This workflow designs oligos that hybridize to rRNA targets. First, a consensus sequence is built for each target. Next, potential probe target sites are screened against the genome and the transcriptome for non-rRNA matches. Finally, probes are selected to cover the targets, while adhering to the specified design parameters and avoiding the predicted non-specific targeting.

• Mary Kay Thompson (@marykthompson)
• Maria Kiourlappou (@mkiourlappou)

To simply download the workflow, go to the 'Clone or download' tab and download the ZIP file.

Prepare an output directory. Prepare a config.yml file matching the format of example_outdir/config.yml. You need to prepare the following simple csv files, matching the format of those in example_outdir/design_params/

1. seqs_and_anns.csv: This file contains the urls or absolute paths to the gtf file and the genome, cdna, and ncrna fasta files for each organism in the target set.

2. targets.csv: This file contains the names of the rRNA targets, their corresponding organisms, and the name of fasta file containing their sequences. Note that it is fine to provide all targets in the same fasta file or to provide them in separate fasta files. If the fasta files contain sequences not matching the provided IDs, only those matching the provided IDs will be included. If left blank, the fasta file will default to the ncrna.fa file provided for that organism. Note that the Clustal program used for alignment truncates sequence IDs at 15 characters, so the IDs should be 15 characters or fewer to avoid downstream errors. Special regions in the target transcripts can also be indicated in this file (1-based, closed interval). The transcript-specific indices will be converted to consensus sequence indices during the alignment steps. excluded_regions: Regions that should not overlap with probes. target_subregions: Split the target up into multiple regions for probe design. This feature was used to specify that an equal number of probes should be targeted to the left and right side of the Drosophila 28S, which is cleaved into two fragments.

3. params.csv: This file specifies the design parameters, such as min and max Tm, number of probes for each target, etc. The excluded_regions and target_subregions can instead be specified in this file as excluded_regions_consensus and target_subregions_consensus, but then the indices must be relative to the consensus sequence.

If you use conda, you will not need to pre-install each required package. We recommend the miniconda distribution. Please see here for instructions on how to install miniconda for your operating system. https://docs.conda.io/en/latest/miniconda.html

Create the probe design environment.

conda env create -f ribopop_probe_design.yaml

Activate the probe design environment.

conda activate ribopop_probe_design

Run the pipeline.

snakemake --directory <your output directory>

If you're not using conda, you will need to install snakemake as well as all the other software listed in envs/ribopop_probe_design.yaml in advance of running the workflow using your preferred installation method.

It may be desirable to run probe design again on a different set of targets without rebuilding the transcript index used for off-target screening. This can be accomplished by running a different version of the Snakefile and providing a different parameter file called indices.csv. In this case, you must also provide the path to the fasta containing your target sequences in the targets.csv file. See example in example_outdir/design_params_prebuilt/. You can leave the target_homology field blank for the organism(s) that you are using for building the target consensus, but you must provide it for any additional organisms that you are screening candidate probes against if they are not included in building the target consensus.

Run the pipeline using a pre-built transcript index:

snakemake -s use_prebuilt_indices.smk --directory <your output directory>

If you are unhappy with the probes the pipeline chooses or you want to choose probes other than those at Tm peaks, you can choose probes directly from the potential_probes_filt.csv file that is in the output directory named for the target (e.g. 18S/potential_probes_filt.csv). These probes have all passed screening with the user-selected sequence and specificity checking criteria.

One additional analysis that the pipeline does is to check for potential heterodimers between all selected probes and discard probes that have an unacceptably low delta G for heterodimer formation. If you select probes directly from the potential_probes_filt.csv file, then you may want to check this yourself. A simple way to check heterodimer formation would be to use the calc_dimer() function from choose_probes.py, e.g.:

import pandas as pd
import choose_probes
df = pd.read_csv('my_candidate_probes.csv', index_col = 'unique_id')
df[['dimer_dG','dimer_partner']] = choose_probes.calc_dimer(df)
df.to_csv('my_candidate_probes_wdimers.csv')

If you simply want to add one additional probe to an existing probe set (for example, if you ran the pipeline a second time with more relaxed constraints to find probes in a difficult-to-target region), you can use the add_extra_probe.py script.

python add_extra_probe.py my_probes.csv candidate_extra_probes.csv expanded_probeset -10

This would create a new file named expanded_probeset.csv with one additional probe from candidate_extra_probes.csv that had > -10 delta G predicted heterodimer with any of the other probes. Both input csv files should have the 'unique_id' field. For example, my_probes.csv could be the output probe set from a pipeline run and candidate_extra_probes.csv could be a selection of probes from the protential_probes_filt.csv file.

The pipeline assumes that fasta files used for building the transcript index end in a newline character (\n) as the Ensembl ones used here do. If yours do not, you should add one before running the pipeline.