Search for: All records

Aligning to a linear reference genome can result in a higher percentage of reads going unmapped or being incorrectly mapped owing to variations not captured by the reference, otherwise known as reference bias. Recently, in efforts to mitigate reference bias, there has been a movement to switch to using pangenomes, a collection of genomes, as the reference. In this paper, we introduce Moni-align, the first short-read pangenome aligner built on the r-index, a variation of the classical FM-index that can index collections of genomes in O(r)-space, whereris the number of runs in the Burrows–Wheeler transform. Moni-align uses a seed-and-extend strategy for aligning reads, utilizing maximal exact matches as seeds, which can be efficiently obtained with ther-index. Using both simulated and real short-read data sets, we demonstrate that Moni-align achieves alignment accuracy comparable to vg map and vg giraffe, the leading pangenome aligners. Although currently best suited for aligning to localized pangenomes owing to computational constraints, Moni-align offers a robust foundation for future optimizations that could further broaden its applicability. 

Abstract Metagenomic read classification is a fundamental task in computational biology, yet it remains challenging due to the scale, diversity, and complexity of sequencing datasets. We propose a novel, run-length compressed index based on the move structure that enables efficient multi-class metagenomic classification inO(r) space, whereris the number of character runs in the BWT of the reference text. Our method identifies all super-maximal exact matches (SMEMs) of length at leastLbetween a read and the reference dataset and associates each SMEM with one class identifier using a sampled tag array. A consensus algorithm then compacts these SMEMs with their class identifier into a single classification per read. We are the first to perform run-length compressed read classification based on full SMEMs instead of semi-SMEMs. We evaluate our approach on both long and short reads in two conceptually distinct datasets: a large bacterial pan-genome with few metagenomic classes and a smaller 16S rRNA gene database spanning thousands of genera or classes. Our method consistently outperforms SPUMONI 2 in accuracy and runtime while maintaining the same asymptotic memory complexity ofO(r). Compared to Cliffy, we demonstrate better memory efficiency while achieving superior accuracy on the simpler dataset and comparable performance on the more complex one. Overall, our implementation carefully balances accuracy, runtime, and memory usage, offering a versatile solution for metagenomic classification across diverse datasets. The open-source C++11 implementation is available athttps://github.com/biointec/taggerunder the AGPL-3.0 license. 

Abstract SummaryImprovements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10–100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications. Availability and implementationSigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni. 

For taxonomic classification, we are asked to index the genomes in a phylogenetic tree such that later, given a DNA read, we can quickly choose a small subtree likely to contain the genome from which that read was drawn. Although popular classifiers such as Kraken use k-mers, recent research indicates that using maximal exact matches (MEMs) can lead to better classifications. For example, we can - build an augmented FM-index over the the genomes in the tree concatenated in left-to-right order; - for each MEM in a read, find the interval in the suffix array containing the starting positions of that MEM’s occurrences in those genomes; - find the minimum and maximum values stored in that interval; - take the lowest common ancestor (LCA) of the genomes containing the characters at those positions. This solution is practical, however, only when the total size of the genomes in the tree is fairly small. In this paper we consider applying the same solution to three lossily compressed representations of the genomes' concatenation: - a KATKA kernel, which discards characters that are not in the first or last occurrence of any k_max-tuple, for a parameter k_max; - a minimizer digest; - a KATKA kernel of a minimizer digest. With a test dataset and these three representations of it, simulated reads and various parameter settings, we checked how many reads' longest MEMs occurred only in the sequences from which those reads were generated ("true positive" reads). For some parameter settings we achieved significant compression while only slightly decreasing the true-positive rate. 

Abstract We present a new method and software tool called that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool available athttps://github.com/alshai/rowbowt. 

Abstract Genomics analyses use large reference sequence collections, like pangenomes or taxonomic databases. SPUMONI 2 is an efficient tool for sequence classification of both short and long reads. It performs multi-class classification using a novel sampled document array. By incorporating minimizers, SPUMONI 2’s index is 65 times smaller than minimap2’s for a mock community pangenome. SPUMONI 2 achieves a speed improvement of 3-fold compared to SPUMONI and 15-fold compared to minimap2. We show SPUMONI 2 achieves an advantageous mix of accuracy and efficiency in practical scenarios such as adaptive sampling, contamination detection and multi-class metagenomics classification. 

Summary A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the “scale-agnostic” $$G_{+}$$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $$k$$ groups, we show that $$G_{+}$$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $$G_{+}$$, referred to as $$H_{+}$$, and demonstrate that $$H_{+}$$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $$H_{+}$$, which are available in the $$\mathtt{fasthplus}$$ R package.