An official website of the United States government
Abstract MotivationK-mer-based methods are used ubiquitously in the field of computational biology. However, determining the optimal value of k for a specific application often remains heuristic. Simply reconstructing a new k-mer set with another k-mer size is computationally expensive, especially in metagenomic analysis where datasets are large. Here, we introduce a hashing-based technique that leverages a kind of bottom-m sketch as well as a k-mer ternary search tree (KTST) to obtain k-mer-based similarity estimates for a range of k values. By truncating k-mers stored in a pre-built KTST with a large k=kmax value, we can simultaneously obtain k-mer-based estimates for all k values up to kmax. This truncation approach circumvents the reconstruction of new k-mer sets when changing k values, making analysis more time and space-efficient. ResultsWe derived the theoretical expression of the bias factor due to truncation. And we showed that the biases are negligible in practice: when using a KTST to estimate the containment index between a RefSeq-based microbial reference database and simulated metagenome data for 10 values of k, the running time was close to 10× faster compared to a classic MinHash approach while using less than one-fifth the space to store the data structure. Availability and implementationA python implementation of this method, CMash, is available at https://github.com/dkoslicki/CMash. The reproduction of all experiments presented herein can be accessed via https://github.com/KoslickiLab/CMASH-reproducibles. Supplementary informationSupplementary data are available at Bioinformatics online.
more »
« less
The Naïve Bayes classifier++ for metagenomic taxonomic classification—query evaluation
Abstract MotivationThis study examines the query performance of the NBC++ (Incremental Naive Bayes Classifier) program for variations in canonicality, k-mer size, databases, and input sample data size. We demonstrate that both NBC++ and Kraken2 are influenced by database depth, with macro measures improving as depth increases. However, fully capturing the diversity of life, especially viruses, remains a challenge. ResultsNBC++ can competitively profile the superkingdom content of metagenomic samples using a small training database. NBC++ spends less time training and can use a fraction of the memory than Kraken2 but at the cost of long querying time. Major NBC++ enhancements include accommodating canonical k-mer storage (leading to significant storage savings) and adaptable and optimized memory allocation that accelerates query analysis and enables the software to be run on nearly any system. Additionally, the output now includes log-likelihood values for each training genome, providing users with valuable confidence information. Availability and implementationSource code and Dockerfile are available at http://github.com/EESI/Naive_Bayes.
more »
« less
- PAR ID:
- 10566179
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 41
- Issue:
- 1
- ISSN:
- 1367-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract SummaryBioinformatics applications increasingly rely on ad hoc disk storage of k-mer sets, e.g. for de Bruijn graphs or alignment indexes. Here, we introduce the K-mer File Format as a general lossless framework for storing and manipulating k-mer sets, realizing space savings of 3–5× compared to other formats, and bringing interoperability across tools. Availability and implementationFormat specification, C++/Rust API, tools: https://github.com/Kmer-File-Format/. Supplementary informationSupplementary data are available at Bioinformatics online.more » « less
-
Abstract MotivationCarbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction. ResultsThis new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes. Availability and implementationhttps://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI. Supplementary informationSupplementary data are available at Bioinformatics online.more » « less
-
Abstract MotivationIn 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. ResultsIn 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 implementationDynamic Mantis implementation is available at https://github.com/splatlab/mantis/tree/mergeMSTs. Supplementary informationSupplementary data are available at Bioinformatics online.more » « less
-
Abstract Genome search and/or classification typically involves finding the best-match database (reference) genomes and has become increasingly challenging due to the growing number of available database genomes and the fact that traditional methods do not scale well with large databases. By combining k-mer hashing-based probabilistic data structures (i.e. ProbMinHash, SuperMinHash, Densified MinHash and SetSketch) to estimate genomic distance, with a graph based nearest neighbor search algorithm (Hierarchical Navigable Small World Graphs, or HNSW), we created a new data structure and developed an associated computer program, GSearch, that is orders of magnitude faster than alternative tools while maintaining high accuracy and low memory usage. For example, GSearch can search 8000 query genomes against all available microbial or viral genomes for their best matches (n = ∼318 000 or ∼3 000 000, respectively) within a few minutes on a personal laptop, using ∼6 GB of memory (2.5 GB via SetSketch). Notably, GSearch has an O(log(N)) time complexity and will scale well with billions of genomes based on a database splitting strategy. Further, GSearch implements a three-step search strategy depending on the degree of novelty of the query genomes to maximize specificity and sensitivity. Therefore, GSearch solves a major bottleneck of microbiome studies that require genome search and/or classification.more » « less
