Computational protocol: Contrasting the Genetic Patterns of Microbial Communities in Soda Lakes with and without Cyanobacterial Bloom

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Protocol publication

[…] For the shotgun metagenomic dataset, overlapping reads were paired using PEAR 0.9.5 () and filtered for quality with Phred > 30, and a length longer than 80 nt using Seqyclean 1.9.8. The reads were annotated by BLASTX against the NCBI-nr database (November 2016) using DIAMOND (). Retrieved annotations were later analyzed using MEGAN6 (). The data presented in the manuscript was categorized using the SEED database for functional assignment (). We also provided an overview of the taxonomic distribution of bacteria across the samples by retrieving bacterial 16S rRNA gene sequences that matched the Silva SSU database. For the taxonomic assignation of reads associated with the distinct biogeochemical processes, we used the FOCUS tool () with default parameters.Prior to comparative metagenomic analyses, samples were normalized by taking into account the smallest number of reads of any of the selected samples. To test for statistical differences of SEED annotations across samples, we used the software package STAMP (). The p-values were calculated using the two-sided Welch’s t-test followed by Benjamini-Hochberg FDR correction ().The biogeochemical pathways of C, N, and S were annotated and mapped using the read counts from the SEED annotations and marker genes (Supplementary Table ). The heatmap was constructed based on z-score transformed functional annotations to improve normality and homogeneity of the variances. Predicted functional annotations that segregated significantly between sample types were identified using random forest analysis () with 1,000 trees followed by the Boruta algorithm for feature selection (average z-scores of 1,000 runs > 4) (). […]

Pipeline specifications

Software tools PEAR, seqyclean, BLASTX, MEGAN, STAMP
Application Metagenomic sequencing analysis
Diseases Bloom Syndrome
Chemicals Arsenic, Carbon, Nitrogen, Oxygen, Sulfur