Minimizer sketches summarize sequences with the main purpose of keeping the smallest footprint to be used for the sequence comparison of multiple reads: they are based on the notion of the smallest lexicographic k-mer in a window. Building on the concept of Lyndon factorization and a compact representation of sequences, called fingerprints, that correspond to the length of the factors in the factorization, we extend the notion of minimizer sketches to read fingerprints. By leveraging the conservation property of Lyndon factorization, we propose a novel approach for a fast comparison of long reads, to detect overlapping read pairs. An experimental evaluation of assemblies produced using the overlaps computed by our approach shows that it is competitive with the state-of-the-art tool minimap2 in terms of quality, while being up to 5 times faster at higher coverage levels.

Abstract Several computational methods for the differential analysis of alternative splicing (AS) events among RNA-Seq samples typically rely on estimating isoform-level gene expression. However, these approaches are often error-prone due to the interplay of individual AS events, which results in different isoforms with locally similar sequences. Moreover, methods based on isoform-level quantification usually need annotated transcripts. In this work, we leverage the ability of deep learning networks to learn features from images and propose , a novel method for event-based AS differential analysis between two RNA-Seq samples. Our method does not rely on isoform abundance estimation, neither on a specific annotation. employs an image embedding scheme to represent the alignments of the two samples on the same region and utilizes a residual neural network to predict the AS events possibly expressed within that region. To our knowledge, is the first deep learning approach for performing an event-based AS analysis of RNA-Seq samples. To validate , we also address the lack of high quality AS benchmark datasets. For this purpose, we manually curated a set of regions exhibiting AS events. These regions were used for training our model and for assessing the predictions of our method. Our results highlight that achieves higher precision at the expense of a small reduction in sensitivity. The tool and the manually curated regions are available at https://github.com/sciccolella/deepSpecas .

MOTIVATION: Bacterial genomes present more variability than human genomes, which requires important adjustments in computational tools that are developed for human data. In particular, bacteria exhibit a mosaic structure due to homologous recombinations, but this fact is not sufficiently captured by standard read mappers that align against linear reference genomes. The recent introduction of pangenomics provides some insights in that context, as a pangenome graph can represent the variability within a species. However, the concept of sequence-to-graph alignment that captures the presence of recombinations has not been previously investigated. RESULTS: In this paper, we present the extension of the notion of sequence-to-graph alignment to a variation graph that incorporates a recombination, so that the latter are explicitly represented and evaluated in an alignment. Moreover, we present a dynamic programming approach for the special case where there is at most a recombination-we implement this case as RecGraph. From a modelling point of view, a recombination corresponds to identifying a new path of the variation graph, where the new arc is composed of two halves, each extracted from an original path, possibly joined by a new arc. Our experiments show that RecGraph accurately aligns simulated recombinant bacterial sequences that have at most a recombination, providing evidence for the presence of recombination events. AVAILABILITY AND IMPLEMENTATION: Our implementation is open source and available at https://github.com/AlgoLab/RecGraph.

Abstract Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

MOTIVATION: Recent advances in high-throughput RNA-Seq technologies allow to produce massive datasets. When a study focuses only on a handful of genes, most reads are not relevant and degrade the performance of the tools used to analyze the data. Removing irrelevant reads from the input dataset leads to improved efficiency without compromising the results of the study. RESULTS: We introduce a novel computational problem, called gene assignment and we propose an efficient alignment-free approach to solve it. Given an RNA-Seq sample and a panel of genes, a gene assignment consists in extracting from the sample, the reads that most probably were sequenced from those genes. The problem becomes more complicated when the sample exhibits evidence of novel alternative splicing events. We implemented our approach in a tool called Shark and assessed its effectiveness in speeding up differential splicing analysis pipelines. This evaluation shows that Shark is able to significantly improve the performance of RNA-Seq analysis tools without having any impact on the final results. AVAILABILITY AND IMPLEMENTATION: The tool is distributed as a stand-alone module and the software is freely available at https://github.com/AlgoLab/shark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Being able to efficiently call variants from the increasing amount of sequencing data daily produced from multiple viral strains is of the utmost importance, as demonstrated during the COVID-19 pandemic, in order to track the spread of the viral strains across the globe. RESULTS: We present MALVIRUS, an easy-to-install and easy-to-use application that assists users in multiple tasks required for the analysis of a viral population, such as the SARS-CoV-2. MALVIRUS allows to: (1) construct a variant catalog consisting in a set of variations (SNPs/indels) from the population sequences, (2) efficiently genotype and annotate variants of the catalog supported by a read sample, and (3) when the considered viral species is the SARS-CoV-2, assign the input sample to the most likely Pango lineages using the genotyped variations. CONCLUSIONS: Tests on Illumina and Nanopore samples proved the efficiency and the effectiveness of MALVIRUS in analyzing SARS-CoV-2 strain samples with respect to publicly available data provided by NCBI and the more complete dataset provided by GISAID. A comparison with state-of-the-art tools showed that MALVIRUS is always more precise and often have a better recall.

SUMMARY: Retroviruses and their vector derivatives integrate semi-randomly in the genome of host cells and are inherited by their progeny as stable genetic marks. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites in gene therapy or virally infected patients, essential for monitoring the evolution of genetically modified cells in vivo. However, since ∼30% of insertions land in low complexity or repetitive regions of the host cell genome, they cannot be correctly assigned and are currently discarded, limiting the accuracy and predictive power of clonal tracking studies. Here, we present γ-TRIS, a new graph-based genome-free alignment tool for identifying insertion sites even if embedded in low complexity regions. By using γ-TRIS to reanalyze clinical studies, we observed improvements in clonal quantification and tracking. AVAILABILITY AND IMPLEMENTATION: Source code at https://bitbucket.org/bereste/g-tris. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Haplotype assembly is the computational problem of reconstructing haplotypes in diploid organisms and is of fundamental importance for characterizing the effects of single-nucleotide polymorphisms on the expression of phenotypic traits. Haplotype assembly highly benefits from the advent of 'future-generation' sequencing technologies and their capability to produce long reads at increasing coverage. Existing methods are not able to deal with such data in a fully satisfactory way, either because accuracy or performances degrade as read length and sequencing coverage increase or because they are based on restrictive assumptions. RESULTS: By exploiting a feature of future-generation technologies-the uniform distribution of sequencing errors-we designed an exact algorithm, called HapCol, that is exponential in the maximum number of corrections for each single-nucleotide polymorphism position and that minimizes the overall error-correction score. We performed an experimental analysis, comparing HapCol with the current state-of-the-art combinatorial methods both on real and simulated data. On a standard benchmark of real data, we show that HapCol is competitive with state-of-the-art methods, improving the accuracy and the number of phased positions. Furthermore, experiments on realistically simulated datasets revealed that HapCol requires significantly less computing resources, especially memory. Thanks to its computational efficiency, HapCol can overcome the limits of previous approaches, allowing to phase datasets with higher coverage and without the traditional all-heterozygous assumption. AVAILABILITY AND IMPLEMENTATION: Our source code is available under the terms of the GNU General Public License at http://hapcol.algolab.eu/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Minimum Path Cover (MinPC) problem on directed acyclic graphs (DAGs) is a classical problem in graph theory that provides a clear and simple mathematical formulation for several applications in computational biology. In this paper, we study the computational complexity of three constrained variants of MinPC motivated by the recent introduction of Next-Generation Sequencing technologies. The first variant (MinRPC), given a DAG and a set of pairs of vertices, asks for a minimum-cardinality set of (not necessarily disjoint) paths such that both vertices of each pair belong to the same path. For this problem, we establish a sharp tractability borderline depending on the ‘overlapping degree’ of the instance, a natural parameter in some applications of the problem. The second variant we consider (MinPCRP), given a DAG and a set of pairs of vertices, asks for a minimum-cardinality set of (not necessarily disjoint) paths ‘covering’ all the vertices of the graph and such that both vertices of each pair belong to the same path. For this problem, we show that, while it is NP-hard to compute if there exists a solution consisting of at most three paths, it is possible to decide in polynomial time whether a solution consisting of at most two paths exists. The third variant (MaxRPSP), given a DAG and a set of pairs of vertices, asks for a single path containing the maximum number of the given pairs of vertices. We show that MaxRPSP is W[1]-hard when parameterized by the number of covered pairs and we give a fixed-parameter algorithm when the parameter is the maximum overlapping degree.

BACKGROUND: A challenging issue in designing computational methods for predicting the gene structure into exons and introns from a cluster of transcript (EST, mRNA) sequences, is guaranteeing accuracy as well as efficiency in time and space, when large clusters of more than 20,000 ESTs and genes longer than 1 Mb are processed. Traditionally, the problem has been faced by combining different tools, not specifically designed for this task. RESULTS: We propose a fast method based on ad hoc procedures for solving the problem. Our method combines two ideas: a novel algorithm of proved small time complexity for computing spliced alignments of a transcript against a genome, and an efficient algorithm that exploits the inherent redundancy of information in a cluster of transcripts to select, among all possible factorizations of EST sequences, those allowing to infer splice site junctions that are largely confirmed by the input data. The EST alignment procedure is based on the construction of maximal embeddings, that are sequences obtained from paths of a graph structure, called embedding graph, whose vertices are the maximal pairings of a genomic sequence T and an EST P. The procedure runs in time linear in the length of P and T and in the size of the output.The method was implemented into the PIntron package. PIntron requires as input a genomic sequence or region and a set of EST and/or mRNA sequences. Besides the prediction of the full-length transcript isoforms potentially expressed by the gene, the PIntron package includes a module for the CDS annotation of the predicted transcripts. CONCLUSIONS: PIntron, the software tool implementing our methodology, is available at http://www.algolab.eu/PIntron under GNU AGPL. PIntron has been shown to outperform state-of-the-art methods, and to quickly process some critical genes. At the same time, PIntron exhibits high accuracy (sensitivity and specificity) when benchmarked with ENCODE annotations.

Haplotype Inference (HI) is a computational challenge of crucial importance in a range of genetic studies. Pedigrees allow to infer haplotypes from genotypes more accurately than population data, since Mendelian inheritance restricts the set of possible solutions. In this work, we define a new HI problem on pedigrees, called MINIMUM-CHANGE HAPLOTYPE CONFIGURATION (MCHC) problem, that allows two types of genetic variation events: recombinations and mutations. Our new formulation extends the MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION (MRHC) problem, that has been proposed in the literature to overcome the limitations of classic statistical haplotyping methods. Our contribution is twofold. First, we prove that the MCHC problem is APX-hard under several restrictions. Second, we propose an efficient and accurate heuristic algorithm for MCHC based on an L-reduction to a well-known coding problem. Our heuristic can also be used to solve the original MRHC problem and can take advantage of additional knowledge about the input genotypes. Moreover, the L-reduction proves for the first time that MCHC and MRHC are O(nm/(log nm))-approximable on general pedigrees, where n is the pedigree size and m is the genotype length. Finally, we present an extensive experimental evaluation and comparison of our heuristic algorithm with several other state-of-the-art methods for HI on pedigrees.

The MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION problem (MRHC) has been highly successful in providing a sound combinatorial formulation for the important problem of genotype phasing on pedigrees. Despite several algorithmic advances that have improved the efficiency, its applicability to real data sets has been limited since it does not take into account some important phenomena such as mutations, genotyping errors, and missing data. In this work, we propose the MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION WITH BOUNDED ERRORS problem (MRHCE), which extends the original MRHC formulation by incorporating the two most common characteristics of real data: errors and missing genotypes (including untyped individuals). We describe a practical algorithm for MRHCE that is based on a reduction to the well-known Satisfiability problem (SAT) and exploits recent advances in the constraint programming literature. An experimental analysis demonstrates the biological soundness of the phasing model and the effectiveness (on both accuracy and performance) of the algorithm under several scenarios. The analysis on real data and the comparison with state-of-the-art programs reveals that our approach couples better scalability to large and complex pedigrees with the explicit inclusion of genotyping errors into the model.

The haplotype resolution from xor-genotype data has been recently formulated as a new model for genetic studies. The xor-genotype data is a cheaply obtainable type of data distinguishing heterozygous from homozygous sites without identifying the homozygous alleles. In this paper, we propose a formulation based on a well-known model used in haplotype inference: pure parsimony. We exhibit exact solutions of the problem by providing polynomial time algorithms for some restricted cases and a fixed-parameter algorithm for the general case. These results are based on some interesting combinatorial properties of a graph representation of the solutions. Furthermore, we show that the problem has a polynomial time k-approximation, where k is the maximum number of xor-genotypes containing a given single nucleotide polymorphisms (SNP). Finally, we propose a heuristic and produce an experimental analysis showing that it scales to real-world large instances taken from the HapMap project.

Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disregarded by uniform random sampling, and we introduce new genetic operators to define the neighborhood of tree structures. We compare the fitness landscape induced by two different sets of functional operators (SNand and SXorNot). The different characteristics of the neutral networks seem to justify the different difficulties of these landscapes for genetic programming.