orpheum

What is orpheum?

Orpheum which used to be called sencha is a Python package for directly translating RNA-seq reads into coding protein sequence.

• Free software: MIT license
• Documentation: https://czbiohub.github.io/orpheum

Installation

The package can be installed from PyPI using pip here:

pip install orpheum

Developmental install

To install this code and play around with the code locally, clone this github repository and use pip to install:

git clone https://github.com/czbiohub/orpheum.git
cd orpheum

# The "." means "install *this*, the folder where I am now"
pip install .

Usage

Extract likely protein-coding reads from sequencing data

A reference proteome must be supplied as the first argument.

orpheum translate reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

Save the "coding scores" to a csv or parquet file

The "coding score" of each read is calculated by translating each read in six frames, then is calculatating the Jaccard index between any of the six translated frames of the read and the peptide database. The final coding score is the maximum Jaccard index across all reading frames. If you'd like to see the coding scores for all reads, use the --csv flag or --parquet flag.

csv:

orpheum translate --csv coding_scores.csv reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

parquet:

orpheum translate --parquet coding_scores.parquet reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

Save the coding nucleotides to a fasta

By default, only the coding peptides are output. If you'd like to also output the underlying nucleotide sequence, then use the flag --coding-nucleotide-fasta

orpheum translate --coding-nucleotide-fasta coding_nucleotides.fasta reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

Save the non-coding nucleotides to a fasta

To see the sequence of reads which were deemed non-coding, use the flag --noncoding-nucleotide-fasta.

orpheum translate --noncoding-nucleotide-fasta noncoding_nucleotides.fasta reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

Save the low complexity nucleotides to a fasta

To see the sequence of reads found to have too low complexity of nucleotide sequence to evaluate, use the flag --low-complexity-nucleotide-fasta. Low complexity is determined by the same method as the read trimmer fastp in which we calculate what percentage of the sequence has consecutive runs of the same base, or mathematically, how often seq[i] = seq[i+1]. The default threshold is 0.3. As an example, the sequence CCCCCCCCCACCACCACCCCCCCCACCCCCCCCCCCCCCCCCCCCCCCCCCACCCCCCCACACACCCCCAACACCC would be considered low complexity. While this sequence has many nucleotide k-mers, it is likely a result of a sequencing error and we ignore it.

orpheum translate --low-complexity-nucleotide-fasta low_complexity_nucleotides.fasta reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

Save the low complexity peptides to a fasta

Even if the nucleotide sequence may pass the complexity filter, the peptide sequence may still be low complexity. As an example, all translated frames of the sequence CAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAG would be considered low complexity, as it translates to either QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ (5'3' Frame 1), SSSSSSSSSSSSSSSSSSSSSSSSSSSSS (5'3' Frame 2), AAAAAAAAAAAAAAAAAAAAAAAAAAAAA (5'3' Frame 3 and 3'5' Frame 3), LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL (3'5' Frame 1), or CCCCCCCCCCCCCCCCCCCCCCCCCCCCC (3'5' Frame 2). As these sequences have few k-mers and are difficult to assess for how "coding" they are, we ignore them. Unlike for nucleotides where we look at runs of consecutive bases, we require the translated peptide to contain greater than (L - k + 1)/2 k-mers, where L is the length of the sequence and k is the k-mer size. To save the sequence of low-complexity peptides to a fasta, use the flag --low-complexity-peptides-fasta.

orpheum translate --low-complexity-peptides-fasta low_complexity_peptides.fasta reference-proteome.fa.gz *.fastq.gz > coding_peptides.fasta

History

0.1.0 (2019-04-10)

• First release on PyPI.

1.0.0 (2020-04-28)

• Sencha release on PyPI.