Biology subjects

Denti, L.

Publications and source records attributed to Denti, L..

23 records · Page 2Linked to original sources

bioRxiv2020

MALVIRUS: an integrated web application for viral variant calling

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. We present MALVIRUS, an easy-to-install and easy-to-use web application that assists users in two tasks: O_LIcomputing a variant catalog consisting in a set of population SNP loci from the population sequences and C_LIO_LIefficiently calling variants of the catalog from a read sample. C_LI Tests on Illumina and Nanopore samples prove the efficiency and the effectiveness of MALVIRUS in genotyping SARS-CoV-2 strain samples with respect to GISAID data.

bioinformatics↗

bioRxiv2020

Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels

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. To overcome these limitations, in this paper 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.

bioinformatics↗

bioRxiv2019

Shark: fishing in a sample to discard useless RNA-Seq reads

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 such useless reads from the input dataset leads to improved efficiency without compromising the results of the study.\n\nTo this aim, in this paper we introduce a novel computational problem, called gene assignment and we propose an efficient alignment-free approach to solve it. Given a 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.\n\nWe 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.\n\nThe tool is distributed as a stand-alone module and the software is freely available at https://github.com/AlgoLab/shark.

bioinformatics↗

bioRxiv2019

MALVA: genotyping by Mapping-free ALlele detection of known VAriants

The amount of genetic variation discovered and characterized in human populations is huge, and is growing rapidly with the widespread availability of modern sequencing technologies. Such a great deal of variation data, that accounts for human diversity, leads to various challenging computational tasks, including variant calling and genotyping of newly sequenced individuals. The standard pipelines for addressing these problems include read mapping, which is a computationally expensive procedure. A few mapping-free tools were proposed in recent years to speed up the genotyping process. While such tools have highly efficient run-times, they focus on isolated, bi-allelic SNPs, providing limited support for multi-allelic SNPs, indels, and genomic regions with high variant density.\n\nTo address these issues, we introduce MALVA, a fast and lightweight mapping-free method to genotype an individual directly from a sample of reads. MALVA is the first mapping-free tool that is able to genotype multi-allelic SNPs and indels, even in high density genomic regions, and to effectively handle a huge number of variants such as those provided by the 1000 Genome Project. An experimental evaluation on whole-genome data shows that 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.

bioinformatics↗

bioRxiv2018

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. The 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.\n\nResults: We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the main goal of detecting novel alternative splicing events. ASGAL receives in input the annotated transcripts of a gene and an RNA-Seq sample, and it computes (1) the spliced alignments of each read, and (2) a list of novel events with respect to the gene annotation.\n\nConclusions: An experimental analysis shows that, by aligning reads directly to the splicing graph, ASGAL better predicts alternative splicing events when compared to tools requiring spliced alignments of the RNA-Seq data to a reference genome. 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.\n\nAvailability: Source code, documentation, and data are available for download at http://asgal.algolab.eu.

bioinformatics↗