An incrementally updatable and scalable system for large-scale sequence search using the Bentley-Saxe transformation.

MOTIVATION: In the past few years, researchers have proposed numerous indexing schemes for searching large datasets of raw sequencing experiments. Most of these proposed indexes are approximate (i.e. with one-sided errors) in order to save space. Recently, researchers have published exact indexes-Mantis, VariMerge and Bifrost-that can serve as colored de Bruijn graph representations in addition to serving as k-mer indexes. This new type of index is promising because it has the potential to support more complex analyses than simple searches. However, in order to be useful as indexes for large and growing repositories of raw sequencing data, they must scale to thousands of experiments and support efficient insertion of new data. RESULTS: In this paper, we show how to build a scalable and updatable exact raw sequence-search index. Specifically, we extend Mantis using the Bentley-Saxe transformation to support efficient updates, called Dynamic Mantis. We demonstrate Dynamic Mantis's scalability by constructing an index of ≈40K samples from SRA by adding samples one at a time to an initial index of 10K samples. Compared to VariMerge and Bifrost, Dynamic Mantis is more efficient in terms of index-construction time and memory, query time and memory and index size. In our benchmarks, VariMerge and Bifrost scaled to only 5K and 80 samples, respectively, while Dynamic Mantis scaled to more than 39K samples. Queries were over 24× faster in Mantis than in Bifrost (VariMerge does not immediately support general search queries we require). Dynamic Mantis indexes were about 2.5× smaller than Bifrost's indexes and about half as big as VariMerge's indexes. AVAILABILITY AND IMPLEMENTATION: Dynamic Mantis implementation is available at https://github.com/splatlab/mantis/tree/mergeMSTs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Efficient, Scalable, and Exact Representation of High-Dimensional Color Information Enabled Using de Bruijn Graph Search.

The colored de Bruijn graph (cdbg) and its variants have become an important combinatorial structure used in numerous areas in genomics, such as population-level variation detection in metagenomic samples, large-scale sequence search, and cdbg-based reference sequence indices. As samples or genomes are added to the cdbg, the color information comes to dominate the space required to represent this data structure. In this article, we show how to represent the color information efficiently by adopting a hierarchical encoding that exploits correlations among color classes-patterns of color occurrence-present in the de Bruijn graph (dbg). A major challenge in deriving an efficient encoding of the color information that takes advantage of such correlations is determining which color classes are close to each other in the high-dimensional space of possible color patterns. We demonstrate that the dbg itself can be used as an efficient mechanism to search for approximate nearest neighbors in this space. While our approach reduces the encoding size of the color information even for relatively small cdbgs (hundreds of experiments), the gains are particularly consequential as the number of potential colors (i.e., samples or references) grows into thousands. We apply this encoding in the context of two different applications; the implicit cdbg used for a large-scale sequence search index, Mantis, as well as the encoding of color information used in population-level variation detection by tools such as Vari and Rainbowfish. Our results show significant improvements in the overall size and scalability of representation of the color information. In our experiment on 10,000 samples, we achieved >11 × better compression compared to Ramen, Ramen, Rao (RRR).

Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index.

Sequence-level searches on large collections of RNA sequencing experiments, such as the NCBI Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Existing approaches, such as the sequence Bloom tree, suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and potentially large numbers of false-positives. This paper introduces Mantis, a space-efficient system that uses new data structures to index thousands of raw-read experiments and facilitates large-scale sequence searches. In our evaluation, index construction with Mantis is 6× faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6-108× faster than SSBT and has no false-positives or -negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2,652 RNA sequencing experiments in 82 min; SSBT took close to 4 days.