A New Paradigm for Identifying Reconciliation-Scenario Altering Mutations Conferring Environmental Adaptation

Authors Roni Zoller, Meirav Zehavi, Michal Ziv-Ukelson

Thumbnail PDF


  • Filesize: 0.64 MB
  • 13 pages

Document Identifiers

Author Details

Roni Zoller
  • Ben Gurion University of the Negev, Israel
Meirav Zehavi
  • Ben Gurion University of the Negev, Israel
Michal Ziv-Ukelson
  • Ben Gurion University of the Negev, Israel


We thank the anonymous WABI reviewers for very helpful comments.

Cite AsGet BibTex

Roni Zoller, Meirav Zehavi, and Michal Ziv-Ukelson. A New Paradigm for Identifying Reconciliation-Scenario Altering Mutations Conferring Environmental Adaptation. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 9:1-9:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


An important goal in microbial computational genomics is to identify crucial events in the evolution of a gene that severely alter the duplication, loss and mobilization patterns of the gene within the genomes in which it disseminates. In this paper, we formalize this microbiological goal as a new pattern-matching problem in the domain of Gene tree and Species tree reconciliation, denoted "Reconciliation-Scenario Altering Mutation (RSAM) Discovery". We propose an O(m * n * k) time algorithm to solve this new problem, where m and n are the number of vertices of the input Gene tree and Species tree, respectively, and k is a user-specified parameter that bounds from above the number of optimal solutions of interest. The algorithm first constructs a hypergraph representing the k highest scoring reconciliation scenarios between the given Gene tree and Species tree, and then interrogates this hypergraph for subtrees matching a pre-specified RSAM Pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by Omega(m * n * k). We implement the new algorithm as a tool, denoted RSAM-finder, and demonstrate its application to the identification of RSAMs in toxins and drug resistance elements across a dataset spanning hundreds of species.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Gene tree
  • Species tree
  • Reconciliation


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. L E Alksne and B A Rasmussen. Expression of the AsbA1, OXA-12, and AsbM1 beta-lactamases in Aeromonas jandaei AER 14 is coordinated by a two-component regulon. Journal of bacteriology, 179(6):2006-2013, 1997. Google Scholar
  2. Timothy L Bailey, Nadya Williams, Chris Misleh, and Wilfred W Li. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic acids research, 34(suppl_2):W369-W373, 2006. Google Scholar
  3. M S Bansal, E J Alm, and M Kellis. Efficient algorithms for the reconciliation problem with gene duplication, horizontal transfer and loss. Bioinformatics, 28(12):i283-i291, 2012. Google Scholar
  4. M S Bansal, E J Alm, and M Kellis. Reconciliation revisited: Handling multiple optima when reconciling with duplication, transfer, and loss. Journal of Computational Biology, 20(10):738-754, 2013. Google Scholar
  5. E Bapteste, M A O'Malley, R G Beiko, M Ereshefsky, J P Gogarten, L Franklin-Hall, F-J Lapointe, J Dupré, T Dagan, Y Boucher, et al. Prokaryotic evolution and the tree of life are two different things. Biology direct, 4(1):34, 2009. Google Scholar
  6. V Berry, F Chevenet, J-P Doyon, and E Jousselin. A geography-aware reconciliation method to investigate diversification patterns in host/parasite interactions. Molecular ecology resources, 18(5):1173-1184, 2018. Google Scholar
  7. Baundauna Bose, Jennifer M Auchtung, Catherine A Lee, and Alan D Grossman. A conserved anti-repressor controls horizontal gene transfer by proteolysis. Molecular microbiology, 70(3):570-582, 2008. Google Scholar
  8. C Brandt, S D Braun, C Stein, P Slickers, R Ehricht, M W Pletz, and O Makarewicz. In silico serine β-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species. Scientific reports, 7:43232, 2017. Google Scholar
  9. K Bush. Past and present perspectives on β-lactamases. Antimicrobial agents and chemotherapy, 62(10):e01076-18, 2018. Google Scholar
  10. MA Charleston. Jungles: a new solution to the host/parasite phylogeny reconciliation problem. Mathematical biosciences, 149(2):191-223, 1998. Google Scholar
  11. R Kumar Chaudhary, G Singh, R Naraian, and S Ram. Structural and functional in-silicoanalysis of toxin-antitoxin proteins in persister cells of pseudomonas aeruginosa. Plant Archives, 18(2):1643-1651, 2018. Google Scholar
  12. L A David and E J Alm. Rapid evolutionary innovation during an Archaean genetic expansion. Nature, 469(7328):93, 2011. Google Scholar
  13. B Donati, C Baudet, B Sinaimeri, P Crescenzi, and MF Sagot. EUCALYPT: efficient tree reconciliation enumerator. Algorithms for Molecular Biology, 10(1):3, 2015. Google Scholar
  14. J-P Doyon, C Chauve, and S Hamel. Space of gene/species trees reconciliations and parsimonious models. Journal of Computational Biology, 16(10):1399-1418, 2009. Google Scholar
  15. J-P Doyon, S Hamel, and C Chauve. An efficient method for exploring the space of gene tree/species tree reconciliations in a probabilistic framework. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(1):26-39, 2011. Google Scholar
  16. B A Evans and S GB Amyes. OXA β-lactamases. Clinical microbiology reviews, 27(2):241-263, 2014. Google Scholar
  17. L Huang and D Chiang. Better k-best parsing. In Proceedings of the Ninth International Workshop on Parsing Technology, pages 53-64. Association for Computational Linguistics, 2005. Google Scholar
  18. J Huerta-Cepas, F Serra, and P Bork. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Molecular biology and evolution, 33(6):1635-1638, 2016. Google Scholar
  19. R Libeskind-Hadas and M A Charleston. On the computational complexity of the reticulate cophylogeny reconstruction problem. Journal of Computational Biology, 16(1):105-117, 2009. Google Scholar
  20. A Marchler-Bauer and S H Bryant. CD-Search: protein domain annotations on the fly. Nucleic acids research, 32(suppl_2):W327-W331, 2004. Google Scholar
  21. O Massidda, M P Montanari, M Mingoia, and P E Varaldo. Borderline methicillin-susceptible Staphylococcus aureus strains have more in common than reduced susceptibility to penicillinase-resistant penicillins. Antimicrobial agents and chemotherapy, 40(12):2769-2774, 1996. Google Scholar
  22. D Merkle, M Middendorf, and N Wieseke. A parameter-adaptive dynamic programming approach for inferring cophylogenies. BMC bioinformatics, 11(1):S60, 2010. Google Scholar
  23. S Mukherjee, D Stamatis, J Bertsch, G Ovchinnikova, O Verezemska, M Isbandi, A D Thomas, R Ali, K Sharma, N C Kyrpides, et al. Genomes OnLine Database (GOLD) v. 6: data updates and feature enhancements. Nucleic acids research, page gkw992, 2016. Google Scholar
  24. R Patro and C Kingsford. Predicting protein interactions via parsimonious network history inference. Bioinformatics, 29(13):i237-i246, 2013. Google Scholar
  25. L Poirel, A Carrër, J D Pitout, and P Nordmann. Integron mobilization unit as a source of mobility of antibiotic resistance genes. Antimicrobial agents and chemotherapy, 53(6):2492-2498, 2009. Google Scholar
  26. A-A Popescu, K T Huber, and E Paradis. ape 3.0: New tools for distance-based phylogenetics and evolutionary analysis in R. Bioinformatics, 28(11):1536-1537, 2012. Google Scholar
  27. C Scornavacca, W Paprotny, V Berry, and V Ranwez. Representing a set of reconciliations in a compact way. Journal of bioinformatics and computational biology, 11(02):1250025, 2013. Google Scholar
  28. F Sievers and D G Higgins. Clustal Omega for making accurate alignments of many protein sequences. Protein Science, 27(1):135-145, 2018. Google Scholar
  29. P JM Stapleton, M Murphy, N McCallion, M Brennan, R Cunney, and R J Drew. Outbreaks of extended spectrum beta-lactamase-producing Enterobacteriaceae in neonatal intensive care units: a systematic review. Archives of Disease in Childhood-Fetal and Neonatal Edition, 101(1):72-78, 2016. Google Scholar
  30. M Stolzer, H Lai, M Xu, D Sathaye, B Vernot, and D Durand. Inferring duplications, losses, transfers and incomplete lineage sorting with nonbinary species trees. Bioinformatics, 28(18):i409-i415, 2012. Google Scholar
  31. J Sukumaran and M T Holder. DendroPy: a Python library for phylogenetic computing. Bioinformatics, 26(12):1569-1571, 2010. Google Scholar
  32. D Szklarczyk, J H Morris, H Cook, M Kuhn, S Wyder, M Simonovic, A Santos, N T Doncheva, A Roth, P Bork, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic acids research, page gkw937, 2016. Google Scholar
  33. R L Tatusov, M Y Galperin, D A Natale, and E V Koonin. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic acids research, 28(1):33-36, 2000. Google Scholar
  34. TH To, E Jacox, V Ranwez, and C Scornavacca. A fast method for calculating reliable event supports in tree reconciliations via Pareto optimality. BMC bioinformatics, 16(1):384, 2015. Google Scholar
  35. A Tofigh. Using trees to capture reticulate evolution: lateral gene transfers and cancer progression. PhD thesis, KTH, 2009. Google Scholar
  36. A Tofigh, M Hallett, and J Lagergren. Simultaneous identification of duplications and lateral gene transfers. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 8(2):517-535, 2011. Google Scholar
  37. M Toth, N T Antunes, N K Stewart, H Frase, M Bhattacharya, C A Smith, and S B Vakulenko. Class D β-lactamases do exist in Gram-positive bacteria. Nature chemical biology, 12(1):9, 2016. Google Scholar
  38. L Van Melderen and M S De Bast. Bacterial toxin-antitoxin systems: more than selfish entities? PLoS genetics, 5(3):e1000437, 2009. Google Scholar