Trainings 2019 – Trinity – Practice

Trinity Practice

Description Hands On Lab Exercises for RNASeq assembly
Related-course materials Linux for Dummies
Related-course materials RNAseq
Authors Julie Orjuela-Bouniol ( - i-Trop platform (UMR BOREA / DIADE / IPME - IRD)
Creation Date 02/08/2019
Last Modified Date 21/09/2019
Modified by Christine Tranchant (


0. Going to the i-Trop cluster - ssh,srun,scp

Dataset used in this practical comes from

In this session, we will analyze RNA-seq data from one sample of S. cerevisiae (NCBI SRA
SRS307298). It is from two different origin (CENPK and Batch), with three biological replications for each
origin (rep1, rep2 and rep3).

Connection to the i-Trop Cluster through ssh mode

We will work on the i-Trop Cluster with a "supermem" node using SLURM scheduler.


Opening an interactive bash session on the node25 (supermem partition) - srun -p partition --pty bash -i

Read this survival document containig basic commands to SLURM (

srun -p supermem --mem 50G --time 20:00:00 --cpus-per-task 2 --pty bash -i

Prepare input files

  • Create your subdirectory in the scratch file system /scratch. In the following, please replace X with your own user ID number in formationX.
cd /scratch
mkdir formationX
cd formationX
  • Copy the exercise files from the shared location to your scratch directory (it is essential that all
    calculations take place here)
scp -r  nas:/data2/formation/TP-trinity/SRA_SRS307298/RAWDATA/ /scratch/formationX/
  • When the files transfer is finished, verify by listing the content of the current directory and the subdirectory RAWDATA with the
    command ls -al. You should see 12 gzipped read files in a listing, the samples.txt file and the bash script.
[orjuela@node25 RAWDATA]$ more samples.txt 
CENPK   CENPK_rep1  PATH/SRR453569_1.fastq.gz   PATH/SRR453569_2.fastq.gz
CENPK   CENPK_rep2  PATH/SRR453570_1.fastq.gz   PATH/SRR453570_2.fastq.gz
CENPK   CENPK_rep3  PATH/SRR453571_1.fastq.gz   PATH/SRR453571_2.fastq.gz
Batch   Batch_rep1  PATH/SRR453566_1.fastq.gz   PATH/SRR453566_2.fastq.gz
Batch   Batch_rep2  PATH/SRR453567_1.fastq.gz   PATH/SRR453567_2.fastq.gz
Batch   Batch_rep3  PATH/SRR453568_1.fastq.gz   PATH/SRR453568_2.fastq.gz

1. Check Reads Quality

FastQC perform some simple quality control checks to ensure that the raw data looks good and there are no problems or biases in data which may affect how user can usefully use it.

# make a fastqc repertoty
cd ../

#charge modules 
module load bioinfo/FastQC/0.10.1

# run fastqc in the whole of samples
fastqc -t 2 /scratch/formationX/RAWDATA/*.gz -o /scratch/formationX/FASTQC/

Multiqc is a modular tool to aggregate results from bioinformatics analyses across many samples into a single report. Use this tool to visualise results of quality.

#charge modules 
module load bioinfo/multiqc/1.7

#launch Multiqc to create a report in html containing the whole of informations generated by FastQC
multiqc .

#transfer results to your cluster home 
scp -r multiqc* nas:/home/formationX/

# transfert results to your local machine by scp or filezilla
scp -r* ./

# open in your favorite web navigator
firefox multiqc_report.html .

In this practice, reads quality is ok. You need to observe sequences and check biases. To remove adaptors and primers you can use Trimmomatic. Use PRINSEQ2 to detect Poly A/T tails and low complexity reads. Remove contaminations with SortMeRNA, riboPicker or DeconSeq.

2. Performing a de novo RNA-Seq assembly with trinity

2.1 Running Trinity with trimmomatic and reads normalisation

Preparing assembly sample file and check parameters of trinity assembler

Observe script and adapt to your formation number. This script is not sending in sbatch mode because it could be take time in this training. If you need launch it in a slurm cluster you can use this version containing SBATCH commands run_trinity.slurm or adapt it to SGE.

[orjuela@node25 RAWDATA]$ more 

# loading modules
module load bioinfo/samtools/1.9
module load bioinfo/trinityrnaseq/2.8.5
module load bioinfo/Trimmomatic/0.33

# changing PATH to current directory in samples file
sed -i 's|PATH|'$PWD'|ig' samples.txt 

# Running trinity assembly
# Trinity --seqType fq --max_memory 50G --CPU 2 --samples_file samples.txt --output ../TRINITY_OUT 

# Running Trinity with trimmomatic and normalisation
Trinity --seqType fq --max_memory 50G --CPU 2 --trimmomatic --quality_trimming_params 'ILLUMINACLIP:/usr/local/Trimmomatic-0.33/adapters/TruSeq2-PE.fa:2:30:10 ILLUMINACLIP:/scratch/formationX/RAWDATA/adapt-125pbLib.txt:2:30:10 SLIDINGWINDOW:5:20 LEADING:5 TRAILING:5 MINLEN:25 HEADCROP:10' --normalize_by_read_set --samples_file samples.txt --output ../TRINITY_OUT

Running assembly :

bash > ../trinity.log & 

All screen output (info messages and error messages, if any) will be saved in the file
../trinity.log. The script will start executing in the background (the & at the end), so
that the terminal will return to the prompt right after you hit “Enter”.

You can use jobs to monitor jobs but if you logout the program does not keep running.

While running you can examine steps tail -f ../trinity.log.

After trimmomatic and reads normalisation, Three stages are done by Trinity

  1. Jellyfish: Extracts and counts K-mers (K=25) from reads
  2. Inchworm: Assembles initial contigs by “greedily” extending sequences with most abundant K-mers
  3. Chrysalis: Clusters overlapping Inchworm contigs, builds de Bruijn graphs for each cluster, partitions reads between clusters
  4. Butterfly: resolves alternatively spliced and paralogous transcripts independently for each cluster (in parallel)

Ending up assembly and downloading assembly results

  • WARNING !: This job is expected to run 12 hours. Kill your job using fg and ctl+c

 jobs = shows jobs running
 fg = foreground
 bf = background
 ctrl+z = send job to background
 ctrl+c = kill job in ground
  • Recover assembly results (only Trinity.fasta) generated by trinity from the shared location to your scratch directory :
scp -r  nas:/data2/formation/TP-trinity/TRINITY_OUT/TRINITY_OUT/Trinity.fasta /scratch/formationX/

cd /scratch/formationX/TRINITY_OUT
  • Upon successful completion of Trinity, the assembled transcriptome is written to the FASTA file called
    Trinity.fasta located in the output directory /scratch/formationX/TRINITY_OUT.

3. Assessing transcriptome assembly quality

3.1 Getting basic Assembly metrics with the trinity script

Running trinity script

Trinity.fasta contains transcripts to be evaluated, annotated, and used in downstream analysis of
expression. In this exercise, we only concentrate on basis statistics of the assembled transcriptome,
which can be obtained using a Trinity utility script

#declare the path_to_trinity variable
#check path
echo $path_to_trinity
#launch assembly metrics script
$path_to_trinity/util/ /scratch/formationX/TRINITY_OUT/Trinity.fasta > trinityStats.log
Parsing the results generated by the script

The output file generated (trinityStats.log) will contain basic information about contig length distributions, based on all transcripts and only on the longest isoform per gene.

Besides average and median contig lengths, also given are quantities N10 through N50. Nx is the smallest contig length such that (x/100)% of all assembled bases are in contigs longer than Nx. Specifically, N50 is the contig length such that half of all assembly sequence is contained in contigs longer than that. In whole genome assembly, N50 is often used as a measure (one of many) of assembly quality, since the longer the contigs, the better the assembly. In the case of transcriptome, contig lengths should be correct, which does not imply “large”. If it falls in the right ballpark (about 1000-1,500), N50 can still be used as a check on overall “sanity” of the transcriptome assembly.

[orjuela@node25 orjuela]$ more trinityStats.log 

## Counts of transcripts, etc.
Total trinity 'genes':    7600
Total trinity transcripts:  8616
Percent GC: 39.37

Stats based on ALL transcript contigs:

    Contig N10: 11180
    Contig N20: 8394
    Contig N30: 6793
    Contig N40: 5553
    Contig N50: 4736

    Median contig length: 506
    Average contig: 1909.88
    Total assembled bases: 16455500

## Stats based on ONLY LONGEST ISOFORM per 'GENE':

    Contig N10: 9989
    Contig N20: 7558
    Contig N30: 6192
    Contig N40: 5131
    Contig N50: 4194

    Median contig length: 423
    Average contig: 1625.41
    Total assembled bases: 12353135

3.2 Reads mapping back rate and abundance estimation using the trinity script

Read congruency is an important measure in determining assembly accuracy. Clusters of read pairs that align incorrectly are strong indicators of mis-assembly. A typical "good" assembly has ~80% reads mapping to the assembly and ~80% are properly paired.

Several tools can be used to calculate reads mapping back rate over Trinity.fasta assembly : bwa, bowtie2 (mapping), kallisto, salmon (pseudo mapping). Quantify read counts for each gene/isoform can be calculate. Mapping and quantification can be obtained by using the --est_method argument into the script.

We will performing this analyses step successively with the script :

  • Pseudomapping methods (kallisto or salmon) are faster than mapping based. So firstly we will use salmon to pseudoalign reads from sample to the reference and quantify abondance.
  • Then, we will use bowtie2 and RSEM to align and quantify read counts for each gene/isoform.
Preparing data and environment for our following analysis
#create a directory dans /scratch/formationX/

# Loading modules
module load system/perl/5.24.0
module load bioinfo/trinityrnaseq/2.8.5
module load bioinfo/bowtie2/2.2.9
module load bioinfo/express/1.5.1
module load bioinfo/kallisto/0.43.1
module load bioinfo/RSEM/1.0
module load bioinfo/salmon/0.10.2
module load bioinfo/samtools/1.7

# modifying samples file path because quality-trimmed your reads using the --trimmomatic parameter in Trinity
cp /scratch/formationX/RAWDATA/samples.txt .
sed -i 's|RAWDATA|TRINITY_OUT|ig' samples.txt
sed -i 's|.fastq.gz|.fastq.gz.P.qtrim.gz|ig' samples.txt

# define variables
samplesfile=/scratch/formationX/ALIGN_AND_ABUNDANCE/samples.txt #modified samples file
Launch Salmon
# create a salmon_outdir and go on
mkdir salmon_outdir; cd salmon_outdir

# salmon
perl $path_to_trinity/util/ \
--transcripts $fasta \
--seqType fq \
--samples_file $samplesfile \
--est_method salmon \
--trinity_mode \
--prep_reference > salmon_align_and_estimate_abundance.log 2>&1 &

Check reads percentage mapped to Trinity.fasta reference by sample for salmon and bowtie-rsem est method.

#salmon results
[orjuela@node25 salmon_outdir]$ grep 'Mapping rate =' */logs/salmon_quant.log 
Batch_rep1/logs/salmon_quant.log:[2019-09-17 15:54:32.923] [jointLog] [info] Mapping rate = 96.3027%
Batch_rep2/logs/salmon_quant.log:[2019-09-17 15:55:09.922] [jointLog] [info] Mapping rate = 96.3992%
Batch_rep3/logs/salmon_quant.log:[2019-09-17 15:55:39.246] [jointLog] [info] Mapping rate = 96.7795%
CENPK_rep1/logs/salmon_quant.log:[2019-09-17 15:52:56.609] [jointLog] [info] Mapping rate = 94.4516%
CENPK_rep2/logs/salmon_quant.log:[2019-09-17 15:53:30.146] [jointLog] [info] Mapping rate = 95.0558%
CENPK_rep3/logs/salmon_quant.log:[2019-09-17 15:54:00.196] [jointLog] [info] Mapping rate = 95.0639%
OPTIONAL: Launch bowtie2-rsem
  • WARNING !: this job can take a lot ~1h30 by sample. This step will not run in this practice.

# create a bowtie2-rsem_outdir and go on
cd /scratch/formationX/ALIGN_AND_ABUNDANCE/
mkdir bowtie2-rsem_outdir; cd bowtie2-rsem_outdir

# runnign align_and_estimate_abundance in bowtie2-rsem mode
perl $path_to_trinity/util/ \
--transcripts $fasta \
--seqType fq \
--samples_file $samplesfile \
--est_method RSEM --aln_method bowtie2 \
--trinity_mode \
--prep_reference \
--coordsort_bam > bowtie-rsem_align_and_estimate_abundance.log 2>&1 &

Here, we show you results with bowtie2-rsem OPTIONAL part

#bowtie-rsem results
[orjuela@node25 bowtie2-rsem_outdir]$ grep -B 6 "overall alignment rate" bowtie-rsem_align_and_estimate_abundance.log
CMD: set -o pipefail && bowtie2 --no-mixed --no-discordant --gbar 1000 --end-to-end -k 200  -q -X 800 -x /scratch/orjuela/TRINITY_OUT/Trinity.fasta.bowtie2 -1 /scratch/orjuela/TRINITY_OUT/SRR453569_1.fastq.gz.P.qtrim.gz -2 /scratch/orjuela/TRINITY_OUT/SRR453569_2.fastq.gz.P.qtrim.gz -p 4 | samtools view -@ 4 -F 4 -S -b | samtools sort -@ 4 -n -o bowtie2.bam 
3666948 reads; of these:
  3666948 (100.00%) were paired; of these:
    497221 (13.56%) aligned concordantly 0 times
    2058938 (56.15%) aligned concordantly exactly 1 time
    1110789 (30.29%) aligned concordantly >1 times
86.44% overall alignment rate

3.3. Expression matrix construction

Combine read count from all samples into a matrix, and normalize the read count using the TMM method. This command will take in RSEM output files from each sample, and combine them into a single matrix file.

# go to salmon results repertory
cd /scratch/formationX/ALIGN_AND_ABUNDANCE/salmon_outdir

#load modules
module load bioinfo/R/3.3.3
module load system/perl/5.24.0

#declare bash variables

# calculate expression matrix (if salmon use quant.sf files, if kallisto use abundance.tsv files)
$path_to_trinity/util/ \
--est_method salmon \
--out_prefix Trinity_trans \
--name_sample_by_basedir \
--gene_trans_map none \
CENPK_rep1/quant.sf \
CENPK_rep2/quant.sf \
CENPK_rep3/quant.sf \
Batch_rep1/quant.sf \
Batch_rep2/quant.sf \

You have to obtain two matrices:
The firts one containing the estimated counts ̀Trinity_trans.isoform.counts.matrix and the second one containing the TPM expression values that are cross-sample normalized using the TMM method Trinity_trans.TMM.EXPR.matrix.
TMM normalization assumes that most transcripts are not differentially expressed, and linearly scales the expression values of samples to better enforce this property.

In both files, each column represents a sample, and each row represents a gene, the values are either the raw read counts or normalized FPKM values. The “counts” file will be used for differentially expressed gene identification, and the “fpkm” file will be used for clustering analysis. By default, the fpkm file is normalized with TMM method.

[orjuela@node25 salmon_outdir]$ ll Trinity_trans.*
total 1,4M
-rw-r--r-- 1 orjuela borea-equipe7 539K 29 août  16:41 Trinity_trans.isoform.TMM.EXPR.matrix
-rw-r--r-- 1 orjuela borea-equipe7 386K 29 août  16:41 Trinity_trans.isoform.counts.matrix
-rw-r--r-- 1 orjuela borea-equipe7 474K 29 août  16:41 Trinity_trans.isoform.TPM.not_cross_norm
-rw-r--r-- 1 orjuela borea-equipe7  355 29 août  16:41 Trinity_trans.isoform.TPM.not_cross_norm.TMM_info.txt
-rw-r--r-- 1 orjuela borea-equipe7  542 29 août  16:41 Trinity_trans.isoform.TPM.not_cross_norm.runTMM.R

[orjuela@node25 salmon_outdir]$ head *.matrix
==> Trinity_trans.isoform.counts.matrix <==
    CENPK_rep1  CENPK_rep2  CENPK_rep3  Batch_rep1  Batch_rep2  Batch_rep3
TRINITY_DN3130_c0_g1_i1 0.000000    4.000000    0.000000    0.000000    0.000000    0.000000
TRINITY_DN986_c0_g1_i4  190.000000  210.677452  319.000000  353.571187  420.080043  292.000000
TRINITY_DN4062_c0_g2_i1 0.000000    13.000000   0.000000    0.000000    0.000000    0.000000
TRINITY_DN4840_c0_g1_i1 0.000000    10.000000   0.000000    0.000000    0.000000    0.000000
TRINITY_DN1624_c0_g1_i1 0.000000    5.000000    0.000000    0.000000    0.000000    0.000000
TRINITY_DN3492_c0_g1_i1 0.000000    5.000000    0.000000    0.000000    0.000000    0.000000
TRINITY_DN1386_c0_g1_i1 671.000000  724.000000  997.000000  1325.000000 1726.000000 1229.000000
TRINITY_DN6688_c0_g1_i1 0.000000    5.000000    0.000000    0.000000    0.000000    0.000000
TRINITY_DN1126_c0_g2_i1 29.000000   42.000000   51.000000   14.000000   7.000000    5.000000

==> Trinity_trans.isoform.TMM.EXPR.matrix <==
    CENPK_rep1  CENPK_rep2  CENPK_rep3  Batch_rep1  Batch_rep2  Batch_rep3
TRINITY_DN3130_c0_g1_i1 0.000   32.382  0.000   0.000   0.000   0.000
TRINITY_DN986_c0_g1_i4  141.255 131.008 158.103 195.483 180.432 173.943
TRINITY_DN4062_c0_g2_i1 0.000   20.291  0.000   0.000   0.000   0.000
TRINITY_DN4840_c0_g1_i1 0.000   23.121  0.000   0.000   0.000   0.000
TRINITY_DN1624_c0_g1_i1 0.000   22.187  0.000   0.000   0.000   0.000
TRINITY_DN3492_c0_g1_i1 0.000   30.511  0.000   0.000   0.000   0.000
TRINITY_DN1386_c0_g1_i1 96.472  86.724  94.337  140.804 141.542 140.796
TRINITY_DN6688_c0_g1_i1 0.000   23.740  0.000   0.000   0.000   0.000
TRINITY_DN1126_c0_g2_i1 9.819   11.863  11.412  3.511   1.357   1.351
[orjuela@node25 salmon_outdir]$ more Trinity_trans.isoform.TPM.not_cross_norm.TMM_info.txt
group   lib.size    norm.factors    eff.lib.size
CENPK_rep1  999995  1.14115425493455    1141148.54916327
CENPK_rep2  999993  0.905154412329735   905148.076248848
CENPK_rep3  1000003 1.0939694255262 1093972.70743448
Batch_rep1  999992  0.989872299588587   989864.38061019
Batch_rep2  999990  0.950965458077021   950955.94842244
Batch_rep3  1000015 0.940121262909323   940135.364728266

3.4 Compute N50 based on the top-most highly expressed transcripts (Ex50)

$path_to_trinity/util/misc/ Trinity_trans.isoform.TMM.EXPR.matrix /scratch/formationX/TRINITY_OUT/Trinity.fasta > ExN50.stats

#Plotting ExN50
$path_to_trinity/util/misc/plot_ExN50_statistic.Rscript ExN50.stats

#If you want to know, how many transcripts correspond to the Ex 90 peak, you could:
cat Trinity_trans.isoform.TMM.EXPR.matrix.E-inputs |  egrep -v ^\# | awk '$1 <= 90' | wc -l

# or 50
cat Trinity_trans.isoform.TMM.EXPR.matrix.E-inputs |  egrep -v ^\# | awk '$1 <= 50' | wc -l

TRANSFERT: Observe plots. Remember, you have to transfert *.pdf files to your home before of transfering into your local machine.

#transfering plots
cp *.pdf /home/formationX/
# from your local machine
scp*.pdf .

3.5 Quantifying completness using BUSCO

Assessing gene space is a core aspect of knowing whether or not you have a good assembly.

Benchmarking Universal Single-Copy Orthologs (BUSCO) sets are collections of orthologous groups with near-universally-distributed single-copy genes in each species, selected from OrthoDB root-level orthology delineations across arthropods, vertebrates, metazoans, fungi, and eukaryotes. BUSCO groups were selected from each major radiation of the species phylogeny requiring genes to be present as single-copy orthologs in at least 90% of the species; in others they may be lost or duplicated, and to ensure broad phyletic distribution they cannot all be missing from one sub-clade. The species that define each major radiation were selected to include the majority of OrthoDB species, excluding only those with unusually high numbers of missing or duplicated orthologs, while retaining representation from all major sub-clades. Their widespread presence means that any BUSCO can therefore be expected to be found as a single-copy ortholog in any newly-sequenced genome from the appropriate phylogenetic clade.

Running Busco on Trinity.fasta assembly
#make a BUSCO directory
cd /scratch/formationX/
mkdir BUSCO; cd BUSCO
# download the eukaryota database from
# There is a lot of other databases usable for this dataset (ex: Fungi gene set or Saccaromyceta gene set, with more genes to retrieve, so longer to run)

# uncompress repertory
tar zxvf eukaryota_odb9.tar.gz

# define variables

# run busco
/usr/local/python-3.6.5/bin/python $busco -i $fasta -l $LINEAGE -m transcriptome  -c 2 -o trinity_busco > busco.log &

# check busco running using 
# or 
tail -f busco.log
Displaying the short summary table generated by BUSCO
[orjuela@node25 run_trinity_busco]$ more run_trinity_busco/short_summary_trinity_busco.txt 
# BUSCO version is: 3.0.2 
# The lineage dataset is: eukaryota_odb9 (Creation date: 2016-11-02, number of species: 100, number of BUSCOs: 303)
# To reproduce this run: python /usr/local/BUSCO-3.0.2/scripts/ -i /scratch/formation1/TRINITY_OUT/Trinity.fasta -o trinity_busco_euk -l /scratch/formation1/BUSCO/eukaryota_odb9/ -m transcriptome -c 2

# Summarized benchmarking in BUSCO notation for file /scratch/orjuela/TRINITY_OUT/Trinity.fasta
# BUSCO was run in mode: transcriptome


    283 Complete BUSCOs (C)
    226 Complete and single-copy BUSCOs (S)
    57  Complete and duplicated BUSCOs (D)
    0   Fragmented BUSCOs (F)
    20  Missing BUSCOs (M)
    303 Total BUSCO groups searched

3.6 BLASTX comparison to known protein sequences database

Performing a blastx againt the swissprot database (the manually annotated and reviewed section of the UniProt Knowledgebase)
  • WARNING !: This step will not run in this practice because it will be inclued in annotation script !! But we give you commands lines to lauch with your data.

#load modules
module load bioinfo/blast/2.4.0+

#define variables

# create BLASTX repertory
mkdir /scratch/formationX/BLASTX
cd /scratch/formationX/BLASTX

# First, we downloaded and indexed the database from, 
# uniprotdatabase is available on cluster at  (with the correct index files)

#Then, we ran BLASTX to get the top match hit:
$path_to_blast/blastx \
-db $uniprot \
-query $fasta \
-num_threads 2 \
-max_target_seqs 1 \
-outfmt 6 \
-evalue 1e-20 > SwissProt_1E20_Trinity_blastx.outfmt6 &

#if you want to test blastX practice, please recover results from `/data2/formation/TP-trinity/TRINITY_OUT/BLASTX/`

#Finally, we examined the percent of alignment coverage:
$path_to_trinity/util/misc/ SwissProt_1E20_Trinity_blastx.outfmt6 $fasta $uniprot > SwissProt_1E20_Trinity_blastx.outfmt6.grouped

$path_to_trinity/util/misc/ SwissProt_1E20_Trinity_blastx.outfmt6.grouped \
> SwissProt_1E20_Trinity_blastx.outfmt6.grouped.output
Analyzing blast results
[orjuela@node25 BLASTX]$ more SwissProt_1E20_Trinity_blastx.outfmt6.grouped.output
#hit_pct_cov_bin    count_in_bin    >bin_below
100 242 242
90  8   250
80  4   254
70  5   259
60  4   263
50  5   268
40  4   272
30  2   274
20  2   276
10  1   277

8616 transcripts from Trinity.fasta file were blasted against the swissprot database.

440 hits were reported in SwissProt_1E20_Trinity_blastx.outfmt6 file.

In SwissProt_1E20_Trinity_blastx.outfmt6.grouped.output file we can observed for example that 242 sequences were found with a 100% identity to an uniprot protein (count_in_bin). bin_below column represent a accumulative number of sequences.

4. Differential Expression Analysis (DE)

4.1 Examine your data and your experimental replicates before DE

Before differential expression analysis, examine your data to determine if there are any confounding issues. Trinity comes with a 'PtR' script that we use to simplify making various charts and plots based on a matrix input file. Run these three commands lines :

# go to salmon results repertory
cd /scratch/formationX/ALIGN_AND_ABUNDANCE/salmon_outdir

#create design file from samples.txt
cut -f1,2 ../samples.txt > design.txt

[orjuela@node25 salmon_outdir]$ more design.txt 
Batch   Batch_rep1
Batch   Batch_rep2
Batch   Batch_rep3

Run PtR scripts

$path_to_trinity/Analysis/DifferentialExpression/PtR  --matrix Trinity_trans.isoform.counts.matrix --samples design.txt --log2 --min_rowSums 10 --compare_replicates

$path_to_trinity/Analysis/DifferentialExpression/PtR  --matrix Trinity_trans.isoform.counts.matrix --samples design.txt --log2 --min_rowSums 10 --CPM --sample_cor_matrix

$path_to_trinity/Analysis/DifferentialExpression/PtR  --matrix Trinity_trans.isoform.counts.matrix --samples design.txt --log2 --min_rowSums 10  --CPM --center_rows --prin_comp 

TRANSFERT : Observe plots. Remember, you have to transfert *.pdf files to your home before of transfering into your local machine.

#transfering plots
cp *.pdf /home/formationX/
# from your local machine
scp*.pdf .

4.2 Identify differentially expressed genes between the two tissues.

The tool is a PERL script that use Bioconductor package edgeR.

#run DE analysis
$path_to_trinity/Analysis/DifferentialExpression/ \
--matrix Trinity_trans.isoform.counts.matrix \
--method edgeR \
--samples_file design.txt \
--output edgeR_results

The output files are in the directory edgeR_results. Observe file Trinity_trans.isoform.counts.matrix.Batch_vs_CENPK.edgeR.DE_results. It provides several values for each gene:

1) FDR to indicate whether a gene is differentially expressed or not

2) logFC is the log2 transformed fold change between the two tissues

3) logCPM is the log2 transformed normalized read count of average of the samples.

Usually you have to filter this list of genes/isoforms to FDR <0.05 or below. To be more conservative, you could also use more stringent FDR cutoff (e.g. <0.001), and only keep genes with high logFC (e.g. <-2 and >2) and/or high logCPM (e.g. >1). In the edgeR_results directory there is also a “volcano plot” to visualize the distribution of the DE genes.

[orjuela@node25 salmon_outdir]$ head edgeR_results/Trinity_trans.isoform.counts.matrix.Batch_vs_CENPK.edgeR.DE_results
sampleA sampleB logFC   logCPM  PValue  FDR
TRINITY_DN332_c0_g1_i10 Batch   CENPK   -10.359903780639    8.33512668205486    0   0
TRINITY_DN730_c0_g2_i1  Batch   CENPK   -6.4740873961699    8.70152404919916    0   0
TRINITY_DN741_c0_g1_i1  Batch   CENPK   -6.31231314146213   10.2830859720565    0   0
TRINITY_DN287_c1_g1_i1  Batch   CENPK   -6.26650019458591   8.47203996977476    0   0
TRINITY_DN65_c0_g1_i3   Batch   CENPK   -4.13189957112457   10.730475730689 0   0
TRINITY_DN1298_c0_g1_i1 Batch   CENPK   -3.19109633696326   8.91247526735535    0   0
TRINITY_DN1253_c0_g1_i1 Batch   CENPK   -3.03997649547823   8.99502681145441    6.47317750883953e-301   3.75444295512693e-298
TRINITY_DN51_c0_g1_i1   Batch   CENPK   -4.59451625218422   10.4521118447878    5.94332889429699e-289   3.01623941385572e-286
TRINITY_DN708_c0_g1_i1  Batch   CENPK   -4.1838974791094    7.70916912374135    3.84699757684993e-275   1.73542335133453e-272
#transfering plots
cd edgeR_result
cp *.pdf /home/formationX/
# from your local machine
scp*.pdf .

4.3 Clustering analysis

Hierarchical clustering and k-means clustering for samples and genes can be done using trinity scripts.

The clustering will be performed only on differentially expressed genes, with FDR and logFC cutoff defined by -P and -C parameters.

In this example, we set K=4 for k-means analysis. The genes will be separated into 4 groups based on expression pattern.

There are two prefiltered files produced: *DE_results.P1e-3_C2.Batch-UP.subset and *DE_results.P1e3_C2.CENPK-UP.subset, with differentially expressed genes (FDR cutoff 0.001, logFC cutoff 2 and -2).

#go to edgeR_results
cd edgeR_results
# running analyze_diff_expr
$path_to_trinity/Analysis/DifferentialExpression/ \
--matrix ../Trinity_trans.isoform.TMM.EXPR.matrix --samples ../design.txt -P 1e-3 -C 2 \
--output cluster_results
# running define_clusters_by_cutting_tree
$path_to_trinity/Analysis/DifferentialExpression/ \
-K 4 -R cluster_results.matrix.RData 
#transfering plots
cp *.pdf /home/formationX/
# from your local machine
scp*.pdf .

Before finish ...

Go to here to recover the scripts used in this training.


The resource material is licensed under the Creative Commons Attribution 4.0 International License (here).