Category Archives: Learning bioinformatic tools

Things to do better next time

I have had some errors found in my assembled transcriptomes when they were submitted to NCBI. It appears that I missed a couple of quality control steps at the trimming stage.

What I have learnt:

  • after using the default trimmomatic settings – check the data before proceeding.
  • incorporate a file with all Illumina primers for identifying and trimming these from reads.
  • use bbduk to help with this process – as other residual primers may be there – from previous runs on the machine (
  • screen the raw data for contaminants, eg. bacterial, human and other non-target sequences. Seems my transcriptomes included a  few sequences from humans, gorilla and chimp!!
  • None of this really affected my analysis and, in fact, was such a small proportion of the assembled transcriptome BUT – the assemblies are now a bit questionable and should be done again.

Finally to get the counts

Now with all the bam files, indexed bam files and the bed file made previously we can get read counts per transcript. The moment you are anticipating is not far off. Make sure the bam.bai files are all in the same working directory.

We use BEDtools v2.24.0, with the multiBamCov option.

#PBS -P project
#PBS -N Syzygium_counts
#PBS -l nodes=1:ppn=20
#PBS -l walltime=01:00:00
#PBS -l pmem=24gb
#PBS -e ./Syzygium_counts.txt
#PBS -m abe

# Load modules
module load bedtools

# Working directory
cd /working directory location

# Run bedtools script
multiBamCov -q 30 -p -bams AP0_coordSorted.bam AP1_coordSorted.bam AP2_coordSorted.bam -bed Syzygium.BED > Syzygium_counts.txt

The output from this will be a large text file with the numbers of reads that aligned to each transcript.

TRINITY_DN4630_c0_g1_i1 0 2449 68 0 18 30
TRINITY_DN4630_c0_g2_i1 0 457 2 0 4 4 0
TRINITY_DN4679_c0_g1_i1 0 410 2 0 2 0 0
TRINITY_DN4674_c0_g1_i1 0 227 4 0 0 0 0
TRINITY_DN4609_c0_g1_i1 0 265 0 0 0 0 0
TRINITY_DN4651_c0_g1_i1 0 1030 8 0 4 4

which you can put into a spreadsheet and name the columns. And the columns are in the order that the bam files are placed in the script. Of course you would include all the bam files for all samples and all times in the one run using bedtools.

Gene ID start end AP0 AP1 AP2
TRINITY_DN4630_c0_g1_i1 0 2449 68 0 18
TRINITY_DN4630_c0_g2_i1 0 457 2 0 4
TRINITY_DN4679_c0_g1_i1 0 410 2 0 2
TRINITY_DN4674_c0_g1_i1 0 227 4 0 0
TRINITY_DN4609_c0_g1_i1 0 265 0 0 0
TRINITY_DN4651_c0_g1_i1 0 1030 8 0 4

Running biopython scripts

As I am learning a bit about scripts and how to run them I have made recent errors which probably appear very simple to someone from a computing background.

The word I have learnt is ‘parse‘ which means:

  • to analyze (a sentence) into its parts and describe their syntactic roles.
  • analyze (a string or text) into logical syntactic components, typically in order to test conformability to a logical grammar.
  • examine or analyze minutely.

Another word is to ‘pass‘. You pass some instruction to the software to parse (analyze).

To run software you need:

  • the software and dependencies (other software) to be installed
  • script to tell the program what you want it to do
  • input files

I managed to find this very useful biopython script to make bed files from my newly made transcriptomes. Bed files are tab  delimited files that include the sequence ID, and the start and end location. For new transcriptomes the start is always 0 and the end is the length of the contig. These files can be used to gather the count data from your alignment files per sequence ID (gene).

In case the website above is unavailable, here is the biopython script, slightly altered for the later version of Biopython which requires brackets around the print function:

import sys
from Bio import SeqIO

fasta_handle = open(sys.argv[1], "rU") #Open the fasta file specified by the user on the command line

#Go through all the records in the fasta file
for record in SeqIO.parse(fasta_handle, "fasta"):
    cur_id = #Name of the current feature
    cur_length = len(record.seq) #Size of the current feature
    print (("%s\t%d\t%d") % (cur_id, 0, cur_length)) #Output the name, start, and end coordinates to the screen

This script will take any fasta file you give it and output the bed file. The important thing is to tell python.exe what to do with the input file. You need to pass the script to python.exe so it can parse and act on it. Therefore the order of your command must be correct – the .py script comes directly after the python.exe.

I placed the input file first and could not understand why I kept getting the error that my fasta file had syntax errors.

So in the command line window – I just run this in Windows cmd.exe by holding shift and right-clicking on the folder I want as my working directory;

Open the command window in the directory where your fasta file is, then input the directory path to Python34\python.exe then input the directory path to the script and output to your working directory.

So it will look something like this:

D:\Syzygium\>C:Python34\python.exe D:\scripts\ Syzygium.fasta > Syzygium.bed

The output will look like this showing the length of each ‘gene’.

TRINITY_DN4638_c0_g1_i1 0 486
TRINITY_DN4638_c0_g2_i1 0 486
TRINITY_DN4608_c0_g1_i1 0 442
TRINITY_DN4687_c0_g1_i1 0 207
TRINITY_DN4630_c0_g1_i1 0 2449
TRINITY_DN4630_c0_g2_i1 0 457
TRINITY_DN4679_c0_g1_i1 0 410
TRINITY_DN4674_c0_g1_i1 0 227
TRINITY_DN4609_c0_g1_i1 0 265
TRINITY_DN4651_c0_g1_i1 0 1030
TRINITY_DN4626_c0_g1_i1 0 468
TRINITY_DN4626_c1_g1_i1 0 286
TRINITY_DN4653_c0_g2_i1 0 555
TRINITY_DN4653_c1_g1_i1 0 793
TRINITY_DN4648_c0_g1_i1 0 605
TRINITY_DN4632_c0_g1_i1 0 2026
TRINITY_DN4672_c0_g1_i1 0 239


Clustering the transcriptomes

I have now made eight transcriptomes. Four from the resistant plants and four from the susceptible. To be able to compare expression from different times, and between the different biological replicates, I have to make a single transcriptome for the plant species, in this case, Syzygium (Lilly Pilly). This is a bit concerning as my plants are grown from wild sourced seeds and are likely to be highly heterozygous.

My approach therefore was to use the CD-HIT-EST-2D ( software to combine transcriptomes, initially at 0.95  similarity. I clustered the transcriptomes in pairs from the largest to the smallest until I had a single fasta file.

#PBS -P (project)
#PBS -l nodes=1:ppn=20
#PBS -l walltime=00:15:00
#PBS -l pmem=24gb
#PBS -e ./SYZ1.error
#PBS -M (email address)
#PBS -m abe

# Load modules
module load cd-hit/4.6.1

# Working directory
Input path to directory

# Run CD-Hit script
cd-hit-est-2d -i BU.fasta -i2 BS.fasta -o SYZ1.fasta -c 0.95 -n 10 -d 0 -M 0 -T 0

I then sought out highly conserved genes using local BLAST in BioEdit. I used a couple of Eucalyptus grandis chitinase genes that I know to be well conserved from previous work, currently under review.

I found that the genes were present and intact in the individual transcriptomes but not in the combined one. I therefore ran the process with CD-HIT again with a higher stringency of 0.98.

This time the genes were present in the final transcriptome making me feel more confident about the alignment process.

Building the transcriptome

The purpose of many RNAseq studies is to compare the gene expression patterns across different treatments. In order to do this, as I mentioned in the previous post, all the reads from each treatment (from the one plant) need to be combined first.

Trinity assembly software incorporates other tools as well as assembly, for example Trimmomatic can be run within the trinity pipeline. To do the trimming and assembly you would run a script like this (note no spaces or line breaks):

Trinity –seqType fq –max_memory 24G –CPU 7 –left AP0_R1.fastq,AP1_R1.fastq,AP2_R1.fastq –right AP0_R2.fastq,AP1_R2.fastq,AP2_R2.fastq–trimmomatic –output AP_trinity

This will build all the reads into a complete transcriptome for the plant labelled AP from RNA expressed pre-inoculation, at 24 hours and at 48 hours.

Depending on the success of the sequencing, ie. the number and length of raw reads, the assembly process will take around 1 to 3 days of wall-time. Below is the pbs script to run for trimmed reads.

#PBS -P (project name)
#PBS -N AP_assembly
#PBS -l nodes=1:ppn=24
#PBS -l walltime=100:00:00
#PBS -l pmem=24gb
#PBS -e ./AP_trinity/AP_assembly.txt
#PBS -m abe

# Load modules
module load bowtie
module load java
module load trinity/2.1.1

# Working directory
cd /project/full path name to your working directory (where all the input files are)

# Run trinity
Trinity –seqType fq –max_memory 24G –CPU 24 –left AP0_R1.trimpaired.fastq,AP1_R 1.trimpaired.fastq,AP2_R1.trimpaired.fastq –right AP0_R2.trimpaired.fastq,AP1_R2.trimpaired.fastq,AP2_R2.trimpaired.fastq –output AP_trinity

NB: As I said previously, I am learning on the process as I go. Actually writing this bog is making me find the mistakes I have made along the way. Just found an error in the way I ran the above trinity script. It appears that the paired and unpaired trimmed reads are combined with the trinity assembly. I have only used my paired reads. At this point I am way down the pipeline so this is a big blow! I have decided to continue with my current pipeline as well as re-run the assemblies with both the paired and un-paired reads and then compare the outcomes.

Why do RNAseq?

There is always a scientific question at the basis of doing RNAseq. Here are some images of susceptible Myrtaceae plants infected with myrtle rust. Clockwise from top left: Syzygium luehmannii (Riberry), Syzygium leuhmannii, Syzygium jambos, Chamelaucium uncinatum (Geraldton wax) flower buds, Chamelaucium uncinatum stems and leaves. For highly susceptible plants the pathogen can be devastating. Determining the genetic basis for resistance would therefore be very useful for plant breeding and for population and species management. Whole gene expression studies are one way to determine this genetic basis by identifying the changes in resistant versus susceptible plants.

myrtle rust


Trinity assembly – and how not to go about it

I have used the Trinity assembly software to build denovo transcriptomes for each of my plants. Lots of good information here:

I made several errors to begin with. The first error was to build transcriptomes for each plant at each time-point thinking that this would allow me to compare across the treatments. Running the assembly software can take days to complete – so this has meant a lot of time wasted doing 24 separate assemblies!

The correct thing to do is to combine the data from all times (pre-inoculation, 24 hours post-inoculation and 48 hours post-inoculation) for each plant. In my case I did 8 assemblies. You need all the gene transcripts from all samples to build the most complete ‘potential’ expression profile. You can then use this transcriptome fasta file to map the reads from each treatment later on. The transcriptome is also really useful for other analysis you might want to do later such as searching for specific gene families etc.

more on trinity assembly to come later…


Trimming the reads

Trimming the reads

The raw sequence reads still have Illumina adaptors and some of them might be low quality. The software often used for trimming reads is Trimmomatic (I used trimmomatic v0.33). Manual available here:

Click to access TrimmomaticManual_V0.32.pdf

Trimmomatic requires Java to be installed and loaded, the default java version on Artemis (hpc) is currently  1.8.0 and this works fine. I did not change any default settings for Trimmomatic.

Here is the pbs script for my paired end data

Note: the term ‘module’ means software and there is generally a default version that is uploaded for your work unless you specify the version in the Load Modules section of the pbs),

#PBS -P (project directory)
#PBS -N (job name eg. AP0 trim)
#PBS -l nodes=1:ppn=16
#PBS -l walltime=01:00:00
#PBS -l pmem=4gb
#PBS -m abe

#Load modules
module load java
module load trimmomatic

# Working directory
cd /(pathname of working directory where files are located)

# Run trimmomatic
java -jar /usr/local/trimmomatic/0.33/trimmomatic-0.33.jar PE -phred33 AP0_R1.fastq AP0_R2.fastq AP0_R1_trimpaired.fq AP0_R1_trimunpaired.fq AP0_R2_trimpaired.fq AP0_R2_trimunpaired.fq ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

So the input files are:

AP0_R1.fastq and AP0_R2.fastq (or gzipped versions)

and the output files are:

AP0_R1_trimpaired.fq, AP0_R1_trimunpaired.fq, AP0_R2_trimpaired.fq and AP0_R2_trimunpaired.fq (or gzipped versions)

These output files can be used for the assembly process with Trinity software.