Last updated: 2022-07-05

Checks: 6 1

Knit directory: 20180328_Atkins_RatFracture/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20220705) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 4a20503. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/rawQC.Rmd
    Untracked:  data/gmt/

Unstaged changes:
    Modified:   20180328_Atkins_RatFracture.Rproj
    Modified:   analysis/_site.yml
    Modified:   analysis/index.Rmd
    Modified:   code/runPipeline.sh
    Modified:   data/targets.csv

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


knitr::opts_chunk$set(
    message = FALSE, warning = FALSE,
    fig.height = 6, fig.width = 10
)
library(ngsReports)
library(tidyverse)
library(yaml)
library(scales)
library(pander)
library(glue)
library(plotly)
panderOptions("table.split.table", Inf)
panderOptions("big.mark", ",")
theme_set(theme_bw())
fh <- 6
suffix <- "_L001_R1.fastq.gz"
pattern <- paste0("_CB2YGANXX_.+fastq.gz")
sp <- "Rnorvegicus"
samples <- "data/targets.csv" %>%
  here::here() %>%
  read_csv() %>% 
    mutate(
      Filename = paste0(File, suffix)
    )
group_cols <- hcl.colors(
  n = length(unique(samples$group)), 
  palette = "Zissou 1"
  ) %>%
  setNames(unique(samples$group))

Quality Assessment on Raw Data

rawFqc <- here::here("data/0_rawData/FastQC") %>%
  list.files(pattern = "zip", full.names = TRUE) %>%
  FastqcDataList() %>%
  .[fqName(.) %in% samples$Filename]
plotSummary(rawFqc, pattern = pattern)
*Overall summary of FastQC reports*

Overall summary of FastQC reports

Library Sizes

A total of 6 libraries were contained in this dataset, with read totals ranging between 37,710,559 and 44,890,754 reads.

Across all libraries, reads were 100 bases.

plotReadTotals(rawFqc, pattern = pattern, usePlotly = TRUE)

Library Sizes for all supplied fastq files. Any samples run as multiple libraries are shown as the supplied multiple libraries and have not been merged.

Sequence Quality

plotBaseQuals(
  rawFqc,
  pattern = pattern, 
  usePlotly = TRUE,
  dendrogram = TRUE,
  cluster = TRUE
  )

Mean sequencing quality scores at each base position for each library

GC Content

plotGcContent(
  x = rawFqc, 
  pattern = pattern, 
  species = sp, 
  plotType = "line",
  gcType = "Trans",
  usePlotly = TRUE,
  dendrogram = TRUE,
  cluster = TRUE
  )

GC content shown as the % above and below the theoretical GC content for the Rnorvegicus transcriptome. The peaks at 62% and 71% are strongly suggestive of incomplete rRNA removal

Sequence Content

plotly::ggplotly(
  getModule(rawFqc, module = "Per_base_sequence_content") %>% 
    mutate(Base = fct_inorder(Base)) %>%
    group_by(Base) %>% 
    mutate(
      across(c("A", "C", "G", "T"), function(x){x - mean(x)}) 
    ) %>% 
    pivot_longer(
      cols = c("A", "C", "G", "T"), 
      names_to = "Nuc", 
      values_to = "resid"
    ) %>%
    left_join(samples) %>%
    ggplot(
      aes(Base, resid, group = Filename, colour = group)
    ) + 
    geom_line() +
    facet_wrap(~Nuc) + 
    scale_colour_manual(values = group_cols) +
    labs(
      x = "Read Position", y = "Residual", colour = "Group"
    ) +
    theme(
      axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
    )
)

Base and Position specific residuals for each sample. The mean base content at each position was calculated for each nucleotide, and the sample-specific residuals calculated.

AdapterContent

plotAdapterContent(
  x = rawFqc, 
  pattern = pattern, 
  plotType = "line",
  dendrogram = TRUE,
  cluster = TRUE
  ) +
  scale_y_continuous(expand = expansion(c(0, 0)), limits = c(0, 10))
*Total Adapter Content for each sample shown by starting position in the read.*

Total Adapter Content for each sample shown by starting position in the read.

Overrepresented Sequences

os <- suppressMessages(getModule(rawFqc, "Over"))
if (nrow(os)){
  if (length(unique(os$Filename)) > 1){
    suppressMessages(
      plotOverrep(
        x = rawFqc,
        pattern = suffix, 
        usePlotly = TRUE,
        dendrogram = TRUE,
        cluster = TRUE
      )
    )
  }
}

Summary of over-represented sequences across all libraries

os %>%
  group_by(Sequence, Possible_Source) %>%
  summarise(
    `Found in` = n(),
    Total = sum(Count),
    `Largest Percent` = glue("{round(max(Percentage), 2)}%")
  ) %>%
  pander(
    caption = "*Summary of over-represented sequences within the raw data. Manual checking of over-represented sequences using BLAST indicated most were Ribosomal in origin*"
  )
Summary of over-represented sequences within the raw data. Manual checking of over-represented sequences using BLAST indicated most were Ribosomal in origin
Sequence Possible_Source Found in Total Largest Percent
ACCAGACTTGCCCTCCAATGGATCCTCGTTAAAGGATTTAAAGTGGACTC No Hit 1 45,817 0.1%
ACCAGGTCGATGCGTGGAGTGGACGGAGCAAGCTCCTATTCCAACTCCTA No Hit 6 1,602,565 1.18%
ACCGCGGCTGCTGGCACCAGACTTGCCCTCCAATGGATCCTCGTTAAAGG No Hit 1 47,849 0.11%
ACCGGGTTGGTTTTGATCTGATAAATGCACGCATCCCCCCCACGGGAAGG No Hit 1 65,603 0.15%
ACGACTTTTACTTCCTCTAGATAGTCAAGTTCGACCGTCTTCTCAGCGCT No Hit 1 65,230 0.15%
ACTCATTCCAATTACAGGGCCTCGAAAGAGTCCTGTATTGTTATTTTTCG No Hit 1 99,858 0.22%
AGACGTTCGAATGGGTCGTCGCCGCCACGGAGGGCGTGCGATCGGCCCGA No Hit 1 76,924 0.17%
CACAGTTATCCAAGTAGGAGAGGAGCGAGCGACCAAAGGAACCATAACTG No Hit 2 230,166 0.26%
CACCAGACTTGCCCTCCAATGGATCCTCGTTAAAGGATTTAAAGTGGACT No Hit 1 72,772 0.16%
CACCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCAT No Hit 6 824,551 0.52%
CACTACCACAAATTATGCAGTCGAGTTTCCCGCATTTGGGGAAATCGCAG No Hit 1 86,223 0.19%
CAGACGTTCGAATGGGTCGTCGCCGCCACGGAGGGCGTGCGATCGGCCCG No Hit 1 68,699 0.15%
CAGGTCGATGCGTGGAGTGGACGGAGCAAGCTCCTATTCCAACTCCTAGT No Hit 6 1,328,794 0.91%
CCACAGTTATCCAAGTAGGAGAGGAGCGAGCGACCAAAGGAACCATAACT No Hit 2 239,062 0.29%
CCACTACCACAAATTATGCAGTCGAGTTTCCCGCATTTGGGGAAATCGCA No Hit 1 98,089 0.22%
CCAGGTCGATGCGTGGAGTGGACGGAGCAAGCTCCTATTCCAACTCCTAG No Hit 3 414,384 0.39%
CCCACTACCACAAATTATGCAGTCGAGTTTCCCGCATTTGGGGAAATCGC No Hit 5 839,456 0.87%
CCCCACTACCACAAATTATGCAGTCGAGTTTCCCGCATTTGGGGAAATCG No Hit 5 1,131,951 1.18%
CCCCCACTACCACAAATTATGCAGTCGAGTTTCCCGCATTTGGGGAAATC No Hit 3 176,954 0.21%
CCCCGGCCGTCCCTCTTAATCATGGCCTCAGTTCCGAAAACCAACAAAAT No Hit 1 50,834 0.11%
CCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATAT No Hit 6 2,575,389 1.38%
CCCGAGGTTATCTAGAGTCACCAAAGCCGCCGGCGCCCGACCCCCGGCCG No Hit 2 294,754 0.54%
CCCGCACTTACTGGGAATTCCTCGTTCATGGGGAATAATTGCAATCCCCG No Hit 1 54,847 0.12%
CCCGCATTTGGGGAAATCGCAGGGGTCAGCACATCCGGAGTGCAATGGAT No Hit 1 64,491 0.14%
CCCGGAAGCTGCCCGGCGGGTCATGGGAATAACGCCGCCGCATCGCCAGT No Hit 2 147,329 0.2%
CCCGGCCGTCCCTCTTAATCATGGCCTCAGTTCCGAAAACCAACAAAATA No Hit 2 155,547 0.22%
CCCGGGGCCGCAAGTGCGTTCGAAGTGTCGATGATCAATGTGTCCTGCAA No Hit 3 181,978 0.18%
CCCGGGTCGGGAGTGGGTAATTTGCGCGCCTGCTGCCTTCCTTGGATGTG No Hit 1 81,033 0.18%
CCCGTCACCCGTGGTCACCATGGTAGGCACGGCGACTACCATCGAAAGTT No Hit 1 57,751 0.13%
CCCGTCGGCATGTATTAGCTCTAGAATTACCACAGTTATCCAAGTAGGAG No Hit 2 341,322 0.48%
CCCGTGGTCACCATGGTAGGCACGGCGACTACCATCGAAAGTTGATAGGG No Hit 3 210,456 0.23%
CCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATT No Hit 6 2,138,006 1.07%
CCGAGGTTATCTAGAGTCACCAAAGCCGCCGGCGCCCGACCCCCGGCCGG No Hit 1 108,328 0.24%
CCGGAAGCTGCCCGGCGGGTCATGGGAATAACGCCGCCGCATCGCCAGTC No Hit 1 76,694 0.17%
CCGGGTTGGTTTTGATCTGATAAATGCACGCATCCCCCCCACGGGAAGGG No Hit 2 128,537 0.19%
CCGTCGGCATGTATTAGCTCTAGAATTACCACAGTTATCCAAGTAGGAGA No Hit 2 212,925 0.29%
CCTCATTTGGATGTGTCTGGAGTCTTGGAAGCTTGACTACCCTACGTTCT No Hit 2 132,149 0.24%
CCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTG No Hit 6 1,244,652 0.59%
CCTCGTTCATGGGGAATAATTGCAATCCCCGATCCCCATCACGAATGGGG No Hit 1 53,902 0.12%
CCTGCTTTGAACACTCTAATTTTTTCAAAGTAAACGCTTCGGGCCCCGCG No Hit 1 55,785 0.13%
CCTGTATTGTTATTTTTCGTCACTACCTCCCCGGGTCGGGAGTGGGTAAT No Hit 2 185,159 0.28%
CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG No Hit 6 1,363,560 0.76%
CCTTCCGTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCCCCCGG No Hit 1 76,012 0.17%
CGCCAGTCGGCATCGTTTATGGTCGGAACTACGACGGTATCTGATCGTCT No Hit 1 45,108 0.1%
CGCCTGCTGCCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTCC No Hit 2 211,183 0.31%
CGCGCCTGCTGCCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCT No Hit 1 44,923 0.1%
CGCGGCTGCTGGCACCAGACTTGCCCTCCAATGGATCCTCGTTAAAGGAT No Hit 1 46,293 0.1%
CGCGTAACTAGTTAGCATGCCAGAGTCTCGTTCGTTATCGGAATTAACCA No Hit 1 101,017 0.23%
CGGGGAAGGTCGTCCTCTTCGACCGAGCGCGCAGCTTCGGGAGGGACGCA No Hit 1 81,803 0.22%
CGGGTTGGTTTTGATCTGATAAATGCACGCATCCCCCCCACGGGAAGGGG No Hit 1 49,280 0.11%
CGTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCCCCCGGAACCC No Hit 2 105,954 0.13%
CGTCGGCATGTATTAGCTCTAGAATTACCACAGTTATCCAAGTAGGAGAG No Hit 2 180,218 0.27%
CTAAGAAGTTGGGGGACGCCGACCGCTCGGGGGTCGCGTAACTAGTTAGC No Hit 1 77,886 0.17%
CTCAGTTCCGAAAACCAACAAAATAGAACCGCGGTCCTATTCCATTATTC No Hit 1 66,580 0.15%
CTCATTCCAATTACAGGGCCTCGAAAGAGTCCTGTATTGTTATTTTTCGT No Hit 1 101,980 0.23%
CTCATTTGGATGTGTCTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTC No Hit 3 263,392 0.36%
CTCCAATGGATCCTCGTTAAAGGATTTAAAGTGGACTCATTCCAATTACA No Hit 1 61,149 0.14%
CTCCCATCCAAGTACTAACCAGGCCCGACCCTGCTTAGCTTCCGAGATCA No Hit 6 675,991 0.49%
CTCCGACTTTCGTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCG No Hit 2 124,691 0.17%
CTCCGATTCTGCAATAGCAGGCTTGACCTGCCTTCACAACTGCATCTTGG No Hit 1 41,165 0.11%
CTCCGGAATCGAACCCTGATTCCCCGTCACCCGTGGTCACCATGGTAGGC No Hit 1 60,565 0.14%
CTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGGT No Hit 6 684,533 0.45%
CTCCTATTCCAACTCCTAGTTCCAAAAATCCATTTAATATATTGTCCTCG No Hit 4 230,458 0.17%
CTCCTCTATTCGGGGAAGGTCGTCCTCTTCGACCGAGCGCGCAGCTTCGG No Hit 3 154,373 0.14%
CTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGA No Hit 6 1,401,485 0.71%
CTCGCCACCAACATCATCAGGGTTCACCTTTCCCCACAGGCCATTAACAG No Hit 2 89,013 0.12%
CTCGTATACCCTTGACCGAAGACCGGTCCTCCTCTATTCGGGGAAGGTCG No Hit 1 49,929 0.11%
CTCGTTCATGGGGAATAATTGCAATCCCCGATCCCCATCACGAATGGGGT No Hit 1 88,581 0.2%
CTCGTTCGTTATCGGAATTAACCAGACAAATCGCTCCACCAACTAAGAAC No Hit 1 57,369 0.13%
CTCTAAGAAGTTGGGGGACGCCGACCGCTCGGGGGTCGCGTAACTAGTTA No Hit 1 61,916 0.14%
CTCTATTCGGGGAAGGTCGTCCTCTTCGACCGAGCGCGCAGCTTCGGGAG No Hit 1 45,048 0.1%
CTCTGGTCCGTCTTGCGCCGGTCCAAGAATTTCACCTCTAGCGGCGCAAT No Hit 1 129,334 0.29%
CTCTTAATCATGGCCTCAGTTCCGAAAACCAACAAAATAGAACCGCGGTC No Hit 3 443,318 0.57%
CTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGT No Hit 2 96,563 0.11%
CTGCTGCCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTCCGGA No Hit 2 218,958 0.33%
CTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTCCTACAATGGACCTTG No Hit 5 1,176,836 1.08%
CTGTATTGTTATTTTTCGTCACTACCTCCCCGGGTCGGGAGTGGGTAATT No Hit 4 857,169 1.05%
CTTAATCATGGCCTCAGTTCCGAAAACCAACAAAATAGAACCGCGGTCCT No Hit 1 54,457 0.12%
CTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC No Hit 6 386,884 0.23%
CTTCACCTTAGGGTTACCCATGATAGCAGAGGCAGAGGACAGGTCCCCAA No Hit 1 39,295 0.1%
CTTCCGTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCCCCCGGA No Hit 2 319,873 0.48%
CTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTCCGGAATCGAAC No Hit 2 146,238 0.22%
CTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCTCTGGTCCGTCTT No Hit 1 164,171 0.37%
GCAGACGTTCGAATGGGTCGTCGCCGCCACGGAGGGCGTGCGATCGGCCC No Hit 1 79,199 0.18%
GCATCGTTTATGGTCGGAACTACGACGGTATCTGATCGTCTTCGAACCTC No Hit 1 54,697 0.12%
GCCAGAGTCTCGTTCGTTATCGGAATTAACCAGACAAATCGCTCCACCAA No Hit 1 56,084 0.12%
GCCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATA No Hit 1 40,199 0.11%
GCCCGAGGTTATCTAGAGTCACCAAAGCCGCCGGCGCCCGACCCCCGGCC No Hit 1 119,886 0.27%
GCCCTTCCGTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCCCCC No Hit 1 78,539 0.18%
GCCGGCTTCACGCTCAGGAGGGGACGCTATGTCTCGTCCTCGTGGTTTCG No Hit 1 43,693 0.11%
GCCTCAGTTCCGAAAACCAACAAAATAGAACCGCGGTCCTATTCCATTAT No Hit 2 172,403 0.23%
GCCTCATTTGGATGTGTCTGGAGTCTTGGAAGCTTGACTACCCTACGTTC No Hit 1 74,957 0.2%
GCCTGCTGCCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTCCG No Hit 3 484,746 0.61%
GCCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTCCGGAATCGA No Hit 2 127,409 0.19%
GCGCCTGCTGCCTTCCTTGGATGTGGTAGCCGTTTCTCAGGCTCCCTCTC No Hit 2 135,401 0.2%
GCGGTGTGTACAAAGGGCAGGGACTTAATCAACGCAAGCTTATGACCCGC No Hit 1 49,415 0.11%
GCTCAATCTCGGGTGGCTGAACGCCACTTGTCCCTCTAAGAAGTTGGGGG No Hit 1 49,062 0.11%
GCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGG No Hit 6 413,413 0.23%
GGACGCACATGGAGCGGTGAGGGAGGAAGGGGACACCCGCCTAGCCAGCC No Hit 2 99,505 0.15%
GGACTCATTCCAATTACAGGGCCTCGAAAGAGTCCTGTATTGTTATTTTT No Hit 1 73,056 0.16%
GGAGGTTCTAGCAGGGGAGCGCAGCTACTCGTATACCCTTGACCGAAGAC No Hit 1 79,903 0.21%
GGCAGACGTTCGAATGGGTCGTCGCCGCCACGGAGGGCGTGCGATCGGCC No Hit 4 1,365,014 1.98%
GGCATCGTTTATGGTCGGAACTACGACGGTATCTGATCGTCTTCGAACCT No Hit 1 49,624 0.11%
GGCCTCAGTTCCGAAAACCAACAAAATAGAACCGCGGTCCTATTCCATTA No Hit 1 52,967 0.12%
GGCGGTGTGTACAAAGGGCAGGGACTTAATCAACGCAAGCTTATGACCCG No Hit 1 79,266 0.18%
GGGGAAGGTCGTCCTCTTCGACCGAGCGCGCAGCTTCGGGAGGGACGCAC No Hit 1 52,057 0.14%
GGGGCTTACATCAATGTGAGAGAAGTAGGTCTTGGTGGTGGGGAAGGCAG No Hit 1 39,514 0.1%
GGGGGGTCAGCGCCCGTCGGCATGTATTAGCTCTAGAATTACCACAGTTA No Hit 2 207,892 0.27%
GGGGGTCAGCGCCCGTCGGCATGTATTAGCTCTAGAATTACCACAGTTAT No Hit 1 52,050 0.12%
GGGGTCAGCGCCCGTCGGCATGTATTAGCTCTAGAATTACCACAGTTATC No Hit 2 113,824 0.13%
GGGTCGATGCGTGGAGTGGACGGAGCAAGCTCCTATTCCAACTCCTAGTT No Hit 1 52,061 0.14%
GGGTCGCGTAACTAGTTAGCATGCCAGAGTCTCGTTCGTTATCGGAATTA No Hit 1 68,510 0.15%
GGTCGATGCGTGGAGTGGACGGAGCAAGCTCCTATTCCAACTCCTAGTTC No Hit 6 1,539,951 1.09%
GGTGCCCTTCCGTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCC No Hit 1 47,955 0.11%
GTACAAAGGGCAGGGACTTAATCAACGCAAGCTTATGACCCGCACTTACT No Hit 1 80,575 0.18%
GTATTGTTATTTTTCGTCACTACCTCCCCGGGTCGGGAGTGGGTAATTTG No Hit 1 65,065 0.15%
GTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCCCCCGGAACCCA No Hit 2 109,533 0.12%
GTCACTACCTCCCCGGGTCGGGAGTGGGTAATTTGCGCGCCTGCTGCCTT No Hit 1 69,064 0.15%
GTCCCCCACTACCACAAATTATGCAGTCGAGTTTCCCGCATTTGGGGAAA No Hit 4 472,827 0.46%
GTCCCTCTTAATCATGGCCTCAGTTCCGAAAACCAACAAAATAGAACCGC No Hit 2 167,043 0.24%
GTCCTGTATTGTTATTTTTCGTCACTACCTCCCCGGGTCGGGAGTGGGTA No Hit 4 612,866 0.66%
GTCGATGCGTGGAGTGGACGGAGCAAGCTCCTATTCCAACTCCTAGTTCC No Hit 6 1,390,188 0.95%
GTCGCGTAACTAGTTAGCATGCCAGAGTCTCGTTCGTTATCGGAATTAAC No Hit 1 174,882 0.39%
GTCGGCATCGTTTATGGTCGGAACTACGACGGTATCTGATCGTCTTCGAA No Hit 1 61,947 0.14%
GTCGGCATGTATTAGCTCTAGAATTACCACAGTTATCCAAGTAGGAGAGG No Hit 4 877,632 1%
GTCTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTCCTACAATGGACCT No Hit 5 805,129 0.76%
GTGCCCTTCCGTCAATTCCTTTAAGTTTCAGCTTTGCAACCATACTCCCC No Hit 2 206,070 0.31%
GTGCGATCGGCCCGAGGTTATCTAGAGTCACCAAAGCCGCCGGCGCCCGA No Hit 1 50,483 0.11%
GTGGACTCATTCCAATTACAGGGCCTCGAAAGAGTCCTGTATTGTTATTT No Hit 1 57,242 0.13%
GTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGTTTTGA No Hit 2 139,349 0.22%
GTGTGTACAAAGGGCAGGGACTTAATCAACGCAAGCTTATGACCCGCACT No Hit 1 73,383 0.16%
GTTATTTTTCGTCACTACCTCCCCGGGTCGGGAGTGGGTAATTTGCGCGC No Hit 1 46,188 0.1%
GTTCGAAGTGTCGATGATCAATGTGTCCTGCAATTCACATTAATTCTCGC No Hit 1 57,777 0.13%
GTTCGTTATCGGAATTAACCAGACAAATCGCTCCACCAACTAAGAACGGC No Hit 1 84,329 0.19%
GTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCTCTGGTCCGT No Hit 1 77,063 0.17%
GTTGGTTTTGATCTGATAAATGCACGCATCCCCCCCACGGGAAGGGGGGT No Hit 2 174,018 0.2%
TCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGAT No Hit 3 158,106 0.14%
TCTTAATCATGGCCTCAGTTCCGAAAACCAACAAAATAGAACCGCGGTCC No Hit 1 58,599 0.13%

R version 4.2.0 (2022-04-22)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_AU.UTF-8, LC_NUMERIC=C, LC_TIME=en_AU.UTF-8, LC_COLLATE=en_AU.UTF-8, LC_MONETARY=en_AU.UTF-8, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: plotly(v.4.10.0), glue(v.1.6.2), pander(v.0.6.5), scales(v.1.2.0), yaml(v.2.3.5), forcats(v.0.5.1), stringr(v.1.4.0), dplyr(v.1.0.9), purrr(v.0.3.4), readr(v.2.1.2), tidyr(v.1.2.0), tidyverse(v.1.3.1), ngsReports(v.1.13.0), tibble(v.3.1.7), ggplot2(v.3.3.6), BiocGenerics(v.0.42.0) and workflowr(v.1.7.0)

loaded via a namespace (and not attached): bitops(v.1.0-7), fs(v.1.5.2), bit64(v.4.0.5), lubridate(v.1.8.0), RColorBrewer(v.1.1-3), httr(v.1.4.3), rprojroot(v.2.0.3), GenomeInfoDb(v.1.32.1), tools(v.4.2.0), backports(v.1.4.1), bslib(v.0.3.1), utf8(v.1.2.2), R6(v.2.5.1), DT(v.0.22), DBI(v.1.1.2), lazyeval(v.0.2.2), colorspace(v.2.0-3), withr(v.2.5.0), tidyselect(v.1.1.2), processx(v.3.5.3), bit(v.4.0.4), compiler(v.4.2.0), git2r(v.0.30.1), rvest(v.1.0.2), cli(v.3.3.0), xml2(v.1.3.3), ggdendro(v.0.1.23), labeling(v.0.4.2), sass(v.0.4.1), callr(v.3.7.0), digest(v.0.6.29), rmarkdown(v.2.14), XVector(v.0.36.0), pkgconfig(v.2.0.3), htmltools(v.0.5.2), highr(v.0.9), dbplyr(v.2.1.1), fastmap(v.1.1.0), htmlwidgets(v.1.5.4), rlang(v.1.0.2), readxl(v.1.4.0), rstudioapi(v.0.13), farver(v.2.1.0), jquerylib(v.0.1.4), generics(v.0.1.2), zoo(v.1.8-10), jsonlite(v.1.8.0), crosstalk(v.1.2.0), vroom(v.1.5.7), RCurl(v.1.98-1.6), magrittr(v.2.0.3), GenomeInfoDbData(v.1.2.8), Rcpp(v.1.0.8.3), munsell(v.0.5.0), S4Vectors(v.0.34.0), fansi(v.1.0.3), lifecycle(v.1.0.1), stringi(v.1.7.6), whisker(v.0.4), MASS(v.7.3-57), zlibbioc(v.1.42.0), plyr(v.1.8.7), grid(v.4.2.0), parallel(v.4.2.0), promises(v.1.2.0.1), crayon(v.1.5.1), lattice(v.0.20-45), Biostrings(v.2.64.0), haven(v.2.5.0), hms(v.1.1.1), knitr(v.1.39), ps(v.1.7.0), pillar(v.1.7.0), reshape2(v.1.4.4), stats4(v.4.2.0), reprex(v.2.0.1), evaluate(v.0.15), getPass(v.0.2-2), data.table(v.1.14.2), modelr(v.0.1.8), vctrs(v.0.4.1), tzdb(v.0.3.0), httpuv(v.1.6.5), cellranger(v.1.1.0), gtable(v.0.3.0), assertthat(v.0.2.1), xfun(v.0.30), broom(v.0.8.0), later(v.1.3.0), viridisLite(v.0.4.0), IRanges(v.2.30.0), ellipsis(v.0.3.2) and here(v.1.0.1)


sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] plotly_4.10.0       glue_1.6.2          pander_0.6.5       
 [4] scales_1.2.0        yaml_2.3.5          forcats_0.5.1      
 [7] stringr_1.4.0       dplyr_1.0.9         purrr_0.3.4        
[10] readr_2.1.2         tidyr_1.2.0         tidyverse_1.3.1    
[13] ngsReports_1.13.0   tibble_3.1.7        ggplot2_3.3.6      
[16] BiocGenerics_0.42.0 workflowr_1.7.0    

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           fs_1.5.2               bit64_4.0.5           
 [4] lubridate_1.8.0        RColorBrewer_1.1-3     httr_1.4.3            
 [7] rprojroot_2.0.3        GenomeInfoDb_1.32.1    tools_4.2.0           
[10] backports_1.4.1        bslib_0.3.1            utf8_1.2.2            
[13] R6_2.5.1               DT_0.22                DBI_1.1.2             
[16] lazyeval_0.2.2         colorspace_2.0-3       withr_2.5.0           
[19] tidyselect_1.1.2       processx_3.5.3         bit_4.0.4             
[22] compiler_4.2.0         git2r_0.30.1           rvest_1.0.2           
[25] cli_3.3.0              xml2_1.3.3             ggdendro_0.1.23       
[28] labeling_0.4.2         sass_0.4.1             callr_3.7.0           
[31] digest_0.6.29          rmarkdown_2.14         XVector_0.36.0        
[34] pkgconfig_2.0.3        htmltools_0.5.2        highr_0.9             
[37] dbplyr_2.1.1           fastmap_1.1.0          htmlwidgets_1.5.4     
[40] rlang_1.0.2            readxl_1.4.0           rstudioapi_0.13       
[43] farver_2.1.0           jquerylib_0.1.4        generics_0.1.2        
[46] zoo_1.8-10             jsonlite_1.8.0         crosstalk_1.2.0       
[49] vroom_1.5.7            RCurl_1.98-1.6         magrittr_2.0.3        
[52] GenomeInfoDbData_1.2.8 Rcpp_1.0.8.3           munsell_0.5.0         
[55] S4Vectors_0.34.0       fansi_1.0.3            lifecycle_1.0.1       
[58] stringi_1.7.6          whisker_0.4            MASS_7.3-57           
[61] zlibbioc_1.42.0        plyr_1.8.7             grid_4.2.0            
[64] parallel_4.2.0         promises_1.2.0.1       crayon_1.5.1          
[67] lattice_0.20-45        Biostrings_2.64.0      haven_2.5.0           
[70] hms_1.1.1              knitr_1.39             ps_1.7.0              
[73] pillar_1.7.0           reshape2_1.4.4         stats4_4.2.0          
[76] reprex_2.0.1           evaluate_0.15          getPass_0.2-2         
[79] data.table_1.14.2      modelr_0.1.8           vctrs_0.4.1           
[82] tzdb_0.3.0             httpuv_1.6.5           cellranger_1.1.0      
[85] gtable_0.3.0           assertthat_0.2.1       xfun_0.30             
[88] broom_0.8.0            later_1.3.0            viridisLite_0.4.0     
[91] IRanges_2.30.0         ellipsis_0.3.2         here_1.0.1