ArticleEvolutionary bioinformatics online2019
Versatile Quality Control Methods for Nanopore Sequencing.
Article in Evolutionary bioinformatics online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Mutational analysis of primary and advanced chordoma tissue using next-generation sequencing.Cancer · 2025Article
- Can long-read sequencing tackle the barriers, which the next-generation could not? A review.Pathology oncology research : POR · 2024Review
- Structural variants in the Chinese population and their impact on phenotypes, diseases and population adaptation.Nature communications · 2021Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Third-generation sequencing using nanopores as biosensors has recently emerged as a strategy capable to overcome next-generation sequencing drawbacks and pitfalls. Assessing the quality of the data produced by nanopore sequencing platforms is essential to decide how useful these may be in making biological discoveries. Here, we briefly contextualized NanoR, a quality control method for nanopore sequencing data we developed, in the scenario of preexistent similar tools. We also illustrated 2 quality control pipelines, readily applicable to nanopore sequencing data, respectively, based on NanoR and PyPore, a second quality control method published by our group.
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