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A big data version of dada2 pipeline for processing high throughput amplicon data for the Fierer lab. NOTE: the original repository hosted by Angela Oliverio (amoliverio) is no longer being updated. Please return to this page for updates.
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README.md

dada2 tutorial with MiSeq dataset for Fierer Lab

This tutorial created by Angela Oliverio and Hannah Holland-Moritz, and is maintained by current members of the Fierer Lab (Corinne Walsh, Matt Gebert, Kunkun Fan)
Updated March 2nd, 2020

This pipeline runs the dada2 workflow for Big Data (paired-end) from RStudio on the microbe server.

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

Preliminary Checklist (part 0) - Before You Begin

  1. 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/

  2. 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.

  3. Make note of which sequencing platform was used, as this can impact both read quality and downstream analysis.

  4. 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

  5. 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.

Set up (part 1) - Steps before starting pipeline

If you are running it through Fierer Lab "microbe" server:

Logging in:

To use the microbe server, open a terminal window, and type and hit return:

ssh <your microbe user name>@microbe.colorado.edu 

For your first time:

Setting up your password:

When you log in for the first time, you will have to set a new password. First, log in using your temporary password. The command prompt will then ask you to write a new password. Type it in once and hit return, then type it in again to verify. When you set a new password, make sure that it is something secure (i.e. has at least letters and numbers in it). Note, nothing will show up on the screen when you enter passwords.

Important: Please be respectful and do not give your PW out to other people. The server is currently accessible to the whole world, so if your PW falls into the wrong hands, this will make a lot more work for the folks who administer the server.

Downloading this tutorial from github

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/fiererlab/dada2_fiererlab/archive/master.zip
unzip master.zip

If 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.

Login to RStudio on the server

  1. Open a your web browser and start a new empty tab
  2. Type microbe.colorado.edu:8787 in the address bar
  3. Use your server login credentials to log into rstudio server

If you are running it on your own computer (runs slower!):

  1. 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.
  2. Download the tutorial data from here http://cme.colorado.edu/projects/bioinformatics-tutorials
  3. Install idemp and cutadapt.
  4. Download the dada2-formatted reference database of your choice. Link to download here: https://benjjneb.github.io/dada2/training.html

Set up (part 2) - You are logged in to Rstudio on server (or have it open on your computer)

First open the R script in Rstudio. The R 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_fiererlab and dada2_tutorial_16S.R to open the R script.

Now, install DADA2 & other necessary packages. If this is your first time on Rstudio server, when you install a package 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")

Load DADA2 and required packages

library(dada2); packageVersion("dada2") # the dada2 pipeline
## [1] '1.10.1'
library(ShortRead); packageVersion("ShortRead") # dada2 depends on this
## [1] '1.38.0'
library(dplyr); packageVersion("dplyr") # for manipulating data
## [1] '0.8.1'
library(tidyr); packageVersion("tidyr") # for creating the final graph at the end of the pipeline
## [1] '0.8.3'
library(Hmisc); packageVersion("Hmisc") # for creating the final graph at the end of the pipeline
## [1] '4.2.0'
library(ggplot2); packageVersion("ggplot2") # for creating the final graph at the end of the pipeline
## [1] '3.1.1'
library(plotly); packageVersion("plotly") # enables creation of interactive graphs, especially helpful for quality plots
## [1] '4.9.0'

Once the packages are installed, you can check to make sure the auxillary software is working and set up some of the variables that you will need along the way.

NOTE: If you are not working from microbe server, you will need to change the file paths for idemp and 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

# Set up pathway to idemp (demultiplexing tool) and test
idemp <- "/usr/bin/idemp" # CHANGE ME if not on microbe
system2(idemp) # Check that idemp is in your path and you can run shell commands from R

# Set up pathway to cutadapt (primer trimming tool) and test
cutadapt <- "/usr/local/Python27/bin/cutadapt" # CHANGE ME if not on microbe
system2(cutadapt, args = "--version") # Check by running shell command from R

# Set path to shared data folder and contents
data.fp <- "/data/shared/2019_02_20_MicrMethods_tutorial"

# List all files in shared folder to check path
list.files(data.fp)
##  [1] "barcode_demultiplex_short.txt"                                                
##  [2] "CompletedJobInfo.xml"                                                         
##  [3] "cvanderburgh"                                                                 
##  [4] "GenerateFASTQRunStatistics.xml"                                               
##  [5] "Molecular_Methods_18_515fBC_16S_Mapping_File_SHORT_vFinal_Fierer_10252018.txt"
##  [6] "other_files"                                                                  
##  [7] "RunInfo.xml"                                                                  
##  [8] "runParameters.xml"                                                            
##  [9] "Undetermined_S0_L001_I1_001.fastq.gz"                                         
## [10] "Undetermined_S0_L001_R1_001.fastq.gz"                                         
## [11] "Undetermined_S0_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
barcode.fp <- file.path(data.fp, "barcode_demultiplex_short.txt") # .txt file: barcode </t> sampleID
map.fp <- file.path(data.fp, "Molecular_Methods_18_515fBC_16S_Mapping_File_SHORT_vFinal_Fierer_10252018.txt")
I1.fp <- file.path(data.fp, "Undetermined_S0_L001_I1_001.fastq.gz") 
R1.fp <- file.path(data.fp, "Undetermined_S0_L001_R1_001.fastq.gz") 
R2.fp <- file.path(data.fp, "Undetermined_S0_L001_R2_001.fastq.gz") 
NOTE: idemp relies on having a match in length between the index file and and the barcode sequences. Since the index file usually includes a extra linker basepair (making it 13bp long), you should append the barcode sequences with "N" to make sure each is 13bp long. If you are not sure of the length of index reads, check with the sequencing center. If your index reads are 12bp long, you do NOT need to add an "N".

For ITS Sequences: 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

Set up file paths in YOUR directory where you want data; you do not need to create the subdirectories but they are nice to have for organizational purposes.

project.fp <- "/data/YOURUSERNAMEHERE/MicroMethods_dada2_tutorial" # CHANGE ME to project directory; don't append with a "/"

# Set up names of sub directories to stay organized
preprocess.fp <- file.path(project.fp, "01_preprocess")
    demultiplex.fp <- file.path(preprocess.fp, "demultiplexed")
    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") 

Pre-processing data for dada2 - demultiplex, remove sequences with Ns, cutadapt

Call the demultiplexing script

Demultiplexing splits your reads out into separate files based on the barcodes associated with each sample.

flags <- paste("-b", barcode.fp, "-I1", I1.fp, "-R1", R1.fp, "-R2", R2.fp, "-o", demultiplex.fp) 
system2(idemp, args = flags) 

# Look at output of demultiplexing
list.files(demultiplex.fp)
##  [1] "Undetermined_S0_L001_I1_001.fastq.gz.decode"                 
##  [2] "Undetermined_S0_L001_I1_001.fastq.gz.decode.stat"            
##  [3] "Undetermined_S0_L001_R1_001.fastq.gz_ANT7.fastq.gz"          
##  [4] "Undetermined_S0_L001_R1_001.fastq.gz_ANT8.fastq.gz"          
##  [5] "Undetermined_S0_L001_R1_001.fastq.gz_BA1A5566_10_1D.fastq.gz"
##  [6] "Undetermined_S0_L001_R1_001.fastq.gz_BA1A5566_22_1C.fastq.gz"
##  [7] "Undetermined_S0_L001_R1_001.fastq.gz_BA1A5566_45_1J.fastq.gz"
##  [8] "Undetermined_S0_L001_R1_001.fastq.gz_BB1S.fastq.gz"          
##  [9] "Undetermined_S0_L001_R1_001.fastq.gz_BB1W.fastq.gz"          
## [10] "Undetermined_S0_L001_R1_001.fastq.gz_BNS1.fastq.gz"          
## [11] "Undetermined_S0_L001_R1_001.fastq.gz_BNS2.fastq.gz"          
## [12] "Undetermined_S0_L001_R1_001.fastq.gz_C2S.fastq.gz"           
## [13] "Undetermined_S0_L001_R1_001.fastq.gz_C2W.fastq.gz"           
## [14] "Undetermined_S0_L001_R1_001.fastq.gz_CM2A_10_9C.fastq.gz"    
## [15] "Undetermined_S0_L001_R1_001.fastq.gz_CM2A_22_9J.fastq.gz"    
## [16] "Undetermined_S0_L001_R1_001.fastq.gz_COL11.fastq.gz"         
## [17] "Undetermined_S0_L001_R1_001.fastq.gz_COL12.fastq.gz"         
## [18] "Undetermined_S0_L001_R1_001.fastq.gz_COL3.fastq.gz"          
## [19] "Undetermined_S0_L001_R1_001.fastq.gz_COL4.fastq.gz"          
## [20] "Undetermined_S0_L001_R1_001.fastq.gz_DT3S.fastq.gz"          
## [21] "Undetermined_S0_L001_R1_001.fastq.gz_DT3W.fastq.gz"          
## [22] "Undetermined_S0_L001_R1_001.fastq.gz_OM18_BC.fastq.gz"       
## [23] "Undetermined_S0_L001_R1_001.fastq.gz_OM18_BJ.fastq.gz"       
## [24] "Undetermined_S0_L001_R1_001.fastq.gz_unsigned.fastq.gz"      
## [25] "Undetermined_S0_L001_R1_001.fastq.gz_WAB105_22_6J.fastq.gz"  
## [26] "Undetermined_S0_L001_R1_001.fastq.gz_WAB105_45_6D.fastq.gz"  
## [27] "Undetermined_S0_L001_R1_001.fastq.gz_WAB188_10_4D.fastq.gz"  
## [28] "Undetermined_S0_L001_R1_001.fastq.gz_WAB71_45_3D.fastq.gz"   
## [29] "Undetermined_S0_L001_R2_001.fastq.gz_ANT7.fastq.gz"          
## [30] "Undetermined_S0_L001_R2_001.fastq.gz_ANT8.fastq.gz"          
## [31] "Undetermined_S0_L001_R2_001.fastq.gz_BA1A5566_10_1D.fastq.gz"
## [32] "Undetermined_S0_L001_R2_001.fastq.gz_BA1A5566_22_1C.fastq.gz"
## [33] "Undetermined_S0_L001_R2_001.fastq.gz_BA1A5566_45_1J.fastq.gz"
## [34] "Undetermined_S0_L001_R2_001.fastq.gz_BB1S.fastq.gz"          
## [35] "Undetermined_S0_L001_R2_001.fastq.gz_BB1W.fastq.gz"          
## [36] "Undetermined_S0_L001_R2_001.fastq.gz_BNS1.fastq.gz"          
## [37] "Undetermined_S0_L001_R2_001.fastq.gz_BNS2.fastq.gz"          
## [38] "Undetermined_S0_L001_R2_001.fastq.gz_C2S.fastq.gz"           
## [39] "Undetermined_S0_L001_R2_001.fastq.gz_C2W.fastq.gz"           
## [40] "Undetermined_S0_L001_R2_001.fastq.gz_CM2A_10_9C.fastq.gz"    
## [41] "Undetermined_S0_L001_R2_001.fastq.gz_CM2A_22_9J.fastq.gz"    
## [42] "Undetermined_S0_L001_R2_001.fastq.gz_COL11.fastq.gz"         
## [43] "Undetermined_S0_L001_R2_001.fastq.gz_COL12.fastq.gz"         
## [44] "Undetermined_S0_L001_R2_001.fastq.gz_COL3.fastq.gz"          
## [45] "Undetermined_S0_L001_R2_001.fastq.gz_COL4.fastq.gz"          
## [46] "Undetermined_S0_L001_R2_001.fastq.gz_DT3S.fastq.gz"          
## [47] "Undetermined_S0_L001_R2_001.fastq.gz_DT3W.fastq.gz"          
## [48] "Undetermined_S0_L001_R2_001.fastq.gz_OM18_BC.fastq.gz"       
## [49] "Undetermined_S0_L001_R2_001.fastq.gz_OM18_BJ.fastq.gz"       
## [50] "Undetermined_S0_L001_R2_001.fastq.gz_unsigned.fastq.gz"      
## [51] "Undetermined_S0_L001_R2_001.fastq.gz_WAB105_22_6J.fastq.gz"  
## [52] "Undetermined_S0_L001_R2_001.fastq.gz_WAB105_45_6D.fastq.gz"  
## [53] "Undetermined_S0_L001_R2_001.fastq.gz_WAB188_10_4D.fastq.gz"  
## [54] "Undetermined_S0_L001_R2_001.fastq.gz_WAB71_45_3D.fastq.gz"
WARNING: The demultiplexing step may take a while. If it takes too long you can safely close RStudio on the server and the demultiplexing will run in the background. You should be able to resume the pipeline after demultiplexing is complete by logging back into RStudio on the server.

Clean up the output from idemp

# Change names of unassignable reads so they are not included in downstream processing
unassigned_1 <- paste0("mv", " ", demultiplex.fp, "/Undetermined_S0_L001_R1_001.fastq.gz_unsigned.fastq.gz",
                       " ", demultiplex.fp, "/Unassigned_reads1.fastq.gz")
unassigned_2 <- paste0("mv", " ", demultiplex.fp, "/Undetermined_S0_L001_R2_001.fastq.gz_unsigned.fastq.gz", 
                       " ", demultiplex.fp, "/Unassigned_reads2.fastq.gz")
system(unassigned_1)
system(unassigned_2)

# Rename files - use gsub to get names in order!
R1_names <- gsub(paste0(demultiplex.fp, "/Undetermined_S0_L001_R1_001.fastq.gz_"), "", 
                 list.files(demultiplex.fp, pattern="R1", full.names = TRUE))
file.rename(list.files(demultiplex.fp, pattern="R1", full.names = TRUE), 
            paste0(demultiplex.fp, "/R1_", R1_names))
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

R2_names <- gsub(paste0(demultiplex.fp, "/Undetermined_S0_L001_R2_001.fastq.gz_"), "", 
                 list.files(demultiplex.fp, pattern="R2", full.names = TRUE))
file.rename(list.files(demultiplex.fp, pattern="R2", full.names = TRUE),
            paste0(demultiplex.fp, "/R2_", R2_names))
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

# Get full paths for all files and save them for downstream analyses
# Forward and reverse fastq filenames have format: 
fnFs <- sort(list.files(demultiplex.fp, pattern="R1_", full.names = TRUE))
fnRs <- sort(list.files(demultiplex.fp, pattern="R2_", full.names = TRUE))

Pre-filter to remove sequence reads with Ns

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.

Prepare the primers sequences and custom functions for analyzing the results from cutadapt

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 806br those are the primers below. Change if you sequenced with other primers.

For ITS data: CTTGGTCATTTAGAGGAAGTAA is the ITS forward primer sequence (ITS1F) and GCTGCGTTCTTCATCGATGC is ITS reverse primer sequence (ITS2)

# Set up the primer sequences to pass along to cutadapt
FWD <- "GTGYCAGCMGCCGCGGTAA"  ## CHANGE ME # this is 515f
REV <- "GGACTACNVGGGTWTCTAAT"  ## CHANGE ME # this is 806Br

# 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       2          0       0       0
## FWD.ReverseReads       0          0       0      35
## REV.ForwardReads       0          0       0      56
## REV.ReverseReads       0          0       0       0

Remove primers with cutadapt and assess the output

# 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(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       0

Now start DADA2 pipeline

# 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.rename(from = fnFs.cut, to = fnFs.Q)
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
file.rename(from = fnRs.cut, to = fnRs.Q)
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

# 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.")

1. FILTER AND TRIM FOR QUALITY

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.

Inspect read quality profiles

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 lenghth, 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) {
  plotQualityProfile(paste0(subF.fp, "/", fastqFs))
  plotQualityProfile(paste0(subR.fp, "/", fastqRs))
} else {
  rand_samples <- sample(size = 20, 1:length(fastqFs)) # grab 20 random samples to plot
  fwd_qual_plots <- plotQualityProfile(paste0(subF.fp, "/", fastqFs[rand_samples]))
  rev_qual_plots <- plotQualityProfile(paste0(subR.fp, "/", fastqRs[rand_samples]))
}

fwd_qual_plots

rev_qual_plots

# Or, to make these quality plots interactive, just call the plots through plotly
ggplotly(fwd_qual_plots)
ggplotly(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 = 10, height = 10, dpi = "retina")
ggsave(plot = rev_qual_plots, filename = paste0(filter.fp, "/rev_qual_plots.png"), 
       width = 10, height = 10, dpi = "retina")

Filter the data

WARNING: THESE PARAMETERS ARE NOT OPTIMAL FOR ALL DATASETS. Make sure you determine the trim and filtering parameters for your data. The following settings are generally appropriate for MiSeq runs that are 2x150 bp. These are 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(150,140), 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
## R1_ANT7.fastq.gz              40165     38746
## R1_ANT8.fastq.gz              25526     24723
## R1_BA1A5566_10_1D.fastq.gz    50515     48763
## R1_BA1A5566_22_1C.fastq.gz    42238     40623
## R1_BA1A5566_45_1J.fastq.gz    31057     29830
## R1_BB1S.fastq.gz              34353     33063

# 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         86.5%           96.28%         95.93%        97.15%

Plot the quality of the filtered fastq files.

# figure out which samples, if any, have been filtered out
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")

2. INFER sequence variants

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.

Housekeeping step - set up and verify the file names for the output:

# 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 <- substring(basename(filtFs), regexpr("_", basename(filtFs)) + 1) # doesn't drop fastq.gz
sample.names <- gsub(".fastq.gz", "", sample.names)
sample.namesR <- substring(basename(filtRs), regexpr("_", basename(filtRs)) + 1) # doesn't drop fastq.gz
sample.namesR <- gsub(".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.names

Learn the error rates

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)
## 105605700 total bases in 704038 reads from 23 samples will be used for learning the error rates.

# Learn reverse error rates
errR <- learnErrors(filtRs, nbases = 1e8, multithread = TRUE, randomize = TRUE)
## 102598720 total bases in 732848 reads from 24 samples will be used for learning the error rates.

Plot Error Rates

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 alows the machine learning algorthim to train on a larger portion of your data and may help imporve the fit.

errF_plot <- plotErrors(errF, nominalQ = TRUE)
errR_plot <- plotErrors(errR, nominalQ = TRUE)

errF_plot

errR_plot

# write to disk
saveRDS(errF_plot, paste0(filtpathF, "/errF_plot.rds"))
saveRDS(errR_plot, paste0(filtpathR, "/errR_plot.rds"))

Dereplication, sequence inference, and merging of paired-end reads

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.

Default: SAMPLES NOT POOLED

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, multithread = TRUE)
    ddF[[sam]] <- dadaF
    # Dereplicate reverse reads
    derepR <- derepFastq(filtRs[[sam]])
    # Infer sequences for reverse reads
    dadaR <- dada(derepR, err = errR, multithread = TRUE)
    ddR[[sam]] <- dadaR
    # Merge reads together
    merger <- mergePairs(ddF[[sam]], derepF, ddR[[sam]], derepR)
    mergers[[sam]] <- merger
}
## Processing: ANT7 
## Sample 1 - 38746 reads in 16505 unique sequences.
## Sample 1 - 38746 reads in 14810 unique sequences.
## Processing: ANT8 
## Sample 1 - 24723 reads in 10738 unique sequences.
## Sample 1 - 24723 reads in 9620 unique sequences.
## Processing: BA1A5566_10_1D 
## Sample 1 - 48763 reads in 12161 unique sequences.
## Sample 1 - 48763 reads in 11339 unique sequences.
## Processing: BA1A5566_22_1C 
## Sample 1 - 40623 reads in 9985 unique sequences.
## Sample 1 - 40623 reads in 8732 unique sequences.
## Processing: BA1A5566_45_1J 
## Sample 1 - 29830 reads in 7601 unique sequences.
## Sample 1 - 29830 reads in 6225 unique sequences.
## Processing: BB1S 
## Sample 1 - 33063 reads in 11503 unique sequences.
## Sample 1 - 33063 reads in 9938 unique sequences.
## Processing: BB1W 
## Sample 1 - 38337 reads in 10213 unique sequences.
## Sample 1 - 38337 reads in 7858 unique sequences.
## Processing: BNS1 
## Sample 1 - 3684 reads in 886 unique sequences.
## Sample 1 - 3684 reads in 809 unique sequences.
## Processing: BNS2 
## Sample 1 - 16362 reads in 3931 unique sequences.
## Sample 1 - 16362 reads in 3576 unique sequences.
## Processing: C2S 
## Sample 1 - 37037 reads in 14537 unique sequences.
## Sample 1 - 37037 reads in 12349 unique sequences.
## Processing: C2W 
## Sample 1 - 40359 reads in 9705 unique sequences.
## Sample 1 - 40359 reads in 6851 unique sequences.
## Processing: CM2A_10_9C 
## Sample 1 - 33206 reads in 5265 unique sequences.
## Sample 1 - 33206 reads in 4317 unique sequences.
## Processing: CM2A_22_9J 
## Sample 1 - 29844 reads in 5688 unique sequences.
## Sample 1 - 29844 reads in 4663 unique sequences.
## Processing: COL11 
## Sample 1 - 30423 reads in 15072 unique sequences.
## Sample 1 - 30423 reads in 13813 unique sequences.
## Processing: COL12 
## Sample 1 - 23608 reads in 11851 unique sequences.
## Sample 1 - 23608 reads in 10657 unique sequences.
## Processing: COL3 
## Sample 1 - 38933 reads in 19379 unique sequences.
## Sample 1 - 38933 reads in 17624 unique sequences.
## Processing: COL4 
## Sample 1 - 31406 reads in 15661 unique sequences.
## Sample 1 - 31406 reads in 14126 unique sequences.
## Processing: DT3S 
## Sample 1 - 35316 reads in 12977 unique sequences.
## Sample 1 - 35316 reads in 11359 unique sequences.
## Processing: DT3W 
## Sample 1 - 42982 reads in 11632 unique sequences.
## Sample 1 - 42982 reads in 8518 unique sequences.
## Processing: OM18_BC 
## Sample 1 - 4603 reads in 1285 unique sequences.
## Sample 1 - 4603 reads in 1101 unique sequences.
## Processing: OM18_BJ 
## Sample 1 - 391 reads in 130 unique sequences.
## Sample 1 - 391 reads in 112 unique sequences.
## Processing: WAB105_22_6J 
## Sample 1 - 43423 reads in 9283 unique sequences.
## Sample 1 - 43423 reads in 8167 unique sequences.
## Processing: WAB105_45_6D 
## Sample 1 - 36026 reads in 7663 unique sequences.
## Sample 1 - 36026 reads in 7031 unique sequences.
## Processing: WAB188_10_4D 
## Sample 1 - 29793 reads in 6088 unique sequences.
## Sample 1 - 29793 reads in 4871 unique sequences.
## Processing: WAB71_45_3D 
## Sample 1 - 41726 reads in 10190 unique sequences.
## Sample 1 - 41726 reads in 9586 unique sequences.

rm(derepF); rm(derepR)

Alternative: SAMPLES POOLED

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)

Construct sequence table

seqtab <- makeSequenceTable(mergers)

# Save table as an r data object file
dir.create(table.fp)
saveRDS(seqtab, paste0(table.fp, "/seqtab.rds"))

3. REMOVE Chimeras and ASSIGN Taxonomy

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 v132, 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): /db_files/dada2/silva_nr_v132_train_set.fa

  • ITS fungi (UNITE db): /db_files/dada2/sh_general_release_dynamic_02.02.2019.fasta

  • 18S protists (PR2 db): /db_files/dada2/pr2_version_4.11.1_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] 98.33841

# Assign taxonomy
tax <- assignTaxonomy(seqtab.nochim, "/db_files/dada2/silva_nr_v132_train_set.fa", 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"))

4. Optional - FORMAT OUTPUT to obtain ASV IDs and repset, and input for mctoolsr

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)

Summary of output files:

  1. seqtab_final.txt - A tab-delimited sequence-by-sample (i.e. OTU) table
  2. tax_final.txt - a tab-demilimited file showing the relationship between ASVs, ASV IDs, and their taxonomy
  3. 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
  4. repset.fasta - a fasta file with the representative sequence of each ASV. Fasta headers are the ASV ID and taxonomy string.

5. Summary of reads throughout pipeline

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)","\\2",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
## ANT7                     ANT7 40165    38746     36996     37224  30901
## ANT8                     ANT8 25526    24723     23301     23550  18907
## BA1A5566_10_1D BA1A5566_10_1D 50515    48763     48222     48059  46754
## BA1A5566_22_1C BA1A5566_22_1C 42238    40623     40146     40228  38936
## BA1A5566_45_1J BA1A5566_45_1J 31057    29830     29386     29522  28609
## BB1S                     BB1S 34353    33063     31883     32190  28317
##                nonchim
## ANT7             30770
## ANT8             18795
## BA1A5566_10_1D   45394
## BA1A5566_22_1C   37113
## BA1A5566_45_1J   25798
## BB1S             28183

# 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         95.93076          97.19867          97.85853       91.06978
##   nonchim_pct_avg total_pct_avg
## 1        98.61551      84.18802

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         95.93076          97.19867          97.85853       91.06978
##   nonchim_pct_avg total_pct_avg
## 1        98.61551      84.18802
head(track_pct_med)
##   filtered_pct_avg denoisedF_pct_avg denoisedR_pct_avg merged_pct_avg
## 1         96.27596          98.82579          98.96748       97.11989
##   nonchim_pct_avg total_pct_avg
## 1         99.3116      87.86638

# 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"))

Next Steps

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.

Post-pipeline considerations

After following this pipeline, you will need to think about the following in downstream applications:

  1. Remove mitochondrial and chloroplast sequences
  2. Remove reads assigned as eukaryotes
  3. Remove reads that are unassigned at domain level
  4. Normalize or rarefy your ASV table
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