TransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks.
TransDecoder identifies likely coding sequences based on the following criteria:
a minimum length open reading frame (ORF) is found in a transcript sequence
a log-likelihood score similar to what is computed by the GeneID software is > 0.
the above coding score is greatest when the ORF is scored in the 1st reading frame as compared to scores in the other 5 reading frames.
if a candidate ORF is found fully encapsulated by the coordinates of another candidate ORF, the longer one is reported. However, a single transcript can report multiple ORFs (allowing for operons, chimeras, etc).
optional the putative peptide has a match to a Pfam domain above the noise cutoff score.
Predicting coding regions from a transcript fasta file
The TransDecoder utility is run on a fasta file containing the target transcript sequences. The simplest usage is as follows:
# Step 1: extract the long open reading frames TransDecoder.LongOrfs -t target_transcripts.fasta
|By default, TransDecoder.LongOrfs will identify ORFs that are at least 100 amino acids long. You can lower this via the -m parameter, but know that the rate of false positive ORF predictions increases drastically with shorter minimum length criteria.|
# Step 2: (optional) Optionally, identify ORFs with homology to known proteins via blast or pfam searches.
See Including homology searches as ORF retention criteria section below.
# Step 3: predict the likely coding regions TransDecoder.Predict -t target_transcripts.fasta [ homology options ]
If the transcripts are oriented according to the sense strand, then include the -S flag to examine only the top strand. Full usage info is below.
The final set of candidate coding regions can be found as files .transdecoder. where extensions include .pep, .cds, .gff3, and .bed
Starting from a genome-based transcript structure GTF file (eg. cufflinks)
The process here is identical to the above with the exception that we must first generate a fasta file corresponding to the transcript sequences, and in the end, we recompute a genome annotation file in GFF3 format that describes the predicted coding regions in the context of the genome.
Construct the transcript fasta file using the genome and the transcripts.gtf file like so:
util/cufflinks_gtf_genome_to_cdna_fasta.pl transcripts.gtf test.genome.fasta > transcripts.fasta
Next, convert the transcript structure GTF file to an alignment-GFF3 formatted file (this is done only because our processes operate on gff3 rather than the starting gtf file - nothing of great consequence). Convert gtf to alignment-gff3 like so, using cufflinks GTF output as an example:
util/cufflinks_gtf_to_alignment_gff3.pl transcripts.gtf > transcripts.gff3
Now, run the process described above to generate your best candidate ORF predictions:
TransDecoder.LongOrfs -t transcripts.fasta (optionally, identify peptides with homology to known proteins) TransDecoder.Predict -t transcripts.fasta [ homology options ]
And finally, generate a genome-based coding region annotation file:
util/cdna_alignment_orf_to_genome_orf.pl transcripts.fasta.transdecoder.gff3 transcripts.gff3 transcripts.fasta > transcripts.fasta.transdecoder.genome.gff3
Sample data and execution
The sample_data/ directory includes a runMe.sh script that you can execute to demonstrate the entire process, starting from a cufflinks GTF file. Note, the typical use-case for TransDecoder is starting from a fasta file containing target Transcripts, however, in the case of genome analysis, transcripts are often inferred from annotation coordinates, such as in this Cufflinks GTF formatted file. In this example, transcript sequences are reconstructed based on the GTF annotation coordinates, and then TransDecoder is executed on that fasta file. We include an additional utility for converting the transcript ORF coordinates into genome-coordinates so these regions can be examined in the genomic context.
Output files explained
A working directory (ex. transcripts.transdecoder_dir/) is created to run and store intermediate parts of the pipeline, and contains:
longest_orfs.pep : all ORFs meeting the minimum length criteria, regardless of coding potential. longest_orfs.gff3 : positions of all ORFs as found in the target transcripts longest_orfs.cds : the nucleotide coding sequence for all detected ORFs
longest_orfs.cds.top_500_longest : the top 500 longest ORFs, used for training a Markov model for coding sequences.
hexamer.scores : log likelihood score for each k-mer (coding/random)
longest_orfs.cds.scores : the log likelihood sum scores for each ORF across each of the 6 reading frames longest_orfs.cds.scores.selected : the accessions of the ORFs that were selected based on the scoring criteria (described at top) longest_orfs.cds.best_candidates.gff3 : the positions of the selected ORFs in transcripts
Then, the final outputs are reported in your current working directory:
transcripts.fasta.transdecoder.pep : peptide sequences for the final candidate ORFs; all shorter candidates within longer ORFs were removed. transcripts.fasta.transdecoder.cds : nucleotide sequences for coding regions of the final candidate ORFs transcripts.fasta.transdecoder.gff3 : positions within the target transcripts of the final selected ORFs transcripts.fasta.transdecoder.bed : bed-formatted file describing ORF positions, best for viewing using GenomeView or IGV.
Including homology searches as ORF retention criteria
To further maximize sensitivity for capturing ORFs that may have functional significance, regardless of coding likelihood score as mentioned above, you can scan all ORFs for homology to known proteins and retain all such ORFs. This can be done in two popular ways: a BLAST search against a database of known proteins, and searching PFAM to identify common protein domains. In the context of TransDecoder, this is done as follows:
Search a protein database such as Swissprot (fast) or Uniref90 (slow but more comprehensive) using BLAST+.
An example command would be like so:
blastp -query transdecoder_dir/longest_orfs.pep -db uniprot_sprot.fasta -max_target_seqs 1 -outfmt 6 -evalue 1e-5 -num_threads 10 > blastp.outfmt6
If you have access to a computing grid, consider using HPC GridRunner for more efficient parallel computing.
hmmscan --cpu 8 --domtblout pfam.domtblout /path/to/Pfam-A.hmm transdecoder_dir/longest_orfs.pep
Just as with the blast search, if you have access to a computing grid, consider using HPC GridRunner.
Integrating the Blast and Pfam search results into coding region selection
The outputs generated above can be leveraged by TransDecoder to ensure that those peptides with blast hits or domain hits are retained in the set of reported likely coding regions. Run TransDecoder.Predict like so:
TransDecoder.Predict -t target_transcripts.fasta --retain_pfam_hits pfam.domtblout --retain_blastp_hits blastp.outfmt6
The final coding region predictions will now include both those regions that have sequence characteristics consistent with coding regions in addition to those that have demonstrated blast homology or pfam domain content.
Viewing the ORF predictions in a genome browser
Viewing ORFs on target transcripts
java -jar $GENOMEVIEW/genomeview.jar transcripts.fasta transcripts.fasta.transdecoder.bed
If you lack a genome sequence and are working exclusively with the target transcripts, you can load the transcript fasta file and the ORF predictions (bed file) into GenomeView (see below).
Viewing ORFs in the context of the transcript structures on the genome
java -jar $GENOMEVIEW/genomeview.jar test.genome.fasta transcripts.bed transcripts.fasta.transdecoder.genome.bed
The original cufflinks-based transcript structures are shown in black, and the predicted coding regions are shown in cyan.
Technical Support and Project Announcements
Join our TransDecoder google group at https://groups.google.com/forum/#!forum/transdecoder-users