NanoPipe-a web server for nanopore MinION sequencing data analysis

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Abstract

Background: The fast-moving progress of the third-generation long-read sequencing technologies will soon bring the biological and medical sciences to a new era of research. Altogether, the technique and experimental procedures are becoming more straightforward and available to biologists from diverse fields, even without any profound experience in DNA sequencing. Thus, the introduction of the MinION device by Oxford Nanopore Technologies promises to "bring sequencing technology to the masses"and also allows quick and operative analysis in field studies. However, the convenience of this sequencing technology dramatically contrasts with the available analysis tools, which may significantly reduce enthusiasm of a "regular"user. To really bring the sequencing technology to every biologist, we need a set of user-friendly tools that can perform a powerful analysis in an automatic manner. Findings: NanoPipe was developed in consideration of the specifics of the MinION sequencing technologies, providing accordingly adjusted alignment parameters. The range of the target species/sequences for the alignment is not limited, and the descriptive usage page of NanoPipe helps a user to succeed with NanoPipe analysis. The results contain alignment statistics, consensus sequence, polymorphisms data, and visualization of the alignment. Several test cases are used to demonstrate the efficiency of the tool. Conclusions: Freely available NanoPipe software allows effortless and reliable analysis of MinION sequencing data for experienced bioinformaticians, as well for wet-lab biologists with minimum bioinformatics knowledge. Moreover, for the latter group, we describe the basic algorithm necessary for MinION sequencing analysis from the first to last step.

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Shabardina, V., Kischka, T., Manske, F., Grundmann, N., Frith, M. C., Suzuki, Y., & Makałowski, W. (2019). NanoPipe-a web server for nanopore MinION sequencing data analysis. GigaScience, 8(2). https://doi.org/10.1093/gigascience/giy169

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