Errors in sequencing are a major obstacle in the interpretation of next-generation sequencing (NGS) results. In the present study, sequencing errors identified from analysis of single nucleotide variants (SNVs) identified during exome sequencing of human germline DNA were studied using the Thermo Fisher Ion Proton System. Two consanguineous cases were selected for sequencing using the AmpliSeq Exome capture kit, and SNVs found in both cases were validated using Sanger sequencing. A total of 98 SNVs detected by NGS were randomly selected for further analysis. Nine of the analyzed SNVs were shown to be false positives when confirmed by Sanger sequencing. All but one SNV were considered to be homopolymer regions, mainly through the insertion or deletion of nucleotides. The remaining error was considered to be related to the primer. The present results revealed that the majority of the SNV sequencing errors originated from homopolymer insertion/deletion errors, which are commonly observed when using the Ion Torrent system.
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http://dx.doi.org/10.3892/br.2017.911 | DOI Listing |
Comput Struct Biotechnol J
December 2024
Institute of Bioinformatics, University of Münster, Münster, Germany.
Microbiome studies aim to answer the following questions: which organisms are in the sample and what is their impact on the patient or the environment? To answer these questions, investigators have to perform comparative analyses on their classified sequences based on the collected metadata, such as treatment, condition of the patient, or the environment. The integrity of sequences, classifications, and metadata is paramount for the success of such studies. Still, the area of data management for the preliminary study results appears to be neglected.
View Article and Find Full Text PDFACS Omega
January 2025
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
It is of great significance to realize the accurate prediction of the key output response of the chemical synthetic ammonia process for optimizing system performance and operation monitoring. Because many key intermediate variables of complex systems are difficult to measure comprehensively, there are great difficulties and errors in mechanism analysis and identification modeling techniques. Based on random forest (RF) variable selection, a deep neural network combining temporal convolutional network (TCN) and transformer is proposed to predict the output variables of the synthetic ammonia process.
View Article and Find Full Text PDFACS Omega
January 2025
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion.
View Article and Find Full Text PDFClin Epigenetics
January 2025
Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Institute of Fluid Mechanics, University of Rostock, Rostock, Germany.
Purpose: To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.
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