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pipeline
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pipeline
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##Loading required packages
library(dada2)
library(ape)
library(DECIPHER)
library(phangorn)
library(ggplot2)
library(phyloseq)
library("plyr")
library("Rcpp")
library('Matrix')
library(seqinr)
library(dplyr)
library(biomformat)
#Had to split pipeline into 3 sections, each dataset had its own filter / trim parameters, and different methods for chimera removal. Push each set mostly
#through dada, combine the 3 seqtab.nochim tables at the end and assign taxonomy to all three
setwd("~/bodke/celiacWithNewControl/")
path <- "~/bodke/celiacWithNewControl/" # CHANGE ME to the directory containing the fastq files after unzipping.
list.files(path)
# Forward and reverse fastq filenames have format: SAMPLENAME_R1_001.fastq and SAMPLENAME_R2_001.fastq
fnFs <- sort(list.files(path, pattern="Amplicon-seq_of_gut_microbiota_Homo_Sapiens_1.fastq.gz", full.names = TRUE))
fnRs <- sort(list.files(path, pattern="Amplicon-seq_of_gut_microbiota_Homo_Sapiens_2.fastq.gz", full.names = TRUE))
# Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq
sample.names <- sapply(strsplit(basename(fnFs), "_"), `[`, 1)
plotQualityProfile(fnFs[1:2])
plotQualityProfile(fnRs[1:2])
# Place filtered files in filtered/ subdirectory
filtFs <- file.path(path, "filtered", paste0(sample.names, "_F_filt.fastq.gz"))
filtRs <- file.path(path, "filtered", paste0(sample.names, "_R_filt.fastq.gz"))
names(filtFs) <- sample.names
names(filtRs) <- sample.names
out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,truncLen=c(240,160),
maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=TRUE,
compress=TRUE, multithread=TRUE) # On Windows set multithread=FALSE
head(out)
errF <- learnErrors(filtFs, multithread=FALSE, randomize = TRUE)
errR <- learnErrors(filtRs, multithread=FALSE, randomize = TRUE)
dadaFs <- dada(filtFs, err=errF, multithread=FALSE)
dadaRs <- dada(filtRs, err=errR, multithread=FALSE)
dadaFs[[1]]
mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose=TRUE)
seqtab<-makeSequenceTable(mergers)
# Inspect distribution of sequence lengths
table(nchar(getSequences(seqtab)))
#Too many reads were being removed by dada2s remove bimera denovo, decided to use a referenced based approach (Vsearch) to keep more data. Referenced base
#chimera removal is fine for gut data, dbs are well maintianed and curated.
#removed dada set
## seqtab.nochim<- removeBimeraDenovo(seqtab, method="consensus", multithread=16 , verbose=TRUE)
#subbed in chimera removal
# used vsearch to remove chimeras using a referenced based approach. following code was run using the UCHIME gold referece db
#vsearch --uchime_ref ASV.fasta --chimeras chimeraList.fa --nonchimeras asvNoChim.fa --db gold.fa --threads 4 --uchimeout stats.txt
# link to db - https://drive5.com/uchime/uchime_download.html
#https://usermanual.wiki/Document/vsearchmanual.523130275/html manual used to run vsearch
#vsearch output
# Found 1041 (9.8%) chimeras, 9524 (89.8%) non-chimeras,
# and 38 (0.4%) borderline sequences in 10603 unique sequences.
# Taking abundance information into account, this corresponds to
# 1041 (9.8%) chimeras, 9524 (89.8%) non-chimeras,
# and 38 (0.4%) borderline sequences in 10603 total sequences.
#creating fasta file for vsearch
bodkeJustsequences <- colnames(seqtab.nochim)
write.fasta(bodkeJustsequences, bodkeJustsequences, 'bodkeJustsequences.fasta', open = "w", nbchar = 60, as.string = FALSE)
#Have to removed sequences identified as chimeric from seqtab, python script was used to do this. First seqtab had to be transposed to for easy pythoning
#Data was output by python was fed back into dada 2
seqtabTransposed <- t(seqtab)
write.table(seqtabTransposed, file = "seqtabTransposed.txt", row.names=TRUE, col.names = TRUE,sep = "\t")
#python script
### remove chimeras, look for lines that start with the chimera (read in from chim list) a
### only write lines which do not have chimeras
##
###reading in chimeras
##
##from Bio import SeqIO
##chimList =[]
##fasta_sequences = SeqIO.parse(open('C:/Users/jjcol/Downloads/chimeraList.fa'),'fasta')
##for fasta in fasta_sequences:
## chimList.append(str(fasta.seq))
##
###reading in table
##seqtab= open("C:/Users/jjcol/Downloads/seqtabTransposed.txt", "r")
##seqtab.nochim =[]
##counter = 0
##
##for line in seqtab:
## counter +=1
## isIn = False
## for chim in chimList:
## if line.startswith(chim)== True:
## isIn = True
## if isIn == False:
## seqtab.nochim.append(line)
##output = open('C:/Users/jjcol/Downloads/seqtab.nochimTransposed.txt','w')
##
##for s in seqtab.nochim:
## output.write(s+ '\n')
##output.close()
seqtab.nochim<-read.table('seqtab.nochimTransposed.txt')
seqtab.nochim <- t(seqtab.nochim)
dim(seqtab.nochim)
sum(seqtab.nochim)/sum(seqtab)
getN <- function(x) sum(getUniques(x))
track <- cbind(out, rowSums(seqtab.nochim))
track <- cbind(out, sapply(dadaFs, getN), sapply(dadaRs, getN), sapply(mergers, getN), rowSums(seqtab.nochim))
# If processing a single sample, remove the sapply calls: e.g. replace sapply(dadaFs, getN) with getN(dadaFs)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim")
rownames(track) <- sample.names
head(track)
taxaBodke <- assignTaxonomy(seqtab.nochim, "~/silva_nr99_v138.1_train_set.fa.gz", multithread=TRUE) ## Change to the correct database
taxaBodke.print <- taxaBodke # Removing sequence rownames for display only
rownames(taxaBodke.print) <- NULL
head(taxaBodke.print)
head(seqtab.nochim)
dim(seqtab.nochim)
# end bodke
#begin Pune
## Dataset was mostly normal, only adjustments made to filter and trim, 240, 60 rather than 240, 160. reverse read data quickly dropped off in quality.
#Coupled with differences in previous illumina data made it hard to process in one step.
# If processing a single sample, remove the sapply calls: e.g. replace sapply(dadaFs, getN) with getN(dadaFs)
#Control set I
fnFs2 <- sort(list.files(path, pattern="rRNA_V4_sequences_from_human_stool_samples_1.fastq.gz", full.names = TRUE))
fnRs2 <- sort(list.files(path, pattern="rRNA_V4_sequences_from_human_stool_samples_2.fastq.gz", full.names = TRUE))
# Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq
sample.names <- sapply(strsplit(basename(fnFs2), "_"), `[`, 1)
# Place filtered files in filtered/ subdirectory
filtFs2 <- file.path(path, "filtered", paste0(sample.names, "_F_filt.fastq.gz"))
filtRs2 <- file.path(path, "filtered", paste0(sample.names, "_R_filt.fastq.gz"))
names(filtFs2) <- sample.names
names(filtRs2) <- sample.names
head(track)
plotQualityProfile(fnFs2)
plotQualityProfile(fnRs2)
out <- filterAndTrim(fnFs2, filtFs2, fnRs2, filtRs2,truncLen=c(240,60),
maxN=0, maxEE=c(2,2), truncQ=3, rm.phix=TRUE,
compress=TRUE, multithread=TRUE) # On Windows set multithread=FALSE
head(out)
errF2 <- learnErrors(filtFs2, multithread=FALSE, randomize = TRUE)
errR2 <- learnErrors(filtRs2, multithread=FALSE, randomize = TRUE)
plotErrors(errF, nominalQ=TRUE)
dadaFs2 <- dada(filtFs2, err=errF2, multithread=TRUE)
dadaRs2 <- dada(filtRs2, err=errR2, multithread=TRUE)
dadaFs2[[1]]
mergers2 <- mergePairs(dadaFs2, filtFs2, dadaRs2, filtRs2, verbose=TRUE)
head(mergers2[[1]])
seqtab3 <- makeSequenceTable(mergers2)
dim(seqtab3)
# Inspect distribution of sequence lengths
table(nchar(getSequences(seqtab3)))
seqtab.nochim3<- removeBimeraDenovo(seqtab3, method="consensus", multithread=FALSE , verbose=TRUE)
dim(seqtab.nochim3)
sum(seqtab.nochim3)/sum(seqtab3)
getN <- function(x) sum(getUniques(x))
track <- cbind(out, sapply(dadaFs2, getN), sapply(dadaRs2, getN), sapply(mergers2, getN), rowSums(seqtab.nochim3))
# If processing a single sample, remove the sapply calls: e.g. replace sapply(dadaFs, getN) with getN(dadaFs)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim")
rownames(track) <- sample.names
head(track)
## end Pune
## begin Dubey - pan India sequencing data, pulled only non-obese data from Delhi region of india (needed to be Delhi, as diet varys greatly in India leading to
#large shifts in microbiome) this dataset utlised pyrosequencing, needed most different script as there is only one read. Nothing to merge.
## We should be able to add the DADAFs to mergers then make seqtab since each is simply a list. DADA2 script edited to work with homopolymers by removing all
#references to reverse reads.
fnFs3 <- sort(list.files(path, pattern="ERR", full.names = TRUE))
# Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq
sample.names <- sapply(strsplit(basename(fnFs3), "_"), `[`, 1)
plotQualityProfile(fnFs3[1:2])
# Place filtered files in filtered/ subdirectory
filtFs3 <- file.path(path, "filtered", paste0(sample.names, "_F_filt.fastq.gz"))
names(filtFs3) <- sample.names
#
out <- filterAndTrim(fnFs3, filtFs3,
maxN=0, maxEE=c(2), truncQ=2, rm.phix=TRUE,
compress=TRUE, multithread=TRUE, trimLeft=100) # On Windows set multithread=FALSE
head(out)
plotQualityProfile(filtFs3[1:2])
errF3 <- learnErrors(filtFs3, multithread=FALSE, randomize = TRUE)
plotErrors(errF3, nominalQ=TRUE)
#additional changes to dada2, added homopolymer_gap_penalty, and band_size in accordance with dada2 FAQs
dadaFs3 <- dada(filtFs3, err=errF3, multithread=TRUE,HOMOPOLYMER_GAP_PENALTY=-1, BAND_SIZE=32)
dadaFs3[[1]]
seqtab4 <- makeSequenceTable(dadaFs3)
dim(seqtab4)
# Inspect distribution of sequence lengths
table(nchar(getSequences(seqtab3)))
seqtab.nochim4 <- removeBimeraDenovo(seqtab4, method="consensus", multithread=FALSE , verbose=TRUE)
dim(seqtab.nochim4)
sum(seqtab.nochim4)/sum(seqtab4)
## combining the three sequence tables and assigning taxonomy
mergetab <- mergeSequenceTables(seqtab.nochim,seqtab.nochim3,seqtab.nochim4)
sequences<-getSequences(mergetab)
names(sequences)<-sequences
taxa <- assignTaxonomy(mergetab, "~/silva_nr99_v138.1_train_set.fa.gz", multithread=TRUE) ## Change to the correct database
taxa.print <- taxa # Removing sequence rownames for display only
rownames(taxa.print) <- NULL
head(taxa.print)
#creating phylogenetic tree, dataset too large to make a tree in accordance with phangorn tutorial. This script was made using docs, builds a UPMA tree. Quick
#and easy, less accurate. However, more accurate trees were impossible to make with data, R continually crashed. Having a tree is better than no tree
alignment <- AlignSeqs(DNAStringSet(sequences), anchor=NA)
phang.align <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phang.align)
treeUPMABodke <- upgma(dm)
plot(treeUPMABodke, main="UPGMA")
write.tree(treeUPMABodke, file = "bodkeTreeNewControls.nwk", append = FALSE,
digits = 10, tree.names = FALSE)
# adding taxonomic classifcation to asvs and writing taxa table to file
colnames(taxa) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus","Species")
write.table(taxa, file = "taxaTableNewControls.txt", row.names=TRUE, col.names = TRUE,sep = "\t")
#writing asv table to file
asvTable<-mergetab
write.table(asvTable, file="ASVTableNewData.txt", row.names=TRUE , sep="\t")
## creating fasta file of ASVs for picrust2
ASV_seqs <- colnames(mergetab)
write.fasta(sequences = as.list(ASV_seqs), names = as.list(ASV_seqs), nbchar = 80, file.out = "ASV.fasta")
### creating biom format table for picrust2
biomTable<-make_biom(t(mergetab), sample_metadata = NULL, observation_metadata = NULL,
id = NULL,matrix_element_type = "int")
write_biom(biomTable, biom_file="table.biom")