Spiritual successor to Find Neighbour 4 - continuing the work of David Wyllie.

SNP matrix generation with caching to disk to allow fast reloading. A test set of 1286 cryptic samples was used. Once parsed and saved, these can be read into memory (on a single thread) in 0.5s - scaling linearly

Defaults to using 20 threads where multithreading is used. This can be updated through use of the --threads flag

Saves default to ./saves. This can be updated through use of the --saves_dir flag

Compile and run locally. Requires git, bash, make, cmake, and g++ or clang. These should be preinstalled on most systems.

git clone [email protected]:oxfordmmm/FN5.git
cd FN5
./build.sh

./fn5 <do stuff>

Docker image is available privately via OCI container registry. Requires authentication.

#Open a shell in the container
docker run -it lhr.ocir.io/lrbvkel2wjot/oxfordmmm/fn5:latest bash

Requires OCI authorisation too.

https://github.com/oxfordmmm/fn5_pipeline

A unit test suite is provided, as well as a set of end-to-end tests

#Compile and run the unit tests
./unit-test.sh

#Run end-to-end tests and ensure expected output
./test/test.sh
python -m pytest -vv

Using the cryptic set of 15229 samples, on a VM with 64 cores (using max 250 threads):

• Time to parse and save to disk: 2min 33s
• Time to construct SNP matrix with cutoff 12: 3min 38s
• Time to construct SNP matrix with cutoff 20: 3min 38s
• Time to construct SNP matrix without cutoff (90000): 9min 22s
• Size of saves on disk: 305M. FASTA files ~65G

By not returning anything in cases where the comparison > cutoff, matrix size shows significant decrease, as does time taken.

From cold (i.e nothing in RAM) and with a single thread, pulling out a row of comparisons on this much data can be performed in ~10.5s - meaning that this is fast enough that we could containerise this to run as a service. This should be able to circumvent the need to have an instance running constantly. Also, the time taken for this should just scale linearly (we are doing N comparisons). It also utilises < 0.5GiB RAM for this... Most of this time taken is also just for loading the saves (this takes somewhere in the region of 7-8s), so extending to add samples in a batch like this would be slow, but could be containerised and orchestrated. This should allow ~1min turn around for small batches

By using a SNP cutoff, the amount of data produced becomes significantly more tractable - cuttof of 20 reduces the comparison matrix for the cryptic samples from ~14GB (>100,000,000 comparisons) to ~200MB

With binary saves:

• Time to parse and save to disk: 54s
• Time to construct SNP matrix with cutoff of 20: 3min 30s

Parse some FASTA files into saves. Pass a path to a line separated file of FASTA paths. Currently only supporting upper case nucleotides. This is multithreaded, so can be performed efficiently.

./fn5 --bulk_load <path to list>

Perform pairwise comparisons of all samples which have been saved already. Dumps to a txt file of format <guid1> <guid2> <dist>. Current path to this is outputs/all.txt. This can be changed with the --output_file flag Cutoff is a mandatory parameter (set arbitrarily high to ignore). 12 is a good value for speed and use, but for several use cases, this cutoff may not be helpful

./fn5 --compute <cutoff>

From cold (i.e nothing in RAM), add a new sample to the matrix. This works fine for adding single samples, but is very slow for building a full matrix from scratch due to reading from disk for every sample

./fn5 --add <FASTA path>

From cold, add a list of samples to the matrix. As multiple comparisons occur without reading from disk each time, this is close to performance of --bulk_load then --compute. Takes a path to a line separated file of FASTA paths. Currently uses a 20 SNP threshold

./fn5 --add_many <path>

In most cases, a cutoff of 20 makes sense, but to change this, use the --cutoff flag. To have no cutoff, just set arbirarily high

By default, most functions lead to outputs to stdout. This allows file redirection/piping to other programs. Querying this output should then be trivial

As this uses Python for the results parsing and database, install all requirements (optionally in a virtualenv) with pip install -r requirements.txt.

1. Create a file called .env at the top level of this directory
2. Populate .env with

bucket="<bucket PAR URl here>"
input_paths="<path to a line separated file of FASTA paths to add>"

3. Setup an SQL database (MySQL works here)
4. Create a file called .db at the top level of this directory
5. Populate .db with

DB_PATH="<DB URL here>"
#Example
DB_PATH="mysql://<user>:<password>@<host>:<port>/<db name>"

Run python run.py

Run python query.py --guid <guid here>

FN5 checks that samples are at least 50% ACGT before allowing them to be saved. Any samples which produce ||QC_FAIL: <guid>|| have failed this check and will not be saved!

• Lower case FASTA support