Pychopper v2 is a tool to identify, orient and trim full-length Nanopore cDNA reads. The tool is also able to rescue fused reads.
The general approach of Pychopper v2 is the following:
- Pychopper first identifies alignment hits of the primers across the length of the sequence. The default method for doing this is using
nhmmscanwith the pre-trained strand specific profile HMMs, included with the package. Alternatively, one can use theedlibbackend, which uses a combination of global and local alignment to identify the primers within the read. - After identifying the primer hits by either of the backends, the reads are divided into segments defined by two consecutive primer hits. The score of a segment is its length if the configuration of the flanking primer hits is valid (such as
SPP,-VNPfor forward reads) or zero otherwise. - The segments are assigned to rescued reads using a dynamic programming algorithm maximizing the sum of used segment scores (hence the amount of rescued bases). A crucial observation about the algorithm is that if a segment is included as a rescued read, then the next segment must be excluded as one of the primer hits defining it was "used up" by the previous segment. This put constraints on the dynamic programming graph, as illustrated in the figure below. The arrows in read define the optimal path for rescuing two fused reads with the a total score of
l1 + l3.
- A crucial parameter of Pychopper v2 is
-q, which determines the stringency of primer alignment (E-value in the case of the pHMM backend). This can be explicitly specified by the user, however by default it is optimized on a random sample of input reads to produce the maximum number of classified reads.
The required Python packages are installed by either pip or conda. The profile HMM alignment backend depends on the latest hmmer package.
This can be easily installed using conda:
conda install -c bioconda "hmmer>=3.0"Install via pip:
pip install git+https://github.com/nanoporetech/pychopper.gitOr install from bioconda:
conda install -c bioconda "pychopper>=2.0"Run the tests:
make testIssue make help to get a list of make targets.
usage: cdna_classifier.py [-h] [-b primers] [-g phmm_file] [-c config_file]
[-k kit] [-q cutoff] [-Q min_qual] [-z min_len]
[-r report_pdf] [-u unclass_output]
[-l len_fail_output] [-w rescue_output]
[-S stats_output] [-K qc_fail_output]
[-Y autotune_nr] [-L autotune_samples]
[-A scores_output] [-m method] [-x rescue] [-p]
[-t threads] [-B batch_size] [-D read stats]
input_fastx output_fastx
Tool to identify, orient and rescue full-length cDNA reads.
positional arguments:
input_fastx Input file.
output_fastx Output file.
optional arguments:
-h, --help show this help message and exit
-b primers Primers fasta.
-g phmm_file File with custom profile HMMs (None).
-c config_file File to specify primer configurations for each
direction (None).
-k kit Use primer sequences from this kit (PCS109).
-q cutoff Cutoff parameter (autotuned).
-Q min_qual Minimum mean base quality (7.0).
-z min_len Minimum segment length (50).
-r report_pdf Report PDF (cdna_classifier_report.pdf).
-u unclass_output Write unclassified reads to this file.
-l len_fail_output Write fragments failing the length filter in this file.
-w rescue_output Write rescued reads to this file.
-S stats_output Write statistics to this file.
-K qc_fail_output Write reads failing mean quality filter to this file.
-Y autotune_nr Approximate number of reads used for tuning the cutoff
parameter (10000).
-L autotune_samples Number of samples taken when tuning cutoff parameter
(30).
-A scores_output Write alignment scores to this BED file.
-m method Detection method: phmm or edlib (phmm).
-x rescue Protocol-specific read rescue: DCS109 (None).
-p Keep primers, but trim the rest.
-t threads Number of threads to use (8).
-B batch_size Maximum number of reads processed in each batch
(1000000).
WARNING: Do not turn on trimming during basecalling as it will remove the primers needed for classifying the reads!
Example usage with default PCS109/DCS109 primers using the default pHMM backend:
cdna_classifier.py -r report.pdf -u unclassified.fq -w rescued.fq input.fq full_length_output.fqExample usage with default PCS109/DCS109 primers using the edlib/parasail backend:
cdna_classifier.py -m edlib -r report.pdf -u unclassified.fq -w rescued.fq input.fq full_length_output.fqExample usage with default PCS109/DCS109 primers using the default pHMM backend:
cdna_classifier.py -r report.pdf -A aln_hits.bed -S statistics.tsv -u unclassified.fq -w rescued.fq input.fq full_length_output.fqThe fasta files with custom primers used by the edlib/parasail backend can be specified through -b, while the valid primer configurations are specified through -c:
cdna_classifier.py -m edlib -b custom_pimers.fas -c primer_config.txt input.fq full_length_output.fqWhere the contents of primer_config.txt looks like +:MySSP,-MyVNP|-:MyVNP,-MySSP.
The pHMM alignment backend takes a "compressed" profile HMM trained from a multiple sequence alignment using the hmmer package. Custom profile HMMs can be trained from a fastq of reads and a fasta file with the primer sequences using the hammerpede package. The path to the custom profile HMM can be specified using -g:
cdna_classifier.py -m phmm -g MySSP_MyVNP.hmm -c primer_config.txt input.fq full_length_output.fqEvaluation on 50k reads from a SIRV E0 mix dataset produced by the PCS109 protocol indicated good performance using both backends:
- More than 85% of the reads were classified, while the percent of classified and rescued reads was higher than 90%:
- The oriented reads came from the + and - strands in a roughly 1:1 proportion as expected:
- When mapping the oriented reads to the transcriptome, virtually all of them map in the forward direction as expected:
- Comparing the percent of reads covered by alignment before and after trimming, we observe that trimming removed the adapters and the primers:
- Comparing the percent of reference transcripts covered by alignment before and after trimming, we can observe that trimming did not change its value in most of the cases, hence it only rarely removed valid sequence portions:
The evaluation can be re-run by issuing make from the evaluation directory.
- Please fork the repository and create a merge request to contribute.
- Use bumpversion to manage package versioning.
- The code should be PEP8 compliant, which can be tested by
make lint.
(c) 2019 Oxford Nanopore Technologies Ltd.
This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
See the post announcing the tools at the Oxford Nanopore Technologies Community here.
Research releases are provided as technology demonstrators to provide early access to features or stimulate Community development of tools. Support for this software will be minimal and is only provided directly by the developers. Feature requests, improvements, and discussions are welcome and can be implemented by forking and pull requests. However much as we would like to rectify every issue and piece of feedback users may have, the developers may have limited resource for support of this software. Research releases may be unstable and subject to rapid iteration by Oxford Nanopore Technologies.