Standardised DNA and RNA sample quality control

The Integrated Primate Biomaterials and Information Resource ( www.IPBIR.org ) provides essential research reagents to the scientific community by establishing, verifying, maintaining, and distributing DNA and RNA derived from primate cell cultures. The IPBIR uses mitochondrial cytochrome c oxidase subunit I sequences to verify the identity of samples for quality control purposes in the accession, cell culture, DNA extraction processes and prior to shipping to end users. As a result, IPBIR is accumulating a database of ‘DNA barcodes’ for many species of primates. However, this quality control process is complicated by taxon specific patterns of ‘universal primer’ failure, as well as the amplification or co-amplification of nuclear pseudogenes of mitochondrial origins. To overcome these difficulties, taxon specific primers have been developed, and reverse transcriptase PCR is utilized to exclude these extraneous sequences from amplification. DNA barcoding of primates has applications to conservation and law enforcement. Depositing barcode sequences in a public database, along with primer sequences, trace files and associated quality scores, makes this species identification technique widely accessible. Reference DNA barcode sequences should be derived from, and linked to, specimens of known provenance in web-accessible collections in order to validate this system of molecular diagnostics.

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Demystification of RNAseq Quality Control

JITA - Journal of Information Technology and Applications (Banja Luka) - APEIRON ◽

10.7251/jit2102073d ◽

2021 ◽

Vol 22 (2) ◽

Author(s):

Dragana Dudić ◽

Bojana Banović Đeri ◽

Vesna Pajić ◽

Gordana Pavlović-Lažetić

Keyword(s):

Quality Control ◽

Next Generation Sequencing ◽

Rna Sequencing ◽

Next Generation ◽

Comprehensive Guidance ◽

Dna And Rna ◽

Control Evaluation ◽

Downstream Analysis ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

Next Generation Sequencing (NGS) analysis has become a widely used method for studying the structure of DNA and RNA, but complexity of the procedure leads to obtaining error-prone datasets which need to be cleansed in order to avoid misinterpretation of data. We address the usage and proper interpretations of characteristic metrics for RNA sequencing (RNAseq) quality control, implemented in and reported by FastQC, and provide a comprehensive guidance for their assessment in the context of total RNAseq quality control of Illumina raw reads. Additionally, we give recommendations how to adequately perform the quality control preprocessing step of raw total RNAseq Illumina reads according to the obtained results of the quality control evaluation step; the aim is to provide the best dataset to downstream analysis, rather than to get better FastQC results. We also tested effects of different preprocessing approaches to the downstream analysis and recommended the most suitable approach.

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Viral Metagenomics in the Clinical Realm: Lessons Learned from a Swiss-Wide Ring Trial

Genes ◽

10.3390/genes10090655 ◽

2019 ◽

Vol 10 (9) ◽

pp. 655 ◽

Cited By ~ 12

Author(s):

Junier ◽

Huber ◽

Schmutz ◽

Kufner ◽

Zagordi ◽

...

Keyword(s):

Quality Control ◽

Viral Infections ◽

Lessons Learned ◽

Viral Metagenomics ◽

Reference Database ◽

Shotgun Metagenomics ◽

Dna And Rna ◽

Definition Of ◽

Next Generation Sequencing Ngs ◽

The Impact

Shotgun metagenomics using next generation sequencing (NGS) is a promising technique to analyze both DNA and RNA microbial material from patient samples. Mostly used in a research setting, it is now increasingly being used in the clinical realm as well, notably to support diagnosis of viral infections, thereby calling for quality control and the implementation of ring trials (RT) to benchmark pipelines and ensure comparable results. The Swiss NGS clinical virology community therefore decided to conduct a RT in 2018, in order to benchmark current metagenomic workflows used at Swiss clinical virology laboratories, and thereby contribute to the definition of common best practices. The RT consisted of two parts (increments), in order to disentangle the variability arising from the experimental compared to the bioinformatics parts of the laboratory pipeline. In addition, the RT was also designed to assess the impact of databases compared to bioinformatics algorithms on the final results, by asking participants to perform the bioinformatics analysis with a common database, in addition to using their own in-house database. Five laboratories participated in the RT (seven pipelines were tested). We observed that the algorithms had a stronger impact on the overall performance than the choice of the reference database. Our results also suggest that differences in sample preparation can lead to significant differences in the performance, and that laboratories should aim for at least 5–10 Mio reads per sample and use depth of coverage in addition to other interpretation metrics such as the percent of coverage. Performance was generally lower when increasing the number of viruses per sample. The lessons learned from this pilot study will be useful for the development of larger-scale RTs to serve as regular quality control tests for laboratories performing NGS analyses of viruses in a clinical setting.

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SNP Genotyping and ApoE Genotyping v1

10.17504/protocols.io.by9ypz7w ◽

2021 ◽

Author(s):

Huw Morris ◽

Manuela MX Tan ◽

Donald G Grosset ◽

Nigel M Williams

Keyword(s):

Parkinson’S Disease ◽

Quality Control ◽

Blood Sample ◽

Dna Extraction ◽

Study Design ◽

Human Blood ◽

Patient Data ◽

Snp Genotyping ◽

Sample Quality ◽

Human Blood Sample

This protocol details the steps for DNA extraction from a human blood sample, quality control, and SNP and APOE genotyping. The protocol has been adapted from the PRoBaND SNP Genotyping and ApoE Genotyping Protocol. The overall protocol for PRoBaND /Tracking Parkinson’s is published: Malek, N., Swallow, D. M. A., Grosset, K. A., Lawton, M. A., Marrinan, S. L., Lehn, A. C., Bresner, C., Bajaj, N., Barker, R. A., Ben-Shlomo, Y., Burn, D. J., Foltynie, T., Hardy, J., Morris, H. R., Williams, N. M., Wood, N., & Grosset, D. G. (2015). Tracking Parkinson’s: Study Design and Baseline Patient Data. Journal of Parkinson’s Disease, 5(4), 947–959. https://doi.org/10.3233/JPD-150662

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