Compact Aggregation of Targets for Comprehensive Hybridization
CATCH is a Python package for designing probe sets to use for nucleic acid capture of diverse sequence.
- Comprehensive coverage: CATCH accepts any collection of unaligned sequences — typically whole genomes of all known genetic diversity of one or more microbial species. It designs oligo sequences that guarantee coverage of this diversity, enabling rapid design of exhaustive probe sets for customizable targets.
- Compact designs: CATCH can design with a specified constraint on the number of oligos (e.g., array size). It searches a space of probe sets, which may pool many species, to find an optimal design. This allows its designs to scale well with known genetic diversity, and also supports cost-effective applications.
- Flexibility: CATCH supports applications beyond whole genome enrichment, such as differential identification of species. It allows avoiding sequence from the design (e.g., background in microbial enrichment), supports customized models of hybridization, enables weighting the sensitivity for different species, and more.
Table of contents
- Setting up CATCH
- Using CATCH
Setting up CATCH
CATCH is tested with Python 3.8, 3.9, and 3.10 on Linux (Ubuntu) and macOS, with the above NumPy and SciPy versions. CATCH may also work with older versions of Python, NumPy, and SciPy, as well as with other operating systems, but is not tested with them.
Installing CATCH with
pip (or conda), as described below, will install NumPy and SciPy if they are not already installed.
Setting up a conda environment
Note: This section is optional, but may be useful to users who are new to Python.
It is generally helpful to install and run Python packages inside of a virtual environment, especially if you have multiple versions of Python installed or use multiple packages. This can prevent problems when upgrading, conflicts between packages with different requirements, installation issues that arise from having different Python versions available, and more.
One option to manage packages and environments is to use conda. A fast way to obtain conda is to install Miniconda: you can download it here and find installation instructions for it here. For example, on Linux you would run:
Once you have conda, you can create an environment for CATCH with Python 3.8:
conda create -n catch python=3.8
Then, you can activate the
conda activate catch
After the environment is created and activated, you can install CATCH as described immediately below or by using the conda package. You will need to activate the environment each time you use CATCH.
Downloading and installing
An easy way to setup CATCH is to clone the repository and install with
git clone https://github.com/broadinstitute/catch.git
pip install -e .
If you do not have write permissions in the installation directory, you may need to supply
Note: This section is optional and not required for using CATCH.
CATCH uses Python's
To execute all tests, run:
python -m unittest discover
Alternative installation approach: conda
conda install -c bioconda catch
There are 3 ways of using CATCH to consider. They are all related — in that they wrap around the command described in Option #1 — but they can have large differences in their required computational resources (runtime and memory) and the design criteria they use. If you want a command that is quick to run without needing close familiarity with CATCH, we recommend Option #2. If you have the time to design a complex probe set that works well in practice and/or carefully read documentation, we recommend Option #1 and Option #3.
Option #1: Cautious defaults and one setting of hybridization criteria
This way of running CATCH uses
To see details on all of the arguments that the program accepts, run:
design.py requires one or more
datasets that specify input sequence data to target, as well as a path to which the probe sequences are written:
design.py [dataset] [dataset ...] -o OUTPUT
dataset can be one of two input formats:
- A path to a FASTA file.
- An NCBI taxonomy ID, for which sequences will be automatically downloaded.
This is specified as
download:TAXIDwhere TAXID is the taxonomy ID. CATCH will fetch all accessions (representing whole genomes) for this taxonomy and download the sequences. For viruses, NCBI taxonomy IDs can be found via the Taxonomy Browser.
The probe sequences are written to OUTPUT in FASTA format.
This program sets cautious hybridization criteria by default (e.g., tolerate no probe-target mismatches) and does not automatically enable options that, in practice, reduce runtime and memory usage.
The program was built and described in our publication as a subroutine of CATCH applied to one viral species at a time.
Nevertheless, it can be applied to multiple taxa and to non-viral taxa with appropriate settings: if
datasets encompass multiple taxa and/or include large genomes, please (i) consider the arguments described below that reduce runtime and memory usage or (ii) consider running CATCH as described in Option #2 or Option #3.
Below are several important arguments to
-ps/--probe-stride PROBE_STRIDE: Design probes to be PROBE_LENGTH nt long, and generate candidate probes using a stride of PROBE_STRIDE nt. (Default: 100 and 50.)
-m/--mismatches MISMATCHES: Tolerate up to MISMATCHES mismatches when determining whether a probe covers a target sequence. Higher values lead to fewer probes. This value can considerably affect runtime, with lower runtime at smaller values. Also, see
--island-of-exact-matchfor adjusting additional hybridization criteria, as described by the output of
design.py --help. (Default: 0.)
-c/--coverage COVERAGE: Guarantee that at least COVERAGE of each target genome is captured by probes, where COVERAGE is either a fraction of a genome or a number of nucleotides. Higher values lead to more probes. (Default: 1.0 — i.e., whole genome.)
-e/--cover-extension COVER_EXTENSION: Assume that a probe will capture both the region of the sequence to which it hybridizes, as well as COVER_EXTENSION nt on each side of that. This parameter is reasonable because library fragments are generally longer than the capture probes, and its value may depend on the library fragment length. Higher values lead to fewer probes, whereas lower values are more stringent in modeling capture. Values of around 50 are commonly used and work well in practice. (Default: 0.)
-i/--identify: Design probes to perform differential identification. This is typically used with small values of COVERAGE and >1 specified
datasets. Probes are designed such that each
datasetshould be captured by probes that are unlikely to hybridize to other
datasets. Also, see
--island-of-exact-match-tolerant, as described by the output of
--avoid-genomes dataset [dataset ...]: Design probes to be unlikely to hybridize to any of these datasets. Also, see
--island-of-exact-match-tolerant, as described by the output of
--add-adapters: Add PCR adapters to the ends of each probe sequence. This selects adapters to add to probe sequences so as to minimize overlap among probes that share an adapter, allowing probes with the same adapter to be amplified together. (See
--adapter-btoo, as described by the output of
--custom-hybridization-fn PATH FN: Specify a function, for CATCH to dynamically load, that implements a custom model of hybridization between a probe and target sequence. See
design.py --helpfor details on the expected input and output of this function. If not set, CATCH uses its default model of hybridization based on
--island-of-exact-match. Relatedly, see
--custom-hybridization-fn-tolerantas described by the output of
Arguments that often lower runtime and memory usage
Several arguments change the design process in a way that can considerably reduce the computational burden needed for design — especially for large and highly diverse inputs. The trade-off of these arguments is an increase in the number of output probes, but this increase is usually small (<10%). The arguments are:
--filter-with-lsh-minhash FILTER_WITH_LSH_MINHASH: Use locality-sensitive hashing (LSH) to reduce the space of candidate probes that are considered by detecting and filtering near-duplicate candidate probes. This can significantly improve runtime and memory requirements when the input is especially large and diverse. FILTER_WITH_LSH_MINHASH should generally be around 0.5 to 0.7; 0.6 is a reasonable choice based on probe-target divergences that are typically desired in practice. In particular, the argument tells CATCH to use LSH with a MinHash family to detect near-duplicates within the set of candidate probes, and LSH_WITH_LSH_MINHASH gives the maximum Jaccard distance (1 minus Jaccard similarity) at which to call, and subsequently filter, near-duplicates; the similarity between two probes is computed by treating each probe as a set of 10-mers and measuring the Jaccard similarity between the two sets. Its value should be accordingly commensurate with parameter values for determining whether a probe hybridizes to a target sequence (i.e., with CATCH's default hybridization model using
-m MISMATCHESand letting the probe-target divergence D be MISMATCHES divided by PROBE_LENGTH, then the value should be, at most, roughly 1 - 1/(2*e^(10*D) - 1); see Ondov et al. 2016 and solve Eq. 4 for 1-j with k=10). One caveat: when requiring low probe-target divergences (e.g., MISMATCHES of ~0, 1, or 2), using this argument may cause the coverage provided for each genome to be less than desired with
--coveragebecause too many candidate probes are filtered, so the argument should be used with caution or avoided in this case;
--write-analysis-to-tsvprovides the resulting coverage. Values of FILTER_WITH_LSH_MINHASH above ~0.7 can require significant memory and runtime that offset the benefits of using this argument, but such values should not be needed in practice.
--filter-with-lsh-hamming FILTER_WITH_LSH_HAMMING: Similar to
--filter-with-lsh-minhash, except uses a different technique for locality-sensitive hashing. Namely, the argument tells CATCH to filter near-duplicate candidate probes using LSH with a Hamming distance family. FILTER_WITH_LSH_HAMMING is the maximum Hamming distance between two probes at which to call those probes near-duplicates. With CATCH's default hybridization model using
-m MISMATCHES, FILTER_WITH_LSH_HAMING should be commensurate with, but not greater than, MISMATCHES. We recommend setting the value to MISMATCHES - 1 or MISMATCHES - 2; for example, if MISMATCHES is 5, we recommend setting this value to 3 or 4. It can be more intuitive to determine an appropriate value for this argument than for
--filter-with-lsh-minhash. However, the improvement in runtime and memory usage is usually less with this argument than with
--filter-with-lsh-minhashbecause the method for near-duplicate detection used by
--filter-with-lsh-minhashis more sensitive; thus, in general, we recommend using
--filter-with-lsh-minhashfor most use cases. The same caveat regarding coverage noted for
--filter-with-lsh-minhashalso applies to this argument.
--cluster-and-design-separately CLUSTER_AND_DESIGN_SEPARATELY: Cluster input sequences prior to design and design probes separately on each cluster, merging the resulting probes to produce the final output; input sequence comparisons are performed rapidly, and in an alignment-free manner, using locality-sensitive hashing. Like the above arguments, this is another option to improve runtime and memory requirements on large and diverse input. CLUSTER_AND_DESIGN_SEPARATELY gives the nucleotide dissimilarity at which to cluster input sequences (i.e., 1-ANI, where ANI is the average nucleotide identity between two sequences). Values must be in (0, 0.5] and generally should be around 0.1 to 0.2 because, for probe-target divergences typically desired in practice, it is reasonable to design probes independently on clusters of sequences determined at this threshold; in general, we recommend using 0.15 (i.e., cluster at 15% nucleotide dissimilarity). In particular, this argument tells CATCH to (1) compute a MinHash signature for each input sequence; (2) cluster the input sequences by comparing their signatures (i.e., the Mash approach); (3) design probes, as usual, separately on each cluster; and (4) merge the resulting probes. The particular clustering method (step (2)) is set by
design.py --helpfor details on this argument) and the default value,
choose, uses a heuristic to decide among them. With the
simpleclustering method, clusters correspond to connected components of a graph in which each vertex is a sequence and two sequences are connected if and only if their nucleotide dissimilarity (estimated by MinHash signatures), is within CLUSTER_AND_DESIGN_SEPARATELY. With the
hierarchicalclustering method, clusters are computed by agglomerative hierarchical clustering and CLUSTER_AND_DESIGN_SEPARATELY is the maximum inter-cluster distance at which to merge clusters. With both clustering methods, higher values of CLUSTER_AND_DESIGN_SEPARATELY result in fewer, larger clusters of input sequences; consequently, higher values may result in better solutions (i.e., when using CATCH with fixed hybridization criteria, fewer probes for the same coverage) at the expense of more computational requirements.
--cluster-from-fragments CLUSTER_FROM_FRAGMENTS: Break input sequences into fragments of length CLUSTER_FROM_FRAGMENTS nt, and proceed with clustering as described for
--cluster-and-design-separately, except cluster these fragments rather than whole input sequences. When using this argument, you must also set
--cluster-and-design-separatelybecause this argument tells CATCH to cluster from fragments of the input sequences rather than whole input sequences; see the information on
--cluster-and-design-separately, above, for information about clustering. This option can be useful for large genomes (e.g., bacterial genomes) in which probes for different chunks can be designed independently. The fragment length must balance a trade-off between (a) yielding too many fragments (owing to a short fragment length), which would slow clustering and potentially lead to outputs that are suboptimal in terms of the number of probes; and (b) yielding too few fragments (owing to a long fragment length), which negates the benefit of this argument in speeding design on large genomes. In practice, we have found that a fragment length of around 50,000 nt achieves a reasonable balance, i.e., setting CLUSTER_FROM_FRAGMENTS to 50000 is our recommendation in general.
In general, a reasonable starting point for enabling these option is to try:
--filter-with-lsh-minhash 0.6 --cluster-and-design-separately 0.15 --cluster-from-fragments 50000.
Specific applications may benefit from different values.
Note that these arguments may slightly increase runtime for small inputs so, when also considering the trade-offs noted above, we do not recommend them for every application. Nevertheless, we recommend trying these arguments for large and diverse input (e.g., large genomes, many genomes, or highly divergent genomes), or in any case when runtime or memory usage presents an obstacle to running CATCH.
See the output of
design.py --help for additional details on arguments.
design.py accepts many other arguments that can be useful for particular design applications, to improve resource requirements, or for debugging;
design.py --help describes all of them.
Option #2: Pragmatic defaults for large, diverse input and one setting of hybridization criteria
This way of running CATCH uses
This program wraps
design.py, setting default hybridization criteria that are reasonable in practice and enabling, by default, options that lower runtime and memory usage on large, highly diverse input.
The format for specifying inputs and outputs follows the description above.
- Sets default hybridization criteria that have worked well in practice:
--mismatches 5 --cover-extension 50
- Turns on options to lower runtime and memory usage:
--filter-with-lsh-minhash 0.6 --cluster-and-design-separately 0.15 --cluster-from-fragments 50000
- Uses all available CPUs to parallelize computations
The values of these arguments, as well as all of the arguments described above, can be overriden by specifying the argument.
To see details on all of the arguments that the program accepts, including their default values, as well as more information about input and output, run:
Option #3: Variable hybridization criteria across many taxa
design_large.py require a particular selection of hybridization criteria, but it may not be appropriate to use a single criterion uniformly across all taxa in a multi-taxa probe set.
pool.py, CATCH can find optimal hybridization settings that are allowed to vary across taxa, under a specified limit on the total number of probes (e.g., synthesis array size).
This process accounts for taxa having different degrees of variation, so that CATCH can balance a probe set's composition between the less diverse species needing few probes with the more diverse species that might otherwise dominate the probe set.
In other words, more diverse species are allowed lower enrichment efficiencies, in order to reduce the number of probes they require, relative to the less diverse species.
Species can also be weighted during this process if there is a greater likelihood of encountering some in a sample than others.
pool.py searches over a space of potential probe sets to solve a constrained optimization problem.
To see details on all the arguments that the program accepts, run:
In practice, the process follows a MapReduce-like model.
design.py on each taxon (or other choice of dataset) over a grid of parameter values that spans a reasonable domain.
These parameters could encompass the number of mismatches (
--mismatches) or cover extension (
--cover-extension), or other parameters in a custom hybridization model.
Then, create a table that provides a probe count for each taxon and choice of parameters; the table must be a TSV, in a format like this.
Now, use this table as input to
pool.py INPUT_TSV TARGET_PROBE_COUNT OUTPUT_TSV
where INPUT_TSV is a path to the table described above, TARGET_PROBE_COUNT is a constraint on the number of probes to allow in the pool, and OUTPUT_TSV is a path to a file to which the program will write the optimal parameter values.
Below are two arguments that generalize the search:
--loss-coeffs COEFF [COEFF ...]: Specify coefficients on parameters in the objective function. This allows you to adjust how conservative each parameter is treated relative to others. (Default: 1 for mismatches and 1/100 for cover extension.)
--dataset-weights WEIGHTS_TSV: Assign a weight for each dataset to use in the objective function, where WEIGHTS_TSV is a path to a table that provides a weight for each dataset. This allows you to seek that probes in the pooled design be more sensitive for some taxa than others, for instance, if you believe you are more likely to encounter some taxa in a sample. (Default: 1 for all datasets.)
design.py, be sure to store the probes output for each choice of parameter values in the search grid.
Then, combine the output FASTA files corresponding to the parameter values written to OUTPUT_TSV.
Each run of
pool.py may yield a slightly different output based on the (random) initial guess.
You could run it multiple times and select the output that has the smallest loss, which is written to standard output at the end of the program.
Below is an example of designing probes to target a single taxon.
design.py download:64320 -pl 75 -m 2 -l 60 -e 50 -o zika-probes.fasta --verbose
This will download whole genomes of Zika virus (NCBI taxonomy ID 64320) and design probes that:
- are 75 nt long (
- capture the entirety of each genome under a model that a probe hybridizes to a region if the longest common substring, up to 2 mismatches (
-m 2), between a probe and target is at least 60 nt (
- assume 50 nt on each side of the hybridization is captured as well (
and will save them to
It will provide detailed output during runtime (
--verbose) and yield about 600 probes.
Note that using
-l 75 here (or, equivalently, leaving out
-l) will run significantly faster, but result in more probes.
Also, note that the input can be a path to any custom FASTA file.
Below is an example of designing probes with larger and more diverse input than that provided in the above example:
design_large.py download:64320 download:12637 -o zika-and-dengue-probes.fasta --verbose
This will download whole genomes of Zika virus (NCBI taxonomy ID 64320) and dengue virus (NCBI taxonomy ID 12637).
It will then design probes to enrich both species, and will save the probes to
The command will provide detailed output during runtime (
--verbose) and yield about 3,200 probes.
It will take about 1 hour to run (with 8 CPUs).
For hybridization criteria,
design_large.py will use default values (listed in the output of
design_large.py --help), which can be overridden with custom values if desired.
Here is a table listing probe counts used in the design of the V-WAfr probe set (provided here and described in our publication).
It provides counts for each dataset (species) and combination of two parameters (mismatches and cover extension) that were allowed to vary in CATCH's design; the table was produced by running
design.py for each species over a grid of parameter values.
Below is an example of designing the V-WAfr probe set using this table as input:
pool.py num-probes.V-WAfr.201506.tsv 90000 params.V-Wafr.201506.tsv --round-params 1 10
This will search for parameters that yield at most 90,000 probes across the species, and will output those to
Because the search is over a continuous space, here we use
--round-params 1 10 to set each value of the mismatches parameter to an integer and each value of the cover extension parameter to a multiple of 10 while still meeting the constraint on probe count.
The pooled design yields about 89,950 probes.
For details on how CATCH works, please refer to our publication in Nature Biotechnology. If you find CATCH useful to your work, please cite our paper as:
- Metsky HC and Siddle KJ et al. Capturing sequence diversity in metagenomes with comprehensive and scalable probe design. Nature Biotechnology, 37(2), 160–168 (2019). doi: 10.1038/s41587-018-0006-x
CATCH is licensed under the terms of the MIT license.