Clc genomics workbench v20

Transconjugants were sequenced by a combination of Oxford Nanopore and Illumina MiSeq technologies. Complete genomic DNA (gDNA) was purified using the Qiagen Genomic-Tip 100 kit according to the manufacturer’s directions. Library preparations for Nanopore sequencing were performed with the Rapid barcoding kit (Nanopore) according to the manufacturer’s recommendations. Multiplexed libraries were concentrated using AMPure XP beads (Nanopore) and sequenced in a flow cell until no active pores remained. MiSeq libraries were prepared and sequenced by 300-bp paired-end read lengths using a Nextera XT DNA library preparation kit (Illumina) according to the manufacturer’s instructions. De novo assembly of the Nanopore data was performed using the long-read support function in CLC Genomics Workbench v.20 (Qiagen). Contigs were reanalyzed by reference assembly using MiSeq data with CLC Genomics Workbench v.20 including the Microbial Analysis Pro suite (Qiagen). Detection of resistance genes and plasmid replicons was done by submitting de novo-assembled contigs to the online resources ResFinder (66 (link)) and PlasmidFinder (67 (link)), respectively, at the Center for Genomic Epidemiology, DTU, Denmark.

Short reads generated and sequenced by the MiSeq platform were processed using the CLC Genomics workbench v20.0.4 (CLC bio, Denmark). Qualified and trimmed reads were mapped to 16S rRNA references curated in the SILVA database. Taxonomic Profiling and Find Best Matches with K-mer Spectra (Microbial Genomics Module; CLC genomics workbench) was applied for microbe identification. Throughout analysis of long-read sequencing, EPI2ME (https://epi2me.nanoporetech.com), a cloud-based algorithm including analytical workflow for classification of 16S rRNA with MinION results, was applied in this study. MinION-generated sequencing data were first uploaded by using EPI2ME desktop agent and accessed through a web-interface. Analytical results were generated using EPI2ME to present classification of 16S rRNA based on the NCBI database, containing 18,927 16S rRNA reference sequences. In addition, taxonomic profiling of MinION data was synchronously accessed using the Microbial Genomics Module (CLC genomics workbench) with 16S rRNA references downloaded from the SILVA database (version 128). The required parameter for analyzing 16S rRNA results using EPI2ME or Microbial Genomics Module is default.

CLC Genomics Workbench v. 20.0.4 (CLC Bio, Denmark) was used to filter and trim reads, map the high-quality reads against the last version of the zebrafish genome (GRCz11) and perform the differential expression analyses. Raw reads were trimmed to remove the adaptor sequences and low-quality reads. RNA-Seq analyses were conducted with the following parameters: length fraction = 0.8, similarity fraction = 0.8, mismatch cost = 2, insertion cost = 3 and deletion cost = 3. The expression values were set as transcripts per million (TPM). Finally, a differential expression analysis test was used to compare gene expression levels and to identify differentially expressed genes (DEGs). Transcripts with fold change (FC) values > |2| and false discovery rate (FDR) values < 0.05 were retained for further analyses. To identify and quantify the directions of variability in the data, a principal component analysis (PCA) plot was constructed using the original expression values. Using the TPM values of the selected DEGs, heatmaps for each sampling point were constructed using the complete linkage method with Euclidean distance. Both PCA and heatmaps were constructed using the web tool Clustvis (55 (link)) (https://biit.cs.ut.ee/clustvis/), and a Venn diagram was constructed with the InteractiVenn web tool (56 (link)) (http://www.interactivenn.net/).