Journal articles: 'Pangenomics'

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Durant, Éloi, François Sabot, Matthieu Conte, and Mathieu Rouard. "Panache: a web browser-based viewer for linearized pangenomes." Bioinformatics 37, no. 23 (October 2, 2021): 4556–58. http://dx.doi.org/10.1093/bioinformatics/btab688.

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Abstract Motivation Pangenomics evolved since its first applications on bacteria, extending from the study of genes for a given population to the study of all of its sequences available. While multiple methods are being developed to construct pangenomes in eukaryotic species there is still a gap for efficient and user-friendly visualization tools. Emerging graph representations come with their own challenges, and linearity remains a suitable option for user-friendliness. Results We introduce Panache, a tool for the visualization and exploration of linear representations of gene-based and sequence-based pangenomes. It uses a layout similar to genome browsers to display presence absence variations and additional tracks along a linear axis with a pangenomics perspective. Availability and implementation Panache is available at github.com/SouthGreenPlatform/panache under the MIT License.

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Llamas, Bastien, Giuseppe Narzisi, Valerie Schneider, Peter A. Audano, Evan Biederstedt, Lon Blauvelt, Peter Bradbury, et al. "A strategy for building and using a human reference pangenome." F1000Research 8 (July 29, 2021): 1751. http://dx.doi.org/10.12688/f1000research.19630.2.

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In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

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Llamas, Bastien, Giuseppe Narzisi, Valerie Schneider, Peter A. Audano, Evan Biederstedt, Lon Blauvelt, Peter Bradbury, et al. "A strategy for building and using a human reference pangenome." F1000Research 8 (October 14, 2019): 1751. http://dx.doi.org/10.12688/f1000research.19630.1.

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In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

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Golicz, Agnieszka A., Jacqueline Batley, and David Edwards. "Towards plant pangenomics." Plant Biotechnology Journal 14, no. 4 (November 23, 2015): 1099–105. http://dx.doi.org/10.1111/pbi.12499.

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Cho, Mildred K., Stephanie Malia Fullerton, Evelynn M. Hammonds, Sandra Soo-Jin Lee, Aaron Panofsky, and Jenny Reardon. "Pangenomics: prioritize diversity in collaborations." Nature 619, no. 7971 (July 25, 2023): 698. http://dx.doi.org/10.1038/d41586-023-02248-7.

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Baaijens, Jasmijn A., Paola Bonizzoni, Christina Boucher, Gianluca Della Vedova, Yuri Pirola, Raffaella Rizzi, and Jouni Sirén. "Computational graph pangenomics: a tutorial on data structures and their applications." Natural Computing 21, no. 1 (March 2022): 81–108. http://dx.doi.org/10.1007/s11047-022-09882-6.

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AbstractComputational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

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Danilevicz, Monica Furaste, Cassandria Geraldine Tay Fernandez, Jacob Ian Marsh, Philipp Emanuel Bayer, and David Edwards. "Plant pangenomics: approaches, applications and advancements." Current Opinion in Plant Biology 54 (April 2020): 18–25. http://dx.doi.org/10.1016/j.pbi.2019.12.005.

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Aggarwal, Sumit Kumar, Alla Singh, Mukesh Choudhary, Aundy Kumar, Sujay Rakshit, Pardeep Kumar, Abhishek Bohra, and Rajeev K. Varshney. "Pangenomics in Microbial and Crop Research: Progress, Applications, and Perspectives." Genes 13, no. 4 (March 27, 2022): 598. http://dx.doi.org/10.3390/genes13040598.

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Advances in sequencing technologies and bioinformatics tools have fueled a renewed interest in whole genome sequencing efforts in many organisms. The growing availability of multiple genome sequences has advanced our understanding of the within-species diversity, in the form of a pangenome. Pangenomics has opened new avenues for future research such as allowing dissection of complex molecular mechanisms and increased confidence in genome mapping. To comprehensively capture the genetic diversity for improving plant performance, the pangenome concept is further extended from species to genus level by the inclusion of wild species, constituting a super-pangenome. Characterization of pangenome has implications for both basic and applied research. The concept of pangenome has transformed the way biological questions are addressed. From understanding evolution and adaptation to elucidating host–pathogen interactions, finding novel genes or breeding targets to aid crop improvement to design effective vaccines for human prophylaxis, the increasing availability of the pangenome has revolutionized several aspects of biological research. The future availability of high-resolution pangenomes based on reference-level near-complete genome assemblies would greatly improve our ability to address complex biological problems.

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Hu, Haifei, Jian Wang, Shuai Nie, Junliang Zhao, Jacqueline Batley, and David Edwards. "Plant pangenomics, current practice and future direction." Agriculture Communications 2, no. 2 (June 2024): 100039. http://dx.doi.org/10.1016/j.agrcom.2024.100039.

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Xiao, Jingfa, Zhewen Zhang, Jiayan Wu, and Jun Yu. "A Brief Review of Software Tools for Pangenomics." Genomics, Proteomics & Bioinformatics 13, no. 1 (February 2015): 73–76. http://dx.doi.org/10.1016/j.gpb.2015.01.007.

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Petereit, Jakob, Philipp E. Bayer, William J. W. Thomas, Cassandria G. Tay Fernandez, Junrey Amas, Yueqi Zhang, Jacqueline Batley, and David Edwards. "Pangenomics and Crop Genome Adaptation in a Changing Climate." Plants 11, no. 15 (July 27, 2022): 1949. http://dx.doi.org/10.3390/plants11151949.

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During crop domestication and breeding, wild plant species have been shaped into modern high-yield crops and adapted to the main agro-ecological regions. However, climate change will impact crop productivity in these regions, and agriculture needs to adapt to support future food production. On a global scale, crop wild relatives grow in more diverse environments than crop species, and so may host genes that could support the adaptation of crops to new and variable environments. Through identification of individuals with increased climate resilience we may gain a greater understanding of the genomic basis for this resilience and transfer this to crops. Pangenome analysis can help to identify the genes underlying stress responses in individuals harbouring untapped genomic diversity in crop wild relatives. The information gained from the analysis of these pangenomes can then be applied towards breeding climate resilience into existing crops or to re-domesticating crops, combining environmental adaptation traits with crop productivity.

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Albert, Korin, Asha Rani, and David A. Sela. "Comparative Pangenomics of the Mammalian Gut Commensal Bifidobacterium longum." Microorganisms 8, no. 1 (December 18, 2019): 7. http://dx.doi.org/10.3390/microorganisms8010007.

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Bifidobacterium longum colonizes mammalian gastrointestinal tracts where it could metabolize host-indigestible oligosaccharides. Although B. longum strains are currently segregated into three subspecies that reflect common metabolic capacities and genetic similarity, heterogeneity within subspecies suggests that these taxonomic boundaries may not be completely resolved. To address this, the B. longum pangenome was analyzed from representative strains isolated from a diverse set of sources. As a result, the B. longum pangenome is open and contains almost 17,000 genes, with over 85% of genes found in ≤28 of 191 strains. B. longum genomes share a small core gene set of only ~500 genes, or ~3% of the total pangenome. Although the individual B. longum subspecies pangenomes share similar relative abundances of clusters of orthologous groups, strains show inter- and intrasubspecies differences with respect to carbohydrate utilization gene content and growth phenotypes.

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Snipen, Lars-Gustav, and David W. Ussery. "A domain sequence approach to pangenomics: applications to Escherichia coli." F1000Research 1 (October 1, 2012): 19. http://dx.doi.org/10.12688/f1000research.1-19.v1.

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The study of microbial pangenomes relies on the computation of gene families, i.e. the clustering of coding sequences into groups of essentially similar genes. There is no standard approach to obtain such gene families. Ideally, the gene family computations should be robust against errors in the annotation of genes in various genomes. In an attempt to achieve this robustness, we propose to cluster sequences by their domain sequence, i.e. the ordered sequence of domains in their protein sequence. In a study of 347 genomes from Escherichia coli we find on average around 4500 proteins having hits in Pfam-A in every genome, clustering into around 2500 distinct domain sequence families in each genome. Across all genomes we find a total of 5724 such families. A binomial mixture model approach indicates this is around 95% of all domain sequences we would expect to see in E. coli in the future. A Heaps law analysis indicates the population of domain sequences is larger, but this analysis is also very sensitive to smaller changes in the computation procedure. The resolution between strains is good despite the coarse grouping obtained by domain sequence families. Clustering sequences by their ordered domain content give us domain sequence families, who are robust to errors in the gene prediction step. The computational load of the procedure scales linearly with the number of genomes, which is needed for the future explosion in the number of re-sequenced strains. The use of domain sequence families for a functional classification of strains clearly has some potential to be explored.

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Snipen, Lars-Gustav, and David W. Ussery. "A domain sequence approach to pangenomics: applications to Escherichia coli." F1000Research 1 (May 29, 2013): 19. http://dx.doi.org/10.12688/f1000research.1-19.v2.

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The study of microbial pangenomes relies on the computation of gene families, i.e. the clustering of coding sequences into groups of essentially similar genes. There is no standard approach to obtain such gene families. Ideally, the gene family computations should be robust against errors in the annotation of genes in various genomes. In an attempt to achieve this robustness, we propose to cluster sequences by their domain sequence, i.e. the ordered sequence of domains in their protein sequence. In a study of 347 genomes from Escherichia coli we find on average around 4500 proteins having hits in Pfam-A in every genome, clustering into around 2500 distinct domain sequence families in each genome. Across all genomes we find a total of 5724 such families. A binomial mixture model approach indicates this is around 95% of all domain sequences we would expect to see in E. coli in the future. A Heaps law analysis indicates the population of domain sequences is larger, but this analysis is also very sensitive to smaller changes in the computation procedure. The resolution between strains is good despite the coarse grouping obtained by domain sequence families. Clustering sequences by their ordered domain content give us domain sequence families, who are robust to errors in the gene prediction step. The computational load of the procedure scales linearly with the number of genomes, which is needed for the future explosion in the number of re-sequenced strains. The use of domain sequence families for a functional classification of strains clearly has some potential to be explored.

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Abondio, Paolo, Elisabetta Cilli, and Donata Luiselli. "Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference." Life 13, no. 6 (June 9, 2023): 1360. http://dx.doi.org/10.3390/life13061360.

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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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Garrigues, Christel, Eric Johansen, and Ross Crittenden. "Pangenomics – an avenue to improved industrial starter cultures and probiotics." Current Opinion in Biotechnology 24, no. 2 (April 2013): 187–91. http://dx.doi.org/10.1016/j.copbio.2012.08.009.

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Eisenstein, Michael. "Every base everywhere all at once: pangenomics comes of age." Nature 616, no. 7957 (April 18, 2023): 618–20. http://dx.doi.org/10.1038/d41586-023-01300-w.

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Abondio, Paolo, Francesco Bruno, Giuseppe Passarino, Alberto Montesanto, and Donata Luiselli. "Pangenomics: A new era in the field of neurodegenerative diseases." Ageing Research Reviews 94 (February 2024): 102180. http://dx.doi.org/10.1016/j.arr.2023.102180.

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Hübner, Sariel. "Are we there yet? Driving the road to evolutionary graph-pangenomics." Current Opinion in Plant Biology 66 (April 2022): 102195. http://dx.doi.org/10.1016/j.pbi.2022.102195.

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Golicz, Agnieszka A., Philipp E. Bayer, Prem L. Bhalla, Jacqueline Batley, and David Edwards. "Pangenomics Comes of Age: From Bacteria to Plant and Animal Applications." Trends in Genetics 36, no. 2 (February 2020): 132–45. http://dx.doi.org/10.1016/j.tig.2019.11.006.

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Perrier, Marion, and Amelia E. Barber. "Unraveling the genomic diversity and virulence of human fungal pathogens through pangenomics." PLOS Pathogens 20, no. 7 (July 11, 2024): e1012313. http://dx.doi.org/10.1371/journal.ppat.1012313.

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Hu, B., G. Xie, C. C. Lo, S. R. Starkenburg, and P. S. G. Chain. "Pathogen comparative genomics in the next-generation sequencing era: genome alignments, pangenomics and metagenomics." Briefings in Functional Genomics 10, no. 6 (November 1, 2011): 322–33. http://dx.doi.org/10.1093/bfgp/elr042.

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Bandoy, DJ Darwin. "Pangenome guided pharmacophore modelling of enterohemorrhagic Escherichia coli sdiA." F1000Research 8 (January 9, 2019): 33. http://dx.doi.org/10.12688/f1000research.17620.1.

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Enterohemorrhagic Escherichia coli (EHEC) continues to be a significant public health risk. With the onset of next generation sequencing, whole genome sequences are a potential resource for predictive modelling of the different regulatory mechanism of pathogens, particularly quorum sensing. We used a pangenome approach to determine EHEC genome clustering, determine the synonymous and nonsynonymous mutations across the EHEC sdiA and modelled the associated amino acid changes. Across the EHEC population, nonsynonymous variants are notably absent in ligand binding site for quorum sensing, indicating that population wide conservation of sdiA ligand site can be targeted for potential prophylactic purposes. Applying pathotype-wide pangenomics as a guide for determining evolution of pharmacophore sites is a potential approach in drug discovery.

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Ciccolella, Simone, Davide Cozzi, Gianluca Della Vedova, Stephen Njuguna Kuria, Paola Bonizzoni, and Luca Denti. "Differential quantification of alternative splicing events on spliced pangenome graphs." PLOS Computational Biology 20, no. 12 (December 9, 2024): e1012665. https://doi.org/10.1371/journal.pcbi.1012665.

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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.

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Bandoy, DJ Darwin. "Large scale enterohemorrhagic E coli population genomic analysis using whole genome typing reveals recombination clusters and potential drug target." F1000Research 8 (October 1, 2019): 33. http://dx.doi.org/10.12688/f1000research.17620.2.

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Enterohemorrhagic Escherichia coli continues to be a significant public health risk. With the onset of next generation sequencing, whole genome sequences require a new paradigm of analysis relevant for epidemiology and drug discovery. A large-scale bacterial population genomic analysis was applied to 702 isolates of serotypes associated with EHEC resulting in five pangenome clusters. Serotype incongruence with pangenome types suggests recombination clusters. Core genome analysis was performed to determine the population wide distribution of sdiA as potential drug target. Protein modelling revealed nonsynonymous variants are notably absent in the ligand binding site for quorum sensing, indicating that population wide conservation of the sdiA ligand site can be targeted for potential prophylactic purposes. Applying pathotype-wide pangenomics as a guide for determining evolution of pharmacophore sites is a potential approach in drug discovery.

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Bandoy, DJ Darwin. "Large scale enterohemorrhagic E coli population genomic analysis using whole genome typing reveals recombination clusters and potential drug target." F1000Research 8 (September 1, 2020): 33. http://dx.doi.org/10.12688/f1000research.17620.3.

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Enterohemorrhagic Escherichia coli continues to be a significant public health risk. With the onset of next generation sequencing, whole genome sequences require a new paradigm of analysis relevant for epidemiology and drug discovery. A large-scale bacterial population genomic analysis was applied to 702 isolates of serotypes associated with EHEC resulting in five pangenome clusters. Serotype incongruence with pangenome types suggests recombination clusters. Core genome analysis was performed to determine the population wide distribution of sdiA as potential drug target. Protein modelling revealed nonsynonymous variants are notably absent in the ligand binding site for quorum sensing, indicating that population wide conservation of the sdiA ligand site can be targeted for potential prophylactic purposes. Applying pathotype-wide pangenomics as a guide for determining evolution of pharmacophore sites is a potential approach in drug discovery.

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Valentin, Guignon, Toure Abdel, Droc Gaëtan, Dufayard Jean-François, Conte Matthieu, and Rouard Mathieu. "GreenPhylDB v5: a comparative pangenomic database for plant genomes." Nucleic Acids Research 49, no. D1 (November 25, 2020): D1464—D1471. http://dx.doi.org/10.1093/nar/gkaa1068.

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Abstract Comparative genomics is the analysis of genomic relationships among different species and serves as a significant base for evolutionary and functional genomic studies. GreenPhylDB (https://www.greenphyl.org) is a database designed to facilitate the exploration of gene families and homologous relationships among plant genomes, including staple crops critically important for global food security. GreenPhylDB is available since 2007, after the release of the Arabidopsis thaliana and Oryza sativa genomes and has undergone multiple releases. With the number of plant genomes currently available, it becomes challenging to select a single reference for comparative genomics studies but there is still a lack of databases taking advantage several genomes by species for orthology detection. GreenPhylDBv5 introduces the concept of comparative pangenomics by harnessing multiple genome sequences by species. We created 19 pangenes and processed them with other species still relying on one genome. In total, 46 plant species were considered to build gene families and predict their homologous relationships through phylogenetic-based analyses. In addition, since the previous publication, we rejuvenated the website and included a new set of original tools including protein-domain combination, tree topologies searches and a section for users to store their own results in order to support community curation efforts.

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Li, Haozhen, Kangkang Song, Xiaohua Zhang, Di Wang, Shaolin Dong, Ying Liu, and Long Yang. "Application of Multi-Perspectives in Tea Breeding and the Main Directions." International Journal of Molecular Sciences 24, no. 16 (August 10, 2023): 12643. http://dx.doi.org/10.3390/ijms241612643.

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Tea plants are an economically important crop and conducting research on tea breeding contributes to enhancing the yield and quality of tea leaves as well as breeding traits that satisfy the requirements of the public. This study reviews the current status of tea plants germplasm resources and their utilization, which has provided genetic material for the application of multi-omics, including genomics and transcriptomics in breeding. Various molecular markers for breeding were designed based on multi-omics, and available approaches in the direction of high yield, quality and resistance in tea plants breeding are proposed. Additionally, future breeding of tea plants based on single-cellomics, pangenomics, plant–microbe interactions and epigenetics are proposed and provided as references. This study aims to provide inspiration and guidance for advancing the development of genetic breeding in tea plants, as well as providing implications for breeding research in other crops.

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Almeida, Otávio Guilherme Gonçalves de, João Pedro Rueda Furlan, Eliana Guedes Stehling, and Elaine Cristina Pereira De Martinis. "Comparative phylo-pangenomics reveals generalist lifestyles in representative Acinetobacter species and proposes candidate gene markers for species identification." Gene 791 (July 2021): 145707. http://dx.doi.org/10.1016/j.gene.2021.145707.

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L Rocha, Joana, Runyang N. Lou, and Peter H. Sudmant. "Structural variation in humans and our primate kin in the era of telomere-to-telomere genomes and pangenomics." Current Opinion in Genetics & Development 87 (August 2024): 102233. http://dx.doi.org/10.1016/j.gde.2024.102233.

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Felice, Andrei Giacchetto, Eduarda Guimarães Sousa, Fabiana Vieira Dominici, Vasco Ariston de Carvalho Azevedo, and Siomar de Castro Soares. "Pangenome Analysis Reveals a High Degree of Genetic Diversity in Gardnerella vaginalis: An In Silico Approach." Venereology 2, no. 4 (September 30, 2023): 132–46. http://dx.doi.org/10.3390/venereology2040012.

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The genus Gardnerella comprises Gram-variable, anaerobic, hemolytic, and non-motile bacilli, with four known species, where Gardnerella vaginalis is the main species responsible for bacterial vaginosis (BV). However, quantifying this species is challenging due to a lack of data and underreporting. Despite its significance, particularly for women, and the availability of several genomes in online databases, genomic analyses and studies on effective treatments still lack details. This study aimed to conduct bioinformatic analyses focused on pangenomics to investigate the complete gene repertoire of the species. Genomes of the bacterium available in online databases were used for comparative genomics, genomic plasticity, gene synteny, and pangenome prediction analyses. The results revealed considerable genome variability, indicating a highly diverse pangenome. The low number of genes in the core genome and similarity analysis confirmed this variability. Three pathogenicity islands, two resistance islands, and nine genomic islands were identified, suggesting horizontal gene transfer events during evolution. These findings underscore the need for sequencing new G. vaginalis genomes to better comprehend its variability and adaptation patterns.

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Rosselli, Riccardo, Nicola La Porta, Rosella Muresu, Piergiorgio Stevanato, Giuseppe Concheri, and Andrea Squartini. "Pangenomics of the Symbiotic Rhizobiales. Core and Accessory Functions Across a Group Endowed with High Levels of Genomic Plasticity." Microorganisms 9, no. 2 (February 16, 2021): 407. http://dx.doi.org/10.3390/microorganisms9020407.

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Pangenome analyses reveal major clues on evolutionary instances and critical genome core conservation. The order Rhizobiales encompasses several families with rather disparate ecological attitudes. Among them, Rhizobiaceae, Bradyrhizobiaceae, Phyllobacteriacreae and Xanthobacteriaceae, include members proficient in mutualistic symbioses with plants based on the bacterial conversion of N2 into ammonia (nitrogen-fixation). The pangenome of 12 nitrogen-fixing plant symbionts of the Rhizobiales was analyzed yielding total 37,364 loci, with a core genome constituting 700 genes. The percentage of core genes averaged 10.2% over single genomes, and between 5% to 7% were found to be plasmid-associated. The comparison between a representative reference genome and the core genome subset, showed the core genome highly enriched in genes for macromolecule metabolism, ribosomal constituents and overall translation machinery, while membrane/periplasm-associated genes, and transport domains resulted under-represented. The analysis of protein functions revealed that between 1.7% and 4.9% of core proteins could putatively have different functions.

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Silva de Oliveira, Mônica, Jorianne Thyeska Castro Alves, Pablo Henrique Caracciolo Gomes de Sá, and Adonney Allan de Oliveira Veras. "PAN2HGENE–tool for comparative analysis and identifying new gene products." PLOS ONE 16, no. 5 (May 28, 2021): e0252414. http://dx.doi.org/10.1371/journal.pone.0252414.

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Advances in next-generation sequencing (NGS) platforms have had a positive impact on biological research, leading to the development of numerous omics approaches, including genomics, transcriptomics, metagenomics, and pangenomics. These analyses provide insights into the gene contents of various organisms. However, to understand the evolutionary processes of these genes, comparative analysis, which is an important tool for annotation, is required. Using comparative analysis, it is possible to infer the functions of gene contents and identify orthologs and paralogous genes via their homology. Although several comparative analysis tools currently exist, most of them are limited to complete genomes. PAN2HGENE, a computational tool that allows identification of gene products missing from the original genome sequence, with automated comparative analysis for both complete and draft genomes, can be used to address this limitation. In this study, PAN2HGENE was used to identify new products, resulting in altering the alpha value behavior in the pangenome without altering the original genomic sequence. Our findings indicate that this tool represents an efficient alternative for comparative analysis, with a simple and intuitive graphical interface. The PAN2HGENE have been uploaded to SourceForge and are available via: https://sourceforge.net/projects/pan2hgene-software

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Soares, Siomar de Castro, Letícia de Castro Oliveira, Arun Kumar Jaiswal, and Vasco Azevedo. "Genomic Islands: an overview of current software tools and future improvements." Journal of Integrative Bioinformatics 13, no. 1 (March 1, 2016): 82–89. http://dx.doi.org/10.1515/jib-2016-301.

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Summary Microbes are highly diverse and widely distributed organisms. They account for ~60% of Earth’s biomass and new predictions point for the existence of 1011 to 1012 species, which are constantly sharing genes through several different mechanisms. Genomic Islands (GI) are critical in this context, as they are large regions acquired through horizontal gene transfer. Also, they present common features like genomic signature deviation, transposase genes, flanking tRNAs and insertion sequences. GIs carry large numbers of genes related to specific lifestyle and are commonly classified in Pathogenicity, Resistance, Metabolic or Symbiotic Islands. With the advent of the next-generation sequencing technologies and the deluge of genomic data, many software tools have been developed that aim to tackle the problem of GI prediction and they are all based on the prediction of GI common features. However, there is still room for the development of new software tools that implements new approaches, such as, machine learning and pangenomics based analyses. Finally, GIs will always hold a potential application in every newly invented genomic approach as they are directly responsible for much of the genomic plasticity of bacteria.

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Wang, Taiquan, Yiling Shi, Mengzhuo Zheng, and Jinshui Zheng. "Comparative Genomics Unveils Functional Diversity, Pangenome Openness, and Underlying Biological Drivers among Bacillus subtilis Group." Microorganisms 12, no. 5 (May 14, 2024): 986. http://dx.doi.org/10.3390/microorganisms12050986.

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The Bacillus subtilis group (Bs group), with Bacillus subtilis as its core species, holds significant research and economic value in various fields, including science, industrial production, food, and pharmaceuticals. However, most studies have been confined to comparative genomics analyses and exploration within individual genomes at the level of species, with few conducted within groups across different species. This study focused on Bacillus subtilis, the model of Gram-positive bacteria, and 14 other species with significant research value, employing comparative pangenomics as well as population enrichment analysis to ascertain the functional enrichment and diversity. Through the quantification of pangenome openness, this work revealed the underlying biological drivers and significant correlation between pangenome openness and various factors, including the distribution of toxin–antitoxin- and integrase-related genes, as well as the number of endonucleases, recombinases, repair system-related genes, prophages, integrases, and transfer mobile elements. Furthermore, the functional enrichment results indicated the potential for secondary metabolite, probiotic, and antibiotic exploration in Bacillus licheniformis, Bacillus paralicheniformis, and Bacillus spizizenii, respectively. In general, this work systematically exposed the quantification of pangenome openness, biological drivers, the pivotal role of genomic instability factors, and mobile elements, providing targeted exploration guidance for the Bs group.

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McKay, Stephanie, Brenda M. Murdoch, and Darren E. Hagen. "53 Establishing a Pan-Epigenome for Cattle and Sheep." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 48–49. http://dx.doi.org/10.1093/jas/skad281.059.

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Abstract Genetic and epigenetic factors contribute towards phenotypic variation in complex traits. Epigenetic modifications can affect transcription and translation and have been shown to influence variation in economically important phenotypes in agricultural species. The onset of pangenomics in livestock species is disconnected from pan-epigenomics and hence lacks a fully comprehensive view of diversity in livestock species. To establish a pan-epigenome for cattle and sheep we are comprehensively characterizing epigenomic conservation and diversity through examination of 5-methylcytosine, 5-hydroxymethylcytosine, histone marks H3K4me3, H3K27ac, H3K4me1 and H3K27me3, open chromatin, chromatin accessibility and n6-methyladenosine in the liver, muscle and prefrontal cortex of Brahman, Angus and Holstein cattle as well as Suffolk, Rambouillet and Katahdin sheep. Finally, comparative epigenomics is being used to identify orthologous regions of the sheep and bovine epigenomes that harbor conserved epigenetic modifications and variants. The establishment of a cattle and sheep pan-epigenome will increase our understanding of how different genomes, epigenomes, and gene products from diverse breeds of cattle and sheep affect a variety of important biological phenotypes, supporting more accurate prediction of traits and improving breeding strategies.

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Zaghum, Muhammad Junaid, Kashir Ali, and Sheng Teng. "Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice (Oryza sativa L.)." Agriculture 12, no. 11 (October 24, 2022): 1757. http://dx.doi.org/10.3390/agriculture12111757.

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The primary considerations in rice (Oryza sativa L.) production evoke improvements in the nutritional quality as well as production. Rice cultivars need to be developed to tackle hunger globally with high yield and better nutrition. The traditional cultivation methods of rice to increase the production by use of non-judicious fertilizers to fulfill the nutritional requirement of the masses. This article provokes nutritional strategies by utilization of available omics techniques to increase the nutritional profiling of rice. Recent scientific advancements in genetic resources provide many approaches for better understanding the molecular mechanisms encircled in a specific trait for its up- or down-regulation for opening new horizons for marker-assisted breeding of new rice varieties. In this perspective, genome-wide association studies, genome selection (GS) and QTL mapping are all genetic analysis that help in precise augmentation of specific nutritional enrichment in rice grain. Implementation of several omics techniques are effective approaches to enhance and regulate the nutritional quality of rice cultivars. Advancements in different types of omics including genomics and pangenomics, transcriptomics, metabolomics, nutrigenomics and proteomics are also relevant to rice development initiatives. This review article compiles genes, locus, mutants and for rice yield and yield attribute enhancement. This knowledge will be useful for now and for the future regarding rice studies.

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Liu, Sheng, Jian Jiao, and Chang-Fu Tian. "Adaptive Evolution of Rhizobial Symbiosis beyond Horizontal Gene Transfer: From Genome Innovation to Regulation Reconstruction." Genes 14, no. 2 (January 20, 2023): 274. http://dx.doi.org/10.3390/genes14020274.

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There are ubiquitous variations in symbiotic performance of different rhizobial strains associated with the same legume host in agricultural practices. This is due to polymorphisms of symbiosis genes and/or largely unexplored variations in integration efficiency of symbiotic function. Here, we reviewed cumulative evidence on integration mechanisms of symbiosis genes. Experimental evolution, in concert with reverse genetic studies based on pangenomics, suggests that gain of the same circuit of key symbiosis genes through horizontal gene transfer is necessary but sometimes insufficient for bacteria to establish an effective symbiosis with legumes. An intact genomic background of the recipient may not support the proper expression or functioning of newly acquired key symbiosis genes. Further adaptive evolution, through genome innovation and reconstruction of regulation networks, may confer the recipient of nascent nodulation and nitrogen fixation ability. Other accessory genes, either co-transferred with key symbiosis genes or stochastically transferred, may provide the recipient with additional adaptability in ever-fluctuating host and soil niches. Successful integrations of these accessory genes with the rewired core network, regarding both symbiotic and edaphic fitness, can optimize symbiotic efficiency in various natural and agricultural ecosystems. This progress also sheds light on the development of elite rhizobial inoculants using synthetic biology procedures.

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Jayakodi, Murukarthick, Hyeonah Shim, and Martin Mascher. "What Are We Learning from Plant Pangenomes?" Annual Review of Plant Biology, December 2, 2024. https://doi.org/10.1146/annurev-arplant-090823-015358.

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A single reference genome does not fully capture species diversity. By contrast, a pangenome incorporates multiple genomes to capture the entire set of nonredundant genes in a given species, along with its genome diversity. New sequencing technologies enable researchers to produce multiple high-quality genome sequences and catalog diverse genetic variations with better precision. Pangenomic studies have detected structural variants in plant genomes, dissected the genetic architecture of agronomic traits, and helped unravel molecular underpinnings and evolutionary origins of plant phenotypes. The pangenome concept has further evolved into a so-called superpangenome that includes wild relatives within a genus or clade and shifted to graph-based reference systems. Nevertheless, building pangenomes and representing complex structural variants remain challenging in many crops. Standardized computing pipelines and common data structures are needed to compare and interpret pangenomes. The growing body of plant pangenomics data requires new algorithms, huge data storage capacity, and training to help researchers and breeders take advantage of newly discovered genes and genetic variants.

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Cummins, Elizabeth A., Rebecca J. Hall, Chris Connor, James O. McInerney, and Alan McNally. "Distinct evolutionary trajectories in the Escherichia coli pangenome occur within sequence types." Microbial Genomics 8, no. 11 (November 23, 2022). http://dx.doi.org/10.1099/mgen.0.000903.

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The Escherichia coli species contains a diverse set of sequence types and there remain important questions regarding differences in genetic content within this population that need to be addressed. Pangenomes are useful vehicles for studying gene content within sequence types. Here, we analyse 21 E. coli sequence type pangenomes using comparative pangenomics to identify variance in both pangenome structure and content. We present functional breakdowns of sequence type core genomes and identify sequence types that are enriched in metabolism, transcription and cell membrane biogenesis genes. We also uncover metabolism genes that have variable core classification, depending on which allele is present. Our comparative pangenomics approach allows for detailed exploration of sequence type pangenomes within the context of the species. We show that ongoing gene gain and loss in the E. coli pangenome is sequence type-specific, which may be a consequence of distinct sequence type-specific evolutionary drivers.

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Contreras-Peruyero, Haydeé, Shaday Guerrero-Flores, Claudia Zirión-Martínez, Paulina M. Mejía-Ponce, Marisol Navarro-Miranda, J. Abel Lovaco-Flores, José M. Ibarra-Rodríguez, Anton Pashkov, Cuauhtémoc Licona-Cassani, and Nelly Sélem-Mojica. "Meeting the Challenge of Genomic Analysis: A Collaboratively Developed Workshop for Pangenomics and Topological Data Analysis." Bioinformatics Advances, September 27, 2024. http://dx.doi.org/10.1093/bioadv/vbae139.

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Abstract Motivation As Genomics data analysis becomes increasingly intricate, researchers face the challenge of mastering various software tools. The rise of Pangenomics analysis, which examines the complete set of genes in a group of genomes, is particularly transformative in understanding genetic diversity. Our interdisciplinary team of biologists and mathematicians developed a short Pangenomics Workshop covering Bash, Python scripting, Pangenome, and Topological Data Analysis. These skills provide deeper insights into genetic variations and their implications in Evolutionary Biology. The workshop uses a Conda environment for reproducibility and accessibility. Developed in The Carpentries Incubator infrastructure, the workshop aims to equip researchers with essential skills for Pangenomics research. By emphasizing the role of a community of practice, this work underscores its significance in empowering multidisciplinary professionals to collaboratively develop training that adheres to best practices. Results Our Workshop delivers tangible outcomes by enhancing the skill sets of Computational Biology professionals. Participants gain hands-on experience using real data from the first described pangenome. We share our paths toward creating an open-source, multidisciplinary, and public resource where learners can develop expertise in Pangenomic Analysis. This initiative goes beyond advancing individual capabilities, aligning with the broader mission of addressing educational needs in Computational Biology. Availability https://carpentries-incubator.github.io/pangenomics-workshop/ Supplementary Material online.

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Matthews, Chelsea A., Nathan S. Watson-Haigh, Rachel A. Burton, and Anna E. Sheppard. "A gentle introduction to pangenomics." Briefings in Bioinformatics 25, no. 6 (September 23, 2024). http://dx.doi.org/10.1093/bib/bbae588.

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Abstract Pangenomes have emerged in response to limitations associated with traditional linear reference genomes. In contrast to a traditional reference that is (usually) assembled from a single individual, pangenomes aim to represent all of the genomic variation found in a group of organisms. The term ‘pangenome’ is currently used to describe multiple different types of genomic information, and limited language is available to differentiate between them. This is frustrating for researchers working in the field and confusing for researchers new to the field. Here, we provide an introduction to pangenomics relevant to both prokaryotic and eukaryotic organisms and propose a formalization of the language used to describe pangenomes (see the Glossary) to improve the specificity of discussion in the field.

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Edwards, David, and Jacqueline Batley. "Teatime for pangenomics." Nature Plants, November 27, 2023. http://dx.doi.org/10.1038/s41477-023-01566-y.

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Tonkin-Hill, Gerry, Jukka Corander, and Julian Parkhill. "Challenges in prokaryote pangenomics." Microbial Genomics 9, no. 5 (May 25, 2023). http://dx.doi.org/10.1099/mgen.0.001021.

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Horizontal gene transfer (HGT) and the resulting patterns of gene gain and loss are a fundamental part of bacterial evolution. Investigating these patterns can help us to understand the role of selection in the evolution of bacterial pangenomes and how bacteria adapt to a new niche. Predicting the presence or absence of genes can be a highly error-prone process that can confound efforts to understand the dynamics of horizontal gene transfer. This review discusses both the challenges in accurately constructing a pangenome and the potential consequences errors can have on downstream analyses. We hope that by summarizing these issues researchers will be able to avoid potential pitfalls, leading to improved bacterial pangenome analyses.

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Du, Ze-Zhen, Jia-Bao He, and Wen-Biao Jiao. "Plant graph-based pangenomics: techniques, applications, and challenges." aBIOTECH, March 28, 2025. https://doi.org/10.1007/s42994-025-00206-7.

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Abstract Innovations in DNA sequencing technologies have greatly boosted population-level genomic studies in plants, facilitating the identification of key genetic variations for investigating population diversity and accelerating the molecular breeding of crops. Conventional methods for genomic analysis typically rely on small variants, such as SNPs and indels, and use single linear reference genomes, which introduces biases and reduces performance in highly divergent genomic regions. By integrating the population level of sequences, pangenomes, particularly graph pangenomes, offer a promising solution to these challenges. To date, numerous algorithms have been developed for constructing pangenome graphs, aligning reads to these graphs, and performing variant genotyping based on these graphs. As demonstrated in various plant pangenomic studies, these advancements allow for the detection of previously hidden variants, especially structural variants, thereby enhancing applications such as genetic mapping of agronomically important genes. However, noteworthy challenges remain to be overcome in applying pangenome graph approaches to plants. Addressing these issues will require the development of more sophisticated algorithms tailored specifically to plants. Such improvements will contribute to the scalability of this approach, facilitating the production of super-pangenomes, in which hundreds or even thousands of de novo–assembled genomes from one species or genus can be integrated. This, in turn, will promote broader pan-omic studies, further advancing our understanding of genetic diversity and driving innovations in crop breeding.

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Garg, Shilpa, Renzo Balboa, and Josiah Kuja. "Chromosome-scale haplotype-resolved pangenomics." Trends in Genetics, July 2022. http://dx.doi.org/10.1016/j.tig.2022.06.011.

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Mourkas, Evangelos, Sion Bayliss, Koji Yahara, Jessica Calland, Ben Pascoe, and Samuel Sheppard. "Comparative pangenomics of Campylobacter species." Access Microbiology 1, no. 1A (March 1, 2019). http://dx.doi.org/10.1099/acmi.ac2019.po0520.

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Eizenga, Jordan M., Adam M. Novak, Emily Kobayashi, Flavia Villani, Cecilia Cisar, Simon Heumos, Glenn Hickey, Vincenza Colonna, Benedict Paten, and Erik Garrison. "Efficient dynamic variation graphs." Bioinformatics, July 16, 2020. http://dx.doi.org/10.1093/bioinformatics/btaa640.

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Abstract Motivation Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic datasets can be challenging to work at. These challenges have impeded progress in this field. Results Here, we present a stack of two C++ libraries, libbdsg and libhandlegraph, which use a simple, field-proven interface, designed to expose elementary features of these graphs while preventing common graph manipulation mistakes. The libraries also provide a Python binding. Using a diverse collection of pangenome graphs, we demonstrate that these tools allow for efficient construction and manipulation of large genome graphs with dense variation. For instance, the speed and memory usage are up to an order of magnitude better than the prior graph implementation in the VG toolkit, which has now transitioned to using libbdsg’s implementations. Availability and implementation libhandlegraph and libbdsg are available under an MIT License from https://github.com/vgteam/libhandlegraph and https://github.com/vgteam/libbdsg.

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Outten, Joseph, and Andrew Warren. "Methods and Developments in Graphical Pangenomics." Journal of the Indian Institute of Science, August 24, 2021. http://dx.doi.org/10.1007/s41745-021-00255-z.

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Bayliss, Sion C., Harry A. Thorpe, Nicola M. Coyle, Samuel K. Sheppard, and Edward J. Feil. "PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria." GigaScience 8, no. 10 (October 1, 2019). http://dx.doi.org/10.1093/gigascience/giz119.

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Abstract Background Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Advances in whole genome sequencing technologies have led to a vast expansion in the amount of bacterial genomes deposited in public databases. There is a pressing need for software solutions which are able to cluster, catalogue and characterise genes, or other features, in increasingly large genomic datasets. Results Here we present a pangenomics toolbox, PIRATE (Pangenome Iterative Refinement and Threshold Evaluation), which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino acid or nucleotide identity thresholds and uses the clustering information to rapidly identify paralogous gene families and putative fission/fusion events. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation, and estimates sequence divergence for each gene family. Conclusions We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species.

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Journal articles on the topic 'Pangenomics'

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