The methodology is demonstrated through application to a dataset consisting of 1292 male samples from diverse populations and haplogroups, with the majority coming from low-coverage sequencing by the 1000 Genomes Project. Application of the tree-generation approach to these data produces a tree involving over 120,000 chrY variant sites (about 45,000 sites if “singletons” are excluded). The utility of this approach in refining the Y-chromosome phylogenetic tree is demonstrated by examining results for several haplogroups. The results indicate a number of new branches on the Y-chromosome phylogenetic tree, many of them subdividing known branches, but also including some that inform the presence of additional levels along the “trunk” of the tree. Finally, opportunities for extensions of this phylogenetic analysis approach to other types of genetic data are noted.

scholarly journals BATCH-GE: Batch analysis of Next-Generation Sequencing data for genome editing assessment

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Annekatrien Boel ◽  
Woutert Steyaert ◽  
Nina De Rocker ◽  
Björn Menten ◽  
Bert Callewaert ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genome Editing ◽  
Targeted Mutagenesis ◽  
Read Length ◽  
Next Generation Sequencing Data ◽  
Model Organisms ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Batch Analysis

Abstract Targeted mutagenesis by the CRISPR/Cas9 system is currently revolutionizing genetics. The ease of this technique has enabled genome engineering in-vitro and in a range of model organisms and has pushed experimental dimensions to unprecedented proportions. Due to its tremendous progress in terms of speed, read length, throughput and cost, Next-Generation Sequencing (NGS) has been increasingly used for the analysis of CRISPR/Cas9 genome editing experiments. However, the current tools for genome editing assessment lack flexibility and fall short in the analysis of large amounts of NGS data. Therefore, we designed BATCH-GE, an easy-to-use bioinformatics tool for batch analysis of NGS-generated genome editing data, available from http://. BATCH-GE detects and reports indel mutations and other precise genome editing events and calculates the corresponding mutagenesis efficiencies for a large number of samples in parallel. Furthermore, this new tool provides flexibility by allowing the user to adapt a number of input variables. The performance of BATCH-GE was evaluated in two genome editing experiments, aiming to generate knock-out and knock-in zebrafish mutants. This tool will not only contribute to the evaluation of CRISPR/Cas9-based experiments, but will be of use in any genome editing experiment and has the ability to analyze data from every organism with a sequenced genome.

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