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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.

    

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2. Constructing small genome graphs via string compression

   https://doi.org/10.1093/bioinformatics/btab281  

   Qiu, Yutong ; Kingsford, Carl ( July 2021 , Bioinformatics)

   The size of a genome graph—the space required to store the nodes, node labels and edges—affects the efficiency of operations performed on it. For example, the time complexity to align a sequence to a graph without a graph index depends on the total number of characters in the node labels and the number of edges in the graph. This raises the need for approaches to construct space-efficient genome graphs.

   We point out similarities in the string encoding mechanisms of genome graphs and the external pointer macro (EPM) compression model. We present a pair of linear-time algorithms that transform between genome graphs and EPM-compressed forms. The algorithms result in an upper bound on the size of the genome graph constructed in terms of an optimal EPM compression. To further reduce the size of the genome graph, we propose the source assignment problem that optimizes over the equivalent choices during compression and introduce an ILP formulation that solves that problem optimally. As a proof-of-concept, we introduce RLZ-Graph, a genome graph constructed based on the relative Lempel–Ziv algorithm. Using RLZ-Graph, across all human chromosomes, we are able to reduce the disk space to store a genome graph on average by 40.7% compared to colored compacted de Bruijn graphs constructed by Bifrost under the default settings. The RLZ-Graph scales well in terms of running time and graph sizes with an increasing number of human genome sequences compared to Bifrost and variation graphs produced by VGtoolkit.

   The RLZ-Graph software is available at: https://github.com/Kingsford-Group/rlzgraph.

   Supplementary data are available at Bioinformatics online.

    

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3. 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.

    

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4. deBGR: an efficient and near-exact representation of the weighted de Bruijn graph

   https://doi.org/10.1093/bioinformatics/btx261  

   Pandey, Prashant ; Bender, Michael A. ; Johnson, Rob ; Patro, Rob ( July 2017 , Bioinformatics)

   Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using ‘long read’ technologies like those offered by PacBio or Oxford Nanopore), efficient k-mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k-mer occurs, which is key in transcriptome assemblers.

   We present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18–28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems.

   https://github.com/splatlab/debgr.

   Supplementary data are available at Bioinformatics online.

    

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5. Modeling polypharmacy side effects with graph convolutional networks

   https://doi.org/10.1093/bioinformatics/bty294  

   Zitnik, Marinka ; Agrawal, Monica ; Leskovec, Jure ( June 2018 , Bioinformatics)

   The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug–drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.

   Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein–protein interactions, drug–protein target interactions and the polypharmacy side effects, which are represented as drug–drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug–drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.

   Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.

    

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