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Xu Q, Jin L, Zhang Y, Zhang X, Zheng C, Leebens-Mack JH, Sankoff D. Ancestral Flowering Plant Chromosomes and Gene Orders Based on Generalized Adjacencies and Chromosomal Gene Co-Occurrences. J Comput Biol 2021; 28:1156-1179. [PMID: 34783601 DOI: 10.1089/cmb.2021.0340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Recurrent whole genome duplication and the ensuing loss of redundant genes-fractionation-complicate efforts to reconstruct the gene orders and chromosomes of the ancestors associated with the nodes of a phylogeny. Loss of genes disrupts the gene adjacencies key to current techniques. With our RACCROCHE pipeline, instead of starting with the inference of short ancestral segments, we suggest delaying the choice of gene adjacencies while we accumulate many more syntenically validated generalized (gapped) adjacencies. We obtain longer ancestral contigs using maximum weight matching (MWM). Similarly, we do not construct chromosomes by successively piecing together contigs into larger segments, but rather compile counts of pairwise contig co-occurrences on the set of extant genomes and use these to cluster the contigs. Chromosome-level contig assemblies for a monoploid genome emerge naturally at each node of the phylogeny and the contigs then can be ordered along the chromosome. Sampling alternative MWM solutions, visualizing heat maps, and applying gap statistics allow us to estimate the number of chromosomes in the reconstruction. We introduce several measures of quality: length of contigs, continuity of contig structure on successive ancestors, coverage of the extant genome by the reconstruction, and rearrangement relations among the inferred chromosomes. The reconstructed ancestors are visualized by painting the ancestral projections on the descendant genomes. We submit genomes drawn from a broad range of monocot orders to our pipeline, confirming the tetraploidization event "tau" in the stem lineage between the alismatids and the lilioids. We show additional applications to the Solanaceae and to four Brassica genomes, producing evidence about the monoploid ancestor in each case.
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Affiliation(s)
- Qiaoji Xu
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada
| | - Lingling Jin
- Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
| | - Yue Zhang
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada
| | - Xiaomeng Zhang
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada
| | - Chunfang Zheng
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada
| | | | - David Sankoff
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada
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Barchi L, Pietrella M, Venturini L, Minio A, Toppino L, Acquadro A, Andolfo G, Aprea G, Avanzato C, Bassolino L, Comino C, Molin AD, Ferrarini A, Maor LC, Portis E, Reyes-Chin-Wo S, Rinaldi R, Sala T, Scaglione D, Sonawane P, Tononi P, Almekias-Siegl E, Zago E, Ercolano MR, Aharoni A, Delledonne M, Giuliano G, Lanteri S, Rotino GL. A chromosome-anchored eggplant genome sequence reveals key events in Solanaceae evolution. Sci Rep 2019; 9:11769. [PMID: 31409808 PMCID: PMC6692341 DOI: 10.1038/s41598-019-47985-w] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 07/05/2019] [Indexed: 11/30/2022] Open
Abstract
With approximately 450 species, spiny Solanum species constitute the largest monophyletic group in the Solanaceae family, but a high-quality genome assembly from this group is presently missing. We obtained a chromosome-anchored genome assembly of eggplant (Solanum melongena), containing 34,916 genes, confirming that the diploid gene number in the Solanaceae is around 35,000. Comparative genomic studies with tomato (S. lycopersicum), potato (S. tuberosum) and pepper (Capsicum annuum) highlighted the rapid evolution of miRNA:mRNA regulatory pairs and R-type defense genes in the Solanaceae, and provided a genomic basis for the lack of steroidal glycoalkaloid compounds in the Capsicum genus. Using parsimony methods, we reconstructed the putative chromosomal complements of the key founders of the main Solanaceae clades and the rearrangements that led to the karyotypes of extant species and their ancestors. From 10% to 15% of the genes present in the four genomes were syntenic paralogs (ohnologs) generated by the pre-γ, γ and T paleopolyploidy events, and were enriched in transcription factors. Our data suggest that the basic gene network controlling fruit ripening is conserved in different Solanaceae clades, and that climacteric fruit ripening involves a differential regulation of relatively few components of this network, including CNR and ethylene biosynthetic genes.
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Affiliation(s)
- Lorenzo Barchi
- University of Torino - DISAFA - Plant Genetics and Breeding, Largo Braccini 2, 10095, Grugliasco, Torino, Italy
| | - Marco Pietrella
- Italian National Agency for New Technologies, Energy and Sustainable Development (ENEA), Casaccia Res Ctr, Via Anguillarese 301, 00123, Roma, Italy.,Council for Agricultural Research and Economics (CREA), Research Centre for Olive, Citrus and Tree Fruit, 47121, Forlì, Italy
| | - Luca Venturini
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy.,Department of Life Sciences, Natural History Museum, Cromwell Rd, Kensington, London, United Kingdom
| | - Andrea Minio
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - Laura Toppino
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics, 26836, Montanaso Lombardo, LO, Italy
| | - Alberto Acquadro
- University of Torino - DISAFA - Plant Genetics and Breeding, Largo Braccini 2, 10095, Grugliasco, Torino, Italy
| | - Giuseppe Andolfo
- Department of Agricultural Sciences, University of Naples Federico II, 80055, Portici, Italy
| | - Giuseppe Aprea
- Italian National Agency for New Technologies, Energy and Sustainable Development (ENEA), Casaccia Res Ctr, Via Anguillarese 301, 00123, Roma, Italy
| | - Carla Avanzato
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - Laura Bassolino
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics, 26836, Montanaso Lombardo, LO, Italy
| | - Cinzia Comino
- University of Torino - DISAFA - Plant Genetics and Breeding, Largo Braccini 2, 10095, Grugliasco, Torino, Italy
| | - Alessandra Dal Molin
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - Alberto Ferrarini
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - Louise Chappell Maor
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Ezio Portis
- University of Torino - DISAFA - Plant Genetics and Breeding, Largo Braccini 2, 10095, Grugliasco, Torino, Italy
| | - Sebastian Reyes-Chin-Wo
- UC Davis Genome Center-GBSF, 451 Health Sciences Drive, University of California, Davis, CA, 95616, USA
| | - Riccardo Rinaldi
- University of Torino - DISAFA - Plant Genetics and Breeding, Largo Braccini 2, 10095, Grugliasco, Torino, Italy
| | - Tea Sala
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics, 26836, Montanaso Lombardo, LO, Italy
| | - Davide Scaglione
- IGA Technology Services, Via J. Linussio, 51, 33100, Udine, Italy
| | - Prashant Sonawane
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Paola Tononi
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - Efrat Almekias-Siegl
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Elisa Zago
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | | | - Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Massimo Delledonne
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy.
| | - Giovanni Giuliano
- Italian National Agency for New Technologies, Energy and Sustainable Development (ENEA), Casaccia Res Ctr, Via Anguillarese 301, 00123, Roma, Italy.
| | - Sergio Lanteri
- University of Torino - DISAFA - Plant Genetics and Breeding, Largo Braccini 2, 10095, Grugliasco, Torino, Italy.
| | - Giuseppe Leonardo Rotino
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics, 26836, Montanaso Lombardo, LO, Italy
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Feng B, Zhou L, Tang J. Ancestral Genome Reconstruction on Whole Genome Level. Curr Genomics 2017; 18:306-315. [PMID: 29081686 PMCID: PMC5635614 DOI: 10.2174/1389202918666170307120943] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 10/08/2016] [Accepted: 11/03/2016] [Indexed: 11/22/2022] Open
Abstract
Comparative genomics, evolutionary biology, and cancer researches require tools to elucidate the evolutionary trajectories and reconstruct the ancestral genomes. Various methods have been developed to infer the genome content and gene ordering of ancestral genomes by using such genomic structural variants. There are mainly two kinds of computational approaches in the ancestral genome reconstruction study. Distance/event-based approaches employ genome evolutionary models and reconstruct the ancestral genomes that minimize the total distance or events over the edges of the given phylogeny. The homology/adjacency-based approaches search for the conserved gene adjacencies and genome structures, and assemble these regions into ancestral genomes along the internal node of the given phylogeny. We review the principles and algorithms of these approaches that can reconstruct the ancestral genomes on the whole genome level. We talk about their advantages and limitations of these approaches in dealing with various genome datasets, evolutionary events, and reconstruction problems. We also talk about the improvements and developments of these approaches in the subsequent researches. We select four most famous and powerful approaches from both distance/event-based and homology/adjacency-based categories to analyze and compare their performances in dealing with different kinds of datasets and evolutionary events. Based on our experiment, GASTS has the best performance in solving the problems with equal genome contents that only have genome rearrangement events. PMAG++ achieves the best performance in solving the problems with unequal genome contents that have all possible complicated evolutionary events.
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Affiliation(s)
- Bing Feng
- School of Computer Science and Technology, Tianjin University, Tianjin300350, China
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC29208, USA
| | - Lingxi Zhou
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC29208, USA
| | - Jijun Tang
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC29208, USA
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8
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Abstract
Background During evolution, genomes are modified by large scale structural events, such as rearrangements, deletions or insertions of large blocks of DNA. Of particular interest, in order to better understand how this type of genomic evolution happens, is the reconstruction of ancestral genomes, given a phylogenetic tree with extant genomes at its leaves. One way of solving this problem is to assume a rearrangement model, such as Double Cut and Join (DCJ), and find a set of ancestral genomes that minimizes the number of events on the input tree. Since this problem is NP-hard for most rearrangement models, exact solutions are practical only for small instances, and heuristics have to be used for larger datasets. This type of approach can be called event-based. Another common approach is based on finding conserved structures between the input genomes, such as adjacencies between genes, possibly also assigning weights that indicate a measure of confidence or probability that this particular structure is present on each ancestral genome, and then finding a set of non conflicting adjacencies that optimize some given function, usually trying to maximize total weight and minimizing character changes in the tree. We call this type of methods homology-based. Results In previous work, we proposed an ancestral reconstruction method that combines homology- and event-based ideas, using the concept of intermediate genomes, that arise in DCJ rearrangement scenarios. This method showed better rate of correctly reconstructed adjacencies than other methods, while also being faster, since the use of intermediate genomes greatly reduces the search space. Here, we generalize the intermediate genome concept to genomes with unequal gene content, extending our method to account for gene insertions and deletions of any length. In many of the simulated datasets, our proposed method had better results than MLGO and MGRA, two state-of-the-art algorithms for ancestral reconstruction with unequal gene content, while running much faster, making it more scalable to larger datasets. Conclusion Studing ancestral reconstruction problems under a new light, using the concept of intermediate genomes, allows the design of very fast algorithms by greatly reducing the solution search space, while also giving very good results. The algorithms introduced in this paper were implemented in an open-source software called RINGO (ancestral Reconstruction with INtermediate GenOmes), available at https://github.com/pedrofeijao/RINGO.
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Affiliation(s)
- Pedro Feijão
- Technische Fakultät and CeBiTec, Universität Bielefeld, Universitätsstr. 25, Bielefeld, 33615, Germany.
| | - Eloi Araujo
- Technische Fakultät and CeBiTec, Universität Bielefeld, Universitätsstr. 25, Bielefeld, 33615, Germany.,Faculdade de Computação, Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande, MS, Brazil
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Rajaraman A, Ma J. Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction. BMC Bioinformatics 2016; 17:414. [PMID: 28185565 PMCID: PMC5123302 DOI: 10.1186/s12859-016-1262-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Background Reconstructing ancestral gene orders in the presence of duplications is important for a better understanding of genome evolution. Current methods for ancestral reconstruction are limited by either computational constraints or the availability of reliable gene trees, and often ignore duplications altogether. Recently, methods that consider duplications in ancestral reconstructions have been developed, but the quality of reconstruction, counted as the number of contiguous ancestral regions found, decreases rapidly with the number of duplicated genes, complicating the application of such approaches to mammalian genomes. However, such high fragmentation is not encountered when reconstructing mammalian genomes at the synteny-block level, although the relative positions of genes in such reconstruction cannot be recovered. Results We propose a new heuristic method, MultiRes, to reconstruct ancestral gene orders with duplications guided by homologous synteny blocks for a set of related descendant genomes. The method uses a synteny-level reconstruction to break the gene-order problem into several subproblems, which are then combined in order to disambiguate duplicated genes. We applied this method to both simulated and real data. Our results showed that MultiRes outperforms other methods in terms of gene content, gene adjacency, and common interval recovery. Conclusions This work demonstrates that the inclusion of synteny-level information can help us obtain better gene-level reconstructions. Our algorithm provides a basic toolbox for reconstructing ancestral gene orders with duplications. The source code of MultiRes is available on https://github.com/ma-compbio/MultiRes. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1262-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ashok Rajaraman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, USA
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, USA.
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Xu R, Wang Q. A genomics-based systems approach towards drug repositioning for rheumatoid arthritis. BMC Genomics 2016; 17 Suppl 7:518. [PMID: 27557330 PMCID: PMC5001200 DOI: 10.1186/s12864-016-2910-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data. We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA. Results Our algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets (“direct genetics-based” approach), our algorithm (“indirect genetics-based”) achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01). Conclusions In summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.
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Affiliation(s)
- Rong Xu
- Department of Epidemiology and Biostatistics, Institute of Computational Biology, School of Medicine, Case Western Reserve University, 2103 Cornell Road, Cleveland, 44106, OH, USA.
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