Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology. |
| Συγγραφείς: |
Weißbecker, Christina, Schnabel, Beatrix, Heintz-Buschart, Anna |
| Πηγή: |
GigaScience; Dec2020, Vol. 9 Issue 12, p1-8, 8p |
| Θεματικοί όροι: |
Microbial ecology, Ribosomal RNA, Nucleotide sequence, Microbial communities, Sequence analysis, Electronic data processing |
| Περίληψη: |
Background Amplicon sequencing of phylogenetic marker genes, e.g. 16S, 18S, or ITS ribosomal RNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to use the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Results We present dadasnake, a user-friendly, 1-command Snakemake pipeline that wraps the preprocessing of sequencing reads and the delineation of exact sequence variants by using the favorably benchmarked and widely used DADA2 algorithm with a taxonomic classification and the post-processing of the resultant tables, including hand-off in standard formats. The suitability of the provided default configurations is demonstrated using mock community data from bacteria and archaea, as well as fungi. Conclusions By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. It is easy to install dadasnake via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake. [ABSTRACT FROM AUTHOR] |
|
Copyright of GigaScience is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Complementary Index |