Dataset: SPOT virus ARISA
Deployment: lab_Fuhrman_2014

SPOT Microbial Observatory Bacterial ARISA relative peak abundance
Principal Investigator: 
Jed A. Fuhrman (University of Southern California, USC-WIES)
Cheryl-Emiliane T. Chow (University of Southern California, USC-WIES)
BCO-DMO Data Manager: 
Nancy Copley (Woods Hole Oceanographic Institution, WHOI BCO-DMO)
Current State: 
Final no updates expected
Deployment Synonyms:
 San Pedro Ocean Time-Series,  SPOT,  Microbial Observatory,  MO,  Wrigley Time Series Station,  WTSS
Version Date: 

Bacterial community composition was determined by Automated Ribosomal Intergenic Spacer Analysis (ARISA) (Fisher and Triplett, 1999; Brown et al., 2005; Chow et al., 2013; Needham et al., 2013). ARISA products were run in duplicate on non-adjacent lanes on an ABI377 by slab gel electrophoresis with internal size standards (Bioventures, Murfreesboro, TN, USA) every 25 bp (50–900 bp) or 50 bp (900–1400 bp). Peaks were identified in DAx (van Mierlo, Inc, Eindhoven, The Netherlands). Fragments (400–1210 bp) were rounded to the nearest 0.1 bp and dynamically binned (Ruan, et al., 2006b; Chow and Fuhrman, 2012). The resulting bins were manually curated to merge bins <0.1 bp wide with the nearest neighbor. ARISA OTUs were assigned an identity by matching ARISA lengths with known sequences and their ARISA products (Chow et al., 2013; Needham et al., 2013. Unique fragment lengths were considered as individual OTUs. Relative abundance of each OTU was calculated by dividing a peak’s area by the total area within the monthly fingerprint. Bacterial OTUs <0.1% of the community were removed from further analysis, and the remaining peaks were normalized by sample to determine relative abundance per month; each community thus totaled to 100%.

Relevant References:

* Chow CET, Kim DY, Sachdeva R, Caron DA, and Fuhrman JA. (2014). Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME Journal, 8: 816-829


Brown MV, Schwalbach MS, Hewson I, Fuhrman JA.(2005). Coupling 16S-ITS rDNA clone libraries and automated ribosomal intergenic spacer analysis to show marine microbial diversity: development and application to a time series. Environ Microbiol 7: 1466–1479.

 Chow C-ET, Fuhrman JA. (2012). Seasonality and monthly dynamics of marine myovirus communities. Environ Microbiol 14: 2171–2183.

 Chow C-ET, Sachdeva R, Cram JA, Steele JA, Needham DM, Patel A et al. (2013). Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight. ISME J; epub ahead of print 18 July 2013; doi:10.1038/ismej.2013.122.

 Countway PD, Gast RJ, Savai P, Caron DA. (2005). Protistan diversity estimates based on 18S rDNA from seawater incubations in the western north Atlantic1. J Eukaryot Microbiol 52: 95–106.

 Countway PD, Vigil PD, Schnetzer A, Moorthi SD, Caron DA. (2010). Seasonal analysis of protistan community structure and diversity at the USC Microbial Observatory (San Pedro Channel, North Pacific Ocean). Limnol Oceanogr 55: 2381–2396.

 Fisher MM, Triplett EW. (1999). Automated approach for ribosomal intergenic spacer analysis of microbial diversity and its application to freshwater bacterial communities. Appl Environ Microbiol 65: 4630–4636.

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