Alignment-free analysis of sequences has revolutionized the high-throughput processing of sequencing data within numerous bioinformatics pipelines. Hashing -mers represents a common function across various alignment-free applications, serving as a crucial tool for indexing, querying, and rapid similarity searching. More recently, spaced seeds, a specialized pattern that accommodates errors or mutations, have become a standard choice over traditional -mers. Spaced seeds offer enhanced sensitivity in many applications when compared to -mers. However, it's important to note that hashing spaced seeds significantly increases computational time. Furthermore, if multiple spaced seeds are employed, accuracy can be further improved, albeit at the expense of longer processing times. This paper addresses the challenge of efficiently hashing multiple spaced seeds. The proposed algorithms leverage the similarity of adjacent spaced seed hash values within an input sequence, allowing for the swift computation of subsequent hashes. Our experimental results, conducted across various tests, demonstrate a remarkable performance improvement over previously suggested algorithms, with potential speedups of up to 20 times. Additionally, we apply these efficient spaced seed hashing algorithms to a metagenomic application, specifically the classification of reads using Clark-S (Ounit and Lonardi, 2016). Our findings reveal a substantial speedup, effectively mitigating the slowdown caused by the utilization of multiple spaced seeds.

The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.

Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA). We performed an extensive experimental campaign showing that our algorithms, beside being faster, make it possible the analysis of longer sequences, even for high degrees of resolution, than state of the art algorithms.

The growing number of sequencing projects in medicine and environmental sciences calls for the development of efficient approaches for the analysis of very large sets of metagenomic reads. Among the challenging tasks in metagenomics, the ability to agglomerate, or “bin” together, reads of the same species, without reference genomes, plays a crucial role in building a comprehensive description of relative abundances and diversity of the species in the sample. Recently, we have proposed an algorithm, called MetaProb, for metagenomic reads binning that reaches a precision that is currently unmatched. The competitive advantage of MetaProb depends on the use of probabilistic sequence signatures based on contiguous fc-mers. In this work we explore the use of spaced seeds, rather than contiguous kmers, to build such signatures. The experimental results show that allowing mismatches in carefully chosen predefined positions leads to further benefits both in terms of improved accuracy and of reduction of the memory requirements. Availability: https://bitbucket.org/samu661/metaprob.

Hashing k-mers is a common function across many bioinformatics applications and it is widely used for indexing, querying and rapid similarity search. Recently, spaced seeds, a special type of pattern that accounts for errors or mutations, are routinely used instead of k-mers. Spaced seeds allow to improve the sensitivity, with respect to k-mers, in many applications, however the hashing of spaced seeds increases substantially the computational time. Hence, the ability to speed up hashing operations of spaced seeds would have a major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient. In this paper we address the problem of efficient spaced seed hashing. The proposed algorithm exploits the similarity of adjacent spaced seed hash values in an input sequence in order to efficiently compute the next hash. We report a series of experiments on NGS reads hashing using several spaced seeds. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6x to 5.3x, depending on the structure of the spaced seed.

MOTIVATION: Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Taxonomic analysis of microbial communities, a process referred to as binning, is one of the most challenging tasks when analyzing metagenomic reads data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species and the limitations due to short read lengths and sequencing errors. RESULTS: MetaProb is a novel assembly-assisted tool for unsupervised metagenomic binning. The novelty of MetaProb derives from solving a few important problems: how to divide reads into groups of independent reads, so that k-mer frequencies are not overestimated; how to convert k-mer counts into probabilistic sequence signatures, that will correct for variable distribution of k-mers, and for unbalanced groups of reads, in order to produce better estimates of the underlying genome statistic; how to estimate the number of species in a dataset. We show that MetaProb is more accurate and efficient than other state-of-the-art tools in binning both short reads datasets (F-measure 0.87) and long reads datasets (F-measure 0.97) for various abundance ratios. Also, the estimation of the number of species is more accurate than MetaCluster. On a real human stool dataset MetaProb identifies the most predominant species, in line with previous human gut studies. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/samu661/metaprob CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A growing number of measures of sequence similarity is being based on some underlying notion of relative compressibility. Within this paradigm, similar sequences are expected to share a large number of common substrings, or subsequences, or more complex patterns or motifs, and so on. The computational complexity of such measures varies, and it increases with the complexion of the patterns taken into account. At the low end of the spectrum, most measures based on the bags of shared substrings are typically afforded in linear time. This performance is no longer achievable as soon as some degree of distortion is accepted. In this paper, measures of sequence similarity are introduced and studied in which patterns in a pair are considered similar if they coincide up to a preset number of mismatches, that is, within a bounded Hamming distance. It is shown here that for some such measures bounds are achievable that are slightly better than O(n2). Preliminary experiments demonstrate the potential applicability to phylogeny and classification of similarity measures that are rougher than previously adopted ones.

Position weight matrices are an important method for modeling signals or motifs in biological sequences, both in DNA and protein contexts. In this paper, we present fast algorithms for the problem of finding significant matches of such matrices. Our algorithms are of the online type, and they generalize classical multipattern matching, filtering, and superalphabet techniques of combinatorial string matching to the problem of weight matrix matching. Several variants of the algorithms are developed, including multiple matrix extensions that perform the search for several matrices in one scan through the sequence database. Experimental performance evaluation is provided to compare the new techniques against each other as well as against some other online and index-based algorithms proposed in the literature. Compared to the brute-force O(mn) approach, our solutions can be faster by a factor that is proportional to the matrix length m. Our multiple-matrix filtration algorithm had the best performance in the experiments. On a current PC, this algorithm finds significant matches (p = 0.0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.

UNLABELLED: MOODS (MOtif Occurrence Detection Suite) is a software package for matching position weight matrices against DNA sequences. MOODS implements state-of-the-art online matching algorithms, achieving considerably faster scanning speed than with a simple brute-force search. MOODS is written in C++, with bindings for the popular BioPerl and Biopython toolkits. It can easily be adapted for different purposes and integrated into existing workflows. It can also be used as a C++ library. AVAILABILITY: The package with documentation and examples of usage is available at http://www.cs.helsinki.fi/group/pssmfind. The source code is also available under the terms of a GNU General Public License (GPL).

BACKGROUND: Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space and of appropriate search parameters, in order to develop a method for identification of a tractable number of sequence motifs. RESULTS: Novel software (COOP) was developed for extraction of sequence motifs, based on clustering of exact or approximate patterns according to the frequency of their overlapping occurrences. Genomic sequences of 1 Kb upstream of 91 genes differentially expressed and/or encoding proteins with relevant function in adult human retina were analyzed. Methodology and results were tested by analysing 1,000 groups of putatively unrelated sequences, randomly selected among 17,156 human gene promoters. When applied to a sample of human promoters, the method identified 279 putative motifs frequently occurring in retina promoters sequences. Most of them are localized in the proximal portion of promoters, less variable in central region than in lateral regions and similar to known regulatory sequences. COOP software and reference manual are freely available upon request to the Authors. CONCLUSION: The approach described in this paper seems effective for identifying a tractable number of sequence motifs with putative regulatory role.