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SuperTranscripts

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Brian Haas edited this page Apr 21, 2020 · 6 revisions

SuperTranscripts provide a gene-like view of the transcriptional complexity of a gene. SuperTranscripts were originally defined by Nadia Davidson, Anthony Hawkins, and Alicia Oshlack as described in their publication "SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes" Genome Biology, 2017. SuperTranscripts are useful in the context of genome-free de novo transcriptome assembly in that they provide a genome-like reference for studying aspects of the gene including differential transcript usage (aka. differential exon usage) and as a substrate for mapping reads and identifying allelic polymorphisms.

A SuperTranscript is constructed by collapsing unique and common sequence regions among splicing isoforms into a single linear sequence. An illustration of this is shown below:

In the Trinity toolkit, we provide a utility for constructing SuperTranscripts based on the gene-to-isoform relationships and the sequence graph structure leveraged by Trinity during assembly.

Note, if you have transcriptome assemblies generated by an assembler other than Trinity, or are interested in exploring the earlier published methods, see Lace.

Generate Trinity SuperTranscripts like so:

%  $TRINITY_HOME/Analysis/SuperTranscripts/Trinity_gene_splice_modeler.py \
       --trinity_fasta Trinity.fasta

and this should generate two output files:

 trinity_genes.fasta   :supertranscripts in fasta format
 trinity_genes.gtf     :transcript structure annotation in gtf format

If you're interested in capturing a multiple alignment view that contrasts the different candidate splicing isoforms, you can include parameter '--incl_malign', and it'll generate a file 'trinity_genes.malign'.

An example of such a multiple alignment view is shown below:

TRINITY_DN22_c0_g2_i5   TGTCTCTGACAAATTCTCTCCAGAGGCTGCGTCTCGGAGGGGGCTGAGCACAGCAGAGATGAATGCAGTAGAAGCCATCCACAGAGCTGTGGAATTTAAT
TRINITY_DN22_c0_g2_i4   TGTCTCTGACAAATTCTCTCCAGAGGCTGCGTCTCGGAGGGGGCTGAGCACAGCAGAGATGAATGCAGTAGAAGCCATCCACAGAGCTGTGGAATTTAAT
TRINITY_DN22_c0_g2_i6   TGTCTCTGACAAATTCTCTCCAGAGGCTGCGTCTCGGAGGGGGCTGAGCACAGCAGAGATGAATGCAGTAGAAGCCATCCACAGAGCTGTGGAATTTAAT
TRINITY_DN22_c0_g2_i1   TGTCTCTGACAAATTCTCTCCAGAGGCTGCGTCTCGGAGGGGGCTGAGCACAGCAGAGATGAATGCAGTAGAAGCCATCCACAGAGCTGTGGAATTTAAT
TRINITY_DN22_c0_g2_i3   TGTCTCTGACAAATTCTCTCCAGAGGCTGCGTCTCGGAGGGGGCTGAGCACAGCAGAGATGAATGCAGTAGAAGCCATCCACAGAGCTGTGGAATTTAAT

TRINITY_DN22_c0_g2_i5   CCACACGTGCCAAAA......................TATCTACTAGAAATGAAAAGCTTAATCCTCCCACCAGAACACATCCTGAAGAGAGGAGACAGT
TRINITY_DN22_c0_g2_i4   CCACACGTGCCAAAA......................TATCTACTAGAAATGAAAAGCTTAATCCTCCCACCAGAACACATCCTGAAGAGAGGAGACAGT
TRINITY_DN22_c0_g2_i6   CCACACGTGCCAAAA......................TATCTACTAGAAATGAAAAGCTTAATCCTCCCACCAGAACACATCCTGAAGAGAGGAGACAGT
TRINITY_DN22_c0_g2_i1   CCACACGTGCCAAAACTTTCCGGATGATCCCGTATCC...............................................................
TRINITY_DN22_c0_g2_i3   CCACACGTGCCAAAA......................TATCTACTAGAAATGAAAAGCTTAATCCTCCCACCAGAACACATCCTGAAGAGAGGAGACAGT

TRINITY_DN22_c0_g2_i5   GAAGCGATAGCATATGCATTCTTTCATCTTGCACACTGGAAGAGGGTGGAAGGGGCTTTGAATCTCTTGCATTGTACGTGGGAAGGCACTTTCCGGATGA
TRINITY_DN22_c0_g2_i4   GAAGCGATAGCATATGCATTCTTTCATCTTGCACACTGGAAGAGGGTGGAAGGGGCTTTGAATCTCTTGCATTGTACGTGGGAAGGCACTTTCCGGATGA
TRINITY_DN22_c0_g2_i6   GAAGCGATAGCATATGCATTCTTTCATCTTGCACACTGGAAGAGGGTGGAAGGGGCTTTGAATCTCTTGCATTGTACGTGGGAAGGCACTTTCCGGATGA
TRINITY_DN22_c0_g2_i1   ....................................................................................................
TRINITY_DN22_c0_g2_i3   GAAGCGATAGCATATGCATTCTTTCATCTTGCACACTGGAAGAGGGTGGAAGGGGCTTTGAATCTCTTGCATTGTACGTGGGAAGGCACTTTCCGGATGA

TRINITY_DN22_c0_g2_i5   TCCCGTATCCCCTGGAGAAGGGACACCTATTTTATCCATACCCAATCTGTACAGAAACAGCTGACCGGGAGCTGCTTCCCTCTTTCCATGAAGTCTCAGT
TRINITY_DN22_c0_g2_i4   TCCCGTATCCCCTGGAGAAGGGACACCTATTTTATCCATACCCAATCTGTACAGAAACAGCTGACCGGGAGCTGCTTCCCTCTTTCCATGAAGTCTCAGT
TRINITY_DN22_c0_g2_i6   TCCCGTATCCCCTGGAGAAGGGACACCTATTTTATCCATACCCAATCTGTACAGAAACAGCTGACCGGGAGCTGCTTCCCTCTTTCCATGAAGTCTCAGT
TRINITY_DN22_c0_g2_i1   ..........CCTGGAGAAGGGACACCTATTTTATCCATACCCAATCTGTACAGAAACAGCTGACCGGGAGCTGCTTCCCTCTTTCCATGAAGTCTCAGT
TRINITY_DN22_c0_g2_i3   TCCCGTATCCCCTGGAGAAGGGACACCTATTTTATCCATACCCAATCTGTACAGAAACAGCTGACCGGGAGCTGCTTCCCTCTTTCCATGAAGTCTCAGT

TRINITY_DN22_c0_g2_i5   TTACCCAAAGAAGGAACTTCCCTTCTTCATCCTCTTCACTGCTGGACTGTGCTCCTTCACAGCCATGCTGGCCCTCCTGACACATCAGTTTCCGGAACTT
TRINITY_DN22_c0_g2_i4   TTACCCAAAGAAGGAACTTCCCTTCTTCATCCTCTTCACTGCTGGACTGTGCTCCTTCACAGCCATGCTGGCCCTCCTGACACATCAGTTTCCGGAACTT
TRINITY_DN22_c0_g2_i6   TTACCCAAAGAAGGAACTTCCCTTCTTCATCCTCTTCACTGCTGGACTGTGCTCCTTCACAGCCATGCTGGCCCTCCTGACACATCAGTTTCCGGAACTT
TRINITY_DN22_c0_g2_i1   TTACCCAAAGAAGGAACTTCCCTTCTTCATCCTCTTCACTGCTGGACTGTGCTCCTTCACAGCCATGCTGGCCCTCCTGACACATCAGTTTCCGGAACTT
TRINITY_DN22_c0_g2_i3   TTACCCAAAGAAGGAACTTCCCTTCTTCATCCTCTTCACTGCTGGACTGTGCTCCTTCACAGCCATGCTGGCCCTCCTGACACATCAGTTTCCGGAACTT

Next steps

Now that you have SuperTranscripts, follow on with our protocols for:

Note, while supertranscripts are useful for exploring transcript characteristics in the absence of a reference genome, there is noise and bias that should be taken into consideration. See Freedman AH, Clamp M, Sackton TB. Error, noise and bias in de novo transcriptome assemblies. Mol Ecol Resour. 2020 Mar 17. The bioRxiv preprint is also available here.

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