Bioinformatics for Cancer Genomics 2019

Tutorial for the CTAT Fusion Toolkit, leveraging STAR-Fusion, FusionInspector, and Trinity

The Trinity Cancer Transcriptome Analysis Toolkit (CTAT) includes a suite of tools focused on identifying and characterizing fusion transcripts in cancer. The tools and comprehensive workflow for fusion study via Trinity CTAT is illustrated below:

Tools leveraged within CTAT include STAR-Fusion, FusionInspector, and Trinity. The tutorial below demonstrates the use of each of these utilities in identifying and characterizing cancer fusion transcripts. Note that the STAR-Fusion software incorporates FusionInspector and several companion utilities that will be used for fusion analysis.

The Trinity CTAT Fusion workflow involves first running STAR-Fusion to identify candidate fusion transcripts based on discordant read alignments. Predicted fusions are then ‘in silico validated’ using FusionInspector, which performs a more refined exploration of the candidate fusion transcripts, runs Trinity to de novo assemble fusion transcripts from the RNA-Seq reads, and provides the evidence in a suitable format to facilitate visualization. Predicted fusions are annotated according to prior knowledge of fusion transcripts relevant to cancer biology (or previously observed in normal samples and less likely to be relevant to cancer), and assessed for the impact of the predicted fusion event on coding regions, indicating whether the fusion is in-frame or frame-shifted along with combinations of domains that are expected to exist in the chimeric protein.

Tutorial Software

All software and data we’ll be using for this tutorial are installed on the server. To configure the software for your use, you’ll need to copy the bundle to your workspace like so:

mkdir ~/workspace/Module9
cd ~/workspace/Module9
cp -r ~/CourseData/CG_data/Module9/STAR-Fusion/STAR-Fusion-Tutorial .

Set the following environmental variable like so:

export STAR_FUSION_HOME=/usr/local/STAR-Fusion-v1.5.0/

now verify that this variable was set:

echo $STAR_FUSION_HOME

which should print the path to where the STAR-Fusion software is installed.

This should be the only configuration that’s necessary for executing the tutorial.

Tutorial Data

The tutorial includes a small data set that can be leveraged using modest computational resources, so it should run on a any hardware having at least 4G of RAM (so suitable for most contemporary personal computing machines). The tutorial data consists of the following files:

Genome data:
    minigenome.fa    : small genome sequence consisting of ~750 genes.
    minigenome.gtf   : transcript structure annotations for these genes.

RNA-Seq data:
    rnaseq_1.fastq.gz :  RNA-Seq read 1 data ('left' read)
    rnaseq_2.fastq.gz :  RNA-Seq read 2 data ('right' read)

Meta data:
    CTAT_HumanFusionLib.mini.dat.gz : a small fusion annotation library

These data are included in a STAR-Fusion-Tutorial/ subdirectory. Change to this directory:

cd STAR-Fusion-Tutorial

Add write permissions to the files and directory (because we copied them from a directory where we did not have write permissions):

chmod +w *
chmod +w .

and examine the files that exist there:

ls -l

-rw-rw-r-- 1 ubuntu ubuntu     1719 Mar 14 09:05 AnnotFilterRule.pm
-rwxrwxr-x 1 ubuntu ubuntu      188 Mar 14 09:05 cleanMe.sh
-rw-rw-r-- 1 ubuntu ubuntu     6586 Mar 14 09:05 CTAT_HumanFusionLib.mini.dat.gz
-rw-rw-r-- 1 ubuntu ubuntu 11299925 Mar 14 09:05 minigenome.fa
-rw-rw-r-- 1 ubuntu ubuntu 15636276 Mar 14 09:05 minigenome.gtf
-rw-rw-r-- 1 ubuntu ubuntu      129 Mar 14 09:05 README.md
-rw-rw-r-- 1 ubuntu ubuntu 41380839 Mar 14 09:05 rnaseq_1.fastq.gz
-rw-rw-r-- 1 ubuntu ubuntu 45350714 Mar 14 09:05 rnaseq_2.fastq.gz
drwxrwxr-x 2 ubuntu ubuntu     4096 Mar 14 09:05 STAR-Fusion-Tutorial.wiki

The following will take you through some initial data processing followed by fusion-finding via STAR-Fusion.

Preparing a CTAT Genome Lib

Using the Trinity CTAT ecosystem of tools requires a CTAT genome lib, which is effectively a resource package containing a target genome, reference annotations, and various meta data files that support fusion-finding. Normally (outside of this tutorial context), you would use a pre-compiled CTAT genome lib provided to you (as indicated in the STAR-Fusion documentation). If you have a different target than the entire human genome, as is the case here, then we would need to build a custom CTAT fusion lib.

To build our custom tutorial-supporting CTAT genome lib, run the following along with using the provided tutorial data files:

${STAR_FUSION_HOME}/FusionFilter/prep_genome_lib.pl \
        --genome_fa minigenome.fa \
        --gtf minigenome.gtf \
        --fusion_annot_lib CTAT_HumanFusionLib.mini.dat.gz

# takes ~5 minutes

Running the above will create a ‘ctat_genome_lib_build_dir/’ directory and populate it with the reference data, perform blast searches to identify sequence similar sequences, and build some databases that will be leveraged by CTAT fusion tools.

Predict Fusions Using STAR-Fusion

Run STAR-Fusion to predict fusions like so:

${STAR_FUSION_HOME}/STAR-Fusion \
       --left_fq rnaseq_1.fastq.gz \
       --right_fq rnaseq_2.fastq.gz \
       --genome_lib_dir ctat_genome_lib_build_dir 

# takes a couple minutes

By default, the outputs are written to a subdirectory ‘STAR-Fusion_outdir’, where you’ll find the two primary output files:

star-fusion.fusion_predictions.tsv - fusion predictions including the identity of all evidence reads
star-fusion.fusion_predictions.abridged.tsv - shortened version of the above, lacking the voluminous read identities

Take a look at the format of the abridged output file:

head STAR-Fusion_outdir/star-fusion.fusion_predictions.abridged.tsv  | column -t | less -S

#FusionName            JunctionReadCount  SpanningFragCount  SpliceType           LeftGene                     LeftBreakpoint        RightGene      RightBreakpoint       LargeAnchorSupport  FFPM      LeftBreakDinuc  LeftBreakEntropy  RightBreakDinuc  RightBreakEntropy  annots
TATDN1--GSDMB          81                 126                ONLY_REF_SPLICE      TATDN1^TATDN1                minigenome:6584842:-  GSDMB^GSDMB    minigenome:2142920:-  YES_LDAS            282.2894  GT              1.9219            AG               1.5628             ["CCLE","Klijn_CellLines","FA_CancerSupp","ChimerPub","INTRACHROMOSOMAL[minigenome:4.40Mb]"]
TATDN1--GSDMB          30                 126                ONLY_REF_SPLICE      TATDN1^TATDN1                minigenome:6584842:-  GSDMB^GSDMB    minigenome:2138981:-  YES_LDAS            212.7398  GT              1.9219            AG               1.9086             ["CCLE","Klijn_CellLines","FA_CancerSupp","ChimerPub","INTRACHROMOSOMAL[minigenome:4.40Mb]"]
ACACA--STAC2           31                 42                 ONLY_REF_SPLICE      ACACA^ACACA                  minigenome:1869270:-  STAC2^STAC2    minigenome:2067875:-  YES_LDAS            99.5513   GT              1.9656            AG               1.9656             ["ChimerSeq","CCLE","Klijn_CellLines","FA_CancerSupp","INTRACHROMOSOMAL[minigenome:0.07Mb]","LOCAL_REARRANGEMENT:-:[69422]"]
CCDC6--RET             28                 14                 ONLY_REF_SPLICE      CCDC6^CCDC6                  minigenome:609384:-   RET^RET        minigenome:490475:+   YES_LDAS            57.2761   GT              1.8892            AG               1.8323             ["ChimerSeq","ChimerKB","FA_CancerSupp","Cosmic","Mitelman","ChimerPub","HaasMedCancer","Larsson_TCGA","YOSHIHARA_TCGA","INTRACHROMOSOMAL[minigenome:0.08Mb]","LOCAL_INVERSION:-:+:[82786]"]
BCAS4--BCAS3           8                  84                 ONLY_REF_SPLICE      BCAS4^BCAS4                  minigenome:4187310:+  BCAS3^BCAS3    minigenome:2490128:+  NO_LDAS             125.4619  GT              1.6402            AG               1.9899             ["ChimerPub","ChimerSeq","chimerdb_pubmed","CCLE","FA_CancerSupp","INTRACHROMOSOMAL[minigenome:1.68Mb]"]
BCAS4--BCAS3           4                  84                 ONLY_REF_SPLICE      BCAS4^BCAS4                  minigenome:4187310:+  BCAS3^BCAS3    minigenome:2485565:+  NO_LDAS             120.0071  GT              1.6402            AG               1.3996             ["ChimerPub","ChimerSeq","chimerdb_pubmed","CCLE","FA_CancerSupp","INTRACHROMOSOMAL[minigenome:1.68Mb]"]
RPS6KB1--SNF8          18                 21                 ONLY_REF_SPLICE      RPS6KB1^RPS6KB1              minigenome:2354713:+  SNF8^SNF8      minigenome:2265737:-  YES_LDAS            53.185    GT              1.3753            AG               1.8323             ["Klijn_CellLines","FA_CancerSupp","ChimerSeq","CCLE","INTRACHROMOSOMAL[minigenome:0.09Mb]","LOCAL_INVERSION:+:-:[87595]"]
RP11-208G20.2--PSPHP1  12                 42                 INCL_NON_REF_SPLICE  RP11-208G20.2^RP11-208G20.2  minigenome:6394049:+  PSPHP1^PSPHP1  minigenome:6324196:+  YES_LDAS            73.6407   AA              1.3996            TA               1.8323             INTRACHROMOSOMAL[minigenome:0.07Mb],LOCAL_REARRANGEMENT:+:[69653]
TOB1--SYNRG            17                 22                 ONLY_REF_SPLICE      TOB1^TOB1                    minigenome:2296883:-  SYNRG^SYNRG    minigenome:1999668:-  YES_LDAS            53.185    GT              1.4566            AG               1.8892             ["FA_CancerSupp","CCLE","INTRACHROMOSOMAL[minigenome:0.24Mb]"]

The format of the fusion output is described in the STAR-Fusion documentation. The first few columns identify the fused genes and the number of reads supporting the fusion prediction as split-reads or spanning fragments.

In silico Validation Using FusionInspector

FusionInspector performs in silico validation of fusion transcripts by realigning reads to both the whole genome and to fusion contigs where fusion candidate genes are positioned adjacent to one another in the order of their putative fusion event. Fusion reads that would align discordantly in the whole genome should instead align concordantly to the fusion contigs. FusionInspector identifies such reads and re-scores the fusion predictions. The fusion contigs along with the aligned reads are a useful product for visualizing and inspecting the evidence supporting the fusions.

FusionInspector comes bundled with the STAR-Fusion software and can be launched via the STAR-Fusion interface. FusionInspector has two modes: ‘inspect’ or ‘validate’ mode. In ‘inspect’ mode, it just aligns the STAR-Fusion identified evidence reads to the fusion contigs for inspection purposes. In ‘validate’ mode, it aligns all reads simultaneously to the whole genome and to the fusion contigs to identify those reads that align concordantly as fusion evidence to the fusion contigs.

Run FusionInspector via the STAR-Fusion interface like so:

${STAR_FUSION_HOME}/STAR-Fusion \
     --left_fq rnaseq_1.fastq.gz \
     --right_fq rnaseq_2.fastq.gz \
     --genome_lib_dir ctat_genome_lib_build_dir \
     --FusionInspector validate 
 
# takes ~3.5 minutes

All FusionInspector outputs will be found in the directory ‘STAR-Fusion_outdir/FusionInspector-validate/’

Examine the files found there:

ls -ltr STAR-Fusion_outdir/FusionInspector-validate/

The most relevant output files include:

finspector.fusion_predictions.final - the ‘in silico validated’ fusion predictions
finspector.fusion_predictions.final.abridged.FFPM.annotated - the abridged version including annotations

and files that are useful for viewing in IGV:

finspector.fa - custom reference genome fasta file
finspector.bed.sorted.bed.gz - reference transcript structure annotations in BED format
finspector.consolidated.cSorted.bam - reads aligned to the fusion contigs
finspector.junction_reads.bam - junction / split-reads supporting fusions
finspector.spanning_reads.bam - fusion spanning fragment evidence

Load these files into IGV for inspecting the evidence supporting the fusions. Load finspector.fa first (Genomes > Load from URL), then the bed and bam files listed above (File > Load from URL). The URLs can be copied from http://##.oicrcbw.ca/Module9/STAR-Fusion-Tutorial/STAR-Fusion_outdir/FusionInspector-validate (Remember to replace ## with your student number)

If you need the IGV software, you can launch it from here - just find the ‘Launch’ button in the center of that linked web page.

This FusionInspector view of the data would look like so:

More details about FusionInspector can be found on the FusionInspector documentation wiki.

De novo Reconstruct Fusion Transcripts

Use Trinity to de novo reconstruct fusion transcripts like so:

${STAR_FUSION_HOME}/STAR-Fusion \
     --left_fq rnaseq_1.fastq.gz \
     --right_fq rnaseq_2.fastq.gz \
     --genome_lib_dir ctat_genome_lib_build_dir \
     --FusionInspector validate \
     --denovo_reconstruct
 
# takes ~5 minutes

Now, reexamine the contents of the ‘STAR-Fusion_outdir/FusionInspector-validate/’ directory:

ls -ltr STAR-Fusion_outdir/FusionInspector-validate/

You’ll find additional outputs:

finspector.gmap_trinity_GG.fusions.fasta - de novo reconstructed fusion transcripts
finspector.gmap_trinity_GG.fusions.gff3.bed - fusion transcript structure in the fusion contig context

The ‘finspector.gmap_trinity_GG.fusions.gff3.bed’ can be uploaded into IGV for viewing along with the read evidence.

Fusion Coding Effect

To explore the effect the fusion events have on coding regions of the fused genes, you can run:

${STAR_FUSION_HOME}/STAR-Fusion \
     --left_fq rnaseq_1.fastq.gz \
     --right_fq rnaseq_2.fastq.gz \
     --genome_lib_dir ctat_genome_lib_build_dir \
     --examine_coding_effect 

 # takes a second

You should now find an output file ‘STAR-Fusion_outdir/star-fusion.fusion_predictions.abridged.coding_effect.tsv’ that includes additional columns describing the impact of the fusion event on the coding regions. Below, the extra columns and some examples are shown, with the fields transposed for easy viewing:

     #FusionName
     JunctionReadCount
     SpanningFragCount
     SpliceType
     LeftGene
     LeftBreakpoint
     RightGene
     RightBreakpoint
     LargeAnchorSupport
     FFPM
    LeftBreakDinuc
    LeftBreakEntropy
    RightBreakDinuc
    RightBreakEntropy
    annots
    CDS_LEFT_ID
    CDS_LEFT_RANGE
    CDS_RIGHT_ID
    CDS_RIGHT_RANGE
    PROT_FUSION_TYPE
    FUSION_MODEL
    FUSION_CDS
    FUSION_TRANSL
    PFAM_LEFT
    PFAM_RIGHT

ARFGEF2--SULF2
2
13
ONLY_REF_SPLICE
ARFGEF2^ARFGEF2
minigenome:4071002:+
SULF2^SULF2
minigenome:4059796:-
NO_LDAS
20.4557
GT
1.6895
AG
1.9656
["Klijn_CellLines","FA_CancerSupp","CCLE","INTRACHROMOSOMAL[minigenome:0.00Mb]","LOCAL_INVERSION:+:-:[3001]"]
ARFGEF2^ENST00000371917.4
1-121
SULF2^ENST00000359930.4
176-2610
INFRAME
minigenome|+|[0]4070882-4071002[0]<==>minigenome|-|[2]4020222-4020249[2]|[1]4021256-4021309[2]|[1]4021554-4021587[1]|[0]4023630-4023753[0]|[2]4024927-4025069[1]|[0]4025310-4025479[2]|[1]4025987-4026046[2]|[1]4027056-4027150[0]|[2]4027714-4027810[2]|[1]4028117-4028345[1]|[0]4031209-4031404[0]|[2]4035505-4035634[2]|[1]4036089-4036145[2]|[1]4037687-4037815[2]|[1]4042005-4042180[0]|[2]4043442-4043592[2]|[1]4049137-4049306[0]|[2]4051324-4051475[1]|[0]4059557-4059796[1]
atgcaggagagccagaccaagagcatgttcgtgtcccgggccctggagaagatcctagccgacaaggaggtgaagcggccccagcactcccagctgcgcagggcctgccaggtggcgctcgGTTCCATGCAGGTGATGAACAAGACCCGGCGCATCATGGAGCAGGGCGGGGCGCACTTCATCAACGCCTTCGTGACCACACCCATGTGCTGCCCCTCACGCTCCTCCATCCTCACTGGCAAGTACGTCCACAACCACAACACCTACACCAACAATGAGAACTGCTCCTCGCCCTCCTGGCAGGCACAGCACGAGAGCCGCACCTTTGCCGTGTACCTCAATAGCACTGGCTACCGGACAGCTTTCTTCGGGAAGTATCTTAATGAATACAACGGCTCCTACGTGCCACCCGGCTGGAAGGAGTGGGTCGGACTCCTTAAAAACTCCCGCTTTTATAACTACACGCTGTGTCGGAACGGGGTGAAAGAGAAGCACGGCTCCGACTACTCCAAGGATTACCTCACAGACCTCATCACCAATGACAGCGTGAGCTTCTTCCGCACGTCCAAGAAGATGTACCCGCACAGGCCAGTCCTCATGGTCATCAGCCATGCAGCCCCCCACGGCCCTGAGGATTCAGCCCCACAATATTCACGCCTCTTCCCAAACGCATCTCAGCACATCACGCCGAGCTACAACTACGCGCCCAACCCGGACAAACACTGGATCATGCGCTACACGGGGCCCATGAAGCCCATCCACATGGAATTCACCAACATGCTCCAGCGGAAGCGCTTGCAGACCCTCATGTCGGTGGACGACTCCATGGAGACGATTTACAACATGCTGGTTGAGACGGGCGAGCTGGACAACACGTACATCGTATACACCGCCGACCACGGTTACCACATCGGCCAGTTTGGCCTGGTGAAAGGGAAATCCATGCCATATGAGTTTGACATCAGGGTCCCGTTCTACGTGAGGGGCCCCAACGTGGAAGCCGGCTGTCTGAATCCCCACATCGTCCTCAACATTGACCTGGCCCCCACCATCCTGGACATTGCAGGCCTGGACATACCTGCGGATATGGACGGGAAATCCATCCTCAAGCTGCTGGACACGGAGCGGCCGGTGAATCGGTTTCACTTGAAAAAGAAGATGAGGGTCTGGCGGGACTCCTTCTTGGTGGAGAGAGGCAAGCTGCTACACAAGAGAGACAATGACAAGGTGGACGCCCAGGAGGAGAACTTTCTGCCCAAGTACCAGCGTGTGAAGGACCTGTGTCAGCGTGCTGAGTACCAGACGGCGTGTGAGCAGCTGGGACAGAAGTGGCAGTGTGTGGAGGACGCCACGGGGAAGCTGAAGCTGCATAAGTGCAAGGGCCCCATGCGGCTGGGCGGCAGCAGAGCCCTCTCCAACCTCGTGCCCAAGTACTACGGGCAGGGCAGCGAGGCCTGCACCTGTGACAGCGGGGACTACAAGCTCAGCCTGGCCGGACGCCGGAAAAAACTCTTCAAGAAGAAGTACAAGGCCAGCTATGTCCGCAGTCGCTCCATCCGCTCAGTGGCCATCGAGGTGGACGGCAGGGTGTACCACGTAGGCCTGGGTGATGCCGCCCAGCCCCGAAACCTCACCAAGCGGCACTGGCCAGGGGCCCCTGAGGACCAAGATGACAAGGATGGTGGGGACTTCAGTGGCACTGGAGGCCTTCCCGACTACTCAGCCGCCAACCCCATTAAAGTGACACATCGGTGCTACATCCTAGAGAACGACACAGTCCAGTGTGACCTGGACCTGTACAAGTCCCTGCAGGCCTGGAAAGACCACAAGCTGCACATCGACCACGAGATTGAAACCCTGCAGAACAAAATTAAGAACCTGAGGGAAGTCCGAGGTCACCTGAAGAAAAAGCGGCCAGAAGAATGTGACTGTCACAAAATCAGCTACCACACCCAGCACAAAGGCCGCCTCAAGCACAGAGGCTCCAGTCTGCATCCTTTCAGGAAGGGCCTGCAAGAGAAGGACAAGGTGTGGCTGTTGCGGGAGCAGAAGCGCAAGAAGAAACTCCGCAAGCTGCTCAAGCGCCTGCAGAACAACGACACGTGCAGCATGCCAGGCCTCACGTGCTTCACCCACGACAACCAGCACTGGCAGACGGCGCCTTTCTGGACACTGGGGCCTTTCTGTGCCTGCACCAGCGCCAACAATAACACGTACTGGTGCATGAGGACCATCAATGAGACTCACAATTTCCTCTTCTGTGAATTTGCAACTGGCTTCCTAGAGTACTTTGATCTCAACACAGACCCCTACCAGCTGATGAATGCAGTGAACACACTGGACAGGGATGTCCTCAACCAGCTACACGTACAGCTCATGGAGCTGAGGAGCTGCAAGGGTTACAAGCAGTGTAACCCCCGGACTCGAAACATGGACCTGGGACTTAAAGATGGAGGAAGCTATGAGCAATACAGGCAGTTTCAGCGTCGAAAGTGGCCAGAAATGAAGAGACCTTCTTCCAAATCACTGGGACAACTGTGGGAAGGCTGGGAAGGT
MQESQTKSMFVSRALEKILADKEVKRPQHSQLRRACQVALGSMQVMNKTRRIMEQGGAHFINAFVTTPMCCPSRSSILTGKYVHNHNTYTNNENCSSPSWQAQHESRTFAVYLNSTGYRTAFFGKYLNEYNGSYVPPGWKEWVGLLKNSRFYNYTLCRNGVKEKHGSDYSKDYLTDLITNDSVSFFRTSKKMYPHRPVLMVISHAAPHGPEDSAPQYSRLFPNASQHITPSYNYAPNPDKHWIMRYTGPMKPIHMEFTNMLQRKRLQTLMSVDDSMETIYNMLVETGELDNTYIVYTADHGYHIGQFGLVKGKSMPYEFDIRVPFYVRGPNVEAGCLNPHIVLNIDLAPTILDIAGLDIPADMDGKSILKLLDTERPVNRFHLKKKMRVWRDSFLVERGKLLHKRDNDKVDAQEENFLPKYQRVKDLCQRAEYQTACEQLGQKWQCVEDATGKLKLHKCKGPMRLGGSRALSNLVPKYYGQGSEACTCDSGDYKLSLAGRRKKLFKKKYKASYVRSRSIRSVAIEVDGRVYHVGLGDAAQPRNLTKRHWPGAPEDQDDKDGGDFSGTGGLPDYSAANPIKVTHRCYILENDTVQCDLDLYKSLQAWKDHKLHIDHEIETLQNKIKNLREVRGHLKKKRPEECDCHKISYHTQHKGRLKHRGSSLHPFRKGLQEKDKVWLLREQKRKKKLRKLLKRLQNNDTCSMPGLTCFTHDNQHWQTAPFWTLGPFCACTSANNNTYWCMRTINETHNFLFCEFATGFLEYFDLNTDPYQLMNAVNTLDRDVLNQLHVQLMELRSCKGYKQCNPRTRNMDLGLKDGGSYEQYRQFQRRKWPEMKRPSSKSLGQLWEGWEG
DCB-PARTIAL|23-121~|1.8e-41^DUF1981|69-95|0.37^HEAT|73-95|0.013
Sulfatase-PARTIAL|~176-374|3.5e-52^Phosphodiest-PARTIAL|~176-320|0.00054^Mannosyl_trans2|187-211|1.1^DUF4976|377-395|0.023^DUF5082|420-444|0.5^DUF3740|528-664|2.7e-52^CCDC73|619-667|8.5e-05^DUF5082|631-654|0.001^DUF3740|693-709|5.6^Mannosyl_trans2|745-793|0.00023^DUF4976|777-815|0.0014

Do we really have to run each fusion analysis step separately as in the tutorial above?

No… you can actually have one command that includes all relevant parameters and you can run STAR-Fusion followed by FusionInspector, de novo reconstruction, and explore coding region impacts like so:

${STAR_FUSION_HOME}/STAR-Fusion \
     --left_fq rnaseq_1.fastq.gz \
     --right_fq rnaseq_2.fastq.gz \
     --genome_lib_dir ctat_genome_lib_build_dir \
     --FusionInspector validate \
     --denovo_reconstruct \
     --examine_coding_effect

Closing Remarks

Congratulations on making it through the CTAT Fusion tutorial. For more information on Trinity CTAT and to stay current with our activities and developments, visit our Trinity CTAT page and join our Google Forum