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Solution Strategy

Setting Up Your Own Instance

We want to make it as easy as possible for you to set up your own instance of SILO-LAPIS for an organism of your choice. We solve this in two aspects:

  • Configuration: LAPIS and SILO are highly configurable regarding the data that they process. The available data and the reference genome can be configured to fit your needs.
  • Deployment: We provide Docker containers for LAPIS and SILO that are ready to use. You only need to provide the data and the configuration. We also provide examples and tutorials to help you get started.

Query Performance

LAPIS and SILO are designed to process queries as fast as possible. One should be able to search for mutations in millions of samples in a matter of seconds.

SILO contains an in-memory database that holds the data. The data is stored column-wise in bitmaps, since the nature of most queries targets columns.

Example: A common query is to search for a mutation at a certain position in the genome. SILO stores each position in the genome as a separate column, thus the filter becomes trivial (reading the respective precomputed bitmap). The bitmap is interpreted as the filter result (having a 1 in the positions of the samples that match the filter).


Precomputing the bitmaps is a time-consuming task. SILO does this ahead of time in a separate step, the preprocessing. The preprocessing is a separate part of SILO that builds the in-memory database from the input files and serializes it to disk. At runtime, SILO can then load the serialized database from disk. Having the preprocessing as a separate step has major advantages:

  • The preprocessing can be done on a different machine than the one that runs the queries.
  • The startup time of SILO is reduced, since it only needs to load the database from disk.
    • Scalability: Thus, it is possible to quickly launch several instances of SILO from the same preprocessing result.

Storage Efficiency

SILO uses Roaring bitmaps to store the data, since they are designed to be space-efficient. Internally, Roaring bitmaps store data in chunks. SILO aims to sort sequences such that similar sequences (i.e. sequences that have similar mutations) are stored in the same chunk. The goal is to have many bitmaps that are either almost completely empty or almost completely full. This will result in a very high compression ratio.

Easy Access To Data

SILO offers a rather complex query language to query the data. LAPIS aims to simplify the usage of SILO by providing a simple REST API.