Abstract

BackgroundHomology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools.MethodsOur analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome.ResultsThe protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, “KO + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB”, with 16,938 and 27,701 orthologous groups, respectively.Such new orthologous databases were used for a regular OrthoSearch run. By confronting “KO + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB” databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %).Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13 orthologous groups which represent potential protozoan targets; these were found because of our distant homology approach.We also provide the number of species-specific, pair-to-pair and core groups from such analyses, depicted in Venn diagrams.ConclusionsThe orthologous databases generated by our HMM-based methodology provide a broader dataset, with larger amounts of orthologous groups when compared to the original databases used as input. Those may be used for several homology inference analyses, annotation tasks and protozoan targets identification.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1090-0) contains supplementary material, which is available to authorized users.

Highlights

  • Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another

  • OrthoSearch for Orthologous Database building Table 1 shows details on how many orthologous group (OG) remained intact and directly migrated to the n-orthologous databases (OD) created by our methodology as well as those that were expanded

  • We were able to create two n-ODS, “Kegg Orthology (KO) + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB”, with each providing a larger amount of OGs when compared to the original

Read more

Summary

Introduction

Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. We propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools. There are several protozoan related diseases, which affect more than 25 % of the world population, such as Chagas’ disease, Human African Trypanosomosis, Leishmaniosis, Amoebiosis, Giardiosis, Toxoplasmosis, Cryptosporidiosis, Theileriosis, Babesiosis among many others [5,6,7,8,9,10]. Among the 17 NTDs listed by WHO, three are caused by protozoan organisms: Chagas’ disease (Trypanosoma cruzi), Human African Trypanosomosis (Trypanosoma brucei) and Leishmaniosis (Leishmania spp.) [11]

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call