Logan and Logan Search: making the world’s sequencing data searchable

Imagine you have discovered an interesting gene. Perhaps it encodes an enzyme with an unusual function, or a protein associated with an emerging pathogen.

You would like to know where else this gene occurs. Has anyone sequenced it before? Does it appear in marine samples, soil metagenomes, or human microbiome datasets? Could it be present in organisms that nobody has yet studied?

The answers might already exist in public sequencing data.

Over the past two decades, researchers around the world have deposited enormous quantities of DNA and RNA sequencing data into public archives such as the Sequence Read Archive (SRA). This collection represents one of the most valuable resources ever assembled for biological research.

But there is a problem: storing data is not the same as making it searchable.

We can easily search these archives using metadata, such as the species being studied or the type of experiment performed. Searching the actual nucleotide sequences across millions of experiments is much harder.

This is the problem that Logan and Logan Search aim to solve.

Logan transforms raw sequencing datasets into assembled sequence representations. Logan Search then provides an efficient way to discover which experiments may contain a DNA sequence of interest.

Together, they make it possible to explore previously inaccessible parts of the world’s publicly available sequencing data, without having to download and process every dataset individually.