1 Results for Group Analysis

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


00…AllSamples.illumina.pe

01.Age.Effect.illumina.pe

02.Treatment.Effect.illumina.pe

03…All.Comparisons.illumina.pe

2 Raw Data

Click on the link below to download the excel file containing the links to the raw FASTQ files for the samples in this project:

https://zymo-microbiomics-service.s3.amazonaws.com/epiquest/epiquest_zr31987/WFYFYSLJYHRXFFZN/rawdata/260626.metatrans/zr31987_rawdata_links.tsv



3 Materials and Methods

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

RNA Extraction: If applicable, RNA extraction was performed using either the ZymoBIOMICS® RNA Miniprep Kit (R2001, Zymo Research, Irvine, CA) or the ZymoBIOMICS MagBead RNA Kit (R2137, Zymo Research, Irvine, CA), the latter of which utilizes an automated liquid handler. This process starts with mechanical lysis of microbial samples using ZR BashingBead Lysis Tube (0.1 mm and 0.5 mm) to ensure efficient and unbiased lysis of bacteria, archaea, and fungi. Purified RNA is eluted in 40 l ZymoBIOMICS™ DNase/RNase-Free Water.

Library Preparation: Total RNA samples were profiled with Metatranscriptomics sequencing. Sequencing libraries were prepared using the Zymo-Seq RiboFree® Total RNA Library Kit (R3003, Zymo Research, Irvine, CA) with up to 250 ng RNA input following the manufacturers protocol. This method uses unique dual-Index 8 bp barcodes with TruSeq® adapters (Illumina, San Diego, CA). All libraries were quantified with Qubit™ 1X dsDNA High Sensitivity (HS) (Q33231, Invitrogen™ ThermoFisher Scientific, Waltham, MA) and TapeStation® (Agilent Technologies, Santa Clara, CA), then were pooled in equal abundance. The final pool was quantified using Droplet Digital™ PCR (Bio-Rad Laboratories, Hercules, CA).

Sequencing: The final library was sequenced on the Illumina NovaSeq® X (Illumina, San Diego, CA).

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. After that, ribosomal RNA reads are filtered away using RiboDetector (Deng et al., 2022). Host-derived reads were removed using Kraken2 (Wood et al., 2019) against some common Eukaryote host genomes. Low-diversity reads were detected and removed using sdust (https://github.com/lh3/sdust) and fastp (Chen et al., 2019). The surviving reads were subjected to further taxonomy and functional analyses as follows. Antimicrobial resistance and virulence factor gene identification was performed with the DIAMOND sequence aligner (Buchfink et al., 2015) against reference databases internally curated from NCBI repositories. Microbial composition was profiled using Sourmash (Brown and Irber, 2016). The GTDB species representative database (RS207) was used for bacterial and archaea identification. Pre-formatted GenBank databases (v. 2022.03) provided by Sourmash (https://sourmash.readthedocs.io/en/latest/databases.html) were also used for virus, protozoa and fungi identification. Reads were mapped back to the genomes identified by Sourmash using Minimap2 (Li, 2018) and the microbial abundance was determined based on the counts of mapped reads. The resulting taxonomy and abundance information were 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 Humann3 (Beghini, et al., 2021) including identification of UniRef gene family and MetaCyc metabolic pathways.

4 References

Beghini, F., McIver, L. J., et al. (2021). Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife, 10, e65088.

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

Deng, Z. L., Mnch, P. C., Mreches, R., & McHardy, A. C. (2022). Rapid and accurate identification of ribosomal RNA sequences via deep learning. Nucleic acids research, 50(10), e60-e60.

Morgulis, A., Gertz, E. M., Schffer, A. A., & Agarwala, R. (2006). A fast and symmetric DUST implementation to mask low-complexity DNA sequences. Journal of computational biology : a journal of computational molecular cell biology, 13(5), 10281040.

Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884-i890.

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

Brown, C. T., & Irber, L. (2016). sourmash: a library for MinHash sketching of DNA. Journal of open source software, 1(5), 27.

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.

Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100.

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.

Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome biology, 20, 1-13.






  Forsyth Oral Microbiome Core