Back

Universal Amplicon Sequencing of North Imperial Valley Wetlands Microbiomes

Becker, S. A.; Graham, E.; Sager, L.; Spreafico, R.; McCarren, J.

2022-09-30 ecology
10.1101/2022.09.29.509762 bioRxiv
Show abstract

DNA sequencing of complex microbial communities allows for the classification and quantification of thousands of distinct organisms in diverse environmental niches. We present a three domain "Universal Amplicon" (UA) method to simultaneously amplify DNA from the ribosomal small subunit locus from bacteria, archaea, and eukaryotes (and their organelles) using a single pair of amplification primers. We demonstrate the amenability of the UA to multiplexed Illumina library preparation and MiSeq-based sequencing. We validate the UA by sequencing a commercially available microbial community of known quantitative composition and through direct comparison to a shotgun metagenomics dataset. Following validation, we apply the UA to a time-course study of the wetlands of the Northern Imperial Valley in California and show substantial and variable microbial life in the Salton Sea and nearby waters. We demonstrate that the microbial ecology of the Salton Sea varies on at least a monthly basis and is distinct from the surrounding area. Finally, we contribute an open-source Shiny app for real-time analysis of complex metagenomic communities, with application to this study and far beyond.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 7%
22.3%
Similar papers in this journal
2
ISME Communications
◐ 120 papers in training set
Top 0.2%
7.9%
Similar papers in this journal
3
Microbiome
○ 154 papers in training set
Top 0.5%
6.3%
Similar papers in this journal
4
mSystems
● 394 papers in training set
Top 1%
6.3%
Similar papers in this journal
5
Microbial Ecology
○ 29 papers in training set
Top 0.1%
5.5%
Similar papers in this journal
6
Frontiers in Microbiology
○ 427 papers in training set
Top 2%
4.8%
50% of probability mass above
7
Environmental DNA
○ 56 papers in training set
Top 0.2%
4.8%
8
Molecular Ecology Resources
○ 171 papers in training set
Top 0.5%
4.3%
9
Scientific Reports
○ 3612 papers in training set
Top 25%
4.0%
10
The ISME Journal
◐ 228 papers in training set
Top 2%
2.6%
11
Applied and Environmental Microbiology
● 339 papers in training set
Top 3%
2.4%
12
Environmental Microbiome
○ 29 papers in training set
Top 0.3%
2.4%
13
BioTechniques
○ 25 papers in training set
Top 0.1%
2.4%
14
Microbiology Spectrum
● 469 papers in training set
Top 7%
1.5%
15
PeerJ
◐ 308 papers in training set
Top 7%
1.3%
16
Ecology and Evolution
○ 267 papers in training set
Top 4%
1.1%
17
mBio
● 833 papers in training set
Top 10%
1.1%
18
mSphere
● 302 papers in training set
Top 6%
1.0%
19
Molecular Ecology
○ 336 papers in training set
Top 4%
0.9%
20
GigaScience
◐ 212 papers in training set
Top 4%
0.8%
21
Royal Society Open Science
● 214 papers in training set
Top 6%
0.8%
22
BMC Genomics
○ 406 papers in training set
Top 8%
0.8%
23
Metabarcoding and Metagenomics
● 14 papers in training set
Top 0.2%
0.8%
24
Environmental Microbiology
○ 133 papers in training set
Top 3%
0.8%
25
Access Microbiology
● 25 papers in training set
Top 1%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.