SpringerLink
Log in

AGO, a Framework for the Reconstruction of Ancestral Syntenies and Gene Orders

  • Protocol
  • First Online:
Comparative Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2802))

  • 415 Accesses

Abstract

Reconstructing ancestral gene orders from the genome data of extant species is an important problem in comparative and evolutionary genomics. In a phylogenomics setting that accounts for gene family evolution through gene duplication and gene loss, the reconstruction of ancestral gene orders involves several steps, including multiple sequence alignment, the inference of reconciled gene trees, and the inference of ancestral syntenies and gene adjacencies. For each of the steps of such a process, several methods can be used and implemented using a growing corpus of, often parameterized, tools; in practice, interfacing such tools into an ancestral gene order reconstruction pipeline is far from trivial. This chapter introduces AGO, a Python-based framework aimed at creating ancestral gene order reconstruction pipelines allowing to interface and parameterize different bioinformatics tools. The authors illustrate the features of AGO by reconstructing ancestral gene orders for the X chromosome of three ancestral Anopheles species using three different pipelines. AGO is freely available at https://github.com/cchauve/AGO-pipeline.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
EUR 44.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 169.99
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 210.99
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Boussau B, Daubin V (2009) Genomes as documents of evolutionary history. Trends Ecol Evol 25:224–232. https://doi.org/10.1016/j.tree.2009.09.007

    Article  PubMed  Google Scholar 

  2. Joy JB, Liang RH, McCloskey RM, Nguyen T, Poon AFY (2016) Ancestral reconstruction. PLoS Comput Biol 12:e1004763. https://doi.org/10.1371/journal.pcbi.1004763

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Groussin M, Daubin V, Gouy M, Tannier E (2016) Ancestral reconstruction: theory and practice. In: Encyclopedia of evolutionary biology. Elsevier, Oxford, pp 70–77. https://doi.org/10.1016/B978-0-12-800049-6.00166-9

    Chapter  Google Scholar 

  4. Murat F, Van de Peer Y, Salse J (2012) Decoding plant and animal genome plasticity from differential paleo-evolutionary patterns and processes. Genome Biol Evol 4:917–928. https://doi.org/10.1093/gbe/evs066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bakloushinskaya IY (2016) Chromosomal rearrangements, genome reorganization, and speciation. Biol Bull 43:759–775. https://doi.org/10.1134/S1062359016080057

    Article  Google Scholar 

  6. Pont C, Wagner S, Kremer A, Orlando L, Plomion C, Salse J (2019) Paleogenomics: reconstruction of plant evolutionary trajectories from modern and ancient DNA. Genome Biol 20:29. https://doi.org/10.1186/s13059-019-1627-1

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wellenreuther M, Mérot C, Berdan E, Bernatchez L (2019) Going beyond SNPs: the role of structural genomic variants in adaptive evolution and species diversification. Mol Ecol 28:1203–1209. https://doi.org/10.1111/mec.15066

    Article  PubMed  Google Scholar 

  8. El-Mabrouk N (2021) Predicting the evolution of syntenies—an algorithmic review. Algorithms 14:152. https://doi.org/10.3390/a14050152

    Article  Google Scholar 

  9. Anselmetti Y, Luhmann N, Bérard S, Tannier E, Chauve C (2018) Comparative methods for reconstructing ancient genome organization. In: Setubal JC, Stoye J, Stadler PF (eds) Comparative genomic, Methods in molecular biology, vol 1704. Humana, New York. https://doi.org/10.1007/978-1-4939-7463-4_13

    Chapter  Google Scholar 

  10. Moret BME, Wyman SK, Bader DA, Warnow TJ, Yan M (2001) A new implementation and detailed study of breakpoint analysis. In: Altman RB, Dunker AK, Hunter L, Klein TE (eds) Proceedings of the 6th Pacific Symposium on Biocomputing, PSB 2001, Hawaii, USA, 3–7 Jan 2001

    Google Scholar 

  11. Tesler G (2002) GRIMM: genome rearrangements web server. Bioinformatics 18:492–493. https://doi.org/10.1093/bioinformatics/18.3.492

    Article  CAS  PubMed  Google Scholar 

  12. Feijao P, Meidanis J (2011) SCJ: a breakpoint-like distance that simplifies several rearrangement problems. IEEE/ACM Trans Comput Biol Bioinform 8:1318–1329. https://doi.org/10.1109/TCBB.2011.34

    Article  PubMed  Google Scholar 

  13. Thornton JW, DeSalle R (2000) Gene family evolution and homology: genomics meets phylogenetics. Annu Rev Genomics Hum Genet 1:41–73. https://doi.org/10.1146/annurev.genom.1.1.41

    Article  CAS  PubMed  Google Scholar 

  14. Bohnenkämper L, Braga MDV, Doerr D, Stoye J (2021) Computing the rearrangement distance of natural genomes. J Comput Biol 28:410–431. https://doi.org/10.1089/cmb.2020.0434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Earnest-DeYoung JV, Lerat E, Moret BME (2004) Reversing gene erosion – reconstructing ancestral bacterial genomes from gene-content and order data. In: Jonassen I, Kim J (eds) Algorithms in bioinformatics, 4th international workshop, WABI 2004, Bergen, Norway, 17–21 Sept 2004, Proceedings, Lecture notes in computer science, vol 3240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30219-3_1

    Chapter  Google Scholar 

  16. Gagnon Y, Blanchette M, El-Mabrouk N (2012) A flexible ancestral genome reconstruction method based on gapped adjacencies. BMC Bioinform 13:S4. https://doi.org/10.1186/1471-2105-13-S19-S4

    Article  Google Scholar 

  17. Hu F, Zhou J, Zhou L, Tang J (2014) Probabilistic reconstruction of ancestral gene orders with insertions and deletions. IEEE/ACM Trans Comput Biol Bioinform 11:667–672. https://doi.org/10.1109/TCBB.2014.2309602

    Article  PubMed  Google Scholar 

  18. Yang N, Hu F, Zhou L, Tang J (2014) Reconstruction of ancestral gene orders using probabilistic and gene encoding approaches. PLoS One 9:e108796. https://doi.org/10.1371/journal.pone.0108796

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rajaraman A, Ma J (2016) Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction. BMC Bioinform 17:414. https://doi.org/10.1186/s12859-016-1262-8

    Article  Google Scholar 

  20. Avdeyev P, Jiang S Jr, Aganezov S, Hu F, Alekseyev MA (2016) Reconstruction of ancestral genomes in presence of gene gain and loss. J Comput Biol 23:150–164. https://doi.org/10.1089/cmb.2015.0160

    Article  CAS  PubMed  Google Scholar 

  21. Doerr D, Chauve C (2021) Small parsimony for natural genomes in the DCJ-indel model. J Bioinforma Comput Biol 19:2140009. https://doi.org/10.1142/S0219720021400096

    Article  Google Scholar 

  22. Xu Q, ** L, Zheng C, Zhang X, Leebens-Mack J, Sankoff D (2023) From comparative gene content and gene order to ancestral contigs, chromosomes and karyotypes. Sci Rep 13:6095. https://doi.org/10.1038/s41598-023-33029-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Menet H, Daubin V, Tannier E (2022) Phylogenetic reconciliation. PLoS Comput Biol 18:1–29. https://doi.org/10.1371/journal.pcbi.1010621

    Article  CAS  Google Scholar 

  24. Boussau B, Scornavacca C (2020) Reconciling gene trees with species trees. In: Scornavacca C, Delsuc F, Galtier N (eds) Phylogenetics in the genomic era. https://hal.science/hal-02535529

  25. Sankoff D, El-Mabrouk N (2000) Duplication, rearrangement, and reconciliation. In: Sankoff D, Nadeau JH (eds) Comparative genomics, Computational biology, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-4309-7_46

    Chapter  Google Scholar 

  26. Ma J, Ratan A, Raney BJ, Suh BB, Zhang L, Miller W et al (2008) DUPCAR: reconstructing contiguous ancestral regions with duplications. J Comput Biol 15:1007–1027. https://doi.org/10.1089/cmb.2008.0069

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chauve C, El-Mabrouk N, Guéguen L, Semeria M, Tannier E (2013) Duplication, rearrangement and reconciliation: a follow-up 13 years later. In: Chauve C, El-Mabrouk N, Tannier E (eds) Models and algorithms for genome evolution, Computational biology, vol 19. Springer, London. https://doi.org/10.1007/978-1-4471-5298-9_4

    Chapter  Google Scholar 

  28. Ma J, Zhang L, Suh BB, Raney BJ, Burhans R, Kent WJ et al (2006) Reconstructing contiguous regions of an ancestral genome. Genome Res 16:1557–1565. https://doi.org/10.1101/gr.5383506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Szöllősi GJ, Tannier E, Lartillot N, Daubin V (2013) Lateral gene transfer from the dead. Syst Biol 62:386–397. https://doi.org/10.1093/sysbio/syt003

    Article  PubMed  PubMed Central  Google Scholar 

  30. Amos B, Aurrecoechea C, Barba M, Barreto A, Basenko E, Bażant W et al (2021) VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center. Nucleic Acids Res 50:D898–D911. https://doi.org/10.1093/nar/gkab929

    Article  CAS  PubMed Central  Google Scholar 

  31. Herrero J, Muffato M, Beal K, Fitzgerald S, Gordon L, Pignatelli M et al (2016) Ensembl comparative genomics resources. Database 2016:bav096. https://doi.org/10.1093/database/bav096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Altenhoff AM, Glover NM, Dessimoz C (2019) Inferring orthology and paralogy. In: Anisimova M (ed) Evolutionary genomics, Methods in molecular biology, vol 1910. Humana, New York. https://doi.org/10.1007/978-1-4939-9074-0_5

    Chapter  Google Scholar 

  33. Duchemin W, Gence G, Arigon Chifolleau AM, Arvestad L, Bansal MS, Berry V et al (2018) RecPhyloXML: a format for reconciled gene trees. Bioinformatics 34:3646–3652. https://doi.org/10.1093/bioinformatics/bty389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ranwez V, Douzery EJP, Cambon C, Chantret N, Delsuc F (2018) MACSE v2: toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol Biol Evol 35:2582–2584. https://doi.org/10.1093/molbev/msy159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A et al (2020) IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37:1530–1534. https://doi.org/10.1093/molbev/msaa015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Morel B, Kozlov AM, Stamatakis A, Szöllősi GJ (2020) GeneRax: a tool for species-tree-aware maximum likelihood-based gene family tree inference under gene duplication, transfer, and loss. Mol Biol Evol 37:2763–2774. https://doi.org/10.1093/molbev/msaa141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Szöllősi GJ, Rosikiewicz W, Boussau B, Tannier E, Daubin V (2013) Efficient exploration of the space of reconciled gene trees. Syst Biol 62:901–912. https://doi.org/10.1093/sysbio/syt054

    Article  PubMed  PubMed Central  Google Scholar 

  38. Duchemin W, Anselmetti Y, Patterson M, Ponty Y, Bérard S, Chauve C et al (2017) DeCoSTAR: reconstructing the ancestral organization of genes or genomes using reconciled phylogenies. Genome Biol Evol 9:1312–1319. https://doi.org/10.1093/gbe/evx069

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Jacox E, Chauve C, Szöllősi GJ, Ponty Y, Scornavacca C (2016) ecceTERA: comprehensive gene tree-species tree reconciliation using parsimony. Bioinformatics 32:2056–2058. https://doi.org/10.1093/bioinformatics/btw105

    Article  CAS  PubMed  Google Scholar 

  40. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS (2017) ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14:587–589. https://doi.org/10.1038/nmeth.4285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS (2017) UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol 35:518–522. https://doi.org/10.1093/molbev/msx281

    Article  CAS  PubMed Central  Google Scholar 

  42. Chauve C, Ponty Y, Zanetti JPP (2015) Evolution of genes neighborhood within reconciled phylogenies: an ensemble approach. BMC Bioinform 16:S6. https://doi.org/10.1186/1471-2105-16-S19-S6

    Article  Google Scholar 

  43. Chauve C, Tannier E (2008) A methodological framework for the reconstruction of contiguous regions of ancestral genomes and its application to mammalian genomes. PLoS Comput Biol 4:e1000234. https://doi.org/10.1371/journal.pcbi.1000234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Manuch J, Patterson M, Wittler R, Chauve C, Tannier E (2012) Linearization of ancestral multichromosomal genomes. BMC Bioinform 13:S11. https://doi.org/10.1186/1471-2105-13-S19-S11

    Article  Google Scholar 

  45. Luhmann N, Lafond M, Thevenin A, Ouangraoua A, Wittler R, Chauve C (2017) The SCJ small parsimony problem for weighted gene adjacencies. IEEE/ACM Trans Comput Biol Bioinf 16:1374–1373. https://doi.org/10.1109/TCBB.2017.2661761

    Article  Google Scholar 

  46. Ben-Kiki O, Evans C, Ingerson B (2009) YAML ain’t markup language (YAML) (tm) version 1.2. YAML.org; http://www.yaml.org/spec/1.2/spec.html

  47. Yoo AB, Jette MA, Grondona M (2003) SLURM: simple linux utility for resource management. In: Feitelson DG, Rudolph L, Schwiegelshohn U (eds) Job scheduling strategies for parallel processing 9th international workshop, JSSPP 2003, Seattle, WA, USA, June 24, 2003, revised papers, Lecture notes in computer science, vol 2862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10968987_3

    Chapter  Google Scholar 

  48. Neafsey DE, Waterhouse RM, Abai MR, Aganezov SS, Alekseyev MA, Allen JE et al (2015) Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Science 347:1258522. https://doi.org/10.1126/science.1258522

    Article  CAS  PubMed  Google Scholar 

  49. Fontaine MC, Pease JB, Steele A, Waterhouse RM, Neafsey DE, Sharakhov IV et al (2015) Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347:1258524. https://doi.org/10.1126/science.1258524

    Article  CAS  PubMed  Google Scholar 

  50. Chen F, Mackey AJ, Stoeckert J, Christian J, Roos DS (2006) OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups. Nucleic Acids Res 34:D363–D368. https://doi.org/10.1093/nar/gkj123

    Article  CAS  PubMed  Google Scholar 

  51. Hahn MW (2007) Bias in phylogenetic tree reconciliation methods: implications for vertebrate genome evolution. Genome Biol 8:R141. https://doi.org/10.1186/gb-2007-8-7-r141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Redelings BD (2021) BAli-Phy version 3: model-based co-estimation of alignment and phylogeny. Bioinformatics 37:3032–3034. https://doi.org/10.1093/bioinformatics/btab129

    Article  CAS  PubMed  Google Scholar 

  53. Stamatakis A (2006) RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22:2688–2690. https://doi.org/10.1093/bioinformatics/btl446

    Article  CAS  PubMed  Google Scholar 

  54. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S et al (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61:539–542. https://doi.org/10.1093/sysbio/sys029

    Article  PubMed  PubMed Central  Google Scholar 

  55. Comte N, Morel B, Hasić D, Guéguen L, Boussau B, Daubin V et al (2020) Treerecs: an integrated phylogenetic tool, from sequences to reconciliations. Bioinformatics 36:4822–4824. https://doi.org/10.1093/bioinformatics/btaa615

    Article  CAS  PubMed  Google Scholar 

  56. Bansal MS, Kellis M, Kordi M, Kundu S (2018) RANGER-DTL 2.0: rigorous reconstruction of gene- family evolution by duplication, transfer and loss. Bioinformatics 34:3214–3216. https://doi.org/10.1093/bioinformatics/bty314

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Davín AA, Tricou T, Tannier E, de Vienne DM, Szöllősi GJ (2019) Zombi: a phylogenetic simulator of trees, genomes and sequences that accounts for dead linages. Bioinformatics 36:1286–1288. https://doi.org/10.1093/bioinformatics/btz710

    Article  CAS  PubMed Central  Google Scholar 

  58. Briand S, Dessimoz C, El-Mabrouk N, Lafond M, Lobinska G (2020) A generalized Robinson-Foulds distance for labeled trees. BMC Genomics 21:779. https://doi.org/10.1186/s12864-020-07011-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Tannier E, Bazin A, Davín AA, Guéguen L, Bérard S, Chauve C (2020) Ancestral genome organization as a diagnosis tool for phylogenomics. In: Scornavacca C, Delsuc F, Galtier N (eds) Phylogenetics in the genomic era. https://hal.science/hal-02535466

Download references

Acknowledgments

The authors CC and EC were supported by the Natural Sciences and Engineering Research Council of Canada. This work benefited from the support of the Digital Research Alliance of Canada. DD was supported by the MODS project funded from the program “Profilbildung 2020” (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of North Rhine-Westphalia.

Author information

Authors and Affiliations

  1. Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada

    Evan P. Cribbie & Cedric Chauve

  2. Department for Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, German Diabetes Center (DDZ), Leibniz Institute for Diabetes Research, and Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany

    Daniel Doerr

Authors
  1. Evan P. Cribbie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Doerr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cedric Chauve
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cedric Chauve .

Editor information

Editors and Affiliations

  1. Department of Biochemistry, University of Sao Paulo, Sao Paulo, São Paulo, Brazil

    João Carlos Setubal

  2. Bioinformatics Group, Universität Leipzig, Leipzig, Sachsen, Germany

    Peter F. Stadler

  3. Technische Fakultät, Universität Bielefeld, Bielefeld, Germany

    Jens Stoye

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Cribbie, E.P., Doerr, D., Chauve, C. (2024). AGO, a Framework for the Reconstruction of Ancestral Syntenies and Gene Orders. In: Setubal, J.C., Stadler, P.F., Stoye, J. (eds) Comparative Genomics. Methods in Molecular Biology, vol 2802. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3838-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3838-5_10

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3837-8

  • Online ISBN: 978-1-0716-3838-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation