The program is divided into three parts:
-- dumb ORF prediction: ORF prediction that outputs all longest ORFs in all frames. Can be used to create a top training dataset.
-- ANGEL classifier training: train a coding potential classifier based on given training data. Must provide both CDS and UTR for positive and negative training set.
-- ANGEL robust ORF prediction: ORF prediction based on both the ANGEL classifier and the dumb ORF prediction.
The ANGEL classifer is based on the classifer described in the Shimizu et al., "ANGLE: a sequencing errors resistant program for predicting protein coding regions in unfinished cDNA.", J Bioinform Comput Biol, (2006). The naming change to ANGEL is intentional to disambiguate from the author implementation of ANGLE.
You will need to install CD-HIT and have it available in your $PATH variable to run dumb ORF prediction.
It is recommended that you set up and activate a virtual environment before installation. See here howto.
You can download this GitHub repository in many ways, here we assume you will be using git clone:
git clone https://github.com/Magdoll/ANGEL.git
cd ANGEL
python setup.py build
python setup.py install
dumb_predict.py takes as input a fasta file and outputs all longest ORFs (could be overlapping) that exceed the user-defined minimum length. In addition, it uses CD-HIT to remove redundancy to create a "top training set" for ANGEL classifier training.
The usage is:
dumb_predict.py <fasta_filename> <output_prefix>
[--use_top USE_TOP] [--min_aa_length MIN_AA_LENGTH]
[--use_rev_strand] [--cpus CPUS]
We will use the provided training example below. For runtime purpose, we set --use_top to 50 here but for proper training we would recommend setting to 500. By default, only the forward strand is used. This is especially true for PacBio transcriptome sequencing output that often already has correct strand.
cd ANGEL/training_example
dumb_predict.py test.human_1000seqs.fa test.human --use_top 50 --min_aa_length 300 --cpus 24
The output consists of:
test.human.pep, test.human.cds, test.human.utr: which are the results of longest ORF prediction test.human.training_50.cds, test.human.training_50.utr: which are the top 50 non-redundant CDS/UTR sequences that can be used as training data for ANGEL classifier
angel_train.py takes a CDS fasta file and a UTR fasta file and outputs a trained classifier pickle file. Note that the training dataset does not have to be the same as the input to ANGEL ORF prediction below. You can use any curated dataset like Gencode, RefSeq, or another dataset so long as they are from the same or similar species so the classifier can be trained properly.
The usage is:
angel_train.py <cds_filename> <utr_filename> <output_pickle> [--cpus CPUS]
For example:
cd ANGEL/training_example
angel_train.py test.human.training_50.cds test.human.training_50.utr \
test.human.training_50.classifier.pickle --cpus 24
On a typical 500-sequence training dataset, the training may take several hours. The human MCF-7 training set took 4 hours on a 24-core machine.
The usage is:
angel_predict.py <input_fasta> <classifier_pickle> <output_prefix>
[--min_angel_aa_length] [--min_dumb_aa_length] [--use_rev_strand] [--cpus]
For each sequence, ANGEL uses the classifer to predict the coding potential of each codon in each of the three frames then finds the most likely stretch of window that is the open reading frame. It then does the following:
- If there is only one predicted ORF, tag it as "confident". In this case, it is unlikely there are sequencing errors.
- If there are multiple ORFs but only one exceeds
min_angel_aa_lengththreshold, output that one ORF and tag it as "likely". In this case, the program is semi-positive that there are no sequencing errors or that the error occurs at the ends of the CDS, allowing successful prediction of a relatively long continuous ORF. - If there are multiple ORFs (could be in multiple frames) exceeding the length threshold, output all of them and tag them as "suspicious". In this case, the ORFs could be genuine complete ORFs (indicating a polycistronic transcript or alternative ORFs) or fragments of the same ORF that has frame shift due to uncorrected sequencing errors.
The longest ORF length from the ANGEL process is recorded as T. Then, the same dumb ORF prediction (which simply looks for the longest stretch of ORF without stop codon interruption) is done on the forward three frames. Each predicted dumb ORF is also outputted if and only if its length is greater than both T and min_dumb_aa_length. This is a fallback process in case the ANGEL classifier failed to detect coding potential in the CDS region.
If --use_rev_strand is given, then the same process is repeated on the reverse-complement of the sequence.
For example:
angel_predict.py test.human_1000seqs.fa test.human.training_50.classifier.pickle test.human \
--use_rev_strand --min_dumb_aa_length 300
The output files are: <output_prefix>.ANGLE.cds, <output_prefix>.ANGLE.pep, <output_prefix>.ANGLE.utr.
Each output sequence ID has the format:
<seq_id> type:<tag>-<completeness> len:<ORF length (aa)> strand:<strand> pos:<CDS range>
Where tag is "confident", "likely", or "suspicious" for ANGEL predictions, and "dumb" for dumb ORF predictions. completeness is either "complete", "5partial", "3partial", or "internal" based on the presence or absence of start and stop codons.
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