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Squeakr: An Exact and Approximate k-mer Counting System

By Prashant Pandey, Michael A. Bender, Rob Johnson, Rob Patro

Posted 29 Mar 2017
bioRxiv DOI: 10.1101/122077 (published DOI: 10.1093/bioinformatics/btx636)

Motivation: k-mer-based algorithms have become increasingly popular in the processing of high-throughput sequencing (HTS) data. These algorithms span the gamut of the analysis pipeline from k-mer counting (e.g., for estimating assembly parameters), to error correction, genome and transcriptome assembly, and even transcript quantification. Yet, these tasks often use very different k-mer representations and data structures. In this paper, we set forth the fundamental operations for maintaining multisets of k-mers and classify existing systems from a data-structural perspective. We then show how to build a k-mer-counting and multiset-representation system using the counting quotient filter (CQF), a feature-rich approximate membership query (AMQ) data structure. We introduce the k-mer-counting/querying system Squeakr (Simple Quotient filter-based Exact and Approximate Kmer Representation), which is based on the CQF. This off-the shelf data structure turns out to be an efficient (approximate or exact) representation for sets or multisets of k-mers. Results: Squeakr takes 2X-4.3X less time than the state-of-the-art to count and perform a random-point-query workload. Squeakr is memory-efficient, consuming 1.5X-4.3X less memory than the state-of-the-art. It offers competitive counting performance, and answers point queries (i.e. queries for the abundance of a particular k-mer) over an order-of-magnitude faster than other systems. The Squeakr representation of the k-mer multiset turns out to be immediately useful for downstream processing (e.g., de Bruijn graph traversal) because it supports fast queries and dynamic k-mer insertion, deletion, and modification. Availability: https://github.com/splatlab/squeakr

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