On the critical evaluation and confirmation of germline sequence variants identified using massively parallel sequencing
Journal of Biotechnology, ISSN: 0168-1656, Vol: 298, Page: 64-75
2019
- 10Citations
- 20Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations10
- Citation Indexes8
- CrossRef5
- Policy Citations2
- Policy Citation2
- Captures20
- Readers20
- 20
Article Description
Although massively parallel sequencing (MPS) is becoming common practice in both research and routine clinical care, confirmation requirements of identified DNA variants using alternative methods are still topics of debate. When evaluating variants directly from MPS data, different read depth statistics, together with specialized genotype quality scores are, therefore, of high relevance. Here we report results of our validation study performed in two different ways: 1) confirmation of MPS identified variants using Sanger sequencing; and 2) simultaneous Sanger and MPS analysis of exons of selected genes. Detailed examination of false-positive and false-negative findings revealed typical error sources connected to low read depth/coverage, incomplete reference genome, indel realignment problems, as well as microsatellite associated amplification errors leading to base miss-calling. However, all these error types were identifiable with thorough manual revision of aligned reads according to specific patterns of distributions of variants and their corresponding reads. Moreover, our results point to dependence of both basic quantitative metrics (such as total read counts, alternative allele read counts and allelic balance) together with specific genotype quality scores on the used bioinformatics pipeline, stressing thus the need for establishing of specific thresholds for these metrics in each laboratory and for each involved pipeline independently.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0168165619301270; http://dx.doi.org/10.1016/j.jbiotec.2019.04.013; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85064435175&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/30998956; https://linkinghub.elsevier.com/retrieve/pii/S0168165619301270; https://dx.doi.org/10.1016/j.jbiotec.2019.04.013
Elsevier BV
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