Computational protocol: Environmental selection of protistan plankton communities in hypersaline anoxic deep-sea basins, Eastern Mediterranean Sea

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Protocol publication

[…] Electropherograms derived from the T-RFLP runs were aligned into bins and analyzed using GelQuest© (version 2.1.2.SequentiX-digital DNA processing, Klein Raden, Germany) using default settings except for the following parameters: smoothing width, 10; baselining width, 50; minimum peak height for T-RFs, 75; minimum peak height for marker, 200; and hyperbin width, 1.0. Finally, to distinguish signal from noise using a constant percentage threshold, only those T-RFs were taken into account that contributed at least 1% to the relative fluorescence (rA, based on height of T-RFs) of a sample (Noll et al. ). The relative abundance (rA) of each T-RF was calculated as rA = ni × 100/N, in which ni represents the peak height of one distinct T-RF and N is the sum of all peak heights in a given T-RFLP profile. rA values were determined for all T-RFs detected in a size range between 50 and 700 bp for a given T-RFLP profile. Finally, data from six replicate reactions per sample were assembled by calculating the average rA of each individual T-RF to generate consensus T-RFLP profiles (one for each sample). [...] Similarities between communities were calculated with two different indices: (i) the Jaccard index, which is based on the presence/absence of a T-RF (binary variables of peak presence). This coefficient is equal to the ratio of matching T-RFs in two profiles and the total number of T-RFs present in either profile (Legendre and Legendre ); (ii) an abundance-based modification of the Sørensen Index (Chao–Sørensen), which takes into account relative fluorescence units as quantitative data (Chao et al. ). Both indices were calculated using the software EstimateS v.8 (Colwell ), and then translated into distance matrixes (1 minus Jaccard or Chao–Sørensen index value) for UPGMA cluster analyses.To assess an effect of distance on community similarities, Jaccard and Chao–Sørensen indices were plotted against distance data among individual sample sites in a Pearson rank correlation using the Statistica software package. A Student's t-test for paired samples was used for significance testing. Geographic distances were calculated via the subtraction of different depths on a single geographic position, which resulted in the altitude difference within the same basin. For the calculation of the two-dimensional great-circle distance between two points on a sphere from their longitudes and latitudes (same depth), the haversine formula (Sinnott ) was implemented in the script as provided by Chris Veness (2002–2011) at canonical correspondence analysis (CCA) of T-RFLP profiles (including T-RF size and relative abundance data) was conducted to describe the relationships between community composition patterns and underlying environmental gradients, which shape these diversity patterns. Data were log-transformed (Grant and Ogilvie ) and unconstrained permutations (n = 499) were run under a reduced model. Monte Carlo significance tests of first ordination axes and of all canonical axes together were performed. Initially, all available environmental variables (see above) were included in the model. In order to develop a robust model explaining as much variance as possible while avoiding multicollinearity, individual variables were removed in a step-wise manner. We used the Canoco software (Microcomputer Power, Ithaca, NY) for the ordination analysis.A presence/absence map, visualizing the occurrence of specific T-RFs in each individual sample, was generated using the tool Heatmap Builder (King et al. ). […]

Pipeline specifications

Software tools GelQuest, Statistica
Applications Miscellaneous, DNA fingerprinting
Diseases Hypoxia, Brain
Chemicals Magnesium, Oxygen, Sodium