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Avila Cartes J, Bonizzoni P, Ciccolella S, Della Vedova G, Denti L, Didelot X, Monti DC, Pirola Y. RecGraph: recombination-aware alignment of sequences to variation graphs. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae292. [PMID: 38676570 DOI: 10.1093/bioinformatics/btae292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 02/23/2024] [Accepted: 04/25/2024] [Indexed: 04/29/2024]
Abstract
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.
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Affiliation(s)
- Jorge Avila Cartes
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
| | - Paola Bonizzoni
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
| | - Simone Ciccolella
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
| | - Gianluca Della Vedova
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
| | - Luca Denti
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
| | - Xavier Didelot
- Department of Statistics and School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Davide Cesare Monti
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
| | - Yuri Pirola
- Department of Informatics, Systems and Communication, University of Milano - Bicocca. Viale Sarca 336, Milano 20126, Italy
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Abondio P, Bruno F, Passarino G, Montesanto A, Luiselli D. Pangenomics: A new era in the field of neurodegenerative diseases. Ageing Res Rev 2024; 94:102180. [PMID: 38163518 DOI: 10.1016/j.arr.2023.102180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/14/2023] [Accepted: 12/28/2023] [Indexed: 01/03/2024]
Abstract
A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.
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Affiliation(s)
- Paolo Abondio
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy.
| | - Francesco Bruno
- Academy of Cognitive Behavioral Sciences of Calabria (ASCoC), Lamezia Terme, Italy; Regional Neurogenetic Centre (CRN), Department of Primary Care, Azienda Sanitaria Provinciale Di Catanzaro, Viale A. Perugini, 88046 Lamezia Terme, CZ, Italy; Association for Neurogenetic Research (ARN), Lamezia Terme, CZ, Italy
| | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende 87036, Italy
| | - Alberto Montesanto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende 87036, Italy
| | - Donata Luiselli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
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Rajput J, Chandra G, Jain C. Co-linear chaining on pangenome graphs. Algorithms Mol Biol 2024; 19:4. [PMID: 38279113 DOI: 10.1186/s13015-024-00250-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/02/2024] [Indexed: 01/28/2024] Open
Abstract
Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width and how incorporating gap cost in the scoring function improves alignment accuracy. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy. Implementation ( https://github.com/at-cg/PanAligner ).
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Affiliation(s)
- Jyotshna Rajput
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Ghanshyam Chandra
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Chirag Jain
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, Karnataka, India.
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Chandra G, Jain C. Gap-Sensitive Colinear Chaining Algorithms for Acyclic Pangenome Graphs. J Comput Biol 2023; 30:1182-1197. [PMID: 37902967 DOI: 10.1089/cmb.2023.0186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2023] Open
Abstract
A pangenome graph can serve as a better reference for genomic studies because it allows a compact representation of multiple genomes within a species. Aligning sequences to a graph is critical for pangenome-based resequencing. The seed-chain-extend heuristic works by finding short exact matches between a sequence and a graph. In this heuristic, colinear chaining helps identify a good cluster of exact matches that can be combined to form an alignment. Colinear chaining algorithms have been extensively studied for aligning two sequences with various gap costs, including linear, concave, and convex cost functions. However, extending these algorithms for sequence-to-graph alignment presents significant challenges. Recently, Makinen et al. introduced a sparse dynamic programming framework that exploits the small path cover property of acyclic pangenome graphs, enabling efficient chaining. However, this framework does not consider gap costs, limiting its practical effectiveness. We address this limitation by developing novel problem formulations and provably good chaining algorithms that support a variety of gap cost functions. These functions are carefully designed to enable fast chaining algorithms whose time requirements are parameterized in terms of the size of the minimum path cover. Through an empirical evaluation, we demonstrate the superior performance of our algorithm compared with existing aligners. When mapping simulated long reads to a pangenome graph comprising 95 human haplotypes, we achieved 98.7% precision while leaving <2% of reads unmapped.
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Affiliation(s)
- Ghanshyam Chandra
- Department of Computational and Data Sciences, Indian Institute of Science Bengaluru, India
| | - Chirag Jain
- Department of Computational and Data Sciences, Indian Institute of Science Bengaluru, India
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Aylward AJ, Petrus S, Mamerto A, Hartwick NT, Michael TP. PanKmer: k-mer-based and reference-free pangenome analysis. Bioinformatics 2023; 39:btad621. [PMID: 37846049 PMCID: PMC10603592 DOI: 10.1093/bioinformatics/btad621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/29/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023] Open
Abstract
SUMMARY Pangenomes are replacing single reference genomes as the definitive representation of DNA sequence within a species or clade. Pangenome analysis predominantly leverages graph-based methods that require computationally intensive multiple genome alignments, do not scale to highly complex eukaryotic genomes, limit their scope to identifying structural variants (SVs), or incur bias by relying on a reference genome. Here, we present PanKmer, a toolkit designed for reference-free analysis of pangenome datasets consisting of dozens to thousands of individual genomes. PanKmer decomposes a set of input genomes into a table of observed k-mers and their presence-absence values in each genome. These are stored in an efficient k-mer index data format that encodes SNPs, INDELs, and SVs. It also includes functions for downstream analysis of the k-mer index, such as calculating sequence similarity statistics between individuals at whole-genome or local scales. For example, k-mers can be "anchored" in any individual genome to quantify sequence variability or conservation at a specific locus. This facilitates workflows with various biological applications, e.g. identifying cases of hybridization between plant species. PanKmer provides researchers with a valuable and convenient means to explore the full scope of genetic variation in a population, without reference bias. AVAILABILITY AND IMPLEMENTATION PanKmer is implemented as a Python package with components written in Rust, released under a BSD license. The source code is available from the Python Package Index (PyPI) at https://pypi.org/project/pankmer/ as well as Gitlab at https://gitlab.com/salk-tm/pankmer. Full documentation is available at https://salk-tm.gitlab.io/pankmer/.
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Affiliation(s)
- Anthony J Aylward
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Semar Petrus
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Allen Mamerto
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Nolan T Hartwick
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Todd P Michael
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
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Cozzi D, Rossi M, Rubinacci S, Gagie T, Köppl D, Boucher C, Bonizzoni P. μ- PBWT: a lightweight r-indexing of the PBWT for storing and querying UK Biobank data. Bioinformatics 2023; 39:btad552. [PMID: 37688560 PMCID: PMC10502237 DOI: 10.1093/bioinformatics/btad552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/07/2023] [Accepted: 09/07/2023] [Indexed: 09/11/2023] Open
Abstract
MOTIVATION The Positional Burrows-Wheeler Transform (PBWT) is a data structure that indexes haplotype sequences in a manner that enables finding maximal haplotype matches in h sequences containing w variation sites in O(hw) time. This represents a significant improvement over classical quadratic-time approaches. However, the original PBWT data structure does not allow for queries over Biobank panels that consist of several millions of haplotypes, if an index of the haplotypes must be kept entirely in memory. RESULTS In this article, we leverage the notion of r-index proposed for the BWT to present a memory-efficient method for constructing and storing the run-length encoded PBWT, and computing set maximal matches (SMEMs) queries in haplotype sequences. We implement our method, which we refer to as μ-PBWT, and evaluate it on datasets of 1000 Genome Project and UK Biobank data. Our experiments demonstrate that the μ-PBWT reduces the memory usage up to a factor of 20% compared to the best current PBWT-based indexing. In particular, μ-PBWT produces an index that stores high-coverage whole genome sequencing data of chromosome 20 in about a third of the space of its BCF file. μ-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 succinct representation of the RLPBWT that still allows the efficient computation of set maximal matches (SMEMs) over the original panel. AVAILABILITY AND IMPLEMENTATION Our 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.
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Affiliation(s)
- Davide Cozzi
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan 20126, Italy
| | - Massimiliano Rossi
- Department of Computer & Information Science & Engineering, Herbert-Wertheim College of Engineering, University of Florida, Gainesville, Florida 32611, United States
| | - Simone Rubinacci
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
| | - Travis Gagie
- Faculty of Computer Science, Dalhousie University, Halifax B3H 4R2, Canada
| | - Dominik Köppl
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
- Department of Computer Science, University of Muenster, Muenster 48149, Germany
| | - Christina Boucher
- Department of Computer & Information Science & Engineering, Herbert-Wertheim College of Engineering, University of Florida, Gainesville, Florida 32611, United States
| | - Paola Bonizzoni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan 20126, Italy
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Abondio P, Cilli E, Luiselli D. Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference. Life (Basel) 2023; 13:1360. [PMID: 37374141 DOI: 10.3390/life13061360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/02/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.
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Affiliation(s)
- Paolo Abondio
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
| | - Elisabetta Cilli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
| | - Donata Luiselli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
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Duchen D, Clipman S, Vergara C, Thio CL, Thomas DL, Duggal P, Wojcik GL. A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.11.523611. [PMID: 36711598 PMCID: PMC9882026 DOI: 10.1101/2023.01.11.523611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Hepatitis B virus (HBV) remains a global public health concern, with over 250 million individuals living with chronic HBV infection (CHB) and no curative therapy currently available. Viral diversity is associated with CHB pathogenesis and immunological control of infection. Improved methods to characterize the viral genome at both the population and intra-host level could aid drug development efforts. Conventionally, HBV sequencing data are aligned to a linear reference genome and only sequences capable of aligning to the reference are captured for analysis. Reference selection has additional consequences, including sample-specific 'consensus' sequence construction. It remains unclear how to select a reference from available sequences and whether a single reference is sufficient for genetic analyses. Using simulated short-read sequencing data generated from full-length publicly available HBV genome sequences and HBV sequencing data from a longitudinally sampled individual with CHB, we investigate alternative graph-based alignment approaches. We demonstrate that using a phylogenetically representative 'genome graph' for alignment, rather than linear reference sequences, avoids issues of reference ambiguity, improves alignment, and facilitates the construction of sample-specific consensus sequences genetically similar to an individual's infection. Graph-based methods can therefore improve efforts to characterize the genetics of viral pathogens, including HBV, and may have broad implications in host pathogen research.
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Affiliation(s)
- Dylan Duchen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Steven Clipman
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Candelaria Vergara
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Chloe L Thio
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - David L Thomas
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
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Graph Pangenomes Track Genetic Variants for Crop Improvement. Int J Mol Sci 2022; 23:ijms232113420. [DOI: 10.3390/ijms232113420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
Global climate change and the urgency to transform crops require an exhaustive genetic evaluation. The large polyploid genomes of food crops, such as cereals, make it difficult to identify candidate genes with confirmed hereditary. Although genome-wide association studies (GWAS) have been proficient in identifying genetic variants that are associated with complex traits, the resolution of acquired heritability faces several significant bottlenecks such as incomplete detection of structural variants (SV), genetic heterogeneity, and/or locus heterogeneity. Consequently, a biased estimate is generated with respect to agronomically complex traits. The graph pangenomes have resolved this missing heritability and provide significant details in terms of specific loci segregating among individuals and evolving to variations. The graph pangenome approach facilitates crop improvements through genome-linked fast breeding.
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