This tutorial originally created by Angela Oliverio and Hannah Holland-Moritz. It has been updated for the Ernakovich Lab compute environment (UNH’s HPC Premise) by Hannah Holland-Moritz. Other contributors to this pipeline include: Corinne Walsh, Matt Gebert, and Kunkun Fan
If you find this tutorial helpful or useful, please let us know, it
really helps us out to know how many people are finding it useful,
thanks!
Updated May 11, 2023
This pipeline runs the dada2 workflow for Big Data (paired-end) with modifications for NovaSeq sequencing base calls
We suggest opening the dada2 tutorial online to understand more about each step. The original pipeline on which this tutorial is based can be found here: https://benjjneb.github.io/dada2/bigdata_paired.html
| NOTE: There is a slightly different pipeline for ITS and non-“Big data” workflows. The non-“Big data” pipeline, in particular, has very nice detailed explanations for each step and can be found here: https://benjjneb.github.io/dada2/tutorial.html |
-
Check to make sure you know what your target ‘AMPLICON’ length. This can vary between primer sets, as well as WITHIN primer sets. For example, ITS (internal transcribed spacer) amplicon can vary from ~100 bps to 300 bps
For examples regarding commonly used primer sets (515f/806r, Fungal ITS2, 1391f/EukBr) see protocols on the Earth Microbiome Project website: http://press.igsb.anl.gov/earthmicrobiome/protocols-and-standards/
-
Check to make sure you know how long your reads should be (i.e., how long should the reads be coming off the sequencer?) This is not the same as fragment length, as many times, especially with longer fragments, the entire fragment is not being sequenced in one direction. When long amplicons are not sequenced with a read length that allows for substantial overlap between the forward and reverse read, you can potentially insert biases into the data. If you intend to merge your paired end reads, ensure that your read length is appropriate. For example, with a MiSeq 2 x 150, 300 cycle kit, you will get bidirectional reads of 150 base pairs.
-
Make note of which sequencing platform was used, as this can impact both read quality and downstream analysis. In particular, this pipeline is designed to process NovaSeq data which has very different quality scores than HiSeq or MiSeq data.
-
Decide which database is best suited for your analysis needs. Note that DADA2 requires databases be in a custom format! If a custom database is required, further formatting will be needed to ensure that it can run correctly in dada2.
See the following link for details regarding database formatting: https://benjjneb.github.io/dada2/training.html#formatting-custom-databases
-
For additional tutorials and reporting issues, please see link below:
dada2 tutorial: https://benjjneb.github.io/dada2/tutorial.html
dada2 pipeline issues*: https://github.com/fiererlab/dada2_fiererlab/issues*Note by default, only ‘OPEN’ issues are shown. You can look at all issues by removing “is:open” in the search bar at the top.
Once you have logged in, you can download a copy of the tutorial into your directory on the server. To retrieve the folder with this tutorial from github directly to the server, type the following into your terminal and hit return after each line.
wget https://github.com/ernakovichlab/dada2_ernakovichlab/archive/main.zip
unzip main.zipIf there are ever updates to the tutorial on github, you can update the contents of this folder by downloading the new version from the same link as above.
Setup and install software (you will only need to do this the first time, or if you want to update dada2)
- Install the conda environment (this will install all the necessary software to run dada2)
- First start by cleaning up modules, and then loading the anaconda module.
module purge
module load anaconda/colsa- Next create a conda local environment that you can use to run the software. This will install everything you need to run dada2.
cd dada2_ernakovichlab
conda env create -f dada2_ernakovich.yml
conda activate dada2_ernakovich| WARNING: This installation may take a long time, so only run this code if you have a fairly large chunk of time! |
A note about running this on Premise: To run this on Premise, you will need to submit R-scripts to the job scheduler (slurm). The R scripts in this tutorial can be found in the “R” folder and have been carefully designed so that each step can be run with on slurm with minimal changes. The R scripts are numbered according to their steps. When you are called on to modify a particular step, use a terminal text editor (such as nano) to open up the appropriate R script and edit the code accordingly. For your convenience, there is also a folder called “slurm” which contains ready-made slurm scripts that you can use to submit each R script. The slurm scripts are designed to be submitted from the “slurm” folder. You can submit them by using cd slurm to navigate into the slurm folder, and sbatch xxx_dada2_tutorial_16S.slurm to submit each script. Throughout this pipeline you will see STOP notices. These indicate how you should modify the R script at each stage. |
If you are running it on your own computer (runs slower!):
-
Download this tutorial from github. Go to the homepage, and click the green “Clone or download” button. Then click “Download ZIP”, to save it to your computer. Unzip the file to access the R-script.
-
Download the tutorial data from here http://cme.colorado.edu/projects/bioinformatics-tutorials
-
Install cutadapt. If you are using conda, you may also use the .yml file to create an environment with cutadapt and all the necessary R packages pre-installed
- cutadapt can be installed from here: https://cutadapt.readthedocs.io/en/stable/installation.html
-
Download the dada2-formatted reference database of your choice. Link to download here: https://benjjneb.github.io/dada2/training.html
-
Open the Rmarkdown script in Rstudio. The script is located in the tutorial folder you downloaded in the first step. You can navigate to the proper folder in Rstudio by clicking on the files tab and navigating to the location where you downloaded the github folder. Then click dada2_ernakovichlab and dada2_tutorial_16S_all.Rmd to open the script.
Now, install DADA2 & other necessary packages(if you haven’t opted for the conda option). Depending on how you set up Rstudio, you might get a prompt asking if you want to create your own library. Answer ‘yes’ twice in the console to continue.
| WARNING: This installation may take a long time, so only run this code if these packages are not already installed! |
install.packages("BiocManager")
BiocManager::install("dada2", version = "3.8")
source("https://bioconductor.org/biocLite.R")
biocLite("ShortRead")
install.packages("dplyr")
install.packages("tidyr")
install.packages("Hmisc")
install.packages("ggplot2")
install.packages("plotly")Once the packages are installed, you can check to make sure the auxiliary software is working and set up some of the variables that you will need along the way.
| NOTE: If you are not working from premise, you will need to change the file paths for cutadapt to where they are stored on your computer/server. |
For this tutorial we will be working with some samples that we obtained 16S amplicon data for, from a Illumina Miseq run. The data for these samples can be found on the CME website. http://cme.colorado.edu/projects/bioinformatics-tutorials
First load and test the installed packages to make sure they’re working
Load DADA2 and required packages
library(dada2); packageVersion("dada2") # the dada2 pipeline
## [1] '1.26.0'
library(ShortRead); packageVersion("ShortRead") # dada2 depends on this
## [1] '1.56.0'
library(dplyr); packageVersion("dplyr") # for manipulating data
## [1] '1.1.2'
library(tidyr); packageVersion("tidyr") # for creating the final graph at the end of the pipeline
## [1] '1.3.0'
library(Hmisc); packageVersion("Hmisc") # for creating the final graph at the end of the pipeline
## [1] '5.1.0'
library(ggplot2); packageVersion("ggplot2") # for creating the final graph at the end of the pipeline
## [1] '3.4.2'
library(plotly); packageVersion("plotly") # enables creation of interactive graphs, especially helpful for quality plots
## [1] '4.10.1'
# Set up pathway to cutadapt (primer trimming tool) and test
cutadapt <- "cutadapt" # CHANGE ME if not on premise; will probably look something like this: "/usr/local/Python27/bin/cutadapt"
system2(cutadapt, args = "--version") # Check by running shell command from RWe will now set up the directories for the script. We’ll tell the script where our data is, and where we want to put the outputs of the script. We highly recommend NOT putting outputs of this script directly into your home directory, or into this tutorial directory. A better idea is to create a new project directory to hold the output each project you work on.
# Set path to shared data folder and contents
data.fp <- "/mnt/home/ernakovich/shared/dada2_tutorial_data"
# List all files in shared folder to check path
list.files(data.fp)
## [1] "A1-TS_S1_L001_R1_001.fastq.gz" "A1-TS_S1_L001_R2_001.fastq.gz"
## [3] "A2-TS_S9_L001_R1_001.fastq.gz" "A2-TS_S9_L001_R2_001.fastq.gz"
## [5] "A3-TS_S17_L001_R1_001.fastq.gz" "A3-TS_S17_L001_R2_001.fastq.gz"
## [7] "A4-TS_S25_L001_R1_001.fastq.gz" "A4-TS_S25_L001_R2_001.fastq.gz"
## [9] "B1-TS_S2_L001_R1_001.fastq.gz" "B1-TS_S2_L001_R2_001.fastq.gz"
## [11] "B2-TS_S10_L001_R1_001.fastq.gz" "B2-TS_S10_L001_R2_001.fastq.gz"
## [13] "B3-TS_S18_L001_R1_001.fastq.gz" "B3-TS_S18_L001_R2_001.fastq.gz"
## [15] "B4-TS_S26_L001_R1_001.fastq.gz" "B4-TS_S26_L001_R2_001.fastq.gz"
## [17] "C1-TS_S3_L001_R1_001.fastq.gz" "C1-TS_S3_L001_R2_001.fastq.gz"
## [19] "C2-TS_S11_L001_R1_001.fastq.gz" "C2-TS_S11_L001_R2_001.fastq.gz"
## [21] "C3-TS_S19_L001_R1_001.fastq.gz" "C3-TS_S19_L001_R2_001.fastq.gz"
## [23] "C4-TS_S27_L001_R1_001.fastq.gz" "C4-TS_S27_L001_R2_001.fastq.gz"
## [25] "D1-TS_S4_L001_R1_001.fastq.gz" "D1-TS_S4_L001_R2_001.fastq.gz"
## [27] "D2-TS_S12_L001_R1_001.fastq.gz" "D2-TS_S12_L001_R2_001.fastq.gz"
## [29] "D3-TS_S20_L001_R1_001.fastq.gz" "D3-TS_S20_L001_R2_001.fastq.gz"
## [31] "D4-TS_S28_L001_R1_001.fastq.gz" "D4-TS_S28_L001_R2_001.fastq.gz"
## [33] "E1-TS_S5_L001_R1_001.fastq.gz" "E1-TS_S5_L001_R2_001.fastq.gz"
## [35] "E2-TS_S13_L001_R1_001.fastq.gz" "E2-TS_S13_L001_R2_001.fastq.gz"
## [37] "E3-TS_S21_L001_R1_001.fastq.gz" "E3-TS_S21_L001_R2_001.fastq.gz"
## [39] "E4-TS_S29_L001_R1_001.fastq.gz" "E4-TS_S29_L001_R2_001.fastq.gz"
## [41] "F1-TS_S6_L001_R1_001.fastq.gz" "F1-TS_S6_L001_R2_001.fastq.gz"
## [43] "F2-TS_S14_L001_R1_001.fastq.gz" "F2-TS_S14_L001_R2_001.fastq.gz"
## [45] "F3-TS_S22_L001_R1_001.fastq.gz" "F3-TS_S22_L001_R2_001.fastq.gz"
## [47] "F4-TS_S30_L001_R1_001.fastq.gz" "F4-TS_S30_L001_R2_001.fastq.gz"
## [49] "G1-TS_S7_L001_R1_001.fastq.gz" "G1-TS_S7_L001_R2_001.fastq.gz"
## [51] "G2-TS_S15_L001_R1_001.fastq.gz" "G2-TS_S15_L001_R2_001.fastq.gz"
## [53] "G3-TS_S23_L001_R1_001.fastq.gz" "G3-TS_S23_L001_R2_001.fastq.gz"
## [55] "G4-TS_S31_L001_R1_001.fastq.gz" "G4-TS_S31_L001_R2_001.fastq.gz"
## [57] "H1-TS_S8_L001_R1_001.fastq.gz" "H1-TS_S8_L001_R2_001.fastq.gz"
## [59] "H2-TS_S16_L001_R1_001.fastq.gz" "H2-TS_S16_L001_R2_001.fastq.gz"
## [61] "H3-TS_S24_L001_R1_001.fastq.gz" "H3-TS_S24_L001_R2_001.fastq.gz"
## [63] "H4-TS_S32_L001_R1_001.fastq.gz" "H4-TS_S32_L001_R2_001.fastq.gz"
# Set file paths for barcodes file, map file, and fastqs
# Barcodes need to have 'N' on the end of each 12bp sequence for compatability
#map.fp <- file.path(data.fp, "Molecular_Methods_18_515fBC_16S_Mapping_File_SHORT_vFinal_Fierer_10252018.txt")For the tutorial 16S, we will assign taxonomy with Silva db v138, but you might want to use other databases for your data. Below are paths to some of the databases we use often. (If you are on your own computer you can download the database you need from this link https://benjjneb.github.io/dada2/training.html):
-
16S bacteria and archaea (SILVA db):
/mnt/lz01/ernakovich/shared/db_files/dada2/silva_nr99_v138.1_train_set.fa -
ITS fungi (UNITE db):
/mnt/lz01/ernakovich/shared/db_files/dada2/UNITE_sh_general_release_10.05.2021/sh_general_release_dynamic_10.05.2021.fasta -
18S protists (PR2 db):
/mnt/lz01/ernakovich/shared/db_files/dada2/pr2_version_4.14.0_SSU_dada2.fasta
Set file path for the taxonomy database you will use in step 06
db_fp <- "/mnt/lz01/ernakovich/shared/db_files/dada2/silva_nr99_v138.1_train_set.fa" # CHANGE ME, this is silva 138.1, suitable for 16S onlySet up file paths in YOUR directory where you want data; you do not need to create the sub-directories but they are nice to have for organizational purposes.
project.fp <- "~/dada2_tutorial_test" # CHANGE ME to project directory; don't append with a "/"
dir.create(project.fp)
# Set up names of sub directories to stay organized
preprocess.fp <- file.path(project.fp, "01_preprocess")
filtN.fp <- file.path(preprocess.fp, "filtN")
trimmed.fp <- file.path(preprocess.fp, "trimmed")
filter.fp <- file.path(project.fp, "02_filter")
table.fp <- file.path(project.fp, "03_tabletax") | STOP - 00_setup_dada2_tutorial_16S.R: If you are running this on Premise, open up the 00_setup_dada2_tutorial_16S.R script with nano (or your favorite terminal text editor) and adjust the filepaths above appropriately. |
# Get full paths for all files and save them for downstream analyses
# Forward and reverse fastq filenames have format:
fnFs <- sort(list.files(data.fp, pattern="R1_", full.names = TRUE))
fnRs <- sort(list.files(data.fp, pattern="R2_", full.names = TRUE))Note: If your file names contain the pattern “R1_” anywhere other than the part specifying the read direction, you will need to modify the pattern above, so that the files are not incorrectly categorized as read 1 or read 2. This often happens when file names contain “R1” or “R2” in their names in reference to replicates or site IDS. Simply making the pattern recognition longer, often solves the problem.
Ambiguous bases will make it hard for cutadapt to find short primer sequences in the reads. To solve this problem, we will remove sequences with ambiguous bases (Ns)
# Name the N-filtered files to put them in filtN/ subdirectory
fnFs.filtN <- file.path(preprocess.fp, "filtN", basename(fnFs))
fnRs.filtN <- file.path(preprocess.fp, "filtN", basename(fnRs))
# Filter Ns from reads and put them into the filtN directory
filterAndTrim(fnFs, fnFs.filtN, fnRs, fnRs.filtN, maxN = 0, multithread = TRUE)
# CHANGE multithread to FALSE on Windows (here and elsewhere in the program)Note: The multithread = TRUE setting can sometimes generate an error (names not equal). If this occurs, try rerunning the function. The error normally does not occur the second time. |
Assign the primers you used to “FWD” and “REV” below. Note primers should be not be reverse complemented ahead of time. Our tutorial data uses 515f and 926r those are the primers below. Change if you sequenced with other primers.
For ITS data: Depending on how your sequences were run, your
barcodes may need to be reverse-complemented. Here is a link to a handy
tool, that can help you reverse complement your barcodes:
http://arep.med.harvard.edu/labgc/adnan/projects/Utilities/revcomp.html.
Using cutadapt to remove these primers will allow us to retain ITS
sequences of variable biological length. See the dada2 creators’ ITS
tutorial for more details. Below are several common ITS primers that you
might have used: AYTTAAGCATATCAATAAGCGGAGGCT is ITS4-Fun (reverse
complemented, forward primer), AGWGATCCRTTGYYRAAAGTT is 5.8S-Fun
(reverse complemented, reverse primer). CTTGGTCATTTAGAGGAAGTAA is
ITS1F (forward primer sequence, not reverse complemented) and
GCTGCGTTCTTCATCGATGC is ITS2 (reverse primer sequence, not reverse
complemented).
# Set up the primer sequences to pass along to cutadapt
FWD <- "GTGYCAGCMGCCGCGGTAA" ## CHANGE ME # this is 515f
REV <- "CCGYCAATTYMTTTRAGTTT" ## CHANGE ME # this is 926r
# Write a function that creates a list of all orientations of the primers
allOrients <- function(primer) {
# Create all orientations of the input sequence
require(Biostrings)
dna <- DNAString(primer) # The Biostrings works w/ DNAString objects rather than character vectors
orients <- c(Forward = dna, Complement = complement(dna), Reverse = reverse(dna),
RevComp = reverseComplement(dna))
return(sapply(orients, toString)) # Convert back to character vector
}
# Save the primer orientations to pass to cutadapt
FWD.orients <- allOrients(FWD)
REV.orients <- allOrients(REV)
FWD.orients
## Forward Complement Reverse
## "GTGYCAGCMGCCGCGGTAA" "CACRGTCGKCGGCGCCATT" "AATGGCGCCGMCGACYGTG"
## RevComp
## "TTACCGCGGCKGCTGRCAC"
# Write a function that counts how many time primers appear in a sequence
primerHits <- function(primer, fn) {
# Counts number of reads in which the primer is found
nhits <- vcountPattern(primer, sread(readFastq(fn)), fixed = FALSE)
return(sum(nhits > 0))
}Before running cutadapt, we will look at primer detection for the first sample, as a check. There may be some primers here, we will remove them below using cutadapt.
rbind(FWD.ForwardReads = sapply(FWD.orients, primerHits, fn = fnFs.filtN[[1]]),
FWD.ReverseReads = sapply(FWD.orients, primerHits, fn = fnRs.filtN[[1]]),
REV.ForwardReads = sapply(REV.orients, primerHits, fn = fnFs.filtN[[1]]),
REV.ReverseReads = sapply(REV.orients, primerHits, fn = fnRs.filtN[[1]]))
## Forward Complement Reverse RevComp
## FWD.ForwardReads 524235 0 0 0
## FWD.ReverseReads 0 0 0 1236
## REV.ForwardReads 0 0 0 1248
## REV.ReverseReads 511355 0 0 0In this cutadapt command, we also use the --nextseq-trim option to
remove strings of G’s caused by 2-color chemistry. See
here
for more details
# Create directory to hold the output from cutadapt
if (!dir.exists(trimmed.fp)) dir.create(trimmed.fp)
fnFs.cut <- file.path(trimmed.fp, basename(fnFs))
fnRs.cut <- file.path(trimmed.fp, basename(fnRs))
# Save the reverse complements of the primers to variables
FWD.RC <- dada2:::rc(FWD)
REV.RC <- dada2:::rc(REV)
## Create the cutadapt flags ##
# Trim FWD and the reverse-complement of REV off of R1 (forward reads)
R1.flags <- paste("-g", FWD, "-a", REV.RC, "--minimum-length 50")
# Trim REV and the reverse-complement of FWD off of R2 (reverse reads)
R2.flags <- paste("-G", REV, "-A", FWD.RC, "--minimum-length 50")
# Run Cutadapt
for (i in seq_along(fnFs)) {
system2(cutadapt, args = c("-j", 0, "--nextseq-trim=20", R1.flags, R2.flags, "-n", 2, # -n 2 required to remove FWD and REV from reads
"-o", fnFs.cut[i], "-p", fnRs.cut[i], # output files
fnFs.filtN[i], fnRs.filtN[i])) # input files
}
# As a sanity check, we will check for primers in the first cutadapt-ed sample:
## should all be zero!
rbind(FWD.ForwardReads = sapply(FWD.orients, primerHits, fn = fnFs.cut[[1]]),
FWD.ReverseReads = sapply(FWD.orients, primerHits, fn = fnRs.cut[[1]]),
REV.ForwardReads = sapply(REV.orients, primerHits, fn = fnFs.cut[[1]]),
REV.ReverseReads = sapply(REV.orients, primerHits, fn = fnRs.cut[[1]]))
## Forward Complement Reverse RevComp
## FWD.ForwardReads 0 0 0 0
## FWD.ReverseReads 0 0 0 0
## REV.ForwardReads 0 0 0 0
## REV.ReverseReads 0 0 0 0STOP - 01_pre-process_dada2_tutorial_16S.R: If you are running this on Premise, open up the 01_pre-process_dada2_tutorial_16S.R script with nano (or your favorite terminal text editor) and adjust the primer sequences (if need be). After running it, check the slurm output to make sure that there are no primers still in your samples. |
# Put filtered reads into separate sub-directories for big data workflow
dir.create(filter.fp)
subF.fp <- file.path(filter.fp, "preprocessed_F")
subR.fp <- file.path(filter.fp, "preprocessed_R")
dir.create(subF.fp)
dir.create(subR.fp)
# Move R1 and R2 from trimmed to separate forward/reverse sub-directories
fnFs.Q <- file.path(subF.fp, basename(fnFs))
fnRs.Q <- file.path(subR.fp, basename(fnRs))
file.symlink(from = fnFs.cut, to = fnFs.Q)
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
file.symlink(from = fnRs.cut, to = fnRs.Q)
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# File parsing; create file names and make sure that forward and reverse files match
filtpathF <- file.path(subF.fp, "filtered") # files go into preprocessed_F/filtered/
filtpathR <- file.path(subR.fp, "filtered") # ...
fastqFs <- sort(list.files(subF.fp, pattern="fastq.gz"))
fastqRs <- sort(list.files(subR.fp, pattern="fastq.gz"))
if(length(fastqFs) != length(fastqRs)) stop("Forward and reverse files do not match.")Before chosing sequence variants, we want to trim reads where their
quality scores begin to drop (the truncLen and truncQ values) and
remove any low-quality reads that are left over after we have finished
trimming (the maxEE value).
You will want to change this depending on run chemistry and quality:
For 2x250 bp runs you can try truncLen=c(240,160) (as per the dada2
tutorial)
if your reverse reads drop off in quality. Or you may want to choose a
higher value, for example, truncLen=c(240,200), if they do not. In
truncLen=c(xxx,yyy), xxx refers to the forward read truncation
length, yyy refers to the reverse read truncation length.
For ITS data: Due to the expected variable read lengths in ITS data
you should run this command without the trunclen parameter. See here
for more information and appropriate parameters for ITS data:
https://benjjneb.github.io/dada2/ITS_workflow.html.
From dada2 tutorial: >If there is only one part of any amplicon bioinformatics workflow on which you spend time considering the parameters, it should be filtering! The parameters … are not set in stone, and should be changed if they don’t work for your data. If too few reads are passing the filter, increase maxEE and/or reduce truncQ. If quality drops sharply at the end of your reads, reduce truncLen. If your reads are high quality and you want to reduce computation time in the sample inference step, reduce maxEE.
It’s important to get a feel for the quality of the data that we are using. To do this, we will plot the quality of some of the samples.
From the dada2 tutorial: >In gray-scale is a heat map of the frequency of each quality score at each base position. The median quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines. The red line shows the scaled proportion of reads that extend to at least that position (this is more useful for other sequencing technologies, as Illumina reads are typically all the same length, hence the flat red line).
# If the number of samples is 20 or less, plot them all, otherwise, just plot 20 randomly selected samples
if( length(fastqFs) <= 20) {
fwd_qual_plots <- plotQualityProfile(paste0(subF.fp, "/", fastqFs))
rev_qual_plots <- plotQualityProfile(paste0(subR.fp, "/", fastqRs))
} else {
rand_samples <- sample(size = 20, 1:length(fastqFs)) # grab 20 random samples to plot
writeLines(paste0("Samples being plotted are \n", paste(sort(fastqFs[rand_samples]), collapse = ", ")))
fwd_qual_plots <- plotQualityProfile(paste0(subF.fp, "/", fastqFs[rand_samples]))
rev_qual_plots <- plotQualityProfile(paste0(subR.fp, "/", fastqRs[rand_samples]))
}
## Samples being plotted are
## A1-TS_S1_L001_R1_001.fastq.gz, A2-TS_S9_L001_R1_001.fastq.gz, A4-TS_S25_L001_R1_001.fastq.gz, B1-TS_S2_L001_R1_001.fastq.gz, B2-TS_S10_L001_R1_001.fastq.gz, B3-TS_S18_L001_R1_001.fastq.gz, B4-TS_S26_L001_R1_001.fastq.gz, C2-TS_S11_L001_R1_001.fastq.gz, D2-TS_S12_L001_R1_001.fastq.gz, D3-TS_S20_L001_R1_001.fastq.gz, E1-TS_S5_L001_R1_001.fastq.gz, E3-TS_S21_L001_R1_001.fastq.gz, E4-TS_S29_L001_R1_001.fastq.gz, F2-TS_S14_L001_R1_001.fastq.gz, F4-TS_S30_L001_R1_001.fastq.gz, G1-TS_S7_L001_R1_001.fastq.gz, G3-TS_S23_L001_R1_001.fastq.gz, G4-TS_S31_L001_R1_001.fastq.gz, H3-TS_S24_L001_R1_001.fastq.gz, H4-TS_S32_L001_R1_001.fastq.gz
fwd_qual_plots
rev_qual_plots
# write plots to disk
saveRDS(fwd_qual_plots, paste0(filter.fp, "/fwd_qual_plots.rds"))
saveRDS(rev_qual_plots, paste0(filter.fp, "/rev_qual_plots.rds"))
ggsave(plot = fwd_qual_plots, filename = paste0(filter.fp, "/fwd_qual_plots.png"),
width = 12, height = 10, dpi = "retina")
ggsave(plot = rev_qual_plots, filename = paste0(filter.fp, "/rev_qual_plots.png"),
width = 12, height = 10, dpi = "retina")| STOP - 02_check_quality_dada2_tutorial.R: If you are running this on Premise, run this script and download the plots generated here (fwd_qual_plots.png and rev_qual_plots.png). These are the pre-filtering plots, you should use them to make decisions for your filtering choices in the next step. |
| WARNING: THESE PARAMETERS ARE NOT OPTIMAL FOR ALL DATASETS. Make sure you determine the trim and filtering parameters for your data. The following settings may be generally appropriate for NovaSeq runs that are 2x250 bp. For more information you can check the recommended default parameters from the dada2 pipeline. See above for more details. |
filt_out <- filterAndTrim(fwd=file.path(subF.fp, fastqFs), filt=file.path(filtpathF, fastqFs),
rev=file.path(subR.fp, fastqRs), filt.rev=file.path(filtpathR, fastqRs),
truncLen=c(225,220), maxEE=c(2,2), truncQ=2, maxN=0, rm.phix=TRUE,
compress=TRUE, verbose=TRUE, multithread=TRUE)
# look at how many reads were kept
head(filt_out)
## reads.in reads.out
## A1-TS_S1_L001_R1_001.fastq.gz 530215 482685
## A2-TS_S9_L001_R1_001.fastq.gz 19919 18190
## A3-TS_S17_L001_R1_001.fastq.gz 24740 22372
## A4-TS_S25_L001_R1_001.fastq.gz 398733 361859
## B1-TS_S2_L001_R1_001.fastq.gz 3280463 2967452
## B2-TS_S10_L001_R1_001.fastq.gz 128302 115834
# summary of samples in filt_out by percentage
filt_out %>%
data.frame() %>%
mutate(Samples = rownames(.),
percent_kept = 100*(reads.out/reads.in)) %>%
select(Samples, everything()) %>%
summarise(min_remaining = paste0(round(min(percent_kept), 2), "%"),
median_remaining = paste0(round(median(percent_kept), 2), "%"),
mean_remaining = paste0(round(mean(percent_kept), 2), "%"),
max_remaining = paste0(round(max(percent_kept), 2), "%"))
## min_remaining median_remaining mean_remaining max_remaining
## 1 64.29% 90.53% 89.81% 92.16%Plot the quality of the filtered fastq files.
# If the number of samples greater than 20 figure out which samples, if any, have been filtered out
# so we won't try to plot them, otherwise just plot all the samples that remain
if( length(fastqFs) <= 20) {
remaining_samplesF <- fastqFs[
which(fastqFs %in% list.files(filtpathF))] # keep only samples that haven't been filtered out
remaining_samplesR <- fastqRs[
which(fastqRs %in% list.files(filtpathR))] # keep only samples that haven't been filtered out
fwd_qual_plots_filt <- plotQualityProfile(paste0(filtpathF, "/", remaining_samplesF))
rev_qual_plots_filt <- plotQualityProfile(paste0(filtpathR, "/", remaining_samplesR))
} else {
remaining_samplesF <- fastqFs[rand_samples][
which(fastqFs[rand_samples] %in% list.files(filtpathF))] # keep only samples that haven't been filtered out
remaining_samplesR <- fastqRs[rand_samples][
which(fastqRs[rand_samples] %in% list.files(filtpathR))] # keep only samples that haven't been filtered out
fwd_qual_plots_filt <- plotQualityProfile(paste0(filtpathF, "/", remaining_samplesF))
rev_qual_plots_filt <- plotQualityProfile(paste0(filtpathR, "/", remaining_samplesR))
}
fwd_qual_plots_filt
rev_qual_plots_filt
# write plots to disk
saveRDS(fwd_qual_plots_filt, paste0(filter.fp, "/fwd_qual_plots_filt.rds"))
saveRDS(rev_qual_plots_filt, paste0(filter.fp, "/rev_qual_plots_filt.rds"))
ggsave(plot = fwd_qual_plots_filt, filename = paste0(filter.fp, "/fwd_qual_plots_filt.png"),
width = 10, height = 10, dpi = "retina")
ggsave(plot = rev_qual_plots_filt, filename = paste0(filter.fp, "/rev_qual_plots_filt.png"),
width = 10, height = 10, dpi = "retina")| STOP - 03_filter_reads_dada2_tutorial_16S.R: If you are running this on Premise, download the plots generated here (fwd_qual_plots_filt.png and rev_qual_plots_filt.png) and verify that your filtering is working the way you want it. If not, adjust the filterAndTrim() function and re-run this step with slurm. |
In this part of the pipeline dada2 will learn to distinguish error from biological differences using a subset of our data as a training set. After it understands the error rates, we will reduce the size of the dataset by combining all identical sequence reads into “unique sequences”. Then, using the dereplicated data and error rates, dada2 will infer the sequence variants (OTUs) in our data. Finally, we will merge the coresponding forward and reverse reads to create a list of the fully denoised sequences and create a sequence table from the result.
# File parsing
filtFs <- list.files(filtpathF, pattern="fastq.gz", full.names = TRUE)
filtRs <- list.files(filtpathR, pattern="fastq.gz", full.names = TRUE)
# Sample names in order
sample.names <- basename(filtFs) # doesn't drop fastq.gz
sample.names <- gsub("_R1_001.fastq.gz", "", sample.names)
sample.namesR <- basename(filtRs) # doesn't drop fastq.gz
sample.namesR <- gsub("_R2_001.fastq.gz", "", sample.namesR)
# Double check
if(!identical(sample.names, sample.namesR)) stop("Forward and reverse files do not match.")
names(filtFs) <- sample.names
names(filtRs) <- sample.namesIn this step we will learn the error rates for the sequencing run. Typically dada2 expects you to have data that has HiSeq or MiSeq-style quality scores - that is quality scores that range from 0-40. However, NovaSeq uses a technique called “binned” quality scores. This means that as quality scores are calculated from the sequencer, instead of assigning them a number between 0 and 40, they are instead assigned to 4 different quality scores, typically 0-40 scores are converted as shown below:
0-2 -> 2
3-14 -> 11
15-30 -> 25
31-40 -> 37
This means that the learnErrors function has 1/10th of the information
that it usually uses to learn the appropriate error function, which
often leads to error plots with characteristic troughs in odd places.
Although a definitive solution to this has not been found yet, several
have been proposed.
Typically UNH sequencing data will be NovaSeq data, but it’s good to
check. If you have data that doesn’t have binned error scores
(i.e. MiSeq or HiSeq data) you can proceed to learn error rates in the
typical way, and not worry about the modifications below. (Use errF
and errR for the sequence-variant identification in the next step.)
Otherwise, you should carefully inspect the error plots generated by
each method below and choose the one that looks the best. Error rate
plots that look good have black points that are very close to the black
line and are continuously decreasing (especially in the right side of
the plot).
set.seed(100) # set seed to ensure that randomized steps are replicatable# Learn forward error rates (Notes: randomize default is FALSE)
errF <- learnErrors(filtFs, nbases = 1e8, multithread = TRUE, randomize = TRUE)
## 320845950 total bases in 1425982 reads from 4 samples will be used for learning the error rates.
# Learn reverse error rates
errR <- learnErrors(filtRs, nbases = 1e8, multithread = TRUE, randomize = TRUE)
## 207969080 total bases in 945314 reads from 2 samples will be used for learning the error rates.
saveRDS(errF, paste0(filtpathF, "/errF.rds"))
saveRDS(errR, paste0(filtpathR, "/errR.rds"))Option 1 from JacobRPrice alter loess arguments (weights and span
and enforce monotonicity)
benjjneb/dada2#1307
loessErrfun_mod1 <- function(trans) {
qq <- as.numeric(colnames(trans))
est <- matrix(0, nrow=0, ncol=length(qq))
for(nti in c("A","C","G","T")) {
for(ntj in c("A","C","G","T")) {
if(nti != ntj) {
errs <- trans[paste0(nti,"2",ntj),]
tot <- colSums(trans[paste0(nti,"2",c("A","C","G","T")),])
rlogp <- log10((errs+1)/tot) # 1 psuedocount for each err, but if tot=0 will give NA
rlogp[is.infinite(rlogp)] <- NA
df <- data.frame(q=qq, errs=errs, tot=tot, rlogp=rlogp)
# original
# ###! mod.lo <- loess(rlogp ~ q, df, weights=errs) ###!
# mod.lo <- loess(rlogp ~ q, df, weights=tot) ###!
# # mod.lo <- loess(rlogp ~ q, df)
# Gulliem Salazar's solution
# https://github.com/benjjneb/dada2/issues/938
mod.lo <- loess(rlogp ~ q, df, weights = log10(tot),span = 2)
pred <- predict(mod.lo, qq)
maxrli <- max(which(!is.na(pred)))
minrli <- min(which(!is.na(pred)))
pred[seq_along(pred)>maxrli] <- pred[[maxrli]]
pred[seq_along(pred)<minrli] <- pred[[minrli]]
est <- rbind(est, 10^pred)
} # if(nti != ntj)
} # for(ntj in c("A","C","G","T"))
} # for(nti in c("A","C","G","T"))
# HACKY
MAX_ERROR_RATE <- 0.25
MIN_ERROR_RATE <- 1e-7
est[est>MAX_ERROR_RATE] <- MAX_ERROR_RATE
est[est<MIN_ERROR_RATE] <- MIN_ERROR_RATE
# enforce monotonicity
# https://github.com/benjjneb/dada2/issues/791
estorig <- est
est <- est %>%
data.frame() %>%
mutate_all(funs(case_when(. < X40 ~ X40,
. >= X40 ~ .))) %>% as.matrix()
rownames(est) <- rownames(estorig)
colnames(est) <- colnames(estorig)
# Expand the err matrix with the self-transition probs
err <- rbind(1-colSums(est[1:3,]), est[1:3,],
est[4,], 1-colSums(est[4:6,]), est[5:6,],
est[7:8,], 1-colSums(est[7:9,]), est[9,],
est[10:12,], 1-colSums(est[10:12,]))
rownames(err) <- paste0(rep(c("A","C","G","T"), each=4), "2", c("A","C","G","T"))
colnames(err) <- colnames(trans)
# Return
return(err)
}
# check what this looks like
errF_1 <- learnErrors(
filtFs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod1,
verbose = TRUE
)
## 108604125 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## Convergence after 7 rounds.
errR_1 <- learnErrors(
filtRs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod1,
verbose = TRUE
)
## 106190700 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## selfConsist step 8
## selfConsist step 9
## selfConsist step 10Option 2 enforce monotonicity only.
Originally recommended in:
benjjneb/dada2#791
loessErrfun_mod2 <- function(trans) {
qq <- as.numeric(colnames(trans))
est <- matrix(0, nrow=0, ncol=length(qq))
for(nti in c("A","C","G","T")) {
for(ntj in c("A","C","G","T")) {
if(nti != ntj) {
errs <- trans[paste0(nti,"2",ntj),]
tot <- colSums(trans[paste0(nti,"2",c("A","C","G","T")),])
rlogp <- log10((errs+1)/tot) # 1 psuedocount for each err, but if tot=0 will give NA
rlogp[is.infinite(rlogp)] <- NA
df <- data.frame(q=qq, errs=errs, tot=tot, rlogp=rlogp)
# original
# ###! mod.lo <- loess(rlogp ~ q, df, weights=errs) ###!
mod.lo <- loess(rlogp ~ q, df, weights=tot) ###!
# # mod.lo <- loess(rlogp ~ q, df)
# Gulliem Salazar's solution
# https://github.com/benjjneb/dada2/issues/938
# mod.lo <- loess(rlogp ~ q, df, weights = log10(tot),span = 2)
pred <- predict(mod.lo, qq)
maxrli <- max(which(!is.na(pred)))
minrli <- min(which(!is.na(pred)))
pred[seq_along(pred)>maxrli] <- pred[[maxrli]]
pred[seq_along(pred)<minrli] <- pred[[minrli]]
est <- rbind(est, 10^pred)
} # if(nti != ntj)
} # for(ntj in c("A","C","G","T"))
} # for(nti in c("A","C","G","T"))
# HACKY
MAX_ERROR_RATE <- 0.25
MIN_ERROR_RATE <- 1e-7
est[est>MAX_ERROR_RATE] <- MAX_ERROR_RATE
est[est<MIN_ERROR_RATE] <- MIN_ERROR_RATE
# enforce monotonicity
# https://github.com/benjjneb/dada2/issues/791
estorig <- est
est <- est %>%
data.frame() %>%
mutate_all(funs(case_when(. < X40 ~ X40,
. >= X40 ~ .))) %>% as.matrix()
rownames(est) <- rownames(estorig)
colnames(est) <- colnames(estorig)
# Expand the err matrix with the self-transition probs
err <- rbind(1-colSums(est[1:3,]), est[1:3,],
est[4,], 1-colSums(est[4:6,]), est[5:6,],
est[7:8,], 1-colSums(est[7:9,]), est[9,],
est[10:12,], 1-colSums(est[10:12,]))
rownames(err) <- paste0(rep(c("A","C","G","T"), each=4), "2", c("A","C","G","T"))
colnames(err) <- colnames(trans)
# Return
return(err)
}
# check what this looks like
errF_2 <- learnErrors(
filtFs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod2,
verbose = TRUE
)
## 108604125 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## Convergence after 7 rounds.
errR_2 <- learnErrors(
filtRs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod2,
verbose = TRUE
)
## 106190700 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## Convergence after 6 rounds.Option 3 alter loess function (weights only) and enforce
monotonicity
From JacobRPrice benjjneb/dada2#1307
loessErrfun_mod3 <- function(trans) {
qq <- as.numeric(colnames(trans))
est <- matrix(0, nrow=0, ncol=length(qq))
for(nti in c("A","C","G","T")) {
for(ntj in c("A","C","G","T")) {
if(nti != ntj) {
errs <- trans[paste0(nti,"2",ntj),]
tot <- colSums(trans[paste0(nti,"2",c("A","C","G","T")),])
rlogp <- log10((errs+1)/tot) # 1 psuedocount for each err, but if tot=0 will give NA
rlogp[is.infinite(rlogp)] <- NA
df <- data.frame(q=qq, errs=errs, tot=tot, rlogp=rlogp)
# original
# ###! mod.lo <- loess(rlogp ~ q, df, weights=errs) ###!
# mod.lo <- loess(rlogp ~ q, df, weights=tot) ###!
# # mod.lo <- loess(rlogp ~ q, df)
# Gulliem Salazar's solution
# https://github.com/benjjneb/dada2/issues/938
# mod.lo <- loess(rlogp ~ q, df, weights = log10(tot),span = 2)
# only change the weights
mod.lo <- loess(rlogp ~ q, df, weights = log10(tot))
pred <- predict(mod.lo, qq)
maxrli <- max(which(!is.na(pred)))
minrli <- min(which(!is.na(pred)))
pred[seq_along(pred)>maxrli] <- pred[[maxrli]]
pred[seq_along(pred)<minrli] <- pred[[minrli]]
est <- rbind(est, 10^pred)
} # if(nti != ntj)
} # for(ntj in c("A","C","G","T"))
} # for(nti in c("A","C","G","T"))
# HACKY
MAX_ERROR_RATE <- 0.25
MIN_ERROR_RATE <- 1e-7
est[est>MAX_ERROR_RATE] <- MAX_ERROR_RATE
est[est<MIN_ERROR_RATE] <- MIN_ERROR_RATE
# enforce monotonicity
# https://github.com/benjjneb/dada2/issues/791
estorig <- est
est <- est %>%
data.frame() %>%
mutate_all(funs(case_when(. < X40 ~ X40,
. >= X40 ~ .))) %>% as.matrix()
rownames(est) <- rownames(estorig)
colnames(est) <- colnames(estorig)
# Expand the err matrix with the self-transition probs
err <- rbind(1-colSums(est[1:3,]), est[1:3,],
est[4,], 1-colSums(est[4:6,]), est[5:6,],
est[7:8,], 1-colSums(est[7:9,]), est[9,],
est[10:12,], 1-colSums(est[10:12,]))
rownames(err) <- paste0(rep(c("A","C","G","T"), each=4), "2", c("A","C","G","T"))
colnames(err) <- colnames(trans)
# Return
return(err)
}
# check what this looks like
errF_3 <- learnErrors(
filtFs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod3,
verbose = TRUE
)
## 108604125 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## selfConsist step 8
## selfConsist step 9
## selfConsist step 10
# check what this looks like
errR_3 <- learnErrors(
filtRs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod3,
verbose = TRUE
)
## 106190700 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## Convergence after 7 rounds.Option 4 Alter loess function arguments (weights and span and
degree, also enforce monotonicity)
From Jonalim’s comment in
benjjneb/dada2#1307
loessErrfun_mod4 <- function(trans) {
qq <- as.numeric(colnames(trans))
est <- matrix(0, nrow=0, ncol=length(qq))
for(nti in c("A","C","G","T")) {
for(ntj in c("A","C","G","T")) {
if(nti != ntj) {
errs <- trans[paste0(nti,"2",ntj),]
tot <- colSums(trans[paste0(nti,"2",c("A","C","G","T")),])
rlogp <- log10((errs+1)/tot) # 1 psuedocount for each err, but if tot=0 will give NA
rlogp[is.infinite(rlogp)] <- NA
df <- data.frame(q=qq, errs=errs, tot=tot, rlogp=rlogp)
# original
# ###! mod.lo <- loess(rlogp ~ q, df, weights=errs) ###!
# mod.lo <- loess(rlogp ~ q, df, weights=tot) ###!
# # mod.lo <- loess(rlogp ~ q, df)
# jonalim's solution
# https://github.com/benjjneb/dada2/issues/938
mod.lo <- loess(rlogp ~ q, df, weights = log10(tot),degree = 1, span = 0.95)
pred <- predict(mod.lo, qq)
maxrli <- max(which(!is.na(pred)))
minrli <- min(which(!is.na(pred)))
pred[seq_along(pred)>maxrli] <- pred[[maxrli]]
pred[seq_along(pred)<minrli] <- pred[[minrli]]
est <- rbind(est, 10^pred)
} # if(nti != ntj)
} # for(ntj in c("A","C","G","T"))
} # for(nti in c("A","C","G","T"))
# HACKY
MAX_ERROR_RATE <- 0.25
MIN_ERROR_RATE <- 1e-7
est[est>MAX_ERROR_RATE] <- MAX_ERROR_RATE
est[est<MIN_ERROR_RATE] <- MIN_ERROR_RATE
# enforce monotonicity
# https://github.com/benjjneb/dada2/issues/791
estorig <- est
est <- est %>%
data.frame() %>%
mutate_all(funs(case_when(. < X40 ~ X40,
. >= X40 ~ .))) %>% as.matrix()
rownames(est) <- rownames(estorig)
colnames(est) <- colnames(estorig)
# Expand the err matrix with the self-transition probs
err <- rbind(1-colSums(est[1:3,]), est[1:3,],
est[4,], 1-colSums(est[4:6,]), est[5:6,],
est[7:8,], 1-colSums(est[7:9,]), est[9,],
est[10:12,], 1-colSums(est[10:12,]))
rownames(err) <- paste0(rep(c("A","C","G","T"), each=4), "2", c("A","C","G","T"))
colnames(err) <- colnames(trans)
# Return
return(err)
}
# check what this looks like
errF_4 <- learnErrors(
filtFs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod4,
verbose = TRUE
)
## 108604125 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## Convergence after 6 rounds.
errR_4 <- learnErrors(
filtRs,
multithread = TRUE,
nbases = 1e8,
errorEstimationFunction = loessErrfun_mod4,
verbose = TRUE
)
## 106190700 total bases in 482685 reads from 1 samples will be used for learning the error rates.
## Initializing error rates to maximum possible estimate.
## selfConsist step 1 .
## selfConsist step 2
## selfConsist step 3
## selfConsist step 4
## selfConsist step 5
## selfConsist step 6
## selfConsist step 7
## selfConsist step 8
## Convergence after 8 rounds.We want to make sure that the machine learning algorithm is learning the
error rates properly. In the plots below, the red line represents what
we should expect the learned error rates to look like for each of the 16
possible base transitions (A->A, A->C, A->G, etc.) and the black line
and grey dots represent what the observed error rates are. If the black
line and the red lines are very far off from each other, it may be a
good idea to increase the nbases parameter. This allows the machine
learning algorthim to train on a larger portion of your data and may
help improve the fit.
If you have NovaSeq data, you will notice a characteristic dip in the
default error plots and you may have points that are far off of the
line. This is typical and you will likely want to use one of the other
options for error rate functions as simply increasing nbases will not
solve this problem. There are four options, none of which will yield
“ideal” error plots. Instead look for the solution where the black line
is continuously decreasing (i.e. as quality scores improve on the x-axis
the predicted error rate (y-axis) goes down) and for plots that have
points that mostly align with the black lines, although you will likely
have some points along 0 on the y-axis.
# Original default recommended way (not optimal for NovaSeq data!)
errF_plot <- plotErrors(errF, nominalQ = TRUE)
errR_plot <- plotErrors(errR, nominalQ = TRUE)
errF_plot
errR_plot
saveRDS(errF_plot, paste0(filtpathF, "/errF_plot.rds"))
saveRDS(errR_plot, paste0(filtpathR, "/errR_plot.rds"))
ggsave(plot = errF_plot, filename = paste0(filtpathF, "/errF_plot.png"),
width = 10, height = 10, dpi = "retina")
ggsave(plot = errR_plot, filename = paste0(filtpathR, "/errR_plot.png"),
width = 10, height = 10, dpi = "retina")
# Trial 1 (alter span and weight in loess, enforce montonicity)
errF_plot1 <- plotErrors(errF_1, nominalQ = TRUE)
errR_plot1 <-plotErrors(errR_1, nominalQ = TRUE)
errF_plot1
errR_plot1
saveRDS(errF_plot1, paste0(filtpathF, "/errF_plot1.rds"))
saveRDS(errR_plot1, paste0(filtpathR, "/errR_plot1.rds"))
ggsave(plot = errF_plot1, filename = paste0(filtpathF, "/errF_plot1.png"),
width = 10, height = 10, dpi = "retina")
ggsave(plot = errR_plot1, filename = paste0(filtpathR, "/errR_plot1.png"),
width = 10, height = 10, dpi = "retina")
# Trial 2 (only enforce monotonicity - don't change the loess function)
errF_plot2 <- plotErrors(errF_2, nominalQ = TRUE)
errR_plot2 <-plotErrors(errR_2, nominalQ = TRUE)
errF_plot2
errR_plot2
saveRDS(errF_plot2, paste0(filtpathF, "/errF_plot2.rds"))
saveRDS(errR_plot2, paste0(filtpathR, "/errR_plot2.rds"))
ggsave(plot = errF_plot2, filename = paste0(filtpathF, "/errF_plot2.png"),
width = 10, height = 10, dpi = "retina")
ggsave(plot = errR_plot2, filename = paste0(filtpathR, "/errR_plot2.png"),
width = 10, height = 10, dpi = "retina")
# Trial 3 (alter loess (weights only) and enforce monotonicity)
errF_plot3 <- plotErrors(errF_3, nominalQ = TRUE)
errR_plot3 <-plotErrors(errR_3, nominalQ = TRUE)
errF_plot3
errR_plot3
saveRDS(errF_plot3, paste0(filtpathF, "/errF_plot3.rds"))
saveRDS(errR_plot3, paste0(filtpathR, "/errR_plot3.rds"))
ggsave(plot = errF_plot3, filename = paste0(filtpathF, "/errF_plot3.png"),
width = 10, height = 10, dpi = "retina")
ggsave(plot = errR_plot3, filename = paste0(filtpathR, "/errR_plot3.png"),
width = 10, height = 10, dpi = "retina")
# Trial 4 (alter loess (span, weight, and degree) and enforce monotonicity)
errF_plot4 <- plotErrors(errF_4, nominalQ = TRUE)
errR_plot4 <-plotErrors(errR_4, nominalQ = TRUE)
errF_plot4
errR_plot4
saveRDS(errF_plot4, paste0(filtpathF, "/errF_plot4.rds"))
saveRDS(errR_plot4, paste0(filtpathR, "/errR_plot4.rds"))
ggsave(plot = errF_plot4, filename = paste0(filtpathF, "/errF_plot4.png"),
width = 10, height = 10, dpi = "retina")
ggsave(plot = errR_plot4, filename = paste0(filtpathR, "/errR_plot4.png"),
width = 10, height = 10, dpi = "retina")| STOP - 04_learn_error_rates_dada2_tutorial_16S.R: If you are running this on Premise, download the plots generated here (They will be found in the 02_filter/preprocessed_F/filter and 02_filter/preprocessed_R/filter folder) and verify that the error plots look appropriate. If not, adjust the learnErrors() function and re-run this step with slurm. |
In this part of the pipeline, dada2 will make decisions about assigning
sequences to ASVs (called “sequence inference”). There is a major
parameter option in the core function dada() that changes how samples
are handled during sequence inference. The parameter pool = can be set
to: pool = FALSE (default), pool = TRUE, or pool = psuedo. For
details on parameter choice, please see below, and further information
on this blogpost
http://fiererlab.org/2020/02/17/whats-in-a-number-estimating-microbial-richness-using-dada2/,
and explanation on the dada2 tutorial
https://benjjneb.github.io/dada2/pool.html.
Details
pool = FALSE: Sequence information is not shared between samples. Fast
processing time, less sensitivity to rare taxa.
pool = psuedo: Sequence information is shared in a separate “prior”
step. Intermediate processing time, intermediate sensitivity to rare
taxa.
pool = TRUE: Sequence information from all samples is pooled together.
Slow processing time, most sensitivity to rare taxa.
For simple communities or when you do not need high sensitivity for rare taxa
# make lists to hold the loop output
mergers <- vector("list", length(sample.names))
names(mergers) <- sample.names
ddF <- vector("list", length(sample.names))
names(ddF) <- sample.names
ddR <- vector("list", length(sample.names))
names(ddR) <- sample.names
# For each sample, get a list of merged and denoised sequences
for(sam in sample.names) {
cat("Processing:", sam, "\n")
# Dereplicate forward reads
derepF <- derepFastq(filtFs[[sam]])
# Infer sequences for forward reads
dadaF <- dada(derepF, err = errF_4, multithread = TRUE)
ddF[[sam]] <- dadaF
# Dereplicate reverse reads
derepR <- derepFastq(filtRs[[sam]])
# Infer sequences for reverse reads
dadaR <- dada(derepR, err = errR_4, multithread = TRUE)
ddR[[sam]] <- dadaR
# Merge reads together
merger <- mergePairs(ddF[[sam]], derepF, ddR[[sam]], derepR)
mergers[[sam]] <- merger
}
## Processing: A1-TS_S1_L001
## Sample 1 - 482685 reads in 117323 unique sequences.
## Sample 1 - 482685 reads in 140061 unique sequences.
## Processing: A2-TS_S9_L001
## Sample 1 - 18190 reads in 8297 unique sequences.
## Sample 1 - 18190 reads in 9218 unique sequences.
## Processing: A3-TS_S17_L001
## Sample 1 - 22372 reads in 11238 unique sequences.
## Sample 1 - 22372 reads in 12112 unique sequences.
## Processing: A4-TS_S25_L001
## Sample 1 - 361859 reads in 130927 unique sequences.
## Sample 1 - 361859 reads in 149638 unique sequences.
## Processing: B1-TS_S2_L001
## Sample 1 - 2967452 reads in 570000 unique sequences.
## Sample 1 - 2967452 reads in 695919 unique sequences.
## Processing: B2-TS_S10_L001
## Sample 1 - 115834 reads in 43103 unique sequences.
## Sample 1 - 115834 reads in 47970 unique sequences.
## Processing: B3-TS_S18_L001
## Sample 1 - 84791 reads in 28002 unique sequences.
## Sample 1 - 84791 reads in 33055 unique sequences.
## Processing: B4-TS_S26_L001
## Sample 1 - 709382 reads in 211847 unique sequences.
## Sample 1 - 709382 reads in 241340 unique sequences.
## Processing: C1-TS_S3_L001
## Sample 1 - 1845042 reads in 486467 unique sequences.
## Sample 1 - 1845042 reads in 560897 unique sequences.
## Processing: C2-TS_S11_L001
## Sample 1 - 5159 reads in 2870 unique sequences.
## Sample 1 - 5159 reads in 3149 unique sequences.
## Processing: C3-TS_S19_L001
## Sample 1 - 970487 reads in 355132 unique sequences.
## Sample 1 - 970487 reads in 390933 unique sequences.
## Processing: C4-TS_S27_L001
## Sample 1 - 1874933 reads in 433406 unique sequences.
## Sample 1 - 1874933 reads in 530438 unique sequences.
## Processing: D1-TS_S4_L001
## Sample 1 - 97907 reads in 30527 unique sequences.
## Sample 1 - 97907 reads in 34879 unique sequences.
## Processing: D2-TS_S12_L001
## Sample 1 - 76628 reads in 35052 unique sequences.
## Sample 1 - 76628 reads in 39153 unique sequences.
## Processing: D3-TS_S20_L001
## Sample 1 - 477284 reads in 152079 unique sequences.
## Sample 1 - 477284 reads in 177439 unique sequences.
## Processing: D4-TS_S28_L001
## Sample 1 - 1178539 reads in 288076 unique sequences.
## Sample 1 - 1178539 reads in 351514 unique sequences.
## Processing: E1-TS_S5_L001
## Sample 1 - 3278 reads in 1664 unique sequences.
## Sample 1 - 3278 reads in 1860 unique sequences.
## Processing: E2-TS_S13_L001
## Sample 1 - 5282 reads in 3140 unique sequences.
## Sample 1 - 5282 reads in 3287 unique sequences.
## Processing: E3-TS_S21_L001
## Sample 1 - 960130 reads in 341496 unique sequences.
## Sample 1 - 960130 reads in 381106 unique sequences.
## Processing: E4-TS_S29_L001
## Sample 1 - 1034215 reads in 263684 unique sequences.
## Sample 1 - 1034215 reads in 312598 unique sequences.
## Processing: F1-TS_S6_L001
## Sample 1 - 579061 reads in 168721 unique sequences.
## Sample 1 - 579061 reads in 202261 unique sequences.
## Processing: F2-TS_S14_L001
## Sample 1 - 3463 reads in 1707 unique sequences.
## Sample 1 - 3463 reads in 1861 unique sequences.
## Processing: F3-TS_S22_L001
## Sample 1 - 126450 reads in 48216 unique sequences.
## Sample 1 - 126450 reads in 55248 unique sequences.
## Processing: F4-TS_S30_L001
## Sample 1 - 721504 reads in 202168 unique sequences.
## Sample 1 - 721504 reads in 242235 unique sequences.
## Processing: G1-TS_S7_L001
## Sample 1 - 668516 reads in 181069 unique sequences.
## Sample 1 - 668516 reads in 213965 unique sequences.
## Processing: G2-TS_S15_L001
## Sample 1 - 44689 reads in 16071 unique sequences.
## Sample 1 - 44689 reads in 17927 unique sequences.
## Processing: G3-TS_S23_L001
## Sample 1 - 18 reads in 16 unique sequences.
## Sample 1 - 18 reads in 17 unique sequences.
## Processing: G4-TS_S31_L001
## Sample 1 - 794791 reads in 284380 unique sequences.
## Sample 1 - 794791 reads in 319161 unique sequences.
## Processing: H1-TS_S8_L001
## Sample 1 - 520928 reads in 142861 unique sequences.
## Sample 1 - 520928 reads in 178869 unique sequences.
## Processing: H2-TS_S16_L001
## Sample 1 - 880446 reads in 245078 unique sequences.
## Sample 1 - 880446 reads in 290589 unique sequences.
## Processing: H3-TS_S24_L001
## Sample 1 - 32383 reads in 8975 unique sequences.
## Sample 1 - 32383 reads in 10676 unique sequences.
## Processing: H4-TS_S32_L001
## Sample 1 - 366253 reads in 136740 unique sequences.
## Sample 1 - 366253 reads in 154100 unique sequences.
rm(derepF); rm(derepR)For complex communities when you want to preserve rare taxa alternative:
swap pool = TRUE with pool = "pseudo"
# same steps, not in loop
# Dereplicate forward reads
#derepF.p <- derepFastq(filtFs)
#names(derepF.p) <- sample.names
# Infer sequences for forward reads
#dadaF.p <- dada(derepF.p, err = errF, multithread = TRUE, pool = TRUE)
#names(dadaF.p) <- sample.names
# Dereplicate reverse reads
#derepR.p <- derepFastq(filtRs)
#names(derepR.p) <- sample.names
# Infer sequences for reverse reads
#dadaR.p <- dada(derepR.p, err = errR, multithread = TRUE, pool = TRUE)
#names(dadaR.p) <- sample.names
# Merge reads together
#mergers <- mergePairs(dadaF.p, derepF.p, dadaR.p, derepR.p)You will always perform this step whether or not you have pooled or unpooled ASV picking
seqtab <- makeSequenceTable(mergers)
# Save table as an r data object file
dir.create(table.fp)
saveRDS(seqtab, paste0(table.fp, "/seqtab.rds"))STOP - 05_infer_ASVs_dada2_tutorial_16S.R: If you are running this on Premise, decide if you want the pooled or not-pooled option delete the options you don’t want before running this step with slurm. Also make sure to change the error rate model being used if you are not using the default errR and errF. You can change it in the dada() function option err. Make sure that you change it for both the forward and reverse reads. (You will likely need to change it if you have NovaSeq data.) |
Although dada2 has searched for indel errors and subsitutions, there may still be chimeric sequences in our dataset (sequences that are derived from forward and reverse sequences from two different organisms becoming fused together during PCR and/or sequencing). To identify chimeras, we will search for rare sequence variants that can be reconstructed by combining left-hand and right-hand segments from two more abundant “parent” sequences. After removing chimeras, we will use a taxonomy database to train a classifer-algorithm to assign names to our sequence variants.
For the tutorial 16S, we will assign taxonomy with Silva db v138, but you might want to use other databases for your data. Below are paths to some of the databases we use often. (If you are on your own computer you can download the database you need from this link https://benjjneb.github.io/dada2/training.html):
-
16S bacteria and archaea (SILVA db):
/mnt/home/ernakovich/shared/db_files/dada2/silva_nr99_v138.1_train_set.fa -
ITS fungi (UNITE db):
/mnt/home/ernakovich/shared/db_files/dada2/UNITE_sh_general_release_10.05.2021/sh_general_release_dynamic_10.05.2021.fasta -
18S protists (PR2 db):
/mnt/home/ernakovich/shared/db_files/dada2/pr2_version_4.14.0_SSU_dada2.fasta
# Read in RDS
st.all <- readRDS(paste0(table.fp, "/seqtab.rds"))
# Remove chimeras
seqtab.nochim <- removeBimeraDenovo(st.all, method="consensus", multithread=TRUE)
# Print percentage of our seqences that were not chimeric.
100*sum(seqtab.nochim)/sum(seqtab)
## [1] 66.62523
# Assign taxonomy
tax <- assignTaxonomy(seqtab.nochim, db_fp, tryRC = TRUE,
multithread=TRUE)
# Write results to disk
saveRDS(seqtab.nochim, paste0(table.fp, "/seqtab_final.rds"))
saveRDS(tax, paste0(table.fp, "/tax_final.rds"))For convenience sake, we will now rename our ASVs with numbers, output our results as a traditional taxa table, and create a matrix with the representative sequences for each ASV.
# Flip table
seqtab.t <- as.data.frame(t(seqtab.nochim))
# Pull out ASV repset
rep_set_ASVs <- as.data.frame(rownames(seqtab.t))
rep_set_ASVs <- mutate(rep_set_ASVs, ASV_ID = 1:n())
rep_set_ASVs$ASV_ID <- sub("^", "ASV_", rep_set_ASVs$ASV_ID)
rep_set_ASVs$ASV <- rep_set_ASVs$`rownames(seqtab.t)`
rep_set_ASVs$`rownames(seqtab.t)` <- NULL
# Add ASV numbers to table
rownames(seqtab.t) <- rep_set_ASVs$ASV_ID
# Add ASV numbers to taxonomy
taxonomy <- as.data.frame(tax)
taxonomy$ASV <- as.factor(rownames(taxonomy))
taxonomy <- merge(rep_set_ASVs, taxonomy, by = "ASV")
rownames(taxonomy) <- taxonomy$ASV_ID
taxonomy_for_mctoolsr <- unite_(taxonomy, "taxonomy",
c("Kingdom", "Phylum", "Class", "Order","Family", "Genus", "ASV_ID"),
sep = ";")
# Write repset to fasta file
# create a function that writes fasta sequences
writeRepSetFasta<-function(data, filename){
fastaLines = c()
for (rowNum in 1:nrow(data)){
fastaLines = c(fastaLines, as.character(paste(">", data[rowNum,"name"], sep = "")))
fastaLines = c(fastaLines,as.character(data[rowNum,"seq"]))
}
fileConn<-file(filename)
writeLines(fastaLines, fileConn)
close(fileConn)
}
# Arrange the taxonomy dataframe for the writeRepSetFasta function
taxonomy_for_fasta <- taxonomy %>%
unite("TaxString", c("Kingdom", "Phylum", "Class", "Order","Family", "Genus", "ASV_ID"),
sep = ";", remove = FALSE) %>%
unite("name", c("ASV_ID", "TaxString"),
sep = " ", remove = TRUE) %>%
select(ASV, name) %>%
rename(seq = ASV)
# write fasta file
writeRepSetFasta(taxonomy_for_fasta, paste0(table.fp, "/repset.fasta"))
# Merge taxonomy and table
seqtab_wTax <- merge(seqtab.t, taxonomy_for_mctoolsr, by = 0)
seqtab_wTax$ASV <- NULL
# Set name of table in mctoolsr format and save
out_fp <- paste0(table.fp, "/seqtab_wTax_mctoolsr.txt")
names(seqtab_wTax)[1] = "#ASV_ID"
write("#Exported for mctoolsr", out_fp)
suppressWarnings(write.table(seqtab_wTax, out_fp, sep = "\t", row.names = FALSE, append = TRUE))
# Also export files as .txt
write.table(seqtab.t, file = paste0(table.fp, "/seqtab_final.txt"),
sep = "\t", row.names = TRUE, col.names = NA)
write.table(tax, file = paste0(table.fp, "/tax_final.txt"),
sep = "\t", row.names = TRUE, col.names = NA)- seqtab_final.txt - A tab-delimited sequence-by-sample (i.e. OTU) table
- tax_final.txt - a tab-demlimited file showing the relationship between ASVs, ASV IDs, and their taxonomy
- seqtab_wTax_mctoolsr.txt - a tab-delimited file with ASVs as rows, samples as columns and the final column showing the taxonomy of the ASV ID
- repset.fasta - a fasta file with the representative sequence of each ASV. Fasta headers are the ASV ID and taxonomy string.
Here we track the reads throughout the pipeline to see if any step is resulting in a greater-than-expected loss of reads. If a step is showing a greater than expected loss of reads, it is a good idea to go back to that step and troubleshoot why reads are dropping out. The dada2 tutorial has more details about what can be changed at each step.
getN <- function(x) sum(getUniques(x)) # function to grab sequence counts from output objects
# tracking reads by counts
filt_out_track <- filt_out %>%
data.frame() %>%
mutate(Sample = gsub("(\\_R1\\_)(.{1,})(\\.fastq\\.gz)","",rownames(.))) %>%
rename(input = reads.in, filtered = reads.out)
rownames(filt_out_track) <- filt_out_track$Sample
ddF_track <- data.frame(denoisedF = sapply(ddF[sample.names], getN)) %>%
mutate(Sample = row.names(.))
ddR_track <- data.frame(denoisedR = sapply(ddR[sample.names], getN)) %>%
mutate(Sample = row.names(.))
merge_track <- data.frame(merged = sapply(mergers, getN)) %>%
mutate(Sample = row.names(.))
chim_track <- data.frame(nonchim = rowSums(seqtab.nochim)) %>%
mutate(Sample = row.names(.))
track <- left_join(filt_out_track, ddF_track, by = "Sample") %>%
left_join(ddR_track, by = "Sample") %>%
left_join(merge_track, by = "Sample") %>%
left_join(chim_track, by = "Sample") %>%
replace(., is.na(.), 0) %>%
select(Sample, everything())
row.names(track) <- track$Sample
head(track)
## Sample input filtered denoisedF denoisedR merged
## A1-TS_S1_L001 A1-TS_S1_L001 530215 482685 472074 471027 405706
## A2-TS_S9_L001 A2-TS_S9_L001 19919 18190 16544 16230 11287
## A3-TS_S17_L001 A3-TS_S17_L001 24740 22372 19627 19324 11930
## A4-TS_S25_L001 A4-TS_S25_L001 398733 361859 341818 341172 246432
## B1-TS_S2_L001 B1-TS_S2_L001 3280463 2967452 2921579 2927376 2608809
## B2-TS_S10_L001 B2-TS_S10_L001 128302 115834 108959 108610 80758
## nonchim
## A1-TS_S1_L001 312751
## A2-TS_S9_L001 9352
## A3-TS_S17_L001 10036
## A4-TS_S25_L001 152277
## B1-TS_S2_L001 1860194
## B2-TS_S10_L001 58214
# tracking reads by percentage
track_pct <- track %>%
data.frame() %>%
mutate(Sample = rownames(.),
filtered_pct = ifelse(filtered == 0, 0, 100 * (filtered/input)),
denoisedF_pct = ifelse(denoisedF == 0, 0, 100 * (denoisedF/filtered)),
denoisedR_pct = ifelse(denoisedR == 0, 0, 100 * (denoisedR/filtered)),
merged_pct = ifelse(merged == 0, 0, 100 * merged/((denoisedF + denoisedR)/2)),
nonchim_pct = ifelse(nonchim == 0, 0, 100 * (nonchim/merged)),
total_pct = ifelse(nonchim == 0, 0, 100 * nonchim/input)) %>%
select(Sample, ends_with("_pct"))
# summary stats of tracked reads averaged across samples
track_pct_avg <- track_pct %>% summarize_at(vars(ends_with("_pct")),
list(avg = mean))
head(track_pct_avg)
## filtered_pct_avg denoisedF_pct_avg denoisedR_pct_avg merged_pct_avg
## 1 89.80601 91.70902 92.03842 75.66029
## nonchim_pct_avg total_pct_avg
## 1 74.68421 46.56619
track_pct_med <- track_pct %>% summarize_at(vars(ends_with("_pct")),
list(avg = stats::median))
head(track_pct_avg)
## filtered_pct_avg denoisedF_pct_avg denoisedR_pct_avg merged_pct_avg
## 1 89.80601 91.70902 92.03842 75.66029
## nonchim_pct_avg total_pct_avg
## 1 74.68421 46.56619
head(track_pct_med)
## filtered_pct_avg denoisedF_pct_avg denoisedR_pct_avg merged_pct_avg
## 1 90.53239 95.5243 95.27904 75.87245
## nonchim_pct_avg total_pct_avg
## 1 71.79844 45.99255
# Plotting each sample's reads through the pipeline
track_plot <- track %>%
data.frame() %>%
mutate(Sample = rownames(.)) %>%
gather(key = "Step", value = "Reads", -Sample) %>%
mutate(Step = factor(Step,
levels = c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim"))) %>%
ggplot(aes(x = Step, y = Reads)) +
geom_line(aes(group = Sample), alpha = 0.2) +
geom_point(alpha = 0.5, position = position_jitter(width = 0)) +
stat_summary(fun.y = median, geom = "line", group = 1, color = "steelblue", size = 1, alpha = 0.5) +
stat_summary(fun.y = median, geom = "point", group = 1, color = "steelblue", size = 2, alpha = 0.5) +
stat_summary(fun.data = median_hilow, fun.args = list(conf.int = 0.5),
geom = "ribbon", group = 1, fill = "steelblue", alpha = 0.2) +
geom_label(data = t(track_pct_avg[1:5]) %>% data.frame() %>%
rename(Percent = 1) %>%
mutate(Step = c("filtered", "denoisedF", "denoisedR", "merged", "nonchim"),
Percent = paste(round(Percent, 2), "%")),
aes(label = Percent), y = 1.1 * max(track[,2])) +
geom_label(data = track_pct_avg[6] %>% data.frame() %>%
rename(total = 1),
aes(label = paste("Total\nRemaining:\n", round(track_pct_avg[1,6], 2), "%")),
y = mean(track[,6]), x = 6.5) +
expand_limits(y = 1.1 * max(track[,2]), x = 7) +
theme_classic()
track_plot
# Write results to disk
saveRDS(track, paste0(project.fp, "/tracking_reads.rds"))
saveRDS(track_pct, paste0(project.fp, "/tracking_reads_percentage.rds"))
saveRDS(track_plot, paste0(project.fp, "/tracking_reads_summary_plot.rds"))
ggsave(plot = track_plot, filename = paste0(project.fp, "/tracking_reads_summary_plot.png"), width = 10, height = 10, dpi = "retina")| STOP - 06_remove_chimeras_assign_taxonomy_dada2_tutorial_16S.R: If you are running this on Premise, make sure that you are using the appropriate database before running this step with slurm. |
You can now transfer over the output files onto your local computer. The table and taxonomy can be read into R with ‘mctoolsr’ package or another R package of your choosing.
After following this pipeline, you will need to think about the following in downstream applications:
- Remove mitochondrial and chloroplast sequences
- Remove reads assigned as eukaryotes
- Remove reads that are unassigned at domain level (also consider removing those unassigned at phylum level)
- Normalize or rarefy your ASV table
Enjoy your data!