Environmental DNA survey captures patterns of fish and invertebrate diversity across a tropical seascape
Published article: Nguyen, B.N., Shen, E.W., Seemann, J. et al. Environmental DNA survey captures patterns of fish and invertebrate diversity across a tropical seascape. Sci Rep 10, 6729 (2020). https://doi.org/10.1038/s41598-020-63565-9
Bryan N Nguyen†, Elaine W Shen†, Janina Seemann, Adrienne MS Correa, James L O’Donnell, Andrew H Altieri, Nancy Knowlton, Keith A Crandall, Scott P Egan, W Owen McMillan, Matthieu Leray
† These authors contributed equally.
This GitHub page holds the code for processing the Bocas del Toro 2017 environmental DNA project starting after trimming and demultiplexing with Flexbar, amplicon sequence variant-calling with DADA2, clustering with VSEARCH, post-OTU curation with LULU, and taxonomic assignment with BLCA and BLAST. Because these steps above were run on a high-performance cluster and the scripts and organization of other clusters and bioinformaticians may vary, we provide the parameters used for the above softwares below instead of the exact scripts used.
Flexbar (version 3.0.3) was run twice to remove primer indices from both the forward and reverse reads. Reads had to have identical primer indices on both ends to pass quality control. Flexbar was run with the following parameters:
flexbar -r <read 1 FASTQ> -p <read 2 FASTQ> --adapter-trim-end ANY -a <TruSeq adapters FASTA> -ao 7 -b <primer index FASTA> --barcode-trim-end LTAILAny files under 100k in size were removed for quality control then Flexbar was run again on the output files from the first Flexbar run with the following parameters:
flexbar -r <read 2 FASTQ> -p <read 1 FASTQ> -b <primer index FASTA> --barcode-trim-end LTAILAfter adapter-removal and demultiplexing with Flexbar, reads were processed with DADA2 (version 1.9.0) using the following parameters:
BCS_out <- filterAndTrim(forward_reads, forward_reads_filtered, reverse_reads, reverse_reads_filtered, rm.phix = TRUE, maxN = 0, maxEE = c(2,2), truncQ = 10, trimLeft = 26, minLen = 100, multithread = parallel::detectCores())Sequence variants were re-oriented into the forward direction by reverse-complementing if the reverse complement had a better Hamming distance than the original sequence after Needleman-Wunsch alignment to a reference sequence from a 313-bp section of the COI gene from a Tedania ignis sequence, GenBank accession number DQ133904.1. This is to correct for the tendency of the adapter-ligation library preparation method to cause random read directions.
>DQ133904.1 subset
ATTATCTGGGATTCAGGCTCATTCCGGGGGTTCGGTAGATTTGGTTATTTTTAGTTTACATTTAGCGGGTATTTCTTCTATATTGGCGGCTATGAATTTTATAACCACTATTATTAATATGAGGGCACCAGGGATAACAATGGATAGAACGCCATTGTTTGTTTGGTCAATTTTAGTAACTGCGGTTTTATTATTATTATCTTTACCAGTATTAGCAGGCGCAATTACTATGTTATTAACGGATAGAAATTTTAATACTGCTTTTTTTGATCCAGCAGGTGGAGGAGACCCGATTTTATATCAACATTTATTT
Chimeras were then detected and removed using DADA2's removeBimeraDenovo() function.
Sequence variants from DADA2 were checked for chimeras again with VSEARCH and clustered at 97% identity.
OTUs from VSEARCH were then curated using LULU with the following parameters: minimum_ratio_type = "min", minimum_ratio = 1, minimum_match = 84, minimum_relative_cooccurence = 0.95.
Sequence variants from DADA2 were assigned taxonomy using a slightly modified version of BLCA forked from commit fb2bd12. Sequences were compared against the MIDORI UNIQUE database version 20180221. Hits needed to have a minimum identity score of 70 and a minimum coverage of 0.75 to be used.
OTUs that remained unidentified with BLCA were compared to the whole NCBI NT database (retrieved May 2018) using BLAST searches (word size = 7; max e-value = 5e-13) and assigned the taxonomic information of the lowest common ancestor of the top 100 hits.
After this, OTU and taxonomy tables were combined into a single phyloseq object in R (data/curated_Bocas_eDNA_phyloseq.rds in this GitHub repository).
This GitHub repository contains the code for the R analyses accompanying this project, downstream from the analyses described above. The file edna_manuscript_minimal.R contains the bulk of the primary analyses. The file differential_abundance_heatmaps.R contains the code for differential abundance analyses and heatmap figure generation. The file frozen_sample_comparison.R contains code for testing for differences in DNA extraction yield and PCR yield between frozen and freshly-filtered water samples.
These scripts should run properly if you clone the repo and set the repo as the working directory in R when you run the R scripts.