A root nodule microbiome sequencing data set from red alder (Alnus rubra Bong.).

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Callum J Bell, Johnny A Sena, Diego A Fajardo, Evan M Lavelle, Michael A Costa, Barrington Herman, Laurence B Davin, Norman G Lewis, Alison M Berry
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引用次数: 0

Abstract

There have been frequent reports of more than one strain of the nitrogen-fixing symbiont, Frankia, in the same root nodule of plants in the genus Alnus, but quantitative assessments of their relative contributions have not been made to date. Neither has the diversity of other microbes, having potential functional roles in symbiosis, been systematically evaluated. Alnus rubra root nodule microbiota were studied using Illumina short read sequencing and kmer-based read classification. Single end 76 bp sequencing was done to a median depth of 96 million reads per sample. Reads were assigned to taxa using KrakenUniq, with taxon abundances being estimated using its companion program Bracken. This was the first high resolution study of Alnus root nodules using next generation sequencing (NGS), quantifying multiple Cluster 1 A Frankia strains in single nodules, and in some cases, a Cluster 4 strain. Root nodules were found to contain diverse bacteria, including several genera containing species known to have growth-promoting effects. Evidence was found for partitioning of some bacterial strains in older versus younger lobes.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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