Biology subjects

Cozzi, D.

Publications and source records attributed to Cozzi, D..

7 recordsLinked to original sources

bioRxiv2026

Somatic Structural Variant Discovery from Tumor Specific Strings Computed Against a Draft Composite Germline Reference

Somatic structural variants (SVs) are among the most clinically relevant genomic alterations in cancer. However, their accurate detection remains challenging due to complex genomic rearrangements, repetitive genomic regions, difficulties in distinguishing somatic variants from germline variation and sequencing artifacts, and limited sequencing support for low-frequency events. Existing mapping-based somatic SV callers are susceptible to alignment ambiguities in repetitive regions and near complex SV breakpoints, while approaches relying on comparisons of independently generated tumor and normal variant callsets may propagate errors from both callsets. Building on the concept of specific strings, previously applied to germline SV discovery, we introduce VESPA-SS, a novel approach for detecting somatic SVs from paired tumor-normal long-read whole-genome sequencing data. VESPA-SS directly extracts somatic SV signals using specific strings computed from tumor reads against a Draft Composite Germline Reference, without requiring initial read mapping or a high-quality germline assembly. These signals are subsequently clustered and refined through consensus sequence generation and alignment-based procedures to identify somatic SVs. Evaluation on real PacBio HiFi datasets demonstrates that VESPA-SS outperforms state-of-the-art somatic SV callers, achieving F1 scores exceeding 0.92 and 0.90 on the well-characterized HG008 and COLO829 cancer cell lines, respectively, compared with the highest F1 scores of 0.86 and 0.85 achieved by the evaluated methods. These results demonstrate the effectiveness of specific string based signal extraction for accurate somatic SV discovery and establish a new computational paradigm for identifying complex genomic alterations in cancer. VESPA-SS is freely available at https://github.com/dcmonti/VESPA.

bioinformatics↗

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↗

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

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↗