Transcriptome sequencing data pre-processing
Maeva Techer
2024-05-13
Last updated: 2024-05-13
Checks: 7 0
Knit directory:
locust-comparative-genomics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | be09a11 | Maeva A. TECHER | 2024-05-13 | update markdown |
| html | be09a11 | Maeva A. TECHER | 2024-05-13 | update markdown |
| html | d1cebea | Maeva A. TECHER | 2024-02-19 | Build site. |
| Rmd | 55f9385 | Maeva A. TECHER | 2024-02-19 | wflow_publish("analysis/3_seq-data-qc.Rmd") |
| html | df94db2 | Maeva A. TECHER | 2024-02-19 | adding markdown qc |
| Rmd | f6b4961 | Maeva A. TECHER | 2024-02-19 | wflow_publish("analysis/3_seq-data-qc.Rmd") |
| html | e39d280 | Maeva A. TECHER | 2024-01-29 | Build site. |
| html | f701a01 | Maeva A. TECHER | 2024-01-29 | reupdate |
| html | be046c6 | Maeva A. TECHER | 2024-01-24 | Build site. |
| html | 1b09cbe | Maeva A. TECHER | 2024-01-24 | remove |
| html | 141b63c | Maeva A. TECHER | 2023-12-18 | Build site. |
| Rmd | 53877fa | Maeva A. TECHER | 2023-12-18 | add pages |
At this point, we will have organized both SRA and de novo
paired-end sequencing data within our reads directory located
at
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/reads
and will be ready to run the Snakemake pipeline on it. As a reminder,
each library corresponds to the transcriptome of bulk tissues (either
head or thorax) from a single specimen reared under isolated or crowded
conditions.
Load R libraries (install first from CRAN or Bioconductor)
library("knitr")
library("rmdformats")
library("tidyverse")
library("DT") # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")
library("readr")
library("ggplot2")
## Global options
options(max.print="10000")
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
cache = FALSE,
comment = FALSE,
prompt = FALSE,
tidy = TRUE
)
opts_knit$set(width=75)Control the quality of the .fastq files
The first step is to ensure that we have enough reads per library and
remove any potential outlier resulting from library preparation or
sequencing failure. For that, we will assess each .fastq file with
FASTQC. However, considering we are working with a
significant sample size, we will compile the results using
MULTIQC.
The aggregated results can be conveniently viewed by opening the HTML report in a web browser.
module load GCC/12.2.0 OpenMPI/4.1.4 MultiQC/1.14
multiqc --title 'TYPE THE TITLE YOU WANT' -v /PATHTODIRECTORYmontana <- read_table("data/metadata/Stats_RNAseq_QC_19Feb2024.txt", col_names = TRUE, guess_max = 1000)
head(montana)FALSE # A tibble: 6 × 7
FALSE Sample_Name Species Perc_Dups Perc_GC M_Seqs Unique_Reads Duplicate_Reads
FALSE <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
FALSE 1 SAMER_G_Crd_SRR… americ… 0.71 0.46 23.7 6958831 16765516
FALSE 2 SAMER_G_Crd_SRR… americ… 0.67 0.47 23.7 7784916 15939431
FALSE 3 SAMER_G_Crd_SRR… americ… 0.71 0.48 15.7 4570667 11085054
FALSE 4 SAMER_G_Crd_SRR… americ… 0.67 0.48 15.7 5145265 10510456
FALSE 5 SAMER_G_Crd_SRR… americ… 0.84 0.49 37.2 6049864 31125514
FALSE 6 SAMER_G_Crd_SRR… americ… 0.8 0.49 37.2 7301268 29874110# Convert values to millions
montana <- montana %>%
mutate_at(vars(contains("Reads")), list(~ ./1000000))
# Pivot the data
montana_long <- montana %>%
pivot_longer(cols = contains("Reads"), names_to = "Variable", values_to = "Value")
# Define colors
colors.reads <- c("Duplicate_Reads" = "black", "Unique_Reads" = "deepskyblue")
# Plot the stacked bar plot with values in millions and custom colors
ggplot(montana_long, aes(x = Sample_Name, y = Value, fill = Variable)) +
geom_bar(stat = "identity", position = "stack") +
facet_wrap(~Species, scales = "free") +
labs(title = "Stacked Bar Plot of Unique Reads and Duplicate Reads by Sample",
x = NULL, # Remove x-axis label
y = "Millions of Reads") +
theme_minimal() +
theme(axis.text.x = element_blank()) + # Remove x-axis labels
scale_fill_manual(values = colors.reads) + # Set custom colors
geom_hline(yintercept = 30, linetype = "dashed", color = "red") +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray70") +
ylim(0, 62) # Set y-axis limits
| Version | Author | Date |
|---|---|---|
| d1cebea | Maeva A. TECHER | 2024-02-19 |
library(ggConvexHull)
# Define custom colors for each species
species_colors <- c("americana" = "forestgreen", "cubense" = "yellow3", "gregaria" = "orange", "nitens" = "blue", "piceifrons" = "red2", cancellata = "deeppink")
p <- ggplot(montana, aes(x = Perc_Dups, y = M_Seqs, color = Species)) +
geom_point(size =2) +
scale_color_manual(values = species_colors) + # Apply custom colors
labs(title = "M_Seqs vs % Dups by Species",
x = "Percentage of Duplicates",
y = "Millions of Reads") +
theme_minimal()
p + geom_convexhull(aes(fill = Species, color = Species), alpha = 0.2) +
scale_fill_manual(values = species_colors)
| Version | Author | Date |
|---|---|---|
| d1cebea | Maeva A. TECHER | 2024-02-19 |
Trim and adapter removal
After checking the initial sequence quality, we can determine whether
any parameters adjustments are needed for Trimmomatic. This
is particularly pertinent for the ‘trailings’ and ‘leading’ parameters
which removes the nucleotides at the end and start
########################################
# Snakefile rule
########################################
rule trim_adapt:
input:
read1 = OUTdir + "/reads/{locust}_1.fastq.gz",
read2 = OUTdir + "/reads/{locust}_2.fastq.gz",
adaptfile = OUTdir + "/list/TruSeqNextera_PE.fa"
output:
trimmedread1 = OUTdir + "/trimming/{locust}_trim1P_1.fastq.gz",
badread1 = OUTdir + "/trimming/{locust}_trim1U_1.fastq.gz",
trimmedread2 = OUTdir + "/trimming/{locust}_trim2P_2.fastq.gz",
badread2 = OUTdir + "/trimming/{locust}_trim2U_2.fastq.gz",
shell:
"""
module load Trimmomatic/0.39-Java-11
java -jar $EBROOTTRIMMOMATIC/trimmomatic-0.39.jar PE -threads 2 -phred33 {input.read1} {input.read2} {output.trimmedread1} {output.badread1} {output.trimmedread2} {output.badread2} ILLUMINACLIP:{input.adaptfile}:2:30:10 LEADING:30 TRAILING:30 SLIDINGWINDOW:4:15 MINLEN:36
"""
########################################
# Parameters in the cluster.json file
########################################
"trim_adapt":
{
"cpus-per-task" : 2,
"partition" : "medium",
"ntasks": 2,
"mem" : "1G",
"time": "0-04:00:00"
},
SLIDINGWINDOWWe use the 4:15 approach which means that in a 4-base window, if the average quality drops below 15, the bases will be trimmed.MINLEN:36Specifies the minimum length a read must be to be kept after all trimming steps is at 36 bp otherwise it is too short to convey any information.
We used the Illumina adapters for library preparation and as per recommended on the support website, we removed TruSeq.
We created a list called TruSeqNextera_PE.fa that
gathered several adapters. >PrefixPE/1
TACACTCTTTCCCTACACGACGCTCTTCCGATCT
>PrefixPE/2
GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
>PE1
TACACTCTTTCCCTACACGACGCTCTTCCGATCT
>PE1_rc
AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA
>PE2
GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
>PE2_rc
AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
>PrefixNX/1
AGATGTGTATAAGAGACAG
>PrefixNX/2
AGATGTGTATAAGAGACAG
>Trans1
TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG
>Trans1_rc
CTGTCTCTTATACACATCTGACGCTGCCGACGA
>Trans2
GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG
>Trans2_rc
CTGTCTCTTATACACATCTCCGAGCCCACGAGAC
Trim quality control
We always perform quality control checks after trimming to ensure that the clipping and filtering of sequences were not overly aggressive. Given the number of sequences we work with, manually inspecting each file immediately would be time-consuming. Instead, we adopt a random sampling approach across different species, rearing conditions, and tissues to see that the trimming process worked well.
########################################
# Snakefile rule
########################################
#Quality control step after trimming: checked for adapter content in particular and quality scores
rule trim_fastqc:
input:
read1 = OUTdir + "/trimming/{locust}_trim1P_1.fastq.gz",
read2 = OUTdir + "/trimming/{locust}_trim2P_2.fastq.gz",
output:
htmlqc1 = OUTdir + "/trimming/{locust}_trim1P_1_fastqc.html",
htmlqc2 = OUTdir + "/trimming/{locust}_trim2P_2_fastqc.html",
shell:
"""
module load FastQC/0.11.9-Java-11
fastqc {input.read1}
fastqc {input.read2}
"""
########################################
# Parameters in the cluster.json file
########################################
"trim_fastqc":
{
"cpus-per-task" : 2,
"partition" : "medium",
"ntasks": 1,
"mem" : "500M",
"time": "0-03:00:00"
},
EXAMPLE OF READS QUALITY BEFORE TRIMMING
EXAMPLE OF READS QUALITY AFTER TRIMMING
We can see that the sequence length has changed and that the 5’ and 3’
end positions with lower quality have been removed.
EXAMPLE OF READS QUALITY AFTER TRIMMING
We can see that most detected adapter sequences have been adequately removed after trimming.
Non-target sequencing data
Contamination is likely to occur throughout various stages of experiments, including tissue acquisition, RNA extraction and library preparation. One can hope to reduces as much as possible its impact on the final sequencing data which can affect the success rate of reads mapping.
Screen for microbial contamination
We decided to screen for microbes sequences present in the trimmed
paired-end reads FASTQ.gz files using the tool Kaiju.
Kaiju translates metatranscriptomics sequencing reads into
six possible reading frames and searches for maximum exact matches of
amino acid sequences in a given annotation protein database.
We used the most extensive microbial database nr_euk
which encompass the subset of NCBI BLAST nr database containing all
proteins belonging to Archaea, Bacteria, Viruses, Fungi and microbial
Eukaryotes.
## Downloading the 2022-03-10 database from Kaiju webserver
wget https://kaiju.binf.ku.dk/database/kaiju_db_nr_euk_2022-03-10.tgzTo run Kaiju we used the following
Snakemake rule:
########################################
# Snakefile rule
########################################
rule kaiju:
input:
read1 = OUTdir + "/trimming/{locust}_trim1P_1.fastq.gz",
read2 = OUTdir + "/trimming/{locust}_trim2P_2.fastq.gz",
database = KAIJUdir + "/kaiju_db_nr_euk.fmi",
taxonid = KAIJUdir + "/nodes.dmp",
taxonnames = KAIJUdir + "/names.dmp",
output:
report = OUTdir + "/kaiju/{locust}_kaiju.out",
classification = OUTdir + "/kaiju/{locust}_kaiju.tsv",
shell:
"""
ml GCC/8.3.0 OpenMPI/3.1.4 Kaiju/1.7.3-Python-3.7.4
kaiju -z 12 -v -a greedy -f {input.database} -t {input.taxonid} -i {input.read1} -j {input.read2} -o {output.report}
kaiju2table -v -t {input.taxonid} -n {input.taxonnames} -r phylum -o {output.classification} {output.report}
"""
########################################
# Parameters in the cluster.json file
########################################
"kaiju":
{
"cpus-per-task" : 6,
"partition" : "medium",
"ntasks": 2,
"mem" : "200G",
"time": "0-12:00:00"
},
The ouput produced here allows us to see the percentage of reads that map to unclassified (likely our locust host here) and the percentage of microbial contamination ranked in a phylum level.
Visualize the metatranscriptomics result
Kaiju output can be exported to be view in a interactive
and hierarchical multi-layered pie-charts using Krona. The
results are generated by a .html page. We followed the Kaiju tutorial
on the Github page:
########################################
# Snakefile rule
########################################
rule krona:
input:
kaijuout = OUTdir + "/kaiju/{locust}_kaiju.out",
taxonid = KAIJUdir + "/nodes.dmp",
taxonnames = KAIJUdir + "/names.dmp",
output:
conversion = OUTdir + "/kaiju/{locust}_krona.out",
webreport = OUTdir + "/kaiju/{locust}_krona.html",
shell:
"""
ml GCCcore/8.2.0 KronaTools/2.7.1
kaiju2krona -t {input.taxonid} -n {input.taxonnames} -i {input.kaijuout} -o {output.conversion}
ktImportText -o {output.webreport} {output.conversion}
"""
########################################
# Parameters in the cluster.json file
########################################
"krona":
{
"cpus-per-task" : 2,
"partition" : "short",
"ntasks": 1,
"mem" : "500M",
"time": "0-0:10:00"
},Ribosomal RNA
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