Manneheimia

Description of pipeline for processing of Mannheimia amplicon library

Overview

This is a description of a pipeline that was used for processing a set of ~180 amplicon libraries and performing a variety of microbiome analyses.

1. qiime2_master_RWM.sh

The user first must download and install the qiime2 2018.2 package (you're on your own with this).

Open and read through the script. There are a number of things that must be altered for any given project, including project name, data locations, reference database location and indentity, primer sequences, sample filtering thresholds, and CPU thread counts. All important information along these lines is explained on # comment lines in the .sh file.

This script contains all of the qiime2 code needed to:

1. Import and configure a Silva reference database
2. Import a paired-end demultiplexed set of amplicon libraries
3. Import a metadata file
4. Run the project through three different pipelines, all of which will create an OTU table, perform taxonomic placement, and generate stacked bar charts. It is assumed that the user will check the results at this stage and choose a pipeline to move forward with.
5. Filter samples based on a % of median approach, which the user must generate indepently. This can be done by getting seq counts from the appropriate qzv files.
6. Generate full diversity metrics, alpha and beta diversity, ordinations, and corresponding qzv visualization files

At this stage, the user can visualize any .qzv file at the qiime2view website. This pipeline takes the:

1. OTU table
2. taxonomy table
3. rooted tree

and transitions into the phyloseq R package for further analysis.

explicit pipeline details:

cutadapt was used to detect and remove primer sequences using default parameters Taxonomic placement: Naive-Bayes feature classifier (sklearn) trained on Silva 128 reference database filtered using the primers utilized in the study.

VSearch open reference clustering

1. join pairs, 50 bp minimum overlap, 380 minimum and 480 maximum merged sequence length, disposing of sequences that do not meet these specifications.
2. quality score read filtering, q score 25 minimum with default settings
3. de novo chimera searching using uchime
4. vsearch open reference clustering using silva v.128 97% database
5. taxonomic placement using trained sklearn model

deblur16S

1. join pairs, 50 bp minimum overlap, 380 minimum and 480 maximum merged sequence length, disposing of sequences that do not meet these specifications.
2. deblur denoise-16S for exact sequence variant identification and filtering of non-16S sequences
3. taxonomic placement using trained sklearn model

dada2

1. raw data fed directly to data2 denoise-paired module for exact sequence variants, truncating forward read to 250bp and reverse read to 200bp
2. taxonomic placement using trained sklearn model

further processing of the dada2 results

1. remove all samples with read counts below 5% of the median (1200)
2. align using MAFFT
3. midpoint-rooted tree using fasttree

2. preparing qiime2 files for importing to physeq

moving the qiime2 results into physeq is a bit laborious. Qiime2 is new and the hand-off isn't smooth yet. Ways to handle this are discussed here: joey711/phyloseq#821

The explicit instructions for getting data out of qiime2 .qza files and into physeq begin below (taken from the link above):

based on this thread: https://forum.qiime2.org/t/converting-biom-files-with-taxonomic- info-for-import-in-r-with-phyloseq/2542/5

How to export a feature (OTU) table and convert from biom to .tsv (use following codes in QIIME2)

Step 1, export OTU table

qiime tools export \ yourOTUtablename.qza \ --output-dir phyloseq

OTU table exports as feature-table.biom so convert to .tsv

biom convert -i phyloseq/feature-table.biom -o phyloseq/otu_table.txt --to-tsv

now you have otu_table.txt open it up in text edit and change #OTUID to OTUID

Step 2, export taxonomy table

qiime tools export \ taxonomy.qza
--output-dir phyloseq now you have taxonomy.tsv open it up in text edit and change Feature ID to OTUID

Step 3, export tree

qiime tools export \ unrooted-tree.qza \ --output-dir phyloseq

Step 4, if you filtered out any sequences (chloroplasts, mitochondria, etc) then your taxonomy and OTU tables are different lengths. QIIME2 doesn’t filter out taxonomy, so you have to merge the two files in R and output a merged file.

(use following codes in R)

qiime2_to_physeq1.R

This first script will take the qiime2 files and begin to format them for physeq. After adapting it to your project, you will have to do some work by hand:

manually using Excel to format the tables

It seems tedious but you need to open the merged .txt file in excel and split into two files: one for taxonomy (containing only the columns OTUID and taxonomic info) and the other for the OTU matrix (containing only OTUID and abundances in each sample). Note: for the taxonomy file, you need to use data —> text-to-columns in Excel and separate on semicolon to get columns for kingdom, phylum, class, etc... once you make these two separate files in excel, save each as a .csv

qiime2_to_physeq2.R

This second R script will handle taking the files produced above and convert them into physeq objects (within R)

Microbiome analyses and charts within the Phyloseq package

All of the analyses work from Phyloseq package that resulted from the Qiime2 import. A familiarity with Phyloseq is required to adapt them to a given project or any given goal.

These scripts are not "fire and forget" in any case. Each of them has to be carefully altered to perform desired analyses. For example, the stacked.bar.2.R script can be directed at any taxonomic level or any subset of samples. The scripts are commented such they shouldn't be too hard to figure out. Many of the scripts have universal variables at the very beginning that can be used to more rapidly customize them (haven't gotten around to cleaning up all the scripts yet).

split_and_properties.R

In this project, we will first make sure that the sub-5% threshold samples have been removed. Then, the script splits the project by sampling location.

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003531 https://www.bioconductor.org/packages/release/bioc/vignettes/phyloseq/inst/doc/phyloseq-FAQ.html#should-i-normalize-my-data-before-alpha-diversity-analysis

The two references above address an important primary question; should you normalize or rarify your data? The best answer seems to be no, no normalization is appropriate. However, sample depth should be considered.

This script will:

1. Split by samplling location
2. generate read-depth figures
3. produce alpha diversity plots
4. calculate a variety of ordinations

stacked.bar.2.R

This script has all basic tools required to create a variety of stacked bar plots. Care must be taken to focus the script on certain samples and taxonomic levels.

taxa.time.plot.R

Plot taxa over time with a moving average and a confidence interval. This complements stacked bars in that this approach takes variation into account, at the expense of only plotting one taxon at a time.

DEseq2.R

This script does basic differential abundance caluculations and generates plots. This script was not used in the final data analysis.

Installing DEseq2.R is a bit problematic on Mac, although I have misplaced the workaround that I found. Google search should get you there.

DeSeq2.3.R

This is a refinement of the application of the DeSeq package. This script was used to explore control vs. infected abundancees at the genus level, but does not take into account day 1, pre-inoculation, levels; thus, day 1 differences are not taken into account. Results should be further refined using the stacked.bar.2.R script

PERMANOVA.R

This script will run PERMANOVA calculations, which tests the validity of desired clustering metadata variables

MicrobiomSeq.R

This script installs and runs the microbiomseq package and performs network analyses. This package seems a bit buggy and is difficult to work with. Network analysis is the only thing that I could get to work.