Dataset Information

Bacterial distribution in the Equatorial Indian Ocean using Amplicon sequencing of V3-V4 rDNA hypervariable region data.

ABSTRACT: The Equatorial Indian Ocean (EIO) is a complex system strongly influenced by Indian Monsoon. During a RAMA (Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction) mooring maintenance expedition during the Southwest monsoon (August-September 2016) onboard ORV Sagar Kanya, seawater samples from the surface, deep chlorophyll maxima (DCM) and 200m were collected for bacterioplankton community structure. Herein we document our amplicon data of the bacterial community at 4 stations (4.01°S, 1.60°S, 0.36°N and 1.78°N) along the 67°00' E transect. The samples were subjected to next-generation sequencing (NGS), followed by processing with Mothur v 1.48.0, and the taxonomic classification prepared with Silva 138.1nr reference database. Our data indicates Alphaproteobacteria (48 %) and Cyanobacteria (33 %) dominance in the surface and DCM samples.

SUBMITTER: Sinha AK

PROVIDER: S-EPMC9679681 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bacterial distribution in the Equatorial Indian Ocean using Amplicon sequencing of V3-V4 rDNA hypervariable region data.

Sinha Alok K AK Parli Bhaskar V BV Anilkumar N N

Data in brief 20221014

The Equatorial Indian Ocean (EIO) is a complex system strongly influenced by Indian Monsoon. During a RAMA (Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction) mooring maintenance expedition during the Southwest monsoon (August-September 2016) onboard <i>ORV Sagar Kanya,</i> seawater samples from the surface, deep chlorophyll maxima (DCM) and 200m were collected for bacterioplankton community structure. Herein we document our amplicon data of the bacterial communi ...[more]

PMID: 36426021

Similar Datasets

Project description:Meat and seafood spoilage ecosystems harbor extensive bacterial genomic diversity that is mainly found within a small number of species but within a large number of strains with different spoilage metabolic potential. To decipher the intraspecies diversity of such microbiota, traditional metagenetic analysis using the 16S rRNA gene is inadequate. We therefore assessed the potential benefit of an alternative genetic marker, gyrB, which encodes the subunit B of DNA gyrase, a type II DNA topoisomerase. A comparison between 16S rDNA-based (V3-V4) amplicon sequencing and gyrB-based amplicon sequencing was carried out in five types of meat and seafood products, with five mock communities serving as quality controls. Our results revealed that bacterial richness in these mock communities and food samples was estimated with higher accuracy using gyrB than using16S rDNA. However, for Firmicutes species, 35% of putative gyrB reads were actually identified as sequences of a gyrB paralog, parE, which encodes subunit B of topoisomerase IV; we therefore constructed a reference database of published sequences of both gyrB and pare for use in all subsequent analyses. Despite this co-amplification, the deviation between relative sequencing quantification and absolute qPCR quantification was comparable to that observed for 16S rDNA for all the tested species. This confirms that gyrB can be used successfully alongside 16S rDNA to determine the species composition (richness and evenness) of food microbiota. The major benefit of gyrB sequencing is its potential for improving taxonomic assignment and for further investigating OTU richness at the subspecies level, thus allowing more accurate discrimination of samples. Indeed, 80% of the reads of the 16S rDNA dataset were represented by thirteen 16S rDNA-based OTUs that could not be assigned at the species-level. Instead, these same clades corresponded to 44 gyrB-based OTUs, which differentiated various lineages down to the subspecies level. The increased ability of gyrB-based analyses to track and trace phylogenetically different groups of strains will generate improved resolution and more reliable results for studies of the strains implicated in food processes.

Project description:BackgroundStudies on the airway microbiome have been performed using a wide range of laboratory protocols for high-throughput sequencing of the bacterial 16S ribosomal RNA (16S rRNA) gene. We sought to determine the impact of number of polymerase chain reaction (PCR) steps (1- or 2- steps) and choice of target marker gene region (V3 V4 and V4) on the presentation of the upper and lower airway microbiome. Our analyses included lllumina MiSeq sequencing following three setups: Setup 1 (2-step PCR; V3 V4 region), Setup 2 (2-step PCR; V4 region), Setup 3 (1-step PCR; V4 region). Samples included oral wash, protected specimen brushes and protected bronchoalveolar lavage (healthy and obstructive lung disease), and negative controls.ResultsThe number of sequences and amplicon sequence variants (ASV) decreased in order setup1 > setup2 > setup3. This trend appeared to be associated with an increased taxonomic resolution when sequencing the V3 V4 region (setup 1) and an increased number of small ASVs in setups 1 and 2. The latter was considered a result of contamination in the two-step PCR protocols as well as sequencing across multiple runs (setup 1). Although genera Streptococcus, Prevotella, Veillonella and Rothia dominated, differences in relative abundance were observed across all setups. Analyses of beta-diversity revealed that while oral wash samples (high biomass) clustered together regardless of number of PCR steps, samples from the lungs (low biomass) separated. The removal of contaminants identified using the Decontam package in R, did not resolve differences in results between sequencing setups.ConclusionsDifferences in number of PCR steps will have an impact of final bacterial community descriptions, and more so for samples of low bacterial load. Our findings could not be explained by differences in contamination levels alone, and more research is needed to understand how variations in PCR-setups and reagents may be contributing to the observed protocol bias.

Dataset Information

Bacterial distribution in the Equatorial Indian Ocean using Amplicon sequencing of V3-V4 rDNA hypervariable region data.

Publications

Bacterial distribution in the Equatorial Indian Ocean using Amplicon sequencing of V3-V4 rDNA hypervariable region data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets