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Bonizzoni, P.

Publications and source records attributed to Bonizzoni, P..

At least 19 recordsLinked to original sources
bioRxiv2026

A multi-flow approach for binning circular plasmids from short-reads assembly graphs

We address the problem of plasmid binning, that aims to group contigs - from a draft short-read assembly for a bacterial sample - into bins each expected to correspond to a plasmid present in the sequenced bacterial genome. We formulate the plasmid binning problem as a network multi-flow problem in the assembly graph and describe a Mixed-Integer Linear Program to solve it. We compare our new method, PlasBin-HMF, with state-of-the-art methods,MOB-recon, gplasCC, and PlasBin-flow, on a dataset of more than 500 bacterial samples, and show that PlasBin-HMF outperforms the other methods, by preserving the explainability.

genomics↗
bioRxiv2025

Scalable PBWT Queries with Minimum-Length SMEMConstraints

Detecting long shared ancestry tracts in large haplotype panels is central to IBD analysis, imputation, and local ancestry inference, and can be approximated computationally by finding Set-Maximal Exact Matches (SMEMs) between sequences. The Positional Burrows-Wheeler Transform (PBWT) provides an efficient index for these panels, yet current methods often enumerate all SMEMs, producing a large number of short, uninformative matches. We introduce Positional Boyer- Moore-Li (PBML), which restricts enumeration to SMEMs occurring in at least k haplotypes and spanning at least L sites (kL-SMEMs). PBML is the first algorithm for computing KL-SMEMs on top of a single compressed run-length encoded PBWT index reusable for any (k, L) without rebuilding. On the 1000 Genomes Project, PBML achieves 4.6x faster query time than {micro}-PBWT and 2.4x over Durbins PBWT with lower memory, scaling to 15.9x over {micro}-PBWT at 16 threads. On a 10,000-haplotype panel from the Tennessee BIG Initiative, a diverse admixed cohort, PBML outperforms {micro}-PBWT by up to 4.7x in k-SMEM finding. By applying both thresholds during traversal, PBML extracts biologically informative, population-shared segments while filtering millions of short matches, a capability not available in current tools. On the BIG panel, in about 10 seconds PBML finds 2,441 long tracts at (k = 50, L = 5000) shared by an average of 60 haplotypes against 1000 queries, significantly reducing the 4.8 million unfiltered SMEMs shared on average by 2 haplotypes. These results establish PBML as a scalable tool for targeted long-range shared ancestry detection in large, diverse panels.

bioinformatics↗
bioRxiv2025

Anyone can be the best: Impact of diverse methodologies on the evaluation of structural variant callers

Structural variants (SVs) are medium and large-scale genomic alterations that shape phenotypic diversity and disease risk. Numerous methods have been proposed for discovering SVs, however their benchmarking has been inconsistent across studies, often resulting in contradictory findings. One of the main sources of conflicting evaluation re-sults is the lack of consistency in the SV callsets used as ground truth, ranging from curated callsets released by consortia to more recent approaches that construct callsets from high-quality telomere-to-telomere de novo haplotype assemblies. The discrepancies between benchmarks are further compounded by the choice of the reference genome (GRCh37, GRCh38, and T2T-CHM13), where using T2T-CHM13reveals a different deletion/insertion profile, indicating reduced reference bias. We evaluated the performance of several state-of-the-art SV discovery methods from long-read whole-genome sequencing data and observed substantial variation in their performance and rankings, depending on the choice of ground truth, reference genome, and genomic regions used for evaluation. Counter-intuitively, the more complete reference genome T2T-CHM13does not inherently solve the problem of SV benchmarking; instead it reveals the limitations of each detection method in complex genomic regions. The substantial variation in detection accuracy across different genomic regions calls for additional caution in downstream analyses and in drawing conclusions based on predicted SVs. These findings underscore the complexity of evaluating SV detection methods and highlight the need for careful consideration and, ideally, field-standard best practices when reporting performance metrics.

bioinformatics↗
bioRxiv2025

Pangebin: improving plasmid binning in bacterial isolates using pangenome-assembly graphs.

Short-read genome assemblies typically consist of many contigs of variable lengths and their putative connections represented as an assembly graph. Assembly graphs produced by different tools from the same data may differ significantly, posing a challenge to tools for downstream processing tasks. One such task is plasmid binning, that is identifying plasmids in sequenced bacterial isolates, which is crucial for monitoring the spread of antimicrobial resistance. When plasmid binning tools are applied to assembly graphs produced by different tools, they may exhibit different performance, and choosing the best results a priori can be difficult. To address the above issue, we propose the use of a pangenome graph, built from assembly graphs produced by assembling short reads of the same sample with different assemblers. The resulting pangenome-assembly graph highlights similarities between contigs from different assemblies while retaining information on contigs that appear only in one of the input assemblies. We then used the PlasBin-flow plasmid binning tool customized to take into account pangenome information to identify plasmid bins. The results for pangenome-assemblies built by Unicycler and Skesa show an increase in accuracy measures compared to the mean results obtained on single assemblies, leading to an overall more accurate prediction than a blind choice of assemblers. The source code of the pipeline is available at https://github.com/AlgoLab/pangebin along with the dataset used in this study.

bioinformatics↗
bioRxiv2025

PanSpace: Fast and Scalable Indexing for Massive Bacterial Databases

MotivationSpecies identification is a critical task in agriculture, food processing, and health-care. The rapid growth of genomic databases -- driven in part by the increasing investigation of bacterial genomes in clinical microbiology -- has outpaced the capabilities of conventional tools such as BLAST for basic search and query tasks. A key bottleneck in microbiome studies lies in building indexes that allow rapid species identification and classification from assemblies while scaling efficiently to massive resources such as the AllTheBacteria database, thus enabling large-scale analyses to be performed even on a common laptop. ResultsWe introduce PanSpace, the first convolutional neural network-based approach that leverages dense vector (embedding) indexing --- scalable to billions of embeddings --- for indexing and querying massive bacterial genome databases. PanSpace is specifically designed to classify bacterial draft assemblies. Compared to the most recent and competitive tool for this task, PanSpace requires only ~2 GB of disk space to index the AllTheBacteria database, an 8x reduction relative to existing methods. Moreover, it delivers ultra-fast query performance, processing more than 1,000 assemblies in less than two and a half minutes, while preserving the utmost accuracy of state-of-the-art approaches. AvailabilityPanSpace is available at https://github.com/pg-space/panspace.

genomics↗
bioRxiv2025

Pangenome graph augmentation from unassembled long reads

Pangenomes are becoming increasingly popular data structures for genomics analyses due to their ability to compactly represent the genetic diversity within populations. Constructing a pangenome graph, however, is still a time-consuming and expensive process. A promising approach for pangenome construction consists of progressively augmenting a pangenome graph with additional high-quality assemblies. Currently, there is no method for augmenting a pangenome graph with unassembled reads from newly sequenced samples without first aligning the reads to a reference genome and performing variant calling and genotyping on the new individuals. In this work, we present the first assembly-free and mapping-free approach for augmenting an existing pangenome graph using unassembled long reads from an individual not already present in the pangenome. Our approach consists of finding sample specific sequences in reads using efficient indexes, clustering reads corresponding to the same novel variant(s), and then building a consensus sequence to be added to the pangenome graph for each variant separately. Using simulated reads based on Human Pangenome Reference Consortium (HPRC) assemblies, we demonstrate the effectiveness of the proposed approach for progressively augmenting the pangenome with long reads, without the need for de novo assembly or predicting genetic variants of the new sample. The software is freely available at https://github.com/ldenti/palss.

bioinformatics↗
bioRxiv2025

RecAlign: A* recombination-aware sequence to graph mapping

Pangenomics and long reads bring the promise of integrating read mapping with variant calling, since a pangenome encodes a reference genome that incorporates evolutionary or population aspects, while even a single long read can provide a good evidence of different kinds of variants (not only the single nucleotide variants that can be easily observed by short reads). This promise needs to be fulfilled by the development of new read mapping approaches that are tailored for that purpose. This paper focuses on integrating recombination events, that are key in bacteria, into read mapping. A first approach in that direction [ACBC+24] provides an exact dynamic programming algorithm that is too slow to manage multiple recombinations or long reads. We present a novel A* algorithm for recombination-aware sequence-to-graph mapping that significantly reduces running time by incorporating haplotype information and an efficient heuristic function. Our tool, RecAlign, demonstrates up to a two-order magnitude improvement in time and space complexity over [ACBC+24] and efficiently handles multiple recombinations.

bioinformatics↗
bioRxiv2024

Differential Analysis of Alternative Splicing Events in gene regions using Residual Neural Networks

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, to propose deepSpecas, 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. deepSpecas 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 deepSpecas is the first deep learning approach for performing an event-based AS analysis of RNA-seq samples. To validate deepSpecas, 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 comparing our method with state-of-the-art event-based AS analysis tools. Our results highlight that deepSpecas 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.

bioinformatics↗
bioRxiv2024

PangeBlocks: customized construction of pangenome graphs via maximal blocks

BackgroundThe construction of a pangenome graph is a fundamental task in pangenomics. A natural theoretical question is how to formalize the computational problem of building an optimal pangenome graph, making explicit the underlying optimization criterion and the set of feasible solutions. Current approaches build a pangenome graph with some heuristics, without assuming some explicit optimization criteria. Thus it is unclear how a specific optimization criterion affects the graph topology and downstream analysis, like read mapping and variant calling. MethodsIn this paper, by leveraging the notion of maximal block in a Multiple Sequence Alignment (MSA), we reframe the pangenome graph construction problem as an exact cover problem on blocks called Minimum Weighted Block Cover (MWBC). Then we propose an Integer Linear Programming (ILP) formulation for the MWBC problem that allows us to study the most natural objective functions for building a graph. ResultsWe provide an implementation of the ILP approach for solving the MWBC and we evaluate it on SARS-CoV-2 complete genomes, showing how different objective functions lead to pangenome graphs that have different properties, hinting that the specific downstream task can drive the graph construction phase. ConclusionWe show that a customized construction of a pangenome graph based on selecting objective functions has a direct impact on the resulting graphs. In particular, our formalization of the MWBC problem, based on finding an optimal subset of blocks covering an MSA, paves the way to novel practical approaches to graph representations of an MSA where the user can guide the construction.

bioinformatics↗
bioRxiv2024

Diverse somatic Transformer and sex chromosome karyotype pathways regulate gene expression in Drosophila gonad development

The somatic sex determination gene transformer (tra) is required for the highly sexually dimorphic development of most somatic cells, including those of the gonads. In addition, somatic tra is required for the germline development even though it is not required for sex determination within germ cells. Germ cell autonomous gene expression is also necessary for their sex determination. To understand the interplay between these signals, we compared the phenotype and gene expression of larval wild-type gonads and the sex-transformed tra gonads. XX larval ovaries transformed into testes were dramatically smaller than wild-type, with significant reductions in germ cell number, likely due to altered geometry of the stem cell niche. Additionally, there was a defect in progression into spermatocyte stages. XY larval testes transformed into ovaries had excessive germ cells, possibly due to the earlier onset of cell division. We suggest that germ cells are neither fully female nor male following somatic sex transformation, with certain pathways characteristic of each sex expressed in tra mutants. We found multiple patterns of somatic and germline gene expression control exclusively due to tra, exclusively due to sex chromosome karyotype, but usually due to a combination of these factors showing tra and sex chromosome karyotype pathways regulate gene expression during Drosophila gonad development.

developmental biology↗
bioRxiv2023

Differential quantification of alternative splicing events on spliced pangenome graphs

Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure. To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developed pantas, the first pangenomic method for the detection and differential analysis of AS events from short RNA-Seq reads. A comparison with state-of-the-art linear reference-based approaches proves that pantas achieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes. pantas is open-source and freely available at github.com/algolab/pantas. Author summaryThe ever increasing availability of complete genomes is advancing our comprehension of many biological mechanisms and is enhancing the knowledge we can extract from sequencing data. Pange3PM ESTnome graphs are a convenient way to represent multiple genomes and the genetic variability within a population. Integrating genetic variability with transcriptome diversity can improve our understanding of alternative splicing, a regulation mechanism which allows a single gene to code for multiple proteins. However, many unanswered questions are limiting our comprehension of the relationship between genetic and trancriptomic variations. With this work, we start to fill this gap by introducing pantas, the first approach based on pangenome graphs for the detection and differential quantification of alternative splicing events. A comparison with state-of-the-art approaches based on linear genome prove that pangenome graphs can be effectively used to perform such an analysis. By integrating genetic and transcriptome variability in a single structure, pantas can pave the way to next generation bioinformatic approaches for the accurate analysis of the relations between genetic variations and alternative splicing aberrations.

bioinformatics↗
bioRxiv2023

ESGq: Alternative Splicing events quantification across conditions based on Event Splicing Graphs

Alternative Splicing (AS) is a regulation mechanism that contributes to protein diversity and is also associated to many diseases and tumors. Alternative splicing events quantification from RNA-Seq reads is a crucial step in understanding this complex biological mechanism. However, tools for AS events detection and quantification show inconsistent results. This reduces their reliability in fully capturing and explaining alternative splicing. We introduce ESGq, a novel approach for the quantification of AS events across conditions based on read alignment against Event Splicing Graphs. By comparing ESGq to two state-of-the-art tools on real RNA-Seq data, we validate its performance and evaluate the statistical correlation of the results. ESGq is freely available at https://github.com/AlgoLab/ESGq.

bioinformatics↗
bioRxiv2023

μ-PBWT: Enabling the Storage and Use of UK Biobank Data on a Commodity Laptop

MotivationThe positional Burrows-Wheeler Transform (PBWT) has been introduced as a key data structure for indexing haplotype sequences with the main purpose of finding maximal haplotype matches in h sequences containing w variation sites in [Formula]-time with a significant improvement over classical quadratic time approaches. However the original PBWT data structure does not allow queries over the modern biobank panels of haplotypes consisting of several millions of haplotypes, as they must be kept entirely in memory. ResultsIn this paper, we present a method for constructing the run-length encoded PBWT for memory efficient haplotype matching. We implement our method, which we refer to as -PBWT, and evaluate it on datasets of 1000 Genome Project and UK Biobank data. Our experiments demonstrate that the -PBWT reduces the memory usage up to a factor of 25 compared to the best current PBWT-based indexing. In particular, -PBWT produces an index that stores high-coverage whole genome sequencing data of chromosome 20 in half the space of its BCF file. In addition, -PBWT is able to index a dataset with 2 million haplotypes and 2.3 million sites in 4 GB of space, which can be uploaded in 20 seconds on a commodity laptop. -PBWT is an adaptation of techniques for the run-length compressed BWT for the PBWT (RLPBWT) and it is based on keeping in memory only a small representation of the RLPBWT that still allows the efficient computation of set maximal matches (SMEMs) over the original panel. AvailabilityOur implementation is open source and available at https://github.com/dlcgold/muPBWT. The binary is available at https://bioconda.github.io/recipes/mupbwt/README.html ContactPaola Bonizzoni paola.bonizzoni@unimib.it

bioinformatics↗
bioRxiv2022

RecGraph: adding recombinations to sequence-to-graph alignments

The transition towards graph pangenomes is posing several new challenging questions, most notably how to extend the classical notion of read alignment from a sequence-to-sequence to a sequence-to-graph setting. Especially on variation graphs, where paths corresponding to individual genomes are labeled, notions of alignments that are strongly inspired by the classical ones are usually able to capture only variations that can be expressed by mismatches or gaps, such as SNPs or short insertions and deletions. On the other hand the recent investigation of pangenomes at bacterial scale (Colquhoun et al, 2021) shows that most tools are tailored for human pangenomes and are not suited to bacteria which exhibit, among other characteristics, a larger variability. Such variability leads to the need for incorporating a greater flexibility when computing an alignment. In this paper, we extend the usual notion of sequence-to-graph alignment by including recombinations among the variations that explicitly represented and evaluated in an alignment. From a computational modeling point of view, a recombination corresponds to identifying a new path of the variation graph which is a mosaic of two different paths, possibly joined by a new arc. We provide a dynamic programming algorithm for computing an optimal alignment that allows recombinations with an affine penalty. We have implemented our approach with the tool RecGraph and we have analyzed its accuracy over some over some bacterial pangenome graphs.

bioinformatics↗
bioRxiv2022

Compressed Data Structures for Population-Scale Positional Burrows-Wheeler Transforms

AO_SCPLOWBSTRACTC_SCPLOWThe positional Burrows-Wheeler Transform (PBWT) was presented in 2014 by Durbin as a means to find all maximal haplotype matches in h sequences containing w variation sites in [O](hw)-time. This time complexity of finding maximal haplotype matches using the PBWT is a significant improvement over the naive pattern-matching algorithm that requires [O](h2w)-time. Compared to the more famous Burrows-Wheeler Transform (BWT), however, a relatively little amount of attention has been paid to the PBWT. This has resulted in less space-efficient data structures for building and storing the PBWT. Given the increasing size of available haplotype datasets, and the applicability of the PBWT to pangenomics, the time is ripe for identifying efficient data structures that can be constructed for large datasets. Here, we present a comprehensive study of the memory footprint of data structures supporting maximal haplotype matching in conjunction with the PBWT. In particular, we present several data structure components that act as building blocks for constructing six different data structures that store the PBWT in a manner that supports efficiently finding the maximal haplotype matches. We estimate the memory usage of the data structures by bounding the space usage with respect to the input size. In light of this experimental analysis, we implement the solutions that are deemed to be superior with respect to the memory usage and show the performance on haplotype datasets taken from the 1000 Genomes Project data.

bioinformatics↗
bioRxiv2022

Accurate and Fast Clade Assignment via Deep Learning and Frequency Chaos Game Representation

BackgroundSince the beginning of the COVID-19 pandemic there has been an explosion of sequencing of the SARS-CoV-2 virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus, most notably the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. ResultsIn this paper, we leverage the Frequency Chaos Game Representation (FCGR) and Convolutional Neural Networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieves an 96.29% overall accuracy, while a similar tool, Covidex, obtained a 77, 12% overall accuracy. As far as we know, our method is the first using Deep Learning and FCGR for intra-species classification. Furthermore, by using some feature importance methods CouGaR-g allows to identify k-mers that matches SARS-CoV-2 marker variants. ConclusionsBy combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on Random Forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AvailabilityThe trained models can be tested online providing a FASTA file (with one or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

bioinformatics↗
bioRxiv2022

Improved structural variant discovery in hard-to-call regions using sample-specific string detection 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 to overcome these barriers. Here we present SVDSS, a novel hybrid method for discovery of SVs from long-read sequencing technologies (e.g., 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 significant improvements in calling SVs in repetitive regions of the genome. SVDSS is open source and publicly available at: https://github.com/Parsoa/SVDSS

bioinformatics↗
bioRxiv2021

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

MotivationComparative genome analysis of two or more whole-genome sequenced (WGS) samples is at the core of most applications in genomics. These include discovery of genomic differences segregating in population, case-control analysis in common diseases, and 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 genome (e.g., segmental duplications) and hard-to-detect variants (e.g., complex structural variants). ResultsWe propose a novel framework for addressing the comparative genome analysis by discovery of strings that are specific to one genome ("samples-specific" strings). We have developed an accurate and efficient novel method for discovery of samples-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. We show that the proposed approach is capable of accurately finding samples-specific strings representing nearly all variation (> 98%) reported across pairs or trios of WGS samples using accurate long reads (e.g., PacBio HiFi data). AvailabilityThe proposed tool is publicly available at https://github.com/Parsoa/PingPong.

bioinformatics↗