Mercurial > repos > matthias > dada2_removebimeradenovo

annotate data2.R @ 0:01159d734fc9 draft

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planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit d63c84012410608b3b5d23e130f0beff475ce1f8-dirty
author matthias
date Fri, 08 Mar 2019 06:38:25 -0500
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01159d734fc9 planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit d63c84012410608b3b5d23e130f0beff475ce1f8-dirty
matthias
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diff changeset
1 library(dada2)
01159d734fc9 planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit d63c84012410608b3b5d23e130f0beff475ce1f8-dirty
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3 # library(DBI)
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4 # library(ggplot2)
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5 library(optparse)
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6 # library(RSQLite)
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7 # library(stringr)
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9 ## source required R functions
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10 source('user_input_functions.R')
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12 # print dada2 version
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13 print(paste("dada2 version: ", packageVersion("dada2")))
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15 # # R function to create fasta file from dada2 output data
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16 # outdir is directory to output fasta file
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17 # taxa is taxonomy file generated by dada2
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18 # prefix is string for desired prefix attached to output file names
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20 dada2fasta <- function(outdir, seqtab.nochim, prefix){
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21 seq <- colnames(seqtab.nochim)
01159d734fc9 planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit d63c84012410608b3b5d23e130f0beff475ce1f8-dirty
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22 n <- 0
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23 ASVs <- c()
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24 for(i in seq){
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25 n <- n + 1
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26 ASV <- paste('ASV', as.character(n), sep = '_')
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27 ASVs <- c(ASVs, ASV)
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28 line1 <- paste('>',ASV,sep='')
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29 write(line1, file.path(outdir,sprintf('%s.fasta',prefix)), append=T)
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30 write(i, file.path(outdir,sprintf('%s.fasta',prefix)), append=T)
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31 }
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32 return(ASVs)
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33 }
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36 # # R DADA2 workflow
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37 # wd is path to fastq files
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38 # r_path is path to user_input_functions.R
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39 # outdir is path to output directory
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40 # prefix is string for desired prefix attached to output file names
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42 dada2run <- function(wd, r_path, outdir, prefix){
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43
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44 # read-in files-------------------------------------------------------
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45 ## obtain vectors of forward and reverse reads based on 'R1' and 'R2' in file names
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46 ## additionally obtain the coressponding sample names for these files
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47 p1 <- c()
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48 p2 <- c()
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49 sample.names <- c()
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50 for(f in list.files(wd, full.names=T)){
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51 if(grepl('_1.fq', f)){
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52 sample <- gsub('^.*[/](.*?)_1\\.fq\\.gz', '\\1', f)
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53 sample.names <- c(sample.names, sample)
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54 p1 <- c(p1, f)
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55 }
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56 if(grepl('_2.fq', f)){
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57 p2 <- c(p2, f)
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58 }
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59 }
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60 fnFs <- sort(p1)
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61 fnRs <- sort(p2)
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62 sample.names <- sort(sample.names)
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63
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64 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_test.Rdata')))
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65 #load(file = file.path(outdir, paste0(prefix, 'state_test.Rdata')))
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66
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67 ## print for review
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68 to_view <- data.frame(sample.names, fnFs, fnRs)
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69 cat("The following fastq files were found:\n")
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70 print(to_view)
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71
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72 # Perform quality filtering and trimming---------------------------------
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73 ## assign new names to samples
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74 filtFs <- file.path(outdir, paste0(sample.names, 'F_filt.fastq.gz'))
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75 filtRs <- file.path(outdir, paste0(sample.names, 'R_filt.fastq.gz'))
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76
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77 ## plot forward and reverse quality so that user can decide on filtering parameters
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78 cat('Plotting quality profile of forward reads...\n')
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79 Fqp1 <- plotQualityProfile(fnFs[1])
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80 #print(Fqp1)
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81 ggsave(sprintf('%s_forward_1_qualityprofile.pdf',prefix), Fqp1, path = outdir, width = 20,height = 15,units = c("cm"))
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82 #ggsave(sprintf('%s_forward_1_qualityprofile.emf',prefix), Fqp1, path = outdir, width = 20,height = 15,units = c("cm"))
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83 Fqp2 <- plotQualityProfile(fnFs[2])
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84 #print(Fqp2)
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85 ggsave(sprintf('%s_forward_2_qualityprofile.pdf',prefix),Fqp2, path = outdir, width = 20,height = 15,units = c("cm"))
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86 #ggsave(sprintf('%s_forward_2_qualityprofile.emf',prefix), Fqp2, path = outdir, width = 20,height = 15,units = c("cm"))
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87 #cat('Which position would you like to truncate the forward reads at?\nPlease use the red-dashed lines as a guide, where they stop appearing indicates good quality.\nNOTE: Do NOT over-trim! You still require overlap between your forward and reverse reads in order to merge them later!\n')
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88 len1 <- 240
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89 cat('Plotting quality profile of reverse reads...\n')
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90 Rqp1 <- plotQualityProfile(fnRs[1])
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91 #print(Rqp1)
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92 ggsave(sprintf('%s_reverse_1_qualityprofile.pdf',prefix),Rqp1, path = outdir, width = 20,height = 15,units = c("cm"))
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93 #ggsave(sprintf('%s_reverse_1_qualityprofile.emf',prefix), Rqp1, path = outdir, width = 20,height = 15,units = c("cm"))
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94 Rqp2 <- plotQualityProfile(fnRs[2])
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95 #print(Rqp2)
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96 ggsave(sprintf('%s_reverse_2_qualityprofile.pdf',prefix), Rqp2, path = outdir, width = 20,height = 15,units = c("cm"))
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97 #ggsave(sprintf('%s_reverse_2_qualityprofile.emf',prefix), Rqp2, path = outdir, width = 20,height = 15,units = c("cm"))
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98 #cat('Which position would you like to truncate the forward reads at?\nPlease use the red-dashed lines as a guide, where they stop appearing indicates good quality.\nNOTE: Do NOT over-trim! You still require overlap between your forward and reverse reads in order to merge them later!\n')
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99 len2 <- 240
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100
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101 ## filter and trim
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102 ## remaining parameters set to recommended defaults
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103 ## maxN must be set to 0 (DADA2 requries no Ns)
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104 ## The maxEE parameter sets the maximum number of "expected errors" allowed in a read, which is a better filter than simply averaging quality scores.
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105 ## If not using Windows, you may set multithread to TRUE
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106 ## NOTE: Do not use the truncLen parameter for ITS sequencing
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107 ## trimLeft needs to be varied based on the size of your primers (i.e. it is used to trim your primer sequences)!!
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108 cat('Filtering and trimming sequences...\n')
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109 out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs, truncLen=c(len1,len2), maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=T, compress=T, multithread=threads, trimLeft=15)
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110
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111 ## have user review read count changes, and relax error rate if too many reads are lost
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112 ## for example, you may especially want to relax the number of expected errors on the reverse reads (i.e. maxEE=c(2,5)), as the reverse is prone to error on the Illumina sequencing platform
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113 print(head(out))
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114 check2 <- T
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115 while(check2 == F){
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116 maxF <- numeric_input("What would you like the maximum number of expected errors in the forward reads to be?\nDefault 2:", 2)
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117 maxR <- numeric_input("What would you like the maximum number of expected errors in the reverse reads to be?\nDefault 5:", 5)
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118 out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs, truncLen=c(len1,len2), maxN=0, maxEE=c(maxF,maxR), truncQ=2, rm.phix=T, compress=T, multithread=threads)
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119 print(head(out))
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120 check2 <- yn_input('Proceed? If you lost too many reads, you can choose to not proceed and you will have the option to relax the error rate. Default yes:',T)
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121 }
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122
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123 # Have DADA2 learn the error rates-----------------------------------------------
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124 ## If not using Windows, you may set multithread to TRUE
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125 read.subset <- 1e6
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126 cat('Learning error rate of forward reads...\n')
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127 errF <- learnErrors(filtFs, nreads=read.subset, multithread=threads)
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128
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129 ## have user check estimated error rates
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130 ## note the calculations are done with a subset of the total reads, as it is computationally intensive
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131 ## if the fit is poor, the user has the option to try again with an increased subset number of reads
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132 Error_f <- plotErrors(errF, nominalQ = T)
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133 #print(Error_f)
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134 ggsave(sprintf('%s_forward_Error_plot.pdf',prefix), Error_f, path = outdir, width = 20,height = 15,units = c("cm"))
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135 #ggsave(sprintf('%s_forward_Error_plot.emf',prefix), Error_f, path = outdir, width = 20,height = 15,units = c("cm"))
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136 check3a <- T
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137 while(check3a == F){
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138 read.subset <- numeric_input('Please specify the number of reads you would like dada2 to utilize to calculate the error rate.\nThe default previously used was 1e6.\nThe newly recommended default is 10-fold greater,\n1e7:',1e7)
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139 errF <- learnErrors(filtFs, nreads=read.subset, multithread=threads)
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140 print(Error_f)
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141 ggsave(sprintf('%s_forward_Error_plot.pdf',prefix), path = outdir, width = 20,height = 15,units = c("cm"))
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142 ggsave(sprintf('%s_forward_Error_plot.emf',prefix), path = outdir, width = 20,height = 15,units = c("cm"))
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143 check3a <- yn_input('Proceed?\nThe estimated error rate (black line) should fit to the observed error rates for each consensus quality score (black points).\nAdditionally, the error rates expected under the nominal definition of the Q-value (quality score) should decrease as the quality score increases (or flat-line).\nIf you do not have a good fit, you may want dada2 to re-learn the error rates with a higher number of reads in the utilized subset.\nA subset of reads is always used as the algorithm is computationally intensive.\nDefault yes:',T)
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144 }
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145
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146
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147 ## also do for reverseL
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148 cat('Learning error rate of reverse reads...\n')
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149 errR <- learnErrors(filtRs, nreads=read.subset, multithread=threads)
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150 Error_r <- plotErrors(errR, nominalQ=T)
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151 #print(Error_r)
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152 ggsave(sprintf('%s_reverse_Error_plot.pdf',prefix), Error_r, path = outdir, width = 20,height = 15,units = c("cm"))
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153 #ggsave(sprintf('%s_reverse_Error_plot.emf',prefix), Error_r, path = outdir, width = 20,height = 15,units = c("cm"))
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154 check3b <- T
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155 while(check3b == F){
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156 read.subset <- numeric_input('Please specify the number of reads you would like dada2 to utilize to calculate the error rate.\nThe default previously used was 1e6.\nThe newly recommended default is 10-fold greater,\n1e7:',1e7)
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157 errR <- learnErrors(filtRs, nreads=read.subset, multithread=threads)
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158 print(Error_r)
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159 ggsave(sprintf('%s_reverse_Error_plot.pdf',prefix), path = outdir, width = 20,height = 15,units = c("cm"))
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160 #ggsave(sprintf('%s_reverse_Error_plot.emf',prefix), path = outdir, width = 20,height = 15,units = c("cm"))
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161 check3b <- yn_input('Proceed?\nThe estimated error rate (black line) should fit to the observed error rates for each consensus quality score (black points).\nAdditionally, the error rates expected under the nominal definition of the Q-value (quality score) should decrease as the quality score increases (or flat-line).\nIf you do not have a good fit, you may want dada2 to re-learn the error rates with a higher number of reads in the utilized subset.\nA subset of reads is always used as the algorithm is computationally intensive.\nDefault yes:',T)
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162 }
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163
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164 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_post_learning.Rdata')))
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165 #load(file = file.path(outdir, paste0(prefix, 'state_post_learning.Rdata')))
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166
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167 # Dereplicate sequences to generate unique sequence fastq files with corresponding count tables-------------------------
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168 ## NOTE: if your dataset is huge, you may run out of RAM. Please see https://benjjneb.github.io/dada2/bigdata.html for details.
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169 cat('Dereplicating forward reads...\n')
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170 derepFs <- derepFastq(filtFs, verbose=T)
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171 cat('Dereplicating reverse reads...\n')
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172 derepRs <- derepFastq(filtRs, verbose=T)
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173
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174 ## name the derep-class objects by sample names
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175 names(derepFs) <- sample.names
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176 names(derepRs) <- sample.names
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177
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178 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_post_derep.Rdata')))
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179 #load(file = file.path(outdir, paste0(prefix, 'state_post_derep.Rdata')))
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180
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181 # Infer sequence variants using learned error rates---------------------------------
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182 ## If not using Windows, you may set multithread to TRUE
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183 ## NOTE: if your dataset is huge, you may run out of RAM. Please see https://benjjneb.github.io/dada2/bigdata.html for details.
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184 ## NOTE2: you can use DADA2 for 454 or IonTorrent data as well. Please see https://benjjneb.github.io/dada2/tutorial.html.
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185 s.pool = F
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186 cat('Inferring sequence variants of forward reads...\n')
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187 dadaFs <- dada(derepFs, err=errF, pool=s.pool, multithread=threads)
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188
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189 ## have user inspect detected number of sequence variants, to ensure it is logical based on the biological context of their samples
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190 ## if you have low sampling depths, you may not want to process each sample independently as per default, but set pool=T. It gives better results at increased computation time. The user will have the option to do this if the number of sequence variants doesn't make sense.
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191 print(dadaFs[[1]])
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192 check4 <- T
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193 if(check4 == F){
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194 s.pool = T
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195 dadaFs <- dada(derepFs, err=errF, pool=s.pool, multithread=threads)
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196 print(dadaFs[[1]])
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197 cat('Hopefully, these results make more sense.\nOtherwise, there is not much more you can do except start over!\n')
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198 check <- yn_input('Proceed? Default yes, no to quit:',T)
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199 if(check == F){
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200 stop()
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201 }
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202 }
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203
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204 ## also do for reverse, but don't need to re-check as you need to keep the pool consistent between the forward and reverse!
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205 cat('Inferring sequence variants of reversed reads...\n')
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206 dadaRs <- dada(derepRs, err=errR, pool=s.pool, multithread=threads)
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207
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208 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_post_dada.Rdata')))
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209 #load(file = file.path(outdir, paste0(prefix, 'state_post_dada.Rdata')))
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210
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211
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212 # Merge forward and reverse reads-------------------------------------------------
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213 cat('Merging paired-end reads...\n')
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214 mergers <- mergePairs(dadaFs, derepFs, dadaRs, derepRs, verbose=T)
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215 #cat('Most of your reads should have been retained (i.e. were able to merge, see above).\nOtherwise, there is not much more you can do except start over (i.e. did you over-trim your sequences??)!\n')
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216 check <- T
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217 if(check == F){
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218 stop()
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219 }
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220
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221 # Construct sequences table-------------------------------------------------------
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222 cat('Constructing sequence table...\n')
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223 seqtab <- makeSequenceTable(mergers)
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224
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225 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_post_merge.Rdata')))
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226 #load(file = file.path(outdir, paste0(prefix, 'state_post_merge.Rdata')))
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227
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228
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229 ## inspect distribution of sequence lengths
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230 ## give user the option to filter out overly long or short sequneces
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231 cat('Sequence length distribution listed below:\n')
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232 print(table(nchar(getSequences(seqtab))))
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233 check5 <- T
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234 if(check5 == F){
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235 min.cutoff <- numeric_input('Please input desired minimum length of sequences:',NULL)
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236 max.cutoff <- numeric_input('Please input desired maximum length of sequences:',NULL)
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237 seqtab <- seqtab[,nchar(colnames(seqtab)) %in% seq(min.cutoff,max.cutoff)]
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238 }
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239
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240 # Remove chimeras------------------------------------------------------------------
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241 ## If not using Windows, you may set multithread to TRUE
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242 cat('Removing chimeras...\n')
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243 seqtab.nochim <- removeBimeraDenovo(seqtab, method='consensus', multithread=threads, verbose=T)
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244
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245 ## display percentage of chimeras removed
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246 ## this number should be small (<5%), otherwise some processing parameters need to be revisited
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247 percent.nochim <- (sum(seqtab.nochim)/sum(seqtab))*100
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248 percent.nochim <- paste(as.character(percent.nochim),'of reads retained after chimera removal.\n',sep=' ')
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249 cat(percent.nochim)
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250 #cat('Most of your reads should have been retained.\nOtherwise, there is not much more you can do except start over!\n')
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251 check <- T
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252 if(check == F){
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253 stop()
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254 }
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255
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256 # Final sanity check--------------------------------------------------------------
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257 ## track reads removed throughout the pipeline
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258 ## If processing a single sample, remove the sapply calls: e.g. replace sapply(dadaFs, getN) with getN(dadaFs)
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259 getN <- function(x) sum(getUniques(x))
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260 track <- cbind(out, sapply(dadaFs, getN), sapply(mergers, getN), rowSums(seqtab), rowSums(seqtab.nochim))
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261 colnames(track) <- c("input", "filtered", "denoised", "merged", "tabled", "nonchim")
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262 rownames(track) <- sample.names
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263 print(head(track))
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264 #cat('Most of your reads should have been retained.\nOtherwise, there is not much more you can do except start over!\n')
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265 check <- T
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266 if(check == F){
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267 stop()
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268 }
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269
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270 write.csv(track,file=file.path(outdir, sprintf('%s_read_count-quality_control.csv',prefix)))
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271
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272 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_post_chimera.Rdata')))
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273 # load(file = file.path(outdir, paste0(prefix, 'state_post_chimera.Rdata')))
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274
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275 # Assign taxonomy-----------------------------------------------------------------
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276 ## require silva database files
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277 ## If not using Windows, you may set multithread to TRUE
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278 ## Minimum boot strap should be 80, but for sequnce length =< 250 Minimum bootstrap set to 50 (which is also the default)
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279
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280 ## SILVA
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281 cat('Assigning taxonomy to genus level using SILVA...\n')
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282 taxa_silva <- assignTaxonomy(seqtab.nochim, file.path(wd,"silva_nr_v132_train_set.fa.gz"), multithread=threads, minBoot=80, tryRC=T)
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283 cat('Assigning taxonomy at species level using SILVA...\n')
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284 taxa_silva <- addSpecies(taxa_silva, file.path(wd,"silva_species_assignment_v132.fa.gz"), allowMultiple=T, tryRC=T)
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285 write.csv(taxa_silva,file=file.path(outdir, sprintf('%s_taxonomy_silva.csv',prefix)))
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286
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287 ## RDP - used for copy number correction
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288 cat('Assigning taxonomy to genus level using RDP...\n')
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289 taxa_rdp <- assignTaxonomy(seqtab.nochim, file.path(wd,"rdp_train_set_16.fa.gz"), multithread=threads, minBoot=80, tryRC=T)
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290 cat('Assigning taxonomy at species level using RDP...\n')
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291 taxa_rdp <- addSpecies(taxa_rdp, file.path(wd,"rdp_species_assignment_16.fa.gz"), allowMultiple=T, tryRC=T)
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292 write.csv(taxa_rdp,file=file.path(outdir, sprintf('%s_taxonomy_rdp.csv',prefix)))
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293 save(list = ls(all.names = TRUE), file = file.path(outdir, paste0(prefix, 'state_post_tax.Rdata')))
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294 #load(file = file.path(outdir, paste0(prefix, 'state_post_tax.Rdata')))
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295
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296
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297 # Return data----------------------------------------------------------------------
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298 cat('Returning data...\n')
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299 ## create fasta file
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300 ASVs <- dada2fasta(outdir, seqtab.nochim, prefix)
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301 ## create master dataframe for each classification
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302
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303 ## Assigning ASVs to count table
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304 sequences <- colnames(seqtab.nochim)
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305 colnames(seqtab.nochim) <- ASVs
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306 seqtab.nochim <- t(seqtab.nochim)
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307
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308 ## silva
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309 taxa_silva <- taxa_silva[match(sequences, rownames(taxa_silva)),]
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310 rownames(taxa_silva) <- ASVs
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311 d <- merge(seqtab.nochim, taxa_silva, by='row.names')
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312 colnames(d)[1] <- 'ASV'
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313 ## create database of all information
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314 db <- dbConnect(RSQLite::SQLite(), file.path(outdir, sprintf('%s.sqlite',prefix)))
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315 dbWriteTable(db, 'dada2_results_silva', d)
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316 ## write master dataframe for user, and return it
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317 write.table(d, file.path(outdir, sprintf('%s_dada2_results_silva.txt',prefix)), sep='\t', quote=F, row.names=F)
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318
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319 ## rdp
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320 taxa_rdp <- taxa_rdp[match(sequences, rownames(taxa_rdp)),]
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321 rownames(taxa_rdp) <- ASVs
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322 d <- merge(seqtab.nochim, taxa_rdp, by='row.names')
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323 colnames(d)[1] <- 'ASV'
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324 ## create database of all information
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325 dbWriteTable(db, 'dada2_results_rdp', d)
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326 dbDisconnect(db)
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327 ## write master dataframe for user, and return it
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328 write.table(d, file.path(outdir, sprintf('%s_dada2_results_rdp.txt',prefix)), sep='\t', quote=F, row.names=F)
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329 return(d)
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330
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331 cat('DADA2 processing completed!\n')
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332 }
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333
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334
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335 option_list = list(
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336 make_option(c("-t", "--threads"), type = "integer", default = 1,
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337 help = "number of threads to use", metavar = "THREADS")
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338 );
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339
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340 opt_parser = OptionParser(option_list=option_list);
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341 opt = parse_args(opt_parser);
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342
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343 print(opt)
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344
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345 threads = as.integer(Sys.getenv("NSLOTS", "1"))
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346
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347 exit(1)
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348
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349
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350
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351 dada2run(wd='/work/haange/Leaky_gut/', r_path='/work/haange/Leaky_gut', outdir='/work/haange/Leaky_gut/results', prefix='Leaky_gut')
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352