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2
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1 ---
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15
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2 title: 'Short reads evaluation with [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)'
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14
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3 output:
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4 html_document:
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5 number_sections: true
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6 toc: true
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7 theme: cosmo
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8 highlight: tango
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2
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9 ---
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10
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14
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11 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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12 knitr::opts_chunk$set(
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16
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13 echo = ECHO,
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14 error = TRUE
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14
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15 )
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2
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16 ```
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17
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18
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16
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19 # Fastqc Evaluation
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20
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16
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21 ## Evaluation of reads before trimming
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2
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22
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16
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23 ```{r}
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24 if ('READS_1' == 'None') {
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25 stop("No pre-trimming reads provided!")
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26 } else {
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27 ## run fastqc evaluation
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28 fastqc_command = paste0('fastqc ') %>%
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29 (function(x) {
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30 ifelse('CONTAMINANTS' != 'None', paste0(x, '-c CONTAMINANTS '), x)
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31 }) %>%
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32 (function(x) {
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33 ifelse('LIMITS' != 'None', paste0(x, '-l LIMITS '), x)
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34 }) %>%
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35 (function(x) {
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36 paste0(x, '-o REPORT_DIR ')
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37 })
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38 fastqc_command_reads_1 = paste0(fastqc_command, 'READS_1 > /dev/null 2>&1')
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39 system(fastqc_command_reads_1, intern = TRUE)
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40
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41 # Original html report
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42 reads_1_base = tail(strsplit('READS_1', '/')[[1]], 1)
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43 original_html = tags$a(href=paste0(reads_1_base, '_fastqc.html'), paste0('HTML report: ', opt$name_1))
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44
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45 unzip(paste0('REPORT_DIR/', reads_1_base, '_fastqc.zip'), exdir = 'REPORT_DIR')
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46 reads_1_unzip = paste0('REPORT_DIR/', reads_1_base, '_fastqc/')
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47 # fastqc_data.txt
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48 file.copy(paste0(reads_1_unzip, 'fastqc_data.txt'), 'REPORT_DIR/reads_1_fastqc_data.txt')
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49 fastqc_data = tags$a(href='reads_1_fastqc_data.txt', paste0('fastqc_data.txt: ', opt$name_1))
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50 # summary.txt
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51 file.copy(paste0(reads_1_unzip, 'summary.txt'), 'REPORT_DIR/reads_1_summary.txt')
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52 summary_data = tags$a(href='reads_1_summary.txt', paste0('summary.txt: ', opt$name_1))
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53
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54 tags$ul(
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55 tags$li(original_html),
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56 tags$li(fastqc_data),
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57 tags$li(summary_data)
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58 )
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59 }
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15
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60 ```
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61
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62
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16
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63 ## Evaluation of reads after trimming
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64
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65 ```{r}
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66 if ('READS_2' == 'None') {
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67 stop("No pre-trimming reads provided!")
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68 } else {
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69 ## run fastqc evaluation
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70 fastqc_command = paste0('fastqc ') %>%
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71 (function(x) {
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72 ifelse('CONTAMINANTS' != 'None', paste0(x, '-c CONTAMINANTS '), x)
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73 }) %>%
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74 (function(x) {
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75 ifelse('LIMITS' != 'None', paste0(x, '-l LIMITS '), x)
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76 }) %>%
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77 (function(x) {
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78 paste0(x, '-o REPORT_DIR ')
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79 })
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80 fastqc_command_reads_2 = paste0(fastqc_command, 'READS_2 > /dev/null 2>&1')
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81 system(fastqc_command_reads_2, intern = TRUE)
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82
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83 # Original html report
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84 reads_2_base = tail(strsplit('READS_2', '/')[[1]], 1)
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85 original_html = tags$a(href=paste0(reads_2_base, '_fastqc.html'), paste0('HTML report: ', opt$name_2))
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86
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87 unzip(paste0('REPORT_DIR/', reads_2_base, '_fastqc.zip'), exdir = 'REPORT_DIR')
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88 reads_2_unzip = paste0('REPORT_DIR/', reads_2_base, '_fastqc/')
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89 # fastqc_data.txt
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90 file.copy(paste0(reads_2_unzip, 'fastqc_data.txt'), 'REPORT_DIR/reads_2_fastqc_data.txt')
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91 fastqc_data = tags$a(href='reads_2_fastqc_data.txt', paste0('fastqc_data.txt: ', opt$name_2))
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92 # summary.txt
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93 file.copy(paste0(reads_2_unzip, 'summary.txt'), 'REPORT_DIR/reads_2_summary.txt')
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94 summary_data = tags$a(href='reads_2_summary.txt', paste0('summary.txt: ', opt$name_2))
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95
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96 tags$ul(
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97 tags$li(original_html),
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98 tags$li(fastqc_data),
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99 tags$li(summary_data)
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100 )
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101 }
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2
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102 ```
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103
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15
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104
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105
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106 # Fastqc output visualization
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107
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108 ## Overview
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109
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110 ```{r}
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111 reads_1_summary = read.csv('REPORT_DIR/reads_1_summary.txt', header = FALSE, sep = '\t')[, 2:1]
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112 reads_2_summary = read.csv('REPORT_DIR/reads_2_summary.txt', header = FALSE, sep = '\t')[, 1]
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113 combined_summary = cbind(reads_1_summary, reads_2_summary)
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114 names(combined_summary) = c('MODULE', paste0(opt$name_1, '(before)'), paste0(opt$name_2, '(after)'))
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115 combined_summary[combined_summary == 'FAIL'] = 'FAIL (X)'
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116 combined_summary[combined_summary == 'WARN'] = 'WARN (!)'
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117 knitr::kable(combined_summary)
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15
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118 ```
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119
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17
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120 ## Visualization by data module {.tabset}
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2
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121
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14
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122 * Define a function to extract outputs for each module from fastqc output
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2
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123
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14
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124 ```{r 'function definition'}
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125 extract_data_module = function(fastqc_data, module_name, header = TRUE, comment.char = "") {
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126 f = readLines(fastqc_data)
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127 start_line = grep(module_name, f)
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128 end_module_lines = grep('END_MODULE', f)
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129 end_line = end_module_lines[which(end_module_lines > start_line)[1]]
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130 module_data = f[(start_line+1):(end_line-1)]
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131 writeLines(module_data, 'temp.txt')
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132 read.csv('temp.txt', sep = '\t', header = header, comment.char = comment.char)
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133 }
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134 ```
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135
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15
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136 ### Per base sequence quality
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137
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16
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138 ```{r 'per base sequence quality', fig.width=10}
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139 ## reads 1
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140 pbsq_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per base sequence quality')
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141 pbsq_1$id = 1:length(pbsq_1$X.Base)
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142
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143 melt_pbsq_1 = filter(melt(pbsq_1, id=c('X.Base', 'id')), variable == 'Mean')
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144 melt_pbsq_1$trim = 'before'
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145
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146
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147 ## reads 2
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148 pbsq_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per base sequence quality')
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149 pbsq_2$id = 1:length(pbsq_2$X.Base)
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150
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151 melt_pbsq_2 = filter(melt(pbsq_2, id=c('X.Base', 'id')), variable == 'Mean')
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152 melt_pbsq_2$trim = 'after'
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153
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154 comb_pbsq = rbind(melt_pbsq_1, melt_pbsq_2)
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155 comb_pbsq$trim = factor(levels = c('before', 'after'), comb_pbsq$trim)
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156
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157 p = ggplot(data = comb_pbsq) +
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158 geom_line(mapping = aes(x = id, y = value, group = variable, color = variable)) +
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159 scale_x_continuous(breaks = pbsq_2$id, labels = pbsq_2$X.Base) +
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160 facet_grid(. ~ trim) +
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161 ylim(0, max(comb_pbsq$value) + 5) +
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162 theme(axis.text.x = element_text(angle=45))
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163 ggplotly(p)
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164
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165 ```
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166
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167 ### Per tile sequence quality
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168
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17
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169 ```{r 'per tile sequence quality', fig.width=10}
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170 ## check if 'per tile sequence quality' module exits or not
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171 check_ptsq = grep('Per tile sequence quality', readLines('REPORT_DIR/reads_1_fastqc_data.txt'))
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172 if (length(check_ptsq) > 0) {
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173 ## reads 1
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174 ptsq_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per tile sequence quality')
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175 ptsq_1$trim = 'before'
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176
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177 ## reads 2
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178 ptsq_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per tile sequence quality')
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179 ptsq_2$trim = 'after'
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180
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181 comb_ptsq = rbind(ptsq_1, ptsq_2)
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182 comb_ptsq$trim = factor(levels = c('before', 'after'), comb_ptsq$trim)
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183 comb_ptsq$Base = factor(levels = unique(comb_ptsq$Base), comb_ptsq$Base)
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184
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185 # convert integers to charaters
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186 comb_ptsq$Tile = as.character(comb_ptsq$X.Tile)
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187
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188 p = ggplot(data = comb_ptsq, aes(x = Base, y = Tile, fill = Mean)) +
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189 geom_raster() +
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190 facet_grid(. ~ trim) +
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191 xlab('Position in read (bp)') +
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192 ylab('') +
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193 theme(axis.text.x = element_text(angle=45))
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194 ggplotly(p)
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195 } else {
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196 print('No "per tile sequence quality" data')
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197 }
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198
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199
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200 ```
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201
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202 ### Per sequence quality score
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203
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204 ```{r 'Per sequence quality score', fig.width=10}
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16
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205 ## reads 1
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206 psqs_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per sequence quality scores')
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207 psqs_1$trim = 'before'
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208
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209 ## reads 2
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210 psqs_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per sequence quality scores')
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211 psqs_2$trim = 'after'
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212
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213 comb_psqs = rbind(psqs_1, psqs_2)
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214 comb_psqs$trim = factor(levels = c('before', 'after'), comb_psqs$trim)
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215
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216 p = ggplot(data = comb_psqs, aes(x = X.Quality, y = Count)) +
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217 geom_line(color = 'red') +
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218 facet_grid(. ~ trim) +
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219 xlim(min(comb_psqs$X.Quality), max(comb_psqs$X.Quality)) +
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220 xlab('Mean Sequence Qaulity (Phred Score)') +
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221 ylab('')
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222 ggplotly(p)
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223 ```
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224
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225
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226 ### Per base sequence content
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227
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228 ```{r 'Per base sequence content', fig.width=10}
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229 ## reads 1
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230 pbsc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per base sequence content')
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231 pbsc_1$id = 1:length(pbsc_1$X.Base)
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232
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233 melt_pbsc_1 = melt(pbsc_1, id=c('X.Base', 'id'))
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234 melt_pbsc_1$trim = 'before'
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235
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236
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237 ## reads 2
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238 pbsc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per base sequence content')
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239 pbsc_2$id = 1:length(pbsc_2$X.Base)
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240
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241 melt_pbsc_2 = melt(pbsc_2, id=c('X.Base', 'id'))
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242 melt_pbsc_2$trim = 'after'
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243
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244 comb_pbsc = rbind(melt_pbsc_1, melt_pbsc_2)
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245 comb_pbsc$trim = factor(levels = c('before', 'after'), comb_pbsc$trim)
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246
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247 p = ggplot(data = comb_pbsc, aes(x = id, y = value, color = variable)) +
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248 geom_line() +
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249 facet_grid(. ~ trim) +
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250 xlim(min(comb_pbsc$id), max(comb_pbsc$id)) +
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251 ylim(0, 100) +
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252 xlab('Position in read (bp)') +
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253 ylab('')
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254 ggplotly(p)
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255 ```
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16
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256
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17
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257 ### Per sequence GC content
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258
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259 ```{r 'Per sequence GC content', fig.width=10}
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260 ## reads 1
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261 psGCc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per sequence GC content')
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262 psGCc_1$trim = 'before'
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263
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264 ## reads 2
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265 psGCc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per sequence GC content')
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266 psGCc_2$trim = 'after'
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267
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268 comb_psGCc = rbind(psGCc_1, psGCc_2)
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269 comb_psGCc$trim = factor(levels = c('before', 'after'), comb_psGCc$trim)
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270
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271 p = ggplot(data = comb_psGCc, aes(x = X.GC.Content, y = Count)) +
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272 geom_line(color = 'red') +
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273 facet_grid(. ~ trim) +
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274 xlab('Mean Sequence Qaulity (Phred Score)') +
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275 ylab('')
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276 ggplotly(p)
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277 ```
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278
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16
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279
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17
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280 ### Per base N content
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281
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282 ```{r 'Per base N content', fig.width=10}
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283 ## reads 1
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284 pbNc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per base N content')
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285 pbNc_1$id = 1:length(pbNc_1$X.Base)
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286 pbNc_1$trim = 'before'
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287
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288 ## reads 2
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289 pbNc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per base N content')
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290 pbNc_2$id = 1:length(pbNc_2$X.Base)
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291 pbNc_2$trim = 'after'
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292
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293 comb_pbNc = rbind(pbNc_1, pbNc_2)
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294 comb_pbNc$trim = factor(levels = c('before', 'after'), comb_pbNc$trim)
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295
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296 p = ggplot(data = comb_pbNc, aes(x = id, y = N.Count)) +
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297 geom_line(color = 'red') +
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298 scale_x_continuous(breaks = pbNc_2$id, labels = pbNc_2$X.Base) +
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299 facet_grid(. ~ trim) +
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300 ylim(0, 1) +
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301 xlab('N-Count') +
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302 ylab('') +
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16
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303 theme(axis.text.x = element_text(angle=45))
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304 ggplotly(p)
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15
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305 ```
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306
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307
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17
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308 ### Sequence Length Distribution
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309
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310 ```{r 'Sequence Length Distribution', fig.width=10}
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311 ## reads 1
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312 sld_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Sequence Length Distribution')
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313 sld_1$id = 1:length(sld_1$X.Length)
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314 sld_1$trim = 'before'
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315
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316 ## reads 2
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317 sld_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Sequence Length Distribution')
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318 sld_2$id = 1:length(sld_2$X.Length)
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319 sld_2$trim = 'after'
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320
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321 comb_sld = rbind(sld_1, sld_2)
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322 comb_sld$trim = factor(levels = c('before', 'after'), comb_sld$trim)
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323
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324 p = ggplot(data = comb_sld, aes(x = id, y = Count)) +
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325 geom_line(color = 'red') +
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326 scale_x_continuous(breaks = sld_2$id, labels = sld_2$X.Length) +
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327 facet_grid(. ~ trim) +
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328 xlab('Sequence Length (bp)') +
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329 ylab('') +
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330 theme(axis.text.x = element_text(angle=45))
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331 ggplotly(p)
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332 ```
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333
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334 ### Sequence Duplication Levels
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335
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336 ```{r 'Sequence Duplication Levels', fig.width=10}
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337 ## reads 1
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338 sdl_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Sequence Duplication Levels', header = FALSE, comment.char = '#')
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339 names(sdl_1) = c('Duplication_Level', 'Percentage_of_deduplicated', 'Percentage_of_total')
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340 sdl_1$id = 1:length(sdl_1$Duplication_Level)
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341
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342 melt_sdl_1 = melt(sdl_1, id=c('Duplication_Level', 'id'))
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343 melt_sdl_1$trim = 'before'
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344
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345
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346 ## reads 2
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347 sdl_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Sequence Duplication Levels', header = FALSE, comment.char = '#')
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348 names(sdl_2) = c('Duplication_Level', 'Percentage_of_deduplicated', 'Percentage_of_total')
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349 sdl_2$id = 1:length(sdl_2$Duplication_Level)
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350
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351 melt_sdl_2 = melt(sdl_2, id=c('Duplication_Level', 'id'))
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352 melt_sdl_2$trim = 'after'
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353
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354 comb_sdl = rbind(melt_sdl_1, melt_sdl_2)
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355 comb_sdl$trim = factor(levels = c('before', 'after'), comb_sdl$trim)
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356
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357 p = ggplot(data = comb_sdl, aes(x = id, y = value, color = variable)) +
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358 geom_line() +
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359 scale_x_continuous(breaks = sdl_2$id, labels = sdl_2$Duplication_Level) +
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360 facet_grid(. ~ trim) +
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361 xlab('Sequence Duplication Level') +
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362 ylab('') +
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363 theme(axis.text.x = element_text(angle=45))
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364 ggplotly(p)
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365 ```
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366
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367 ### Adapter Content
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368
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369 ```{r 'Adapter Content', fig.width=10}
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370 ## reads 1
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371 ac_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Adapter Content')
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372 ac_1$id = 1:length(ac_1$X.Position)
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373
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374 melt_ac_1 = melt(ac_1, id=c('X.Position', 'id'))
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375 melt_ac_1$trim = 'before'
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376
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377 ## reads 2
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378 ac_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Adapter Content')
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379 ac_2$id = 1:length(ac_2$X.Position)
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380
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381 melt_ac_2 = melt(ac_2, id=c('X.Position', 'id'))
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382 melt_ac_2$trim = 'after'
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383
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384 comb_ac = rbind(melt_ac_1, melt_ac_2)
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385 comb_ac$trim = factor(levels = c('before', 'after'), comb_ac$trim)
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386
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387 p = ggplot(data = comb_ac, aes(x = id, y = value, color = variable)) +
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388 geom_line() +
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389 facet_grid(. ~ trim) +
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390 xlim(min(comb_ac$id), max(comb_ac$id)) +
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391 ylim(0, 1) +
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392 xlab('Position in read (bp)') +
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393 ylab('')
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394 ggplotly(p)
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395 ```
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396
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397 ### Kmer Content {.tabset}
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398
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399 #### Before
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400
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401 ```{r 'Kmer Content (before)', fig.width=10}
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402 kc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Kmer Content')
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403 knitr::kable(kc_1)
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404 ```
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405
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406 #### After
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407 ```{r 'Kmer Content (after)', fig.width=10}
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408 kc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Kmer Content')
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409 knitr::kable(kc_2)
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410 ```
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411
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2
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412
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14
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413 # Session Info
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2
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414
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14
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415 ```{r 'session info'}
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416 sessionInfo()
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2
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417 ```
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418
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