silodb 0.11.3

Python bindings for SILO - high-performance sequence database

LAPIS-SILO

High-performance analytical database for sequence alignment data

For information on how to build, test, and contribute to SILO, see Contributing.

Python Bindings

SILO provides Python bindings via Cython. The bindings wrap the core C++ Database and are installable by pip install silodb.

See Contributing for build instructions.

Usage

from silodb import Database

# Create a new database
db = Database()

# Or load from a saved state
db = Database("/path/to/saved/database")

# Create a nucleotide sequence table
db.create_nucleotide_sequence_table(
    table_name="sequences",
    primary_key_name="id",
    sequence_name="main",
    reference_sequence="ACGT..."
)

# Append data from file
db.append_data_from_file("sequences", "/path/to/data.ndjson")

# Get reference sequence
ref = db.get_nucleotide_reference_sequence("sequences", "main")

# Get filtered bitmap (list of matching row indices)
indices = db.get_filtered_bitmap("sequences", "some_filter")

# Get prevalent mutations
mutations = db.get_prevalent_mutations(
    table_name="sequences",
    sequence_name="main",
    prevalence_threshold=0.05,
    filter_expression=""
)

# Save database state
db.save_checkpoint("/path/to/save/directory")

# Print all data (to stdout)
db.print_all_data("sequences")

Configuration Files

For SILO, there are three different configuration files:

• DatabaseConfig described in file database_config.h
• PreprocessingConfig used when started with preprocessing and described in file preprocessing_config.h. For details see silo preprocessing --help.
• RuntimeConfig used when started with api and described in file runtime_config.h For details see silo api --help.

The database config contains the schema of the database and is always required when preprocessing data. The database config will be saved together with the output of the preprocessing and is therefore not required when starting SILO as an API.

An example configuration file can be seen in testBaseData/exampleDataset/database_config.yaml.

By default, the config files are expected to be YAML files in the current working directory in snake_case (database_config.yaml, preprocessing_config.yaml, runtime_config.yaml), but their location can be overridden using the options --database-config=X, --preprocessing-config=X, and --runtime-config=X.

Preprocessing and Runtime configurations contain default values for all fields and are thus only optional. Their parameters can also be provided as command-line arguments in snake_case and as environment variables prefixed with SILO_ in capital SNAKE_CASE. (e.g. SILO_INPUT_DIRECTORY).

The precendence is CLI argument > Environment Variable > Configuration File > Default Value

Run The Preprocessing

The preprocessing acts as a program that takes an input directory that contains the to-be-processed data and an output directory where the processed data will be stored. Both need to be mounted to the container.

SILO expects a preprocessing config that can to be mounted to the default location /app/preprocessing_config.yaml.

Additionally, a database config and a ndjson file containing the data are required. They should typically be mounted in /preprocessing/input.

docker run \
  -v your/input/directory:/preprocessing/input \
  -v your/preprocessing/output:/preprocessing/output \
  -v your/preprocessing_config.yaml:/app/preprocessing_config.yaml
  silo preprocessing

Both config files can also be provided in custom locations:

silo preprocessing --preprocessing-config=./custom/preprocessing_config.yaml --database-config=./custom/database_config.yaml

The Docker image contains a default preprocessing config that sets defaults specific for running SILO in Docker. Apart from that, there are default values if neither user-provided nor default config specify fields. The user-provided preprocessing config can be used to overwrite the default values. For a full reference, see the help text.

Run docker container (api)

After preprocessing the data, the api can be started with the following command:

docker run
  -p 8081:8081
  -v your/preprocessing/output:/data
  silo api

The directory where SILO expects the preprocessing output can be overwritten via silo api --data-directory=/custom/data/directory or in a corresponding configuration file.

Acknowledgments

Original genome indexing logic with roaring bitmaps by Prof. Neumann: https://db.in.tum.de/~neumann/gi/