Dataset Information

Detection of Single-Nucleotide and Copy Number Defects Underlying Hyperphenylalaninemia by Next-Generation Sequencing.

ABSTRACT: Hyperphenylalaninemia (HPA) is the most common inherited amino acid metabolism disorder characterized by serious clinical manifestations, including irreversible brain damage, intellectual deficiency and epilepsy. Due to its extensive genic and allelic heterogeneity, next-generation sequencing (NGS) technology may help to identify the molecular basis of this genetic disease. Herein, we describe the development and validation of a targeted NGS (tNGS) approach for the simultaneous detection of single-nucleotide changes and copy number variations (CNVs) in genes associated with HPA (PAH, GCH1, PTS, QDPR, PCBD1, DNAJC12) or useful for its differential diagnosis (SPR). Our tNGS approach offers the possibility to detail, with a high accuracy and in a single workflow, the combined effect of a broader spectrum of genomic variants in a comprehensive view, providing a significant step forward in the development of optimized patient care and management.

SUBMITTER: Tendi EA

PROVIDER: S-EPMC10377317 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

Detection of Single-Nucleotide and Copy Number Defects Underlying Hyperphenylalaninemia by Next-Generation Sequencing.

Tendi Elisabetta Anna EA Morello Giovanna G Guarnaccia Maria M La Cognata Valentina V Petralia Salvatore S Messina Maria Anna MA Meli Concetta C Fiumara Agata A Ruggieri Martino M Cavallaro Sebastiano S

Biomedicines 20230704 7

Hyperphenylalaninemia (HPA) is the most common inherited amino acid metabolism disorder characterized by serious clinical manifestations, including irreversible brain damage, intellectual deficiency and epilepsy. Due to its extensive genic and allelic heterogeneity, next-generation sequencing (NGS) technology may help to identify the molecular basis of this genetic disease. Herein, we describe the development and validation of a targeted NGS (tNGS) approach for the simultaneous detection of sing ...[more]

PMID: 37509538

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Project description:Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity (e.g., bias, noise, alignment errors) of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs.

Dataset Information

Detection of Single-Nucleotide and Copy Number Defects Underlying Hyperphenylalaninemia by Next-Generation Sequencing.

Publications

Detection of Single-Nucleotide and Copy Number Defects Underlying Hyperphenylalaninemia by Next-Generation Sequencing.

Similar Datasets

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