RecGraph: recombination-aware alignment of sequences to variation graphs.

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.

SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads.

Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable these barriers to be overcome. Here we present SV discovery with sample-specific strings (SVDSS)-a method for discovery of SVs from long-read sequencing technologies (for example, PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves notable improvements in calling SVs in repetitive regions of the genome.

Antiparasitic Drugs against SARS-CoV-2: A Comprehensive Literature Survey.

More than two years have passed since the viral outbreak that led to the novel infectious respiratory disease COVID-19, caused by the SARS-CoV-2 coronavirus. Since then, the urgency for effective treatments resulted in unprecedented efforts to develop new vaccines and to accelerate the drug discovery pipeline, mainly through the repurposing of well-known compounds with broad antiviral effects. In particular, antiparasitic drugs historically used against human infections due to protozoa or helminth parasites have entered the main stage as a miracle cure in the fight against SARS-CoV-2. Despite having demonstrated promising anti-SARS-CoV-2 activities in vitro, conflicting results have made their translation into clinical practice more difficult than expected. Since many studies involving antiparasitic drugs are currently under investigation, the window of opportunity might be not closed yet. Here, we will review the (controversial) journey of these old antiparasitic drugs to combat the human infection caused by the novel coronavirus SARS-CoV-2.

Alignment-Free Genotyping of Known Variations with MALVA.

The discovery and characterization of sequence variations in human populations are crucial in genetic studies. Standard methods for addressing this problem are computationally expensive and highly time consuming, thus impractical for clinical applications, where time is often an issue. When the task is to genotype variations that have been previously annotated, alignment-free methods come to the aid. Here, we describe MALVA, an alignment-free approach for genotyping a set of known variations. MALVA is the first mapping-free tool which is able to genotype multi-allelic SNPs and indels, even in high-density genomic regions, and to effectively handle a huge number of variations.

Mechanical Cues, E-Cadherin Expression and Cell "Sociality" Are Crucial Crossroads in Determining Pancreatic Ductal Adenocarcinoma Cells Behavior.

E-cadherin, an epithelial-to-mesenchymal transition (EMT) marker, is coupled to actin cytoskeleton and distributes cell forces acting on cells. Since YAP transduces mechanical signals involving actin cytoskeleton, we aimed to investigate the relationship between YAP and mechanical cues in pancreatic ductal adenocarcinoma (PDAC) cell lines, characterized by different EMT-related phenotypes, cultured in 2D monolayers and 3D spheroids. We observed that the YAP/p-YAP ratio was reduced in HPAC and MIA PaCa-2 cell lines and remained unchanged in BxPC-3 cells when cultured in a 3D setting. CTGF and CYR61 gene expression were down-regulated in all PDAC 3D compared to 2D cultures, without any significant effect following actin cytoskeleton inhibition by Cytochalasin B (CyB) treatment. Moreover, LATS1 mRNA, indicating the activation of the Hippo pathway, was not influenced by CyB and differed in all PDAC cell lines having different EMT-related phenotype but a similar pattern of CTGF and CYR61 expression. Although the role of YAP modulation in response to mechanical cues in cancer cells remains to be completely elucidated, our results suggest that cell arrangement and phenotype can determine variable outcomes to mechanical stimuli in PDAC cells. Moreover, it is possible to speculate that YAP and Hippo pathways may act as parallel and not exclusive inputs that, converging at some points, may impact cell behavior.

MALVIRUS: an integrated application for viral variant analysis.

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.

Simvastatin Prevents Liver Microthrombosis and Sepsis Induced Coagulopathy in a Rat Model of Endotoxemia.

Background: Endotoxemia causes endothelial dysfunction and microthrombosis, which are pathogenic mechanisms of coagulopathy and organ failure during sepsis. Simvastatin has potential anti-thrombotic effects on liver endothelial cells. We investigated the hemostatic changes induced by lipopolysaccharide (LPS) and explored the protective effects of simvastatin against liver vascular microthrombosis. Methods and results: We compared male Wistar rats exposed to LPS (5 mg/kg one i.p. dose) or saline in two experimental protocols­placebo (vehicle) and simvastatin (25 mg/kg die, orally, for 3 days before LPS). Morphological studies were performed by light- and electron-microscopy analyses to show intravascular fibrin deposition, vascular endothelial structure and liver damage. Peripheral- and organ-hemostatic profiles were analyzed using whole blood viscoelastometry by ROTEM, liver biopsy and western-blot/immunohistochemistry of thrombomodulin (TM), as well as immunohistochemistry of the von Willebrand factor (VWF). LPS-induced fibrin deposition and liver vascular microthrombosis were combined with a loss of sinusoidal endothelial TM expression and VWF-release. These changes were associated with parenchymal eosinophilia and necrosis. ROTEM analyses displayed hypo-coagulability in the peripheral blood that correlated with the degree of intrahepatic fibrin deposition (p < 0.05). Simvastatin prevented LPS-induced fibrin deposition by preserving TM expression in sinusoidal cells and completely reverted the peripheral hypo-coagulability caused by endotoxemia. These changes were associated with a significant reduction of liver cell necrosis without any effect on eosinophilia. Conclusions: Simvastatin preserves the antithrombotic properties of sinusoidal endothelial cells disrupted by LPS, deserving pharmacological properties to contrast sepsis-associated coagulopathy and hepatic failure elicited by endotoxemia

Shark: fishing relevant reads in an RNA-Seq sample.

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.

Comparative genome analysis using sample-specific string detection in accurate long reads.

Motivation: Comparative genome analysis of two or more whole-genome sequenced (WGS) samples is at the core of most applications in genomics. These include the discovery of genomic differences segregating in populations, case-control analysis in common diseases and diagnosing rare disorders. With the current progress of accurate long-read sequencing technologies (e.g. circular consensus sequencing from PacBio sequencers), we can dive into studying repeat regions of the genome (e.g. segmental duplications) and hard-to-detect variants (e.g. complex structural variants). Results: We propose a novel framework for comparative genome analysis through the discovery of strings that are specific to one genome ('samples-specific' strings). We have developed a novel, accurate and efficient computational method for the discovery of sample-specific strings between two groups of WGS samples. The proposed approach will give us the ability to perform comparative genome analysis without the need to map the reads and is not hindered by shortcomings of the reference genome and mapping algorithms. We show that the proposed approach is capable of accurately finding sample-specific strings representing nearly all variation (>98%) reported across pairs or trios of WGS samples using accurate long reads (e.g. PacBio HiFi data). Availability and implementation: Data, code and instructions for reproducing the results presented in this manuscript are publicly available at https://github.com/Parsoa/PingPong. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Triplet-based similarity score for fully multilabeled trees with poly-occurring labels.

MOTIVATION: The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies. Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases. RESULTS: To overcome these limitations, in this article, we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data. AVAILABILITY AND IMPLEMENTATION: An open source implementation of MP3 is publicly available at https://github.com/AlgoLab/mp3treesim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants.

The amount of genetic variation discovered in human populations is growing rapidly leading to challenging computational tasks, such as variant calling. Standard methods for addressing this problem include read mapping, a computationally expensive procedure; thus, mapping-free tools have been proposed in recent years. These tools focus on isolated, biallelic SNPs, providing limited support for multi-allelic SNPs and short insertions and deletions of nucleotides (indels). Here we introduce MALVA, a mapping-free method to genotype an individual from a sample of reads. MALVA is the first mapping-free tool able to genotype multi-allelic SNPs and indels, even in high-density genomic regions, and to effectively handle a huge number of variants. MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools.

ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events.

BACKGROUND: While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. This latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph. RESULTS: We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the specific goal of detecting novel splicing events, involving either annotated or unannotated splice sites. ASGAL takes as input the annotated transcripts of a gene and a RNA-Seq sample, and computes (1) the spliced alignments of each read in input, and (2) a list of novel events with respect to the gene annotation. CONCLUSIONS: An experimental analysis shows that ASGAL allows to enrich the annotation with novel alternative splicing events even when genes in an experiment express at most one isoform. Compared with other tools which use the spliced alignment of reads against a reference genome for differential analysis, ASGAL better predicts events that use splice sites which are novel with respect to a splicing graph, showing a higher accuracy. To the best of our knowledge, ASGAL is the first tool that detects novel alternative splicing events by directly aligning reads to a splicing graph. AVAILABILITY: Source code, documentation, and data are available for download at http://asgal.algolab.eu .