1 Results for Group Analysis

Click on the links below to view the results for each group analysis:


Group1.illumina.pe



2 Materials and Methods

The samples were processed and analyzed with the ZymoBIOMICS® Metatranscriptomics Sequencing Service (Zymo Research, Irvine, CA). Specific details for the project can be found in the final report PDF.

RNA Extraction: One of two DNA extraction kits was used depending on the sample type and sample volume. In most cases, the ZymoBIOMICS Magbead RNA Kit (R2137, Zymo Research, Irvine, CA) was used to extract RNA using an automated liquid handler. In some cases, ZymoBIOMICS RNA Miniprep Kit (R2001, Zymo Research, Irvine, CA) was used. Both kits start with mechanical lysis of microbial samples using ZR BashingBead Lysis Tube (0.1 mm and 0.5 mm) to ensure efficient lysis of bacteria, archaea and fungi.

RNA Library Preparation: The RNA-Seq library was prepared using the Zymo-Seq RiboFree Total RNA Library Kit (R3000, Zymo Research, Irvine, CA) with 500 ng RNA as input. All libraries were quantified with TapeStation (Agilent Technologies, Santa Clara, CA) and then pooled in equal abundance. The final pool was quantified using qPCR.

Sequencing: The final library was sequenced on either the Illumina HiSeq® or the Illumina NovaSeq®.

Bioinformatics Analysis: Raw sequence reads were trimmed to remove low quality fractions and adapters with Trimmomatic-0.33 (Bolger et al., 2014): quality trimming by sliding window with 6 bp window size and a quality cutoff of 20, and reads with size lower than 70 bp were removed. Antimicrobial resistance and virulence factor gene identification was performed with the DIAMOND sequence aligner (Buchfink et al., 2015). Microbial composition was profiled with Centrifuge (Kim et al., 2016) using bacterial, viral, fungal, mouse, and human genome datasets. Strain-level abundance information was extracted from the Centrifuge outputs and further analyzed: (1) to perform alpha- and beta-diversity analyses; (2) to create microbial composition barplots with QIIME (Caporaso et al., 2012); (3) to create taxa abundance heatmaps with hierarchical clustering (based on Bray-Curtis dissimilarity); and (4) for biomarker discovery with LEfSe (Segata et al., 2011) with default settings (p>0.05 and LDA effect size >2).
Functional profiling was performed using Humann2 (Franzosa, et al., 2018) including identification of UniRef (Suzek et al., 2014) gene family and MetaCyc (Caspi et al., 2020) metabolic pathways.

3 References

Bolger, A.M., Lohse, M., and Usadel, B. (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114-2120.

Buchfink, B., Xie, C., Huson, D.H. (2015) Fast and sensitive protein alignment using DIAMOND. Nature Methods 12:59-60.

Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K. et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7: 335-336.

Franzosa E.A., McIver L.J., Rahnavard G., Thompson L.R., Schirmer M., Weingart G., Schwarzberg Lipson K., Knight R., Caporaso J.G., Segata N., Huttenhower C. (2018) Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 15: 962-968.

Caspi, R., R. Billington, C. A. Fulcher, I. M. Keseler, A. Kothari, M. Krummenacker, M. Latendresse, P. E. Midford, Q. Ong, W. K. Ong, S. Paley, P. Subhraveti, and P. D. Karp. (2018) The MetaCyc database of metabolic pathways and enzymes. Nucleic acids research 46:D633-D639.

Kim, D., Song, L., Breitwieser, F.P., Salzberg, S.L. (2016) Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 12:1721-1729.

Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., and Huttenhower, C. (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12: R60.