More Like this

1. A space and time-efficient index for the compacted colored de Bruijn graph

   https://doi.org/10.1093/bioinformatics/bty292  

   Almodaresi, Fatemeh ; Sarkar, Hirak ; Srivastava, Avi ; Patro, Rob ( June 2018 , Bioinformatics)

   Indexing reference sequences for search—both individual genomes and collections of genomes—is an important building block for many sequence analysis tasks. Much work has been dedicated to developing full-text indices for genomic sequences, based on data structures such as the suffix array, the BWT and the FM-index. However, the de Bruijn graph, commonly used for sequence assembly, has recently been gaining attention as an indexing data structure, due to its natural ability to represent multiple references using a graphical structure, and to collapse highly-repetitive sequence regions. Yet, much less attention has been given as to how to best index such a structure, such that queries can be performed efficiently and memory usage remains practical as the size and number of reference sequences being indexed grows large.

   We present a novel data structure for representing and indexing the compacted colored de Bruijn graph, which allows for efficient pattern matching and retrieval of the reference information associated with each k-mer. As the popularity of the de Bruijn graph as an index has increased over the past few years, so have the number of proposed representations of this structure. Existing structures typically fall into two categories; those that are hashing-based and provide very fast access to the underlying k-mer information, and those that are space-frugal and provide asymptotically efficient but practically slower pattern search. Our representation achieves a compromise between these two extremes. By building upon minimum perfect hashing and making use of succinct representations where applicable, our data structure provides practically fast lookup while greatly reducing the space compared to traditional hashing-based implementations. Further, we describe a sampling scheme for this index, which provides the ability to trade off query speed for a reduction in the index size. We believe this representation strikes a desirable balance between speed and space usage, and allows for fast search on large reference sequences.

   Finally, we describe an application of this index to the taxonomic read assignment problem. We show that by adopting, essentially, the approach of Kraken, but replacing k-mer presence with coverage by chains of consistent unique maximal matches, we can improve the space, speed and accuracy of taxonomic read assignment.

   pufferfish is written in C++11, is open source, and is available at https://github.com/COMBINE-lab/pufferfish.

   Supplementary data are available at Bioinformatics online.

    

   more » « less
2. ONe Index for All Kernels (ONIAK): A Zero Re-Indexing LSH Solution to ANNS-ALT (After Linear Transformation)

   https://doi.org/10.14778/3565838.3565847  

   Meng, Jingfan ; Wang, Huayi ; Xu, Jun ; Ogihara, Mitsunori ( September 2022 , Proceedings of the VLDB Endowment)

   In this work, we formulate and solve a new type of approximate nearest neighbor search (ANNS) problems called ANNS after linear transformation (ALT). In ANNS-ALT, we search for the vector (in a dataset) that, after being linearly transformed by a user-specified query matrix, is closest to a query vector. It is a very general mother problem in the sense that a wide range of baby ANNS problems that have important applications in databases and machine learning can be reduced to and solved as ANNS-ALT, or its dual that we call ANNS-ALTD. We propose a novel and computationally efficient solution, called ONe Index for All Kernels (ONIAK), to ANNS-ALT and all its baby problems when the data dimension d is not too large (say d ≤ 200). In ONIAK, a universal index is built, once and for all, for answering all future ANNS-ALT queries that can have distinct query matrices. We show by experiments that, when d is not too large, ONIAK has better query performance than linear scan on the mother problem (of ANNS-ALT), and has query performances comparable to those of the state-of-the-art solutions on the baby problems. However, the algorithmic technique behind this universal index approach suffers from a so-called dimension blowup problem that can make the indexing time prohibitively long for a large dataset. We propose a novel algorithmic technique, called fast GOE quadratic form (FGoeQF), that completely solves the (prohibitively long indexing time) fallout of the dimension blowup problem. We also propose a Johnson-Lindenstrauss transform (JLT) based ANNS-ALT (and ANNS-ALTD) solution that significantly outperforms any competitor when d is large. 

   more » « less
3. ONe Index for All Kernels (ONIAK): A Zero Re-Indexing LSH Solution to ANNS-ALT (After Linear Transformation)

   Jingfan Meng, Huayi Wang ( October 2022 , Proceedings of the VLDB Endowment)

   In this work, we formulate and solve a new type of approximate nearest neighbor search (ANNS) problems called ANNS after linear transformation (ALT). In ANNS-ALT, we search for the vector (in a dataset) that, after being linearly transformed by a user-specified query matrix, is closest to a query vector. It is a very general mother problem in the sense that a wide range of baby ANNS problems that have important applications in databases and machine learning can be reduced to and solved as ANNS-ALT, or its dual that we call ANNS-ALTD. We propose a novel and computationally efficient solution, called ONe Index for All Kernels (ONIAK), to ANNS-ALT and all its baby problems when the data dimension 𝑑 is not too large (say 𝑑 ≤ 200). In ONIAK, a universal index is built, once and for all, for answering all future ANNS-ALT queries that can have distinct query matrices. We show by experiments that, when 𝑑 is not too large, ONIAK has better query performance than linear scan on the mother problem (of ANNS-ALT), and has query performances comparable to those of the state-of-the-art solutions on the baby problems. However, the algorithmic technique behind this universal index approach suffers from a so-called dimension blowup problem that can make the indexing time prohibitively long for a large dataset. We propose a novel algorithmic technique, called fast GOE quadratic form (FGoeQF), that completely solves the (prohibitively long indexing time) fallout of the dimension blowup problem. We also propose a Johnson-Lindenstrauss transform (JLT) based ANNS- ALT (and ANNS-ALTD) solution that significantly outperforms any competitor when 𝑑 is large. 

   more » « less
4. Dwell Regions: Generalized Stay Regions For Streaming and Archival Trajectory Data

   https://doi.org/10.1145/3543850  

   Uddin, Reaz ; Mahin, Mehnaz Tabassum ; Rajan, Payas ; Ravishankar, Chinya V. ; Tsotras, Vassilis J. ( January 2022 , ACM Transactions on Spatial Algorithms and Systems)

   A region \(\mathcal {R} \) is a dwell region for a moving object O if, given a threshold distance r q and duration τ q , every point of \(\mathcal {R} \) remains within distance r q from O for at least time τ q . Points within \(\mathcal {R} \) are likely to be of interest to O , so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor, and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ -Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ -Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude. 

   more » « less
5. An incrementally updatable and scalable system for large-scale sequence search using the Bentley–Saxe transformation

   https://doi.org/10.1093/bioinformatics/btac142  

   Almodaresi, Fatemeh ; Khan, Jamshed ; Madaminov, Sergey ; Ferdman, Michael ; Johnson, Rob ; Pandey, Prashant ; Patro, Rob ; Boeva, ed., Valentina ( March 2022 , Bioinformatics)

   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.

   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.

   Dynamic Mantis implementation is available at https://github.com/splatlab/mantis/tree/mergeMSTs.

   Supplementary data are available at Bioinformatics online.

    

   more » « less