Current technologies allow for the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. In order to determine the role of the reference database in taxonomic sequence classification, we examine the influence of the database over time on the performance of Kraken 2 a widely used taxonomic classification and profiling method. We reported that changes in reference database over time influenced the accuracy of metagenomic taxonomic classification, and training the classifier with more data, e.g. new species, worsen the results.

Current technologies allow for the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. In this study, we delve into an evaluation of how variations in the reference database impact classification performance. This aspect assumes paramount significance, particularly when considering the continuous evolution and expansion of the NCBI Reference Sequence database (RefSeq) over time. The aim of this study is to analyze the influence of RefSeq database size and composition on taxonomic identification performance using Kraken 2, a widely used taxonomic classification and profiling method. We found that resource-efficient Kraken 2 Lite (capped) databases, which can be run on standard laptops, generally exhibit lower classification accuracy compared to full reference databases that require high-performance computing infrastructure. The size of the RefSeq database and its growth affect the performance of the k-mer-based algorithms in addition to the computing resources need. We reported that changes in RefSeq reference database over time influenced the accuracy of metagenomic taxonomic classification, and training the classifier with more data, for example, new species, worsen the results especially on the most complex microbial communities.

Aquaculture is a rapidly growing sector in the global food production chain as a recognized fundamental source of high-quality proteins. One of the crucial tasks in aquaculture is phenotype prediction. While machine learning research has mainly focused on classification tasks on Big Data, in many bioinformatics applications, including aquaculture, the real challenge behind prediction problems is dealing with small sample and high-dimensional data. In such contexts, it is in fact common that the number of genetic features (such as SNPs) far exceeds the sample size. As a test case, this study focuses on the prediction of resistance to Viral Nervous Necrosis(VNN) from a population of European sea bass. We explore a range of machine learning techniques, from established methods such as Support Vector Machines and Gradient Boosting, to increasingly popular Deep Learning Approaches, also including a variant of image-based classification based on Chaos Game Representation. Besides standard training-test partitioning, we also considered a more challenging partition of the dataset that maximize the genomic distance among training and testing set to better reflect the kind of generalization problem encountered in breeding practice due to data scarcity typical of non-model species. Although all the animals belong to the same population, this approach offered the most appropriate way to ensure the procedure was sufficiently challenging given the available data. We assessed the performance of learning approaches in different scenarios, reducing the data dimensionality by selecting SNPs on the basis of functional information. Our experiments confirmed the difficult nature of this association task. However, each tested tool showed promising results in at least one scenario. While predicting disease susceptibility remains a challenging task for breeding programs, within the boundaries of the tested scenarios, our results show that machine learning approaches, combined with a controlled amount of additional functional information, can help mitigate the issues arising from high dimensional, low sample size datasets typical in the study of non-model species.

Many state-of-the-art tools for sequence analysis are based on alignment-free techniques to manage high-throughput processing. Several routine tasks such as querying, indexing, and similarity search are based on k-mer statistics. In order to accommodate errors or mutations, spaced seeds have been increasingly used instead of k-mers, enhancing sensitivity in various applications. However, spaced seed hashing is computationally intensive, introducing significant slowdown in the processing.This article addresses the challenge of efficient spaced seed hashing, which is functional for the computation of spaced k-mers counting. We present DuoHash, a framework that enables the efficient computation of hash functions for spaced seeds. DuoHash exploits an efficient spaced seed binary encoding and precomputed tables to speedup the computation of the hash value for both the forward and reverse strands of a DNA sequence. In our experiments, DuoHash substantially outperforms existing algorithms, achieving speedups of up to 11x on short reads with a spaced seed of medium density. Furthermore, we show the applicability of DuoHash to the problem of spaced k-mers counting. The code of DuoHash is available at https://github.com/CominLab/DuoHash/.

A core task in computational genomics is transforming input sequences into their constituent k-mers. Efficiently storing these k-mer collections is crucial for scaling bioinformatics workflows. A common strategy involves representing the k-mers as a de Bruijn graph (dBG) and deriving a compact plain text form through a minimum path cover. In this article, we introduce USTAR-CR (Unitig STitch Advanced constRuction with Colors Reordering), a fast and space-efficient algorithm for compressing multiple k-mer sets. USTAR-CR exploits the structural properties of colored dBGs to construct a succinct plain text representation while also incorporating an effective scheme for encoding k-mer color information. We evaluate USTAR-CR on real sequencing datasets and benchmark it against the state-of-the-art tool GGCAT. USTAR-CR achieves superior compression ratios, significantly reduces memory usage, and offers substantial speed improvements-up to 64× faster-highlighting its effectiveness for large-scale genomic data processing.

A fundamental operation in computational genomics is the reduction of input sequences into their constituent k-mers. Developing space-efficient methods to represent a collection of k-mers is crucial for enhancing the scalability of bioinformatics analyses. A common strategy is to transform the set of k-mers into a de Bruijn graph and then create a streamlined representation by identifying the smallest path cover. In this article, we introduce USTAR2, a novel algorithm for compressing k-mers. USTAR2 leverages node connectivity principles in the de Bruijn graph for more efficient path selection in constructing the path cover. We tested USTAR2 on real read datasets and compared it with several other tools. USTAR2 demonstrated superior performance in terms of compression, requiring less memory and being significantly faster (up to 96x). The code of USTAR2 is available at the repository https://github.com/CominLab/USTAR2 .

Alignment-free genomic sequence analysis has facilitated high-throughput processing within numerous bioinformatics workflows. A central task in alignment -free applications is hashing k-mers, commonly used for indexing, querying, and fast similarity searches. Recently, spaced seeds a specialized pattern designed to accommodate errors or mutations have increasingly replaced k-mers, enhancing sensitivity in various applications. However, spaced seed hashing is computationally intensive, introducing significant delays. This paper addresses the challenge of efficient spaced seed hashing and presents DuoHash, a framework that enables the efficient computation of several hash functions. Our experimental results demonstrate that the proposed method substantially outperforms existing algorithms, achieving speedups of up to 11x. To illustrate practical utility, we further applied DuoHash to the problem of spaced k-mers counting. The code of DuoHash is available at https://github.com/CominLab/DuoHash/.

A fundamental operation in computational genomics is the reduction of input sequences into their constituent k-mers. Designing space-efficient ways to represent a k-mer collection is essential to improve the scalability of bioinformatics analyses. A widely used approach involves converting the k-mer set into a de Bruijn graph and then producing a compact plain text representation by identifying the minimum path cover. In this article, we present USTAR-CR, a novel algorithm for compressing multiple k-mer sets. USTAR-CR leverages node connectivity principles in the colored de Bruijn graph for a more compact plain text representation, combined with an efficient encoding of k-mers colors. We tested USTAR-CR on real read datasets and compared it with the state-of-the-art GGCAT. USTAR-CR demonstrated superior performance in terms of compression, requiring less memory and being significantly faster (up to 51x) https://github.com/enricorox/USTAR-CR.

An essential task in computational genomics involves transforming input sequences into their constituent k-mers. The quest for an efficient representation of k-mer sets is crucial for enhancing the scalability of bioinformatic analyses. One widely used method involves converting the k-mer set into a de Bruijn graph (dBG), followed by seeking a compact graph representation via the smallest path cover. This study introduces USTAR* (Unitig STitch Advanced constRuction), a tool designed to compress both a set of k-mers and their associated counts. USTAR leverages the connectivity and density of dBGs, enabling a more efficient path selection for constructing the path cover. The efficacy of USTAR is demonstrated through its application in compressing real read data sets. USTAR improves the compression achieved by UST (Unitig STitch), the best algorithm, by percentages ranging from 2.3% to 26.4%, depending on the k-mer size, and it is up to 7× times faster.

Alignment-free analysis of sequences has revolutionized the high-throughput processing of sequencing data within numerous bioinformatics pipelines. Hashing k-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 k-mers. Spaced seeds offer enhanced sensitivity in many applications when compared to k-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

A fundamental operation in computational genomics is to reduce the input sequences to their constituent k -mers. Finding a space-efficient way to represent a set of k -mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of k -mers into a de Bruijn graph and then find a compact representation of the graph through the smallest path cover. In this paper, we present USTAR, a tool for compressing a set of k -mers and their counts. USTAR exploits the node connectivity and density of the de Bruijn graph enabling a more effective path selection for the construction of the path cover. We demonstrate the usefulness of USTAR in the compression of read datasets. USTAR can improve the compression of UST, the best algorithm, from 2.3% up to 26,4%, depending on the k -mer size. The code of USTAR and the complete results are available at the repository https://github.com/enricorox/USTAR .

: Alignment-Free analysis of sequences has enabled high-throughput processing of sequencing data in many bioinformatics pipelines. Hashing k-mers is a common function across many alignment-free 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. Moreover, if multiple spaced seeds are used the accuracy can further increases at the cost of running time. In this paper we address the problem of efficient multiple spaced seed hashing. The proposed algorithms exploit the similarity of adjacent spaced seed hash values in an input sequence in order to efficiently compute the next hashes. We report the results on several tests which show that our methods significantly outperform the previously proposed algorithms, with a speedup that can reach 20x. We also apply these efficient spaced seeds hashing algorithms to an application in the field of metagenomic, the classification of reads performed by Clark-S (Ounit and Lonardi, 2016), and we shown that a significant speedup can be obtained, thus resolving the slowdown introduced by the use of multiple spaced seeds. Code available at: https://github.com/CominLab/MISSH.

The spread of Covid-19 virus all around the globe has drastically changed our lifestyles and our habits. Among all aspects affected by the pandemic, mobility has certainly undergone huge changes. Understanding these changes is crucial for the improvement of future urban planning. Since the world is moving in the direction of a more sustainable mobility, our analysis has focused on bike sharing systems and we have utilized the city of Padova (Italy) as case study. In particular we have analysed the data relative to three periods that are representative of Pre-Pandemic, Pandemic and Post-Pandemic worlds. We have highlighted the effects of the pandemic on bike sharing users’ behaviors not only during the emergency period, but mostly with a long-term perspective. We have observed an increase of the total amount of rides, as well as an expansion of the service towards more peripheral areas, and a greater predisposition to use the bike in the free time or when the weather conditions are not ideal.

The major problem when analyzing a metagenomic sample is to taxonomically annotate its reads to identify the species they contain. Most of the methods currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of recall (the actual number of classified reads) the performances fall at around 50%. One of the reasons is the fact that the sequences in a sample can be very different from the corresponding reference genome, e.g., viral genomes are highly mutated. To address this issue, in this paper we study the problem of metagenomic reads classification by improving the reference k-mers library with novel discriminative k-mers from the input sequencing reads. We evaluated the performance in different conditions against several other tools and the results showed an improved F-measure, especially when close reference genomes are not available. Availability: https://github.com.

MOTIVATION:The extraction of k-mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the characterization of RNA-seq datasets. The extraction of all k-mers and their frequencies is extremely demanding in terms of running time and memory, owing to the size of the data and to the exponential number of k-mers to be considered. However, in several applications, only frequent k-mers, which are k-mers appearing in a relatively high proportion of the data, are required by the analysis. RESULTS:In this work, we present SPRISS, a new efficient algorithm to approximate frequent k-mers and their frequencies in next-generation sequencing data. SPRISS uses a simple yet powerful reads sampling scheme, which allows to extract a representative subset of the dataset that can be used, in combination with any k-mer counting algorithm, to perform downstream analyses in a fraction of the time required by the analysis of the whole data, while obtaining comparable answers. Our extensive experimental evaluation demonstrates the efficiency and accuracy of SPRISS in approximating frequent k-mers, and shows that it can be used in various scenarios, such as the comparison of metagenomic datasets, the identification of discriminative k-mers, and SNP (single nucleotide polymorphism) genotyping, to extract insights in a fraction of the time required by the analysis of the whole dataset. AVAILABILITY AND IMPLEMENTATION:SPRISS [a preliminary version (Santoro et al., 2021) of this work was presented at RECOMB 2021] is available at https://github.com/VandinLab/SPRISS. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. The major difficulties of taxonomic analysis are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, and sequencing errors. Microbial communities can be studied with reads clustering, a process referred to as genome binning. In this study, we present MetaProb 2 an unsupervised genome binning method based on reads assembly and probabilistic k-mers statistics. The novelties of MetaProb 2 are the use of minimizers to efficiently assemble reads into unitigs and a community detection algorithm based on graph modularity to cluster unitigs and to detect representative unitigs. The effectiveness of MetaProb 2 is demonstrated in both simulated and real datasets in comparison with state-of-art binning tools such as MetaProb, AbundanceBin, Bimeta, and MetaCluster. On real datasets, it is the only one capable of producing promising results while being parsimonious with computational resources.

Diseases can be tied to changes at the molecular level within affected cells. This can be concerning transcription, translation, or any other mechanism involved in gene expression, such as post-transcriptional regulation. Instrumentation for the measurement of such molecular changes is readily available and produces large amounts of data. For example, DNA and RNA sequencing, as well as protein quantitation, and sequencing can be achieved via next-generation sequencing andmass spectrometry, respectively. One current challenge is the analysis and integration of the resulting heterogeneous and large datasets. Bioinformatics is the field of study which produces algorithms and integrative approaches to attempt suchdata analyses. The primary aim in algorithmic bioinformatics is, however, the development of algorithms and not their application. Typically, novel algorithms are introduced with a proof of principle, and they are applied to some data for that purpose, but usually not comprehensively. Their data might slightly differ from the proof of principle, inducing further data analysis challenges. Additionally, applying such algorithms to their data may be involved for researchers from the biomedical domain. The 1st International Applied Bioinformatics Conference was conceived to bring together representatives from all research fields involved to increase knowledge transfer. First planned for 2020 and then deferred to 2021 due to the pandemic caused by the Coronavirus [1], the conference was held online. Despite the virtual nature of the conference, attentionwas great.We receivedmany goodmanuscripts and invited a few to submit their full versions to this special issue. The range of topics was extensive, but many submissions concerned the interface of bioinformatics and its application. The selected papers for this special issue also discuss various topics such as sequence alignment and gene network reconstruction. The first paper in this special issue concerns a challenging issue in bioinformatics, the usage of pangenomes instead of single reference genomes and offers a fast variation-aware read mapping algorithm [2]. Mapping is also vital to investigate gene expression, which is essential for the secondmanuscript. It discusses how microRNA and mRNA expression profiles can be investigated [3]. From this, modular networks are inferred, describing post-transcriptional regulatory networks. Such networks are challenging to visualize, which is the focus of the third paper [4]. The work summarizes the state-of-the-art in bicluster visualization and is also based on gene expression data. Next, we move from transcriptomics to metabolomics. A disparity filter was applied to perform network analysis for colorectal cancer as a proof of principle [5]. The final two manuscripts focus more on practical application in cancer. First, the prostate, ovary, testes, and embryo