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A database for the taxonomic and phylogenetic identification of the genus Bradyrhizobium using multilocus sequence analysis

  • 1, 2,
  • 1Email author,
  • 2 and
  • 2
BMC Genomics201516 (Suppl 5) :S10

https://doi.org/10.1186/1471-2164-16-S5-S10

  • Published:

Abstract

Background

Biological nitrogen fixation, with an emphasis on the legume-rhizobia symbiosis, is a key process for agriculture and the environment, allowing the replacement of nitrogen fertilizers, reducing water pollution by nitrate as well as emission of greenhouse gases. Soils contain numerous strains belonging to the bacterial genus Bradyrhizobium, which establish symbioses with a variety of legumes. However, due to the high conservation of Bradyrhizobium 16S rRNA genes - considered as the backbone of the taxonomy of prokaryotes - few species have been delineated. The multilocus sequence analysis (MLSA) methodology, which includes analysis of housekeeping genes, has been shown to be promising and powerful for defining bacterial species, and, in this study, it was applied to Bradyrhizobium, species, increasing our understanding of the diversity of nitrogen-fixing bacteria.

Description

Classification of bacteria of agronomic importance is relevant to biodiversity, as well as to biotechnological manipulation to improve agricultural productivity. We propose the construction of an online database that will provide information and tools using MLSA to improve phylogenetic and taxonomic characterization of Bradyrhizobium, allowing the comparison of genomic sequences with those of type and representative strains of each species.

Conclusion

A database for the taxonomic and phylogenetic identification of the Bradyrhizobium, genus, using MLSA, will facilitate the use of biological data available through an intuitive web interface. Sequences stored in the on-line database can be compared with multiple sequences of other strains with simplicity and agility through multiple alignment algorithms and computational routines integrated into the database. The proposed database and software tools are available at http://mlsa.cnpso.embrapa.br, and can be used, free of charge, by researchers worldwide to classify Bradyrhizobium, strains; the database and software can be applied to replicate the experiments presented in this study as well as to generate new experiments. The next step will be expansion of the database to include other rhizobial species.

Keywords

  • Bradyrhizobium database
  • Taxonomic of Prokaryotes
  • Phylogeny of Prokaryotes
  • Multilocus Sequence Analysis
  • 16S rRNA Gene
  • Bioinformatics
  • Pattern Recognition

Background

Taxonomy of prokaryotes is gaining increasing attention duo to both the valoration of biodiversity and the recognition of the economic value of many microorganisms. Phylogenetic studies are also key for determining the exact taxonomic position of organisms, as well as to determine their evolutionary history, indicating their relations with other groups and their places in families and kingdoms.

Bacterial phylogeny is based mainly on sequence data of biological macro-molecules; highly conserved molecules help to compare distantly related organisms, whereas molecules that change rapidly help to elucidate small and recent changes [1]. The 16S rRNA gene is broadly elected as the backbone of prokaryote taxonomy and phylogeny [2] and repositories of both 16S rRNA genes and other biological data are increasing every day, generating large datasets [3]; efficient organization of this information is critical to scientific progress.

The term "rhizobia" applies to soil-borne bacteria that are capable of fixing atmospheric nitrogen N2 in symbioses with, and for the benefit of, plants, the vast majority of which are legumes. Yearly, billions of dollars are saved worldwide thanks to the action of rhizobia, in crops that otherwise would require application of nitrogen fertilizers to achieve optimal yields. However, despite their importance to the agriculture and to the environment, studies on phylogeny and taxonomy of rhizobia are relatively scarce, including in some countries where genetic diversity is high, such as Brazil [4]. The genus Bradyrhizobium, used in this study, is currently composed of 19 species recognized by the International Committee of Taxonomy; it has been suggested to be the ancestor of all rhizobia, having originated in the tropics e.g. [58]. The genus includes important strains, such as those known to contribute superior rates of N2 fixation to grain crops such as soybean (Glycine max (L.) Merr.) [9]. However, one main limitation in taxonomy and phylogeny studies of Bradyrhizobium is that its 16S rRNA gene is highly conserved, making it difficult to capture the diversity observed in other phenotypic and genotypic analyses and to define and delineate species [4, 1013]. Therefore, one interesting approach has been to use the multilocus sequencing analysis (MLSA) methodology, including the analysis of housekeeping genes which is conserved but with a higher rate of evolution, to more precisely detect diversity within the genus Bradyrhizobium [8, 12, 14].

Some technologies have been developed in order to improve the identification process of biological entities, such as PseudoMLSA Database [15] and EZTaxon [16]. The former has a model similar to that proposed in our study, including the possibility of performing similarity searches using Blast [17], phylogenetic inference by CLUSTAL Omega [18] and PHYLIP [19] for Pseudomonas species. With EZTaxon [16] it is possible to identify all types of prokaryotes, using an information database along with 16S rRNA gene sequences. By contrast, our study provides a new database with the combination of different software tools for multiple sequence alignments and techniques for automatic pre-processing and post-processing the genomic sequences that are necessary for carrying out the MLSA, and, hence, identify biological entities.

The database for taxonomic identification and phylogenetics of the genus Bradyrhizobium through MLSA described in our study represents a repository for genomic sequences of Bradyrhizobium species. The main objective is to be an online database, open sourced with helpful information and tools in order to elucidate the taxonomy and phylogenetic analysis of these organisms. The current version of the database represents a selection of genes assigned to the genus Bradyrhizobium that are commonly used and are validated, and were updated through June 2014. The web interface developed for this system enables users to perform analyses of similarity of their datasets, as well as to make queries and downloads in the stored genomic sequences.

The need for a more informative database of species of rhizobia with useful genes for applying the MLSA methodology results from the fact that currently generated sequences for identification and rating of these organisms are scattered across various databases, and gathering this information is a time-consuming process. We started the procedure with the genus Bradyrhizobium - i.e. the most difficult in terms of rhizobial taxonomy - due to its highly conserved 16S rRNA gene sequence [914] and due to interest in its evolution since it is considered as the ancestor of all rhizobia [59]. In due course, the database will be expanded to include other rhizobial species.

Current Taxonomic Analysis

Taxonomic consensus is best achieved when different types of data and information (phenotypic, genotypic, phylogenetic) are combined. This integrated model of information is called polyphasic taxonomy, and a bacterial species is defined as a group of genomically alike strains that share a high degree of similarity in several independent features [20]. The phenotypic data are obtained through studies involving gene expression, protein analysis and function, chemotaxonomic markers, and other characteristics that correspond to the final expression of genes [2123]. For genotyping studies, the information is obtained from both DNA and RNA. Various methodologies can be cited for this purpose, including G+C mol% of DNA; DNA-DNA hybridization (DDH); restriction-fragment-length polymorphism (RFLP); pulsed-field gel electrophoresis (PFGE); gene sequencing; and PCR-fingerprinting [24]. The DDH method is based on physico-chemical properties of the DNA and has been required for the definition of most prokaryote species. However, DDH has several limitations, including low reproducibility among laboratories, high labour demand, cost and time consumption due to the need for hybridization of a large number of strains [23, 25]. Furthermore, there is no database that allows the comparison of results from different studies [26].

Comparisons of the ribosomal 16S rRNA gene represent the basis of modern taxonomic analysis; important databases comprise 16S rRNA genes, such as the ribosomal database project at https://rdp.cme.msu.edu. However, a limitation is the high degree of nucleotide-sequence conservation in this gene across genera-including Bradyrhizobium-makiiig it difficult to distinguish closely related species [24, 2732]. Consequently, it is important to develop new techniques that can complement the results obtained from 16S rRNA gene-sequence data, as well as replace DDH for taxonomic purposes. It is also important to establish databases that facilitate analyses of new strains.

Multilocus Sequence Analysis (MLSA)

Identifying organisms as prokaryotic and the delineation of species are the main foci of the taxonomy of microorganisms [33]. Thus, although the levels of identity-obtained in the analysis of the sequences of the 16S rRNA gene and of DDH are still considered as molecular criteria for classification of species, it is expected that additional taxonomic information can be obtained from complete genome sequences [34], and MLSA has been increasingly suggested as a replacement for DDH [9, 35, 36].

MLSA represents a strategic alternative to avoid the effects of genetic recombination and horizontal transfer occurring in a specific single gene [33, 35]. In addition, it can clarify the distinction between highly related species, or species where the analysis of the 16S rRNA genes shows low resolution, since the chosen housekeeping genes-comprising genes involved in cellular metabolism, i.e. those essential for the survival of the microorganism [35]-present faster evolutionary rates than do the ribosomal genes, but with a level of conservation sufficient to reveal evolutionary information [21, 24, 25, 27, 36]. The choice of housekeeping genes should follow certain criteria, including: i) presence in the genome in a single copy; ii) being distributed in the genome with a minimal distance between the genes of 100 kb; iii) containing sufficient nucleotide length to allow its sequencing; iv) containing sufficient information for its analysis [13, 25, 27, 3638].

The MLSA methodology has been increasingly used to improve bacterial taxonomy, providing a tool suitable for defining species and revealing their taxonomic relationships. Several studies have shown that MLSA may provide high resolution, allowing the discrimination of isolates at the species level [14, 25, 36, 3841], which would not be possible by analysis exclusively by 16S rRNA-gene sequencing [12, 33, 35]. The distinction at the species level is achieved by MLSA analysis through algorithms for estimating evolutionary distance between strains. In the particular case of rhizobia, housekeeping genes used in recent years as phylogenetic markers for the species classification include atpD, recA, glnA, glnB, dnaK, thrC and git A [4]. However, taking into account the large number of microorganisms that remain to be identified and classified, and the improvement of microbiology data generation, there is need for the development of new databases and software tools for their analysis [33, 35].

Construction and content

The computational infrastructure used to provide the set of services described in this work is hosted at the National Soybean Research Center of the Brazilian Agricultural Research Corporation (Embrapa Soja). All applications and tools required for the operation of the database were configured for the platform Linux Ubuntu Server 4.13 with Apache 2.4.7, the MySQL database-management system, and the phpAdmin 4.2.2 data-modelling tool.

The relational model of the proposed database follows the scheme proposed by the BioSQL project [42], considering that it is a standard solution for storing sequences of molecular modelling, and it has compatibility with other bioinformatics projects such as BioPerl, BioPython, BioJava and BioRuby. The database was developed by considering the same data structure used in GenBank [43]. Therefore, it is expected that the database-updating process will not be a time-consuming task, and its usability can be improved in the future. BioSQL allows customization of its schema through extension modules, such as the PhyloDB, which allows the storage of taxonomy and phylogenetic trees. Besides MySQL, relational databases such as PostgreSQL, HSQLDB, Apache Derby and Oracle also support this bioinformatics tool. The adopted BioSQL schema is available as additional file 1.

GenBank files are used to provide the required information and keep it updated in the database. Sequences, resources and notes are included in the database from BioPython scripts and the SeqIO module [44]. Multiple alignments were adopted by means of the algorithms CLUSTAL Omega [18] and MUSCLE [45]. The verification of the homology between nucleotides of the bacterial genes was also integrated as a software tool into the web interface of the proposed database. This process is very important for identifying regions aligned among various species and plays a key role in the application of the MLSA methodology, in order that only after aligning and trimming of all the analysed sequences of equal size, it is possible to perform the phylogenetic and taxonomic inferences of the analysed species. The multiple sequence alignment is performed by means of web services developed by the European Bioinformatics Institute (EMBL-EBI), available for CLUSTAL Omega [http://www.ebi.ac.uk/Tools/webservices/services/msa/clustalo_soap] and for MUSCLE [http://www.ebi.ac.uk/Tools/webservices/services/msa/muscle_soap].

Finally, scripts in PHP and Java Script were developed in order to parameterize and to perform the post processing of the bioinformatics tools available in the database. These scripts are important to make the cropping areas of common genes aligned, allowing individual analyses of these genes and concatenating the loci for the application of the MLSA methodology.

The database presented in this work consists of 286 genomic sequences, distributed in six specific housekeeping genes, namely: atpD, dnaK, glnll, recA, gyrB and rpoB. Nineteen species of the Bradyrhizobium genus were considered: B. betae, B. canariense, B. cytsi, B. daqingense, B. denitrificans, B. diazoefficiens, B. elkanii, B. huanghuaihaiense, B. icense, B. iriomotense, B. japonicum, B. jicamae, B. lablabi, B. liaoningense, B. oligorophicum, B. pachyrhizi, B. paxllaeri, B. rifense and B. yuanmingense.

For species such as B. canariense, B. diazoefficiens, B. elkani, B. japonicum, B. liaoningense and B. yuanmingense other reference strains were included in order to improve the molecular and phylogenetic characterizations and to refine the process of comparison of results. Accession numbers of the sequences used in this work are available in Table 1 and for building the phylogenetic trees, the species Rhodopseudomonas palustris was adopted as an outgroup.
Table 1

GenBank accession numbers of the sequences used in this work.

Strain

Genome

atpD

dnaK

glnll

recA

gyrB

rpoB

B. betae LMG 21987 T

 

FM253129.1

AY923046.1

AB353733.1

AB353734.1

FM253217.1

FM253260.1

B. canariense LMG 22265 T

 

AY386739.1

AY923047.1

AY386765.1

FM253177.1

FM253220.1

FM253263.1

B. cylisiCTAW 11 T

 

GU001613.1

KF532219.1

GU001594.1

GU001575.1

KF532653.1

JN186288.1

B. dagingense CCBAU 15774 T

 

HQ231289.1

KF962684.1

HQ231301.1

HQ231270.1

KF962694.1

JX437676.1

B. deniirificans 8443

 

FM253153.1

KF962685.1

HM047121.1

FM253196.1

FM253239.1

FM253282.1

B. diazoefficiens USDA 110 T

NC 004463.1

NC 004463.1

NC 004463.1

NC 004463.1

NC 004463.1

NC 004463.1

NC 004463.1

B. elkanii USDA 76 T

 

AY386758.1

AY328392.1

AY599117.1

AY591568.1

AM418800.1

AM 295348.1

B. huanghuaihaiense CCBAU 23303 T

 

HQ231682.1

KF962686.1

HQ231639.1

HQ231595.1

KF962695.1

HQ428068.1

B. iriomoiense EK 05 T

 

AB300994.1

JF308944.1

AB300995.1

AB300996.1

AB300997.1

HQ587646.1

B. japonicum USDA 6 T

 

AM168320.1

AM168362.1

AF169582.1

AM182158.1

AM418801.1

AM295349.1

B. jicamae PAC 68 T

 

FJ428211.1

JF308945.1

FJ428204.1

HM047133.1

HQ873309.1

HQ587647.1

B. lablabi CCBAU 23086 T

 

GU433473.1

KF962687.1

GU433498.1

GU433522.1

KF962696.1

JX437677.1

B. liaoningense LMG 18230 T

 

AY386752.1

AY923041.1

AY386775.1

AY591564.1

FM253223.1

FM253266.1

B. pachyrhizi PAC 48 T

 

FJ428208.1

JF308946.1

FJ428201.1

HM047130.1

HQ873310.1

HQ587648.1

B. rifense CTAW 71 T

 

GU001617.1

KF532220.1

GU001604.1

GU001585.1

KF532666.1

KC569468.1

B. yuanmingense LMG 21827 T

 

AY386760.1

AY923039.1

AY386780.1

AM168343.1

FM253226.1

FM253269.1

B. icense LMTR 13

 

KF896192.1

KF896182.1

KF896175.1

JX943615.1

KF896201.1

 

B. oligoirophicum LMG 10732

 

JQ619232.1

KF962688.1

JQ619233.1

JQ619231.1

KF962697.1

KF962713.1

B. paxllaeri LMTR 21

 

KF896186.1

AY923038.1

KF896169.1

JX943617.1

KF896195.1

 

Rhodopseudomonas palusiris CGA009

NC 005296.1

NC 005296.1

NC 005296.1

NC 005296.1

NC 005296.1

NC 005296.1

NC 005296.1

SEMIA 5025

 

FJ390951

FJ390991

FJ391031

FJ391151

  

SEMIA 5045

 

FJ390954

FJ390994

FJ391034

FJ391154

  

SEMIA 5060

 

JX867237.1

JX867240.1

JX867241.1

JX867239.1

JX867245.1

JX867242.1

SEMIA 5062

 

FJ390955

FJ390995

FJ391035

FJ391155

  

SEMIA 5079

CP007569.1

FJ390956.1

FJ390996.1

FJ391036.1

FJ391156.1

CP007569

CP007569

SEMIA 5080

 

FJ390957.1

FJ390997.1

FJ391037.1

FJ391157.1

JX867246.1

JX867243.1

SEMIA 511

 

FJ390942

FJ390982

FJ391022

FJ391142

  

SEMIA 512

 

FJ390943

FJ390983

FJ391023

FJ391143

  

SEMIA 560

 

FJ390944

FJ390984

FJ391024

FJ391144

  

SEMIA 6014

 

FJ390958

FJ390998

FJ391038

FJ391158

  

SEMIA 6028

 

FJ390959

FJ390999

FJ391039

FJ391159

HQ634886

HQ634905

SEMIA 6053

 

FJ390960

FJ391000

FJ391040

FJ391160

HQ634887

HQ634906

SEMIA 6059

 

FJ390961.1

FJ391001.1

FJ391041.1

FJ391161.1

JX867247.1

JX867244.1

SEMIA 6069

 

FJ390962

FJ391002

FJ391042

FJ391162

  

SEMIA 6077

 

FJ390963

FJ391003

FJ391043

FJ391163

  

SEMIA 6093

 

FJ390964

FJ391004

FJ391044

FJ391164

  

SEMIA 6099

 

FJ390965

FJ391005

FJ391045

FJ391165

  

SEMIA 6101

 

FJ390966

FJ391006

FJ391046

FJ391166

  

SEMIA 6144

 

HQ634873

EU196049

HQ634879

HQ634897

HQ634888

HQ634907

SEMIA 6146

 

FJ390967

FJ391007

FJ391047

FJ391167

  

SEMIA 6148

 

FJ390968

FJ391008

FJ391048

FJ391168

HQ634890

HQ634909

SEMIA 6152

 

FJ390969

FJ391009

FJ391049

FJ391169

  

SEMIA 6156

 

FJ390970

FJ391010

FJ391050

FJ391170

  

SEMIA 6160

 

FJ390971

FJ391011

FJ391051

FJ391171

HQ634892

HQ634911

SEMIA 6163

 

FJ390972

FJ391012

FJ391052

FJ391172

  

SEMIA 6164

 

FJ390973

FJ391013

FJ391053

FJ391173

  

SEMIA 6179

 

FJ390974

FJ391014

FJ391054

FJ391174

  

SEMIA 6186

 

FJ390975

FJ391015

FJ391055

FJ391175

  

SEMIA 6187

 

FJ390976

FJ391016

FJ391056

FJ391176

  

SEMIA 6192

 

FJ390977

FJ391017

FJ391057

FJ391177

  

SEMIA 6319

 

FJ390978

FJ391018

FJ391058

FJ391178

  

SEMIA 6374

 

FJ390979

FJ391019

FJ391059

FJ391179

  

SEMIA 6434

 

FJ390980

FJ391020

FJ391060

FJ391180

  

SEMIA 6440

 

FJ390981

FJ391021

FJ391061

FJ391181

 

treeclusta Iomega

SEMIA 656

 

FJ390946

FJ390986

FJ391026

FJ391146

HQ634882

HQ634901

SEMIA 695

 

FJ390947

FJ390987

FJ391027

FJ391147

  

SEMIA 928

 

FJ390948

FJ390988

FJ391028

FJ391148

  

Rhizobium pisi strain DSM 30132

 

EF113149.1

JQ795193.1

JN580715.1

EF113134.1

JQ795183.1

JQ795190.1

All genes chosen in our work were verified for the MLSA requirements stated previously [13, 25, 27, 3638]. Our main goal is to allow, in a web environment, the search, analysis and phylogenetic inferences of the genus Bradyrhizobium. An overview of the steps and how they are interconnected is shown in Figure 1.
Figure 1
Figure 1

Workflow for the taxonomic identification of Bradyrhizobium genus.

Observing Figure 1, we see that the user must provide data from one to six genes in the analysis. The next step consists of loading of the sequences stored in the database according to the sequences of the genes inserted by the user. Thus, the multiple alignment is performed by considering the input and the database sequences through the EBI-EML web service from which the user can choose to use the CLUSTAL Omega or MUSCLE algorithms. After performing the multiple alignment, a script will select and cut off the aligned regions of all sequences related to each specific gene. This task will produce sequences of equal sizes. After the alignment of all sequences for each one of the three genes, a new script will perform a concatenation of the gene sequences, thus producing a new sequence. At the end of this process, a new multiple alignment is performed with the concatenated sequences, and the results are processed by a script in order to produce the following outputs:

  • Similarity Matrix/score;

  • Text with the results of the multiple gene alignments;

  • Parameters for phylogenetic tree generating;

which will assist in the classification of the organism.

The similarity matrix (score) produces an objective result, from which it is possible to verify the proximity between sequenced species (input) and all species available in the Bradyrhizobium database containing the three selected genes by the user.

Utility and Discussion

In our study, validation was performed by using 16 strains, 14 of which represent type strains of the genus Bradyrhizobium: B. betae LMG 21987 T , B. canariense LMG 22265 T , B. cytsi CTAW11 T , B. diazoefficiens SEMI A 5060, B. diazoefficiens SEMIA 5080, B. diazoefficiens SEMIA 6059, B. diazoefficiens USDA 110 T , B. elkanii USDA 76 T , B. iriomotense EK05 T , B. japonicum USDA 6 T , B. japonicum SEMIA 5079, B. jicamae PAC 68 T , B. lablabi CCBAU 23086 T , B. lianingense LMG 18230T. A sequence representing an outgroup was included in the database: Rhodopseudomonas palustris CGA009. The last adopted sequence belongs to R. pisi DSM 30132T, included as a negative control, i.e. a strain belonging to the genus Rhizobium rather than Bradyrhizobium. All genome sequences were collected from GenBank [43].

As presented in Sec. "Multilocus Sequence Analysis (MLSA)", the analysis of multiple genes in bacterial taxonomy consists of the joint sequencing (one concatenated sequence) analysis of housekeeping genes, and it has been proposed that, initially, at least five genes should be analysed [21, 24, 38]. For the MLSA methodology in this study, we proposed the use from one to six housekeeping genes, based on results obtained in recent studies, that similar results were obtained with three and with five genes [14, 3941, 4649]. However, as mentioned before, our site allows the analysis from one to six genes. The genes chosen as an input test were combined in three subgroups: (atpD, dnaK, glnll), (atpD, dnaK, glnII, recA) and (dnaK, recA, gyrB). Table 2 shows how the subsets of tests were assembled. Although it is present in the database, the rpoB gene was not used in the test because there were no available sequences for 29 strains. The default values to perform the alignment algorithms can be observed in Table 3. It has been generally accepted that strains with 16S rRNA gene similarities higher than 97.00% belong to the same species [23, 50], but later, with the analyses of several 16S rRNA gene sequences, [51] proposed a cut-off value of 98.70-99.00%. However, when genes other than those for 16S rRNA gene are considered, lower values can be accepted. For example, [52] proposed an average nucleotide identity (ANI) value of 96.00%. However, for this study, we were strict, and for the tests, we assumed an initial cut-off of 98.70% in the MLSA analysis.
Table 2

Subset of genes used to test the proposed database by the MLSA methodology.

Quantity of Strains

Quantity of Strains for Genes Used

Quantity Genes

Algorithm for the Multiple Sequence Alignment

Genes Used

16

57

3

CLUSTAL Omega

atpD, dnaK, glnll

16

30

3

CLUSTAL Omega

dnaK, recA, gyrB

16

57

4

CLUSTAL Omega

atpD, dnaK, glnll, recA

16

57

3

MUSCLE

atpD, dnaK, glnll

16

30

3

MUSCLE

dnaK, recA, gyrB

16

57

4

MUSCLE

atpD, dnaK, glnll, recA

Table 3

Parameters for the execution of multiple sequence alignment algorithm.

Algorithm

Parameter

Value

Algorithm

Parameter

Value

CLUSTAL Omega

Sequence type

DNA

MUSCLE

Output format

Pearson/Fasta

CLUSTAL Omega

Output format

Pearson/Fasta

MUSCLE

Output tree

none

CLUSTAL Omega

Dealing input sequences

false

MUSCLE

Output order

aligned

CLUSTAL Omega

Mbed-like clustering guide-tree

true

   

CLUSTAL Omega

Mbed-like clustering iteration

true

   

CLUSTAL Omega

Number of combined iterations

0

   

CLUSTAL Omega

Max guide tree iterations

-1

   

CLUSTAL Omega

Max hmm iterations

-1

   

CLUSTAL Omega

Order

aligned

   

The table available as additional file 2 shows an identity matrix created where the values represent the similarity values between the sequences of the species of the database and the species used for the input test, Bradyrhizobium betae LMG 21987 t . AS expected, the similarity rate of 100% was found between the input test and the species B. betae LMG 21987 T . The similarity matrix also allows confirms the current taxonomy of the Bradyrhizobium genus (5), with B. betae LMG 21987 T showing higher similarity with B. diazoefficiens strains SEMIA 5060, SEMIA 5080, SEMIA 6059 and with the type strain B. diazoefficiens USDA 110 T , of 96.29%, 96.13%, 96.06% and 96.06, respectively. None of the three strains was found to be the same species as the input test because they are all below the cut-off of 98.70%.

The table available as additional file 3 shows the values for the accuracy, precision, recall and f-score achieved with the software and strains available in the proposed database, these measures were calculated using data from the result of Matrix Identity generated by the analysis of multiple genes, with the use of the proposed cut-off of 98.70% for minimum similarity. The data sets used for the tests are described in additional file 3 and represent how the genomic sequences were grouped for analysis of multiple genes, along with the chosen implementation for multiple alignment algorithm. The data sets SI, S2 and S3 were analysed by the algorithm CLUSTAL OMEGA, and the combinations of the genes for these assemblies were arranged as (ATPD+DnaK+glnll), (dnaK+recA+gyrB) and (atpD+DnaK+glnll+recA) respectively. In the case of data sets S4, S5 and S6, the selected genes were the same as previous data sets, including maintaining the order, however, the analysis was performed using the MUSCLE algorithm. Each of the sequences was tested six times taking into consideration the parameters described above, and the results are shown in Tables 4 and additional file 3. The different subsets of genes resulted in differences in the results of the multiple alignments.
Table 4

Summary of the results.

Algorithm

Genes

Analysed Organisms

Cut Off Used

True Positive

False Positive

True Negative

False Negative

Muscle

atpD dnaK glnll recA

57

98.70%

33

0

853

26

Clustal Omega

atpD dnaK glnll recA

57

98.70%

30

0

857

25

Clustal Omega

dnaK recA gyrB

30

98.70%

27

0

445

8

Muscle

atpD dnaK glnll

57

98.70%

26

6

847

33

Muscle

dnaK recA gyrB

30

98.70%

25

0

445

10

Clustal Omega

atpD dnaK glnll

57

98.70%

24

0

853

35

Using algorithm CLUSTAL Omega the subset of genes atpD+dnaK+glnll shows values of 96.16% for accuracy, 100.00% for precision, 65.83% for recall and 73.64% for f-score, while considering the subset of genes dnaK+recA+gyrB, the values were of 98.33%, 100.00%, 85.78% and 88.89%, for subset with 4 genes atpD+dnaK+glnll, the values were of 97.26%, 100.00%, 75.39% and 81.39% for accuracy, precision, recall and f-score, respectively.

Using MUSCLE algorithm for analyse the same subset of genes atpD+dnaK+glnll shows values of 95.72% for accuracy, 92.00% for precision, 66.94% for recall and 71.26% for f-score, while considering the subset of genes dnaK+recA+gyrB, the values were of 97.92%, 100.00%, 82.44% and 86.98%, and for subset with 4 genes atpD+dnaK+glnll, the values were of 97.15%, 100.00%, 77.50% and 82.59% for accuracy, precision, recall and f-score, respectively.

Using the CLUSTAL Omega algorithm and the dnaK+recA+gyrB genes, the strain B. diazofficiens SEMIA 5080 was correctly identified as B. diazoefficiens; the classification indicated similarities of 99.92% with strain SEMIA 5060, of 99.52% with SEMIA 6059 and of 99.20% with the type strain B. diazoefficiens USDA 110 T . This result indicates the correctness of the method for the classification of these SEMIA strains, which are different but fit into the same B. diazoefficiens species. The genes atpD+dnak+glnll analysed with the same algorithm showed similarities of 99.84% with B. diazoefficiens SEMIA 5060, 99.59% with B. diazoefficiens USDA 110 T and 85.28% for B. diazoefficiens 6059.

In an additional test, considering the sequences related to B. japonicum strain SEMIA 5079 as input, we found that genes dnaK+atpD+glnll analysed with the CLUSTAL algorithm Omega resulted in the correct identification of the species and that the strain showed similarity with other strains, of 99.69% with B. japonicum USDA 6 T and of 98.84% with SEMIA 511. When analysed with the MUSCLE algorithm, the results were of 99.69% with B. japonicum UADA 6 T , of 99.30% with SEMIA 512 and of 98.83% with SEMIA 511.

Another result demonstrating increased precision from the selection of certain genes was observed in the analysis of the species B. liaoningense LMG 18230 T . When atpD+dnaK+glnll+recA genes were chosen, the algorithm CLUSTAL Omega presented a similarity of 97.60% between the type strain with the strain SEMIA 5025, while Muscle algorithm shows a 97.50% of similarity, whereas the analysis of atpD+dnaK+glnll genes resulted in a similarity of 97.17% using the Omega CLUSTAL and of 97.20% using the MUSCLE algorithm.

When the test set was used with genomic sequences of the species Rhizobium pisi, the classification resulted in values ranging from 30.00% to 82.15%, considering all the combinations involving alignment algorithms and subsets of genes. The results indicate the correct classification of Rhizobium pisi as not belonging to a species of Bradyrhizobium as described in additional file 3.

Figure 1 shows the outputs for taxonomic and phylogenetic identification available in the proposed database. The identification of the genus Bradyrhizobium through MLSA also brings the results of multiple alignment and parameters for creating phylogenetic trees, both of which have bearing on the phylogenetic implications regarding the organisms of interest [53]. The alignment of a single sequence obtained from the concatenation of three genes produced by the application of the MLSA methodology was used to better explain how the phylogenetic tree can be inferred from the database analysis. A phylogenetic tree was produced with Mega software version 6 [54] shows in Figure 2, by considering the previous results shown in Table available as additional file 2. In this figure, it is possible to verify the correct classification of the test species, as well as the species B. betae LMG 21987 T with 100% similarity.
Figure 2
Figure 2

Phylogenetic tree created from the results of three genes concatenated by the proposed methodology, strain of test based in B. betae LMG 21987, the evolutionary history was inferred using the Neighbour-Joining [55]. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches [56]. The evolutionary distances were computed using the Tamura-Nei method [57] and are in the units of the number of base substitutions per site. The analysis involved 25 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 1152 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 [54].

Conclusion

This work was developed in order to provide a database for the taxonomic and phylogenetic identification of the genus Bradyrhizobium by using the multilocus sequence analysis (MLSA) methodology. More specifically, the following tools and database functionality were developed:

  • a database based on a relational model using BioSQL to store data and to maintain the interoperability between bioinformatics projects such as BioPerl, BioPython and BioJava;

  • a database with validated information of Bradyrhizobium species through a friendly web interface for users;

  • computational tools suitable for the automatic data mining, analysis and classification of genomic sequences;

  • computational scripts for the automatic updating of the database with sequences used in the identification and classification process;

The experimental results indicate that the proposed database and the computational tools correctly distinguished species of the same genus and with high similarity rates, reinforcing the efficiency of the MLSA methodology. The Results also show that for the efficient use of the MLSA database it is important to know the combinations of genes that will be used in the taxonomic analysis, as well as the similarity rates that could be used for each genus. Therefore, it is necessary to perform previous tests in order to achieve the best results. The proposed database provides useful information for research in taxonomy and molecular phylogeny of the genus Bradyrhizobium, taking into account the possibility of gathering into a single database information that is commonly needed for studies of these microorganisms and is fragmented in various sources and formats. The current database contains 286 entries of gene sequences of the Bradyrhizobium genus. However, further studies are planned to include sequences of other rhizobial genera: Rhizobium, Sinorhizobium, Azorhizobium, Mesorhizobium and Neorhizobium. There is also the possibility of increasing the number of genes to be analysed. Finally, it is important to integrate the current results with other software packages that allow the visualization of the results directly into a web page, creating an association that will make it even more simple and practical to interpret phylogenetic implications from the proposed database.

Declarations

Acknowledgements

This work was supported by CNPq and Fundação Araucária. We thank to Dr. Renan A. Ribeiro and Jakeline Delamuta for helping in providing sequences and discussion.

Declarations

The authors declare that funding for publication of the article was sponsored by UTFPR - Federal University of Technology - Paraná and CNPq grant # 562008/2010-1.

This article has been published as part of BMC Genomics Volume 16 Supplement 5, 2015: Proceedings of the 10th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2014). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/16/S5.

Authors’ Affiliations

(1)
Federal University of Technology - Paraná, Av. Alberto Carazzai, 1640, 86300-000 Cornélio Procópio, Brazil
(2)
Empresa Brasileira de Pesquisa Agropecuária - Embrapa, João Carlos Strass, Londrina, Brazil

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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