Mismatch-tolerant, alignment-free sequence classification using multiple spaced seeds and multiindex Bloom filters

Abstract

<jats:p>Alignment-free classification tools have enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines primarily due to their computational efficiency. Originally <jats:italic>k</jats:italic>-mer based, such tools often lack sensitivity when faced with sequencing errors and polymorphisms. In response, some tools have been augmented with spaced seeds, which are capable of tolerating mismatches. However, spaced seeds have seen little practical use in classification because they bring increased computational and memory costs compared to methods that use <jats:italic>k-</jats:italic>mers. These limitations have also caused the design and length of practical spaced seeds to be constrained, since storing spaced seeds can be costly. To address these challenges, we have designed a probabilistic data structure called a multiindex Bloom Filter (miBF), which can store multiple spaced seed sequences with a low memory cost that remains static regardless of seed length or seed design. We formalize how to minimize the false-positive rate of miBFs when classifying sequences from multiple targets or references. Available within BioBloom Tools, we illustrate the utility of miBF in two use cases: read-binning for targeted assembly, and taxonomic read assignment. In our benchmarks, an analysis pipeline based on miBF shows higher sensitivity and specificity for read-binning than sequence alignment-based methods, also executing in less time. Similarly, for taxonomic classification, miBF enables higher sensitivity than a conventional spaced seed-based approach, while using half the memory and an order of magnitude less computational time.</jats:p>

btllib: A C++ library with Python interface for efficient genomic sequence processing

“…Like the counting Bloom filter, the race conditions are minimized for multithreaded insertion. Code adapted from the Multi-index Bloom filter publication (Chu et al, 2020). • Indexlr An optimized and versatile minimizer calculator.…”

Classifying DNA barcode sequences of four insects belonging to Orthoptera order using tensor network

“…In contrast, the alignment-free methods for biological sequence classification have proven to be efficient and accurate. In terms of memory utilization, a machine-learning model for sequence classification can be more efficient than an alignmentbased method (Chu et al, 2020). Recently, researchers have used a machine-learning method for biological sequence classification but their method was limited for kingdom level classification (Nugent and Adamowicz, 2020).…”

GapPredict – A Language Model for Resolving Gaps in Draft Genome Assemblies

“…From each gap, we extracted 500 bp flanks from both sides to construct a FASTA file using a combination of in-house scripts, SAMtools (v1.9) [ 24 ], and BEDtools (v2.27.1) [ 25 ]. Finally, we used the BioBloomMIMaker utility from BioBloom Tools (v2.3.2) [ 26 ] to construct a multi-index Bloom filter for each flank. Next, using Bio-BloomMICategorizer [ 26 ] we built a FASTǪ file by selecting any read, along with its mate, that mapped to a gap flank sequence.…”

“…Finally, we used the BioBloomMIMaker utility from BioBloom Tools (v2.3.2) [ 26 ] to construct a multi-index Bloom filter for each flank. Next, using Bio-BloomMICategorizer [ 26 ] we built a FASTǪ file by selecting any read, along with its mate, that mapped to a gap flank sequence. For each gap, this pair of FASTA and FASTǪ files was the input used to run GapPredict.…”