Dataset Information

Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel.

ABSTRACT: Freshwater bodies are critical components of terrestrial ecosystems. The microbial communities of freshwater ecosystems are intimately linked water quality. These microbes interact with, utilize and recycle inorganic elements and organic matter. Here, we present three metagenomic sequence datasets (total of 182.9 Gbp) from different freshwater environments in Israel. The first dataset is from diverse freshwater bodies intended for different usages - a nature reserve, irrigation and aquaculture facilities, a tertiary wastewater treatment plant and a desert rainfall reservoir. The second represents a two-year time-series, collected during 2013-2014 at roughly monthly intervals, from a water reservoir connected to an aquaculture facility. The third is from several time-points during the winter and spring of 2015 in Lake Kinneret, including a bloom of the cyanobacterium Microcystis sp. These datasets are accompanied by physical, chemical, and biological measurements at each sampling point. We expect that these metagenomes will facilitate a wide range of comparative studies that seek to illuminate new aspects of freshwater microbial ecosystems and inform future water quality management approaches.

SUBMITTER: Al-Ashhab A

PROVIDER: S-EPMC9606356 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel.

Al-Ashhab Ashraf A Marmen Sophi S Schweitzer-Natan Orna O Bolotin Evgeni E Patil Hemant H Viner-Mozzini Diti D Aharonovich Dikla D Hershberg Ruth R Minz Dror D Carmeli Shmuel S Cytryn Eddie E Sukenik Assaf A Sher Daniel D

Scientific data 20221026 1

Freshwater bodies are critical components of terrestrial ecosystems. The microbial communities of freshwater ecosystems are intimately linked water quality. These microbes interact with, utilize and recycle inorganic elements and organic matter. Here, we present three metagenomic sequence datasets (total of 182.9 Gbp) from different freshwater environments in Israel. The first dataset is from diverse freshwater bodies intended for different usages - a nature reserve, irrigation and aquaculture f ...[more]

PMID: 36289228

Similar Datasets

Project description:Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18-24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.

Dataset Information

Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel.

Publications

Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets