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Variant detection sensitivity and biases in whole genome and exome sequencing

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Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. |

TÀI LIỆU LIÊN QUAN

Systematic comparative analysis of singlenucleotide variant detection methods from single-cell RNA sequencing data

QQ-SNV: Single nucleotide variant detection at low frequency by comparing the quality quantiles

PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection

VARSCOT: Variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9

TNER: A novel background error suppression method for mutation detection in circulating tumor DNA

Analysis of optimal alignments unfolds aligners’ bias in existing variant profiles

PRINCESS: Comprehensive detection of haplotype resolved SNVs, SVs, and methylation

MutScan: Fast detection and visualization of target mutations by scanning FASTQ data

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Technical advance in targeted NGS analysis enables identification of lung cancer riskassociated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells
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