MotivationThe minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g. too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.ResultsWe provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.Availability and ImplementationThe software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.

Many bacteria contain plasmids, but separating between contigs that originate on the plasmid and those that are part of the bacterial genome can be difficult. This is especially true in metagenomic assembly, which yields many contigs of unknown origin. Existing tools for classifying sequences of plasmid origin give less reliable results for shorter sequences, are trained using a fraction of the known plasmids, and can be difficult to use in practice. We present PlasClass, a new plasmid classifier. It uses a set of standard classifiers trained on the most current set of known plasmid sequences for different sequence lengths. We tested PlasClass sequence classification on held-out data and simulations, as well as publicly available bacterial isolates and plasmidome samples and plasmids assembled from metagenomic samples. PlasClass outperforms the state-of-the-art plasmid classification tool on shorter sequences, which constitute the majority of assembly contigs, allowing it to achieve higher F1 scores in classifying sequences from a wide range of datasets. PlasClass also uses significantly less time and memory. PlasClass can be used to easily classify plasmid and bacterial genome sequences in metagenomic or isolate assemblies.

With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data structures are needed. Minimizers are a central recent paradigm that has improved various sequence analysis tasks, including hashing for faster read overlap detection, sparse suffix arrays for creating smaller indexes, and Bloom filters for speeding up sequence search. Here, we propose an alternative paradigm that can lead to substantial further improvement in these and other tasks. For integers k and L > k, we say that a set of k-mers is a universal hitting set (UHS) if every possible L-long sequence must contain a k-mer from the set. We develop a heuristic called DOCKS to find a compact UHS, which works in two phases: The first phase is solved optimally, and for the second we propose several efficient heuristics, trading set size for speed and memory. The use of heuristics is motivated by showing the NP-hardness of a closely related problem. We show that DOCKS works well in practice and produces UHSs that are very close to a theoretical lower bound. We present results for various values of k and L and by applying them to real genomes show that UHSs indeed improve over minimizers. In particular, DOCKS uses less than 30% of the 10-mers needed to span the human genome compared to minimizers. The software and computed UHSs are freely available at github.com/Shamir-Lab/DOCKS/ and acgt.cs.tau.ac.il/docks/, respectively. Author summaryHigh-throughput sequencing data has been accumulating at an extreme pace. The need to efficiently analyze and process it has become a critical challenge of the field. Many of the data structures and algorithms for this task rely on k-mer sets (DNA words of length k) to represent the sequences in a dataset. The runtime and memory usage of these highly depend on the size of the k-mer sets used. Thus, a minimum-size k-mer hitting set, namely, a set of k-mers that hit (have non-empty overlap with) all sequences, is desirable. In this work, we create universal k-mer hitting sets that hit any L-long sequence. We present several heuristic approaches for constructing such small sets; the approaches vary in the trade-off between the size of the produced set and runtime and memory usage. We show the benefit in practice of using the produced universal k-mer hitting sets compared to minimizers and randomly created hitting sets on the human genome.

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AbstractBackgroundMany bacteria contain plasmids, but separating between contigs that originate on the plasmid and those that are part of the bacterial genome can be difficult. This is especially true in metagenomic assembly, which yields many contigs of unknown origin. Existing tools for classifying sequences of plasmid origin give less reliable results for shorter sequences, are trained using a fraction of the known plasmids, and can be difficult to use in practice.ResultsWe present PlasClass, a new plasmid classifier. It uses a set of standard classifiers trained on the most current set of known plasmid sequences for different sequence lengths. PlasClass outperforms the state-of-the-art plasmid classification tool on shorter sequences, which constitute the majority of assembly contigs, while using less time and memory.ConclusionsPlasClass can be used to easily classify plasmid and bacterial genome sequences in metagenomic or isolate assemblies. It is available from: https://github.com/Shamir-Lab/PlasClass