Skip to main content

Genomic comparative analysis and gene function prediction in infectious diseases: application to the investigation of a meningitis outbreak



Next generation sequencing (NGS) is being increasingly used for the detection and characterization of pathogens during outbreaks. This technology allows rapid sequencing of pathogen full genomes, useful not only for accurate genotyping and molecular epidemiology, but also for identification of drug resistance and virulence traits.


In this study, an approach based on whole genome sequencing by NGS, comparative genomics, and gene function prediction was set up and retrospectively applied for the investigation of two N. meningitidis serogroup C isolates collected from a cluster of meningococcal disease, characterized by a high fatality rate.


According to conventional molecular typing methods, all the isolates had the same typing results and were classified as outbreak isolates within the same N. meningitidis sequence type ST-11, while full genome sequencing demonstrated subtle genetic differences between the isolates. Looking for these specific regions by means of 9 PCR and cycle sequencing assays in other 7 isolates allowed distinguishing outbreak cases from unrelated cases. Comparative genomics and gene function prediction analyses between outbreak isolates and a set of reference N. meningitidis genomes led to the identification of differences in gene content that could be relevant for pathogenesis. Most genetic changes occurred in the capsule locus and were consistent with recombination and horizontal acquisition of a set of genes involved in capsule biosynthesis.


This study showed the added value given by whole genome sequencing by NGS over conventional sequence-based typing methods in the investigation of an outbreak. Routine application of this technology in clinical microbiology will significantly improve methods for molecular epidemiology and surveillance of infectious disease and provide a bulk of data useful to improve our understanding of pathogens biology.

Peer Review reports


The recent success of next generation sequencing technologies (NGS), that are now available in many laboratories and sequencing centers, is deeply impacting our capability to investigate biological samples in many fields of research, including microbiology and virology [1, 2]. In this regard, the main applications fostered by NGS are the identification of novel pathogens, the metagenomics analysis of biological and environmental niches, the investigation of transcriptomes, and the characterization of full-length microbial and viral genomes for a wide range of purposes, including phylogenetic and epidemiological studies and the characterization of drug-resistant or immune-escaping mutants [36].

For each of these applications, a plethora of bioinformatics tools is available, both academic and commercial, that has to be applied and coordinated depending on the specific needs. In this study we report the application of an analysis pipeline which combines genomic comparative analysis and gene function prediction to finely characterize microbial genomes. The pipeline was applied to the retrospective analysis of a group of Neisseria meningitidis isolates collected during an outbreak. Epidemiological, clinical, and surveillance data on this outbreak, which occurred in north-eastern Italy during the 2007–2008 winter and was characterize by a high fatality rate, have been previously reported [7, 8]. The results of this study highlight the essential contribution of whole genome sequencing, performed by NGS technology, to distinguish outbreak cases, i.e., related cases with a common epidemiological source, from clusters of temporary and geographically proximate but unrelated cases. In addition, genomic comparative analysis and gene function prediction led to the identification of genetic changes in the capsule locus that could have contributed to pathogenicity.


N. meningitidisisolates

N. meningitidis isolates of an outbreak which occurred in Veneto Region (north-eastern Italy) in December 2007-January 2008 were collected by local hospital laboratories and sent to the Regional Reference Laboratory at Padua University Hospital for confirmation, phenotypic characterization, and molecular typing. The outbreak strains analyzed in the present study included N. meningitidis isolates from seven patients (mean age 23 year, range 15–33 years) from a relatively small geographical area, who had disease onset between December 13, 2007 and January 4, 2008.

The study was approved by the Ethics Committee of Padova University Hospital (protocol no. 53503).

Phenotypic and genotypic characterization of N. meningitidisisolates

Serogrouping, which was performed by slide agglutination using commercial antisera (Remel Europe Ltd, Dartford, UK), classified all the 7 isolates as serogroup C. All isolates were fully susceptible to penicillin, rifampicin, ceftriaxone, and ciprofloxacin. Pulsed-field gel electrophoresis analysis gave the same electrophoresis pattern for all isolates, indicating their relatedness. Molecular characterization by MLST, performed according to Maiden et al. [9], demonstrated that all isolates belonged to the same sequence type and clonal complex ST-11 (all isolates had the following MLST alleles: abcZ 2, adk 3, aroE 4, fumC 3, gdh 8, pdhC 4, pgm 6). In addition, sequencing of N. meningitidis PorA variable regions 1 and 2 [10] confirmed that all isolates had the same PorA subtype 5–1, 10–8.

Whole genome sequencing of N. meningitidisisolates by 454 pyrosequencing

Whole genome sequencing of two N. meningitidis isolates, the index case (named K1207) and the last case (named S0108) of the outbreak, was performed with the objective of confirming their relatedness and to detect genetic differences between the two strains that could have occurred during the short period of the outbreak. The draft genome sequences of these two N. meningitidis isolates were reported in a previous announcement [11].

Genomic DNA was purified from meningococcal isolates using a phenol-chloroform-based method.

Sequencing was performed using a Roche 454 Life Sciences Genome Sequencer FLX platform following the manufacturer’s instructions (Roche 454 Life Sciences, Branford, CT, USA). For each sample, 2 different libraries were prepared, a shotgun and a 3 kb paired-end, starting from 5 μg of genomic DNA. The shotgun library was prepared as follows: after nebulization, purification and adaptors ligation, DNA fragments were clonally amplified using the Emulsion PCR Kit I (Roche). For the preparation of the paired end library, genomic DNA was fragmented by hydrodynamic shearing, followed by a size selection step, hairpin adaptors ligation and circularization of fragments. From this step the procedure was similar to the shotgun one, consisting in the nebulization of circular molecules, paired end adaptors ligation and amplification of the library. The clonal amplification was carried out using the Emulsion PCR Kit II (Roche). Sequencing was performed on a GS FLX instrument, using the Standard LR70 Sequencing Kit (Roche). Images were processed using the runAnalysisPipe and runAnalysisPipePairedEnd commands provided with the DataProcessing package (Roche). With respect to the previous genome announcement [11], we performed a new de novo assembly with the most recent version of the Newbler software (v.2.6), which is more effective and produces a smaller number of contigs with respect to older versions [12].

Gene identification and comparison between the two N. meningitidisgenomes and comparison with FAM18 reference genome

The assembled sequences of K1207 and S0108 isolates were compared to each other and with the most similar genome among those already available in RefSeq database, i.e., the FAM18 strain genome (NC_008767). Whole genome level comparison was made with MUMmer3.23 package, to investigate possible large recombination events.

To compare the CDS content between K1207 and S0108 genomes an approach to find the reciprocal blast best hits was implemented. This method is widely used to identify the coding sequences that are supposed to be “orthologs”. The following steps were performed:

download of protein sequences of genes annotated on each of the following complete N. meningitidis genomes, spanning different serogroups: FAM18 (NC_008767) and 053442 (NC_010120) from serogroup C, Z2491 (NC_003116) from serogroup A, MC58 (NC_003112) from serogroup B and the capsule null strain alpha14 (NC_013016), for which the serogroup cannot be defined.

clustering of protein sequences from the previous step at 90% of sequence identity, using cd-hit [13, 14], in order to eliminate redundancy and create a reference proteome.

de novo gene prediction on isolates K1207 and S0108 using Glimmer3.02 [15] and extraction of the corresponding protein sequences.

search of protein sequences belonging to each isolate against this reference proteome using blastp [16]; since all analyzed protein sequences belong to the same species, only matches with at least 90% of sequence identity and covering at least 90% of sequence length were considered. The threshold was chosen after testing different cutoff values: relaxing this parameter did not produce substantial changes in the number of shared CDSs, while tightening the percentage identity cutoff resulted in a rapid decrease in the number of shared CDSs (see Additional file 1: Figure S1). Nonetheless, this high stringency led to the loss of many similar genes whose function is identical.

starting from blastp output, a table of matches was created in which, for each protein, “0” or “1” indicated its absence or presence in the different genomes. From this table it was possible to extract different lists of genes, common to both genomes, or specific to one or another.

To better characterize the differences between the two isolates, function prediction of unknown genes was done:

genes with unknown function were analyzed by means of the function prediction tool Argot2 [1719]. This algorithm was developed by our bioinformatics group and allows to retrieve scored GO annotations for a given gene or CDS, starting from its nucleotide or protein sequence.

The workflow of the analysis pipeline is summarized in Figure 1.

Figure 1

Analysis algorithm. Diagram of the analysis workflow applied to the investigation of a meningitis outbreak based on genomic comparative analysis and gene function prediction.

Validation of genetic differences among N. meningitidisisolates by target-specific PCR and cycle sequencing

Some of the genes found to be present in only one of the two N. meningitis isolates were selected for validation with target-specific PCR and conventional cycle sequencing. A detailed list of investigated genes is reported in Table 1, along with brief gene function descriptions. These genes were also screened in the other five N. meningitidis isolates from the outbreak and in sporadic N. menigitidis isolates, with identical multilocus sequence type and porA genotype, which were collected in Veneto Region in 2007 and 2008.

Table 1 Gene-specific PCR and Sanger sequencing validation of differences detected by NGS


Pyrosequencing results

The index and the last cases of a Neisseria meningitidis outbreak, named K1207 and S0108 respectively, were sequenced in a 454 FLX platform. For K1207 genome, a total of 257,909 shotgun reads were obtained, with average length of 237 bases, and 63,593 paired end reads with an average distance of 3,113 nucleotides. For genome S0108, the shotgun reads obtained were 241,983 with an average length of 235 bases, while paired end reads were 50,658 with an average distance of 2560 bases. The average depth was 27 to 28 fold for both genomes. Raw reads were assembled with Newbler2.6.

Characteristics of sequenced genomes and plasmids of N. meningitidisisolates

The genome sequences of the two N. meningitidis serogroup C ST-11 isolates were very similar to N. meningitidis serogroup C FAM18 strain, which is a representative of the ST-11/ET-37 clonal complex and whose sequence is completely finished and deposited in RefSeq (NC_008767). A deeper analysis revealed that the two isolates belonged to the ET-15 variant of the ST-11 complex, as demonstrated by the presence of the IS1301 insertion element [20] and the ET-15 fumarate hydratase gene [21].

The sequence of a 7-kb plasmid, identical in both isolates and very similar to pJS-B (RefSeq accession number NC_004758) was also obtained. Draft genome sequences have been deposited in GenBank (accession numbers ADWM02000000 and ADWN02000000 for K1207 and S0108, respectively).

Comparison of N. meningitidisserogroup C genomes through analysis of their coding sequence content

To screen for the presence of genetic variations between the two isolates, the K1207 and S0108 genomes were compared by assessing their coding sequence (CDS) content, looking for shared and genome-specific CDSs. The latter were defined as CDSs for which a reciprocal match between the two isolates was not detected. Following the criteria described in the methods section, 2011 CDSs were identified as present in both genomes, 11 CDSs were exclusively present in K1207 genome, while 10 CDSs were present in S0108 genome only. Since most unique CDSs encoded hypothetical proteins of unknown function, we performed a function prediction step by using Argot2 webservice, an in-house developed tool [1719]; the complete report of predicted functional annotations is available in Additional file 1: Figure S1. These genes were predicted to encode proteins belonging to the membrane compartment, involved in capsular polysaccharide biosynthetic process, or adhesins and transport proteins.

Screening for genetic differences in other N. meningitidisserogroup C ST-11 isolates of the outbreak

Since the dissimilarities between the two N. meningitidis isolates could be the result of genetic variations of the same strain within the outbreak, we selected a subset of these differences for further investigation and validation by using target-specific PCR and cycle sequencing. This analysis was performed in the two sequenced N. meningitidis K1207 and S0108 strains for confirmation and in the other five isolates of the outbreak (i.e., isolates B-F in Table 1), as well as in other two ST-11 isolates (i.e., isolates A and G in Table 1), which were collected from sporadic meningitis cases that occurred in the same area in November 2007 and in April 2008. The results of this analysis confirmed findings provided by NGS and bioinformatics comparison for the K1207 and the S0108 isolates and, unexpectedly, they demonstrated that the S0108 isolate did not belong to the outbreak cluster. In fact, while the other five isolates from the same outbreak shared the same sequences of the outbreak index case K1207, the CDSs of S0108 were found in the strains isolated from sporadic meningitis cases, but differed from those of the outbreak isolates (Table 1). Thus, these results demonstrate that the outbreak was caused by the K1207 strain, while the S0108 strain was circulating in the same region during the same season and caused sporadic meningitis cases.

Analysis of the capsule locus

Comparison among K1207, S0108, and FAM18 N. meningitidis genomes at whole genome level and for CDS content demonstrated that most genetic variations occurred in the capsule locus. The results of whole genome comparison between K1207 and S0108 are illustrated in Figure 2, where an inversion in the capsule locus of S0108 is demonstrated. The whole genome alignment is shown with a dot plot matrix, where a dot is drawn each time there is a match between two regions of the genomes. Ideally, a perfect alignment would result in a continuous diagonal line from the start to the end of genomes; the presence of a chromosome inversion is highlighted by a break in the diagonal, with the change in dots direction indicating an inverse complement alignment. A map summarizing genetic changes among K1207, S0108 and FAM18 genomes is shown in Figure 3, where the blue chiasma indicates the same inversion present in S0108 with respect to FAM18.

Figure 2

Genome-wide comparison of samples K1207 and S0108. Whole genome alignment between isolate K1207 vs. isolate S0108. The inset highlights the 20 Kb inversion in the capsule locus region between the two isolates. The dot plot was generated with MUMmer3.23 ( and indicates matching sequences in forward and reverse direction.

Figure 3

Capsule locus modifications. Map of the capsule locus of K1207 and S0108 N. meningitidis isolates in comparison with FAM18 strain. Changes in CDS content between K1207 and FAM18 are highlighted in yellow, with the percentage similarity of amino acid sequences reported in parenthesis; the 20 kb inversion in the S0108 genome is indicated in blue.

The K1207 genome had remarkable changes in CDS content and nucleotide sequence of the capsule locus in comparison with both S0108 and FAM18 strains, which were consistent with multiple events of recombination and horizontal gene transfer. Genetic variations in the capsule locus of K1207 affected (i) the refB gene, which had a deletion in 3′, (ii) the duplicated galE-rfbBAC operon (for lipooligosaccharide biosynthesis) and the downstream lipAB operon (for phospholipid modification of the capsular polysaccharide, required for its translocation to the cell surface), which had genes with markedly different nucleotide sequences and the insertion of novel CDSs (i.e., SacB, which we predicted to encode a polysaccharide phosphotransferase, and rfbC/D which encodes a TDP-deoxymannose-dehydratase); (iii) and the oatC gene (that encodes polysialic acid-specific O-acetyltransferase), which was interrupted by the insertion of a mobile element of the IS4 transposase family. Search in the GenBank database demonstrated that the SacB gene was present only in the genome of the carriage N. menigitidis strain alpha275 (Genbank AM889138), but not in pathogenic strains. In addition, we found this gene also in Neisseria mucosa, suggesting that the rearranged region of the capsule locus in K1207 might have been acquired by horizontal gene transfer from nonpathogenic meningococci or other commensal Neisseria species.

The CDS content of the S0108 genome showed a high degree of similarity with FAM18 genome, with the exception of a large inversion in the capsule locus of a region of approximately 20 Kb included between the duplicated rfbC and rfbC2 genes (Figures 2 and 3). In addition, the nucleotide sequence of the rfbC2 gene in the FAM18 genome was different from the corresponding gene in K1207 and S0108 genomes.


In this study, an approach based on whole genome sequencing by NGS technology, comparative genomics, and gene function prediction was set up and applied to investigate N. meningitidis isolates, collected during an outbreak, that appeared identical by standard molecular typing methods based on PFGE, MLST, and porA VR1/VR2 sequencing. Whole genome sequencing demonstrated genetic changes between the fully sequenced isolates: then, a specific PCR based strategy applied to other samples from the same outbreak, followed by Sanger sequencing, allowed to distinguish outbreak cases from other temporal and geographically proximate but unrelated cases. This approach was designed in order to be feasible with a small budget. Nonetheless, the continuous decrease of sequencing costs, due to the improvement in the throughput of existing sequencing platforms and the simultaneous development of new technologies, will probably foster a wider employ of next generation sequencing in diagnostics and surveillance activities.

Comparative genomics analysis led to the identification and characterization of genetic differences among strains that might have been relevant for virulence and pathogenicity. In fact, in comparison with the N. meningitidis serogroup C ST-11/ET-37 reference strain FAM18 and with the S0108 isolate, the hyper-virulent K1207 outbreak isolate had marked changes in the capsule locus, consistent with recombination and horizontal acquisition of a set of genes involved in capsule biosynthesis from other meningococci. Although experimental verification is required, we hypothesize that these genetic changes might have enhanced transmissibility and invasiveness or even changed antigens involved in host immunity. In this regard, acquisition of novel genotypes at antigen-encoding loci has been reported to be probably the mechanism of emergence of hyper-virulent N. meningitidis ST-11 complex strains [22].

Homologous recombination and horizontal gene transfer are quite common in pathogenic N. meningitidis strains [23] and frequently affect virulence genes, including the capsule biosynthesis locus which may cause capsule switching [24], genes targeted by vaccines, and antibiotic susceptibility genes [25]. Considering this high variability, the conventional genotyping methods that are used to investigate outbreaks and to monitor the circulation of new variants of hyper-virulent strains may not be accurate enough. Several recent studies have demonstrated the power of whole genome sequencing by NGS technology to improve the identification of the geographical and evolutionary origin of an outbreak and to distinguish cases that belong to the outbreak from cases that do not [26].

The application of NGS technologies for the investigation of outbreaks in real time is becoming feasible thanks to the development of semi-automatic pipelines, as proposed in some recent publications [2729]. In particular, Vogel et al. [27] demonstrated how to apply NGS in clinical practice, focusing on target genes of conventional typing methods (i.e., MLST, porA and fetA typing) and antimicrobial resistance gene testing (i.e., penA and rpoB). This approach can be expanded, as shown by Jolley et al. [28, 29], on different subsets of genes, including the standard MLST loci, the ribosomal genes, personalized lists of specific loci, and whole sets of loci annotated on a complete reference genome. Such tools, which come along web platforms and interactive easy-to-use interfaces, are certainly essential instruments that will allow the introduction of whole genome sequencing in diagnostic routine. Instead, a limitation of their application in research is the capability to identify new determinants, not yet known and annotated [3]. The function prediction step integrated in our approach can provide valuable information to improve our understanding on unknown features, such as horizontal gene transfer and genome rearrangements. Though requiring experimental validation, these predictions could be useful to drive the experimental design and produce annotations: this new information will, on the one hand, enrich public databases and increase our knowledge about bacterial biodiversity and pathogenic mechanisms, and on the other hand it will be available for future routine diagnostic access.


In conclusion, this study represents a practical example on the application of NGS technology to the investigation of an outbreak and how this technology may be helpful in the identification of outbreak cases with a direct clinical impact on the containment measures being taken. This study also shows how the avalanche of genomic information provided by NGS technology and bioinformatics analysis may contribute to our understanding of human pathogens.


  1. 1.

    Barzon L, Lavezzo E, Costanzi G, Franchin E, Toppo S, Palù G: Next-generation sequencing technologies in diagnostic virology. J Clin Virol. 2013, 13: 00086-3. doi: S1386-6532

    Google Scholar 

  2. 2.

    Barzon L, Lavezzo E, Militello V, Toppo S, Palù G: Applications of next-generation sequencing technologies to diagnostic virology. Int J Mol Sci. 2011, 12: 7861-7884. 10.3390/ijms12117861.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Didelot X, Bowden R, Wilson DJ, Peto TE, Crook DW: Transforming clinical microbiology with bacterial genome sequencing. Nat Rev Genet. 2012, 13: 601-612.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Kahvejian A, Quackenbush J, Thompson JF: What would you do if you could sequence everything?. Nat Biotechnol. 2008, 26: 1125-1133. 10.1038/nbt1494.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kostic AD, Ojesina AI, Pedamallu CS, Jung J, Verhaak RG, Getz G, Meyerson M: PathSeq: software to identify or discover microbes by deep sequencing of human tissue. Nat Biotechnol. 2011, 29: 393-396. 10.1038/nbt.1868.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Frasson I, Lavezzo E, Franchin E, Toppo S, Barzon L, Cavallaro A, Richter SN, Palù G: Antimicrobial treatment and containment measures for an extremely drug-resistant Klebsiella pneumoniae ST101 isolate carrying pKPN101-IT, a novel fully sequenced bla(KPC-2) plasmid. J Clin Microbiol. 2012, 50: 3768-3772. 10.1128/JCM.01892-12.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Ferro A, Baldo V, Cinquetti S, Corziali P, Gallo G, Lustro G, Paludetti P, Menegon T, Baldovin T, Palù G, Trivello R: Outbreak of serogroup C meningococcal disease in Veneto region, Italy. Euro Surveill. 2008, 13 (2): 8008-

    PubMed  Google Scholar 

  8. 8.

    Fazio C, Neri A, Tonino S, Carannante A, Caporali MG, Salmaso S, Mastrantonio P, Stefanelli P: Characterisation of Neisseria meningitidis C strains causing two clusters in the north of Italy in 2007 and 2008. Euro Surveill. 2009, 14 (16): 19179-

    PubMed  Google Scholar 

  9. 9.

    Maiden MC, Bygraves JA, Feil E, Morelli G, Russell JE, Urwin R, Zhang Q, Zhou J, Zurth K, Caugant DA, Feavers IM, Achtman M, Spratt BG: Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. Proc Natl Acad Sci USA. 1998, 95: 3140-3145. 10.1073/pnas.95.6.3140.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Suker J, Feavers IM, Achtman M, Morelli G, Wang JF, Maiden MC: The porA gene in serogroup A meningococci: evolutionary stability and mechanism of genetic variation. Mol Microbiol. 1994, 12: 253-265. 10.1111/j.1365-2958.1994.tb01014.x.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Lavezzo E, Toppo S, Barzon L, Cobelli C, Di Camillo B, Finotello F, Franchin E, Peruzzo D, Toffolo GM, Trevisan M, Palù G: Draft genome sequences of two Neisseria meningitidis serogroup C clinical isolates. J Bacteriol. 2010, 192: 5270-5271. 10.1128/JB.00789-10.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Finotello F, Lavezzo E, Fontana P, Peruzzo D, Albiero A, Barzon L, Falda M, Di Camillo B, Toppo S: Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data. Brief Bioinform. 2012, 13: 269-280. 10.1093/bib/bbr063.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Li W, Jaroszewski L, Godzik A: Clustering of highly homologous sequences to reduce the size of large protein database. Bioinformatics. 2001, 17: 282-283. 10.1093/bioinformatics/17.3.282.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Li W, Jaroszewski L, Godzik A: Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics. 2002, 18: 77-82. 10.1093/bioinformatics/18.1.77.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Delcher AL, Bratke KA, Powers EC, Salzberg SL: Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. 2007, 23: 673-679. 10.1093/bioinformatics/btm009.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Fontana P, Cestaro A, Velasco R, Formentin E, Toppo S: Rapid annotation of anonymous sequences from genome projects using semantic similarities and a weighting scheme in gene ontology. PLoS One. 2009, 4: e4619-10.1371/journal.pone.0004619.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Falda M, Toppo S, Pescarolo A, Lavezzo E, Di Camillo B, Facchinetti A, Cilia E, Velasco R, Fontana P: Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms. BMC Bioinformatics. 2012, 13 (Suppl 4): S14-10.1186/1471-2105-13-S4-S14.

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, Graim K, Funk C, Verspoor K, Ben-Hur A, Pandey G, Yunes JM, Talwalkar AS, Repo S, Souza ML, Piovesan D, Casadio R, Wang Z, Cheng J, Fang H, Gough J, Koskinen P, Törönen P, Nokso-Koivisto J, Holm L, Cozzetto D, Buchan DW, Bryson K, Jones DT, Limaye B, et al: A large-scale evaluation of computational protein function prediction. Nat Methods. 2013, 10: 221-227. 10.1038/nmeth.2340.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Elias J, Vogel U: IS1301 fingerprint analysis of Neisseria meningitidis strains belonging to the ET-15 clone. J Clin Microbiol. 2007, 45: 159-167. 10.1128/JCM.01322-06.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Vogel U, Claus H, Frosch M, Caugant DA: Molecular basis for distinction of the ET-15 clone within the ET-37 complex of Neisseria meningitidis. J Clin Microbiol. 2000, 38: 941-

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Jolley KA, Wilson DJ, Kriz P, McVean G, Maiden MC: The influence of mutation, recombination, population history, and selection on patterns of genetic diversity in Neisseria meningitidis. Mol Biol Evol. 2005, 22: 562-569.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Schoen C, Tettelin H, Parkhill J, Frosch M: Genome flexibility in Neisseria meningitidis. Vaccine. 2009, 27 (Suppl 2): B103-B111.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Swartley JS, Marfin AA, Edupugnanti S, Liu LJ, Cieslak P, Perkins B, Wenger JD, Stephens DS: Capsule switching of Neisseria meningitidis. Proc Natl Acad Sci USA. 1997, 94: 271-276. 10.1073/pnas.94.1.271.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Hao W, Ma JH, Warren K, Tsang RS, Low DE, Jamieson FB, Alexander DC: Extensive genomic variation within clonal complexes of Neisseria meningitidis. Genome Biol Evol. 2011, 3: 1406-1418. 10.1093/gbe/evr119.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Didelot X: Genomic analysis to improve the management of outbreaks of bacterial infections. Expert Rev Anti Infect Ther. 2013, 11: 335-337. 10.1586/eri.13.15.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Vogel U, Szczepanowski R, Claus H, Jünemann S, Prior K, Harmsen D: Ion Torrent Personal Genome Machine sequencing for genomic typing of Neisseria meningitidis for rapid determination of multiple layers of typing information. J Clin Microbiol. 2012, 50: 1889-1894. 10.1128/JCM.00038-12.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Jolley KA, Maiden MC: Automated extraction of typing information for bacterial pathogens from whole genome sequence data: Neisseria meningitidis as an exemplar. Euro Surveill. 2013, 18: 20379-

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Jolley KA, Hill DM, Bratcher HB, Harrison OB, Feavers IM, Parkhill J, Maiden MC: Resolution of a meningococcal disease outbreak from whole-genome sequence data with rapid Web-based analysis methods. J Clin Microbiol. 2012, 50: 3046-3453. 10.1128/JCM.01312-12.

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    A.r.g.o.t.2, Functional annotation of proteins using the semantic similarity in the Gene Ontology.,

Pre-publication history

  1. The pre-publication history for this paper can be accessed here:

Download references

Author information



Corresponding authors

Correspondence to Giorgio Palù or Luisa Barzon.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

EF carried out the wet lab procedures; EL, ST, FF, MF, and BDC performed the bioinformatics analyses; RM, GP and LB conceived the study and helped in its design and coordination; LB, ST and EL wrote the manuscript. All authors read and approved the final manuscript.

Electronic supplementary material

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lavezzo, E., Toppo, S., Franchin, E. et al. Genomic comparative analysis and gene function prediction in infectious diseases: application to the investigation of a meningitis outbreak. BMC Infect Dis 13, 554 (2013).

Download citation


  • Neisseria meningitidis
  • Whole genome sequencing
  • Next generation sequencing
  • capsule locus
  • Comparative genomics
  • 454 pyrosequencing
  • Meningitis outbreak
  • Molecular epidemiology
  • Gene function prediction