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GapBlaster—A Graphical Gap Filler for Prokaryote Genomes

  • Pablo H. C. G. de Sá ,

    Contributed equally to this work with: Pablo H. C. G. de Sá, Fábio Miranda

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Fábio Miranda ,

    Contributed equally to this work with: Pablo H. C. G. de Sá, Fábio Miranda

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Adonney Veras,

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Diego Magalhães de Melo,

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Siomar Soares,

    Affiliation Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil

  • Kenny Pinheiro,

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Luis Guimarães,

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Vasco Azevedo,

    Affiliation Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

  • Artur Silva,

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

  • Rommel T. J. Ramos

    rommelthiago@gmail.com

    Affiliation Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil

GapBlaster—A Graphical Gap Filler for Prokaryote Genomes

  • Pablo H. C. G. de Sá, 
  • Fábio Miranda, 
  • Adonney Veras, 
  • Diego Magalhães de Melo, 
  • Siomar Soares, 
  • Kenny Pinheiro, 
  • Luis Guimarães, 
  • Vasco Azevedo, 
  • Artur Silva, 
  • Rommel T. J. Ramos
PLOS
x

Abstract

The advent of NGS (Next Generation Sequencing) technologies has resulted in an exponential increase in the number of complete genomes available in biological databases. This advance has allowed the development of several computational tools enabling analyses of large amounts of data in each of the various steps, from processing and quality filtering to gap filling and manual curation. The tools developed for gap closure are very useful as they result in more complete genomes, which will influence downstream analyses of genomic plasticity and comparative genomics. However, the gap filling step remains a challenge for genome assembly, often requiring manual intervention. Here, we present GapBlaster, a graphical application to evaluate and close gaps. GapBlaster was developed via Java programming language. The software uses contigs obtained in the assembly of the genome to perform an alignment against a draft of the genome/scaffold, using BLAST or Mummer to close gaps. Then, all identified alignments of contigs that extend through the gaps in the draft sequence are presented to the user for further evaluation via the GapBlaster graphical interface. GapBlaster presents significant results compared to other similar software and has the advantage of offering a graphical interface for manual curation of the gaps. GapBlaster program, the user guide and the test datasets are freely available at https://sourceforge.net/projects/gapblaster2015/. It requires Sun JDK 8 and Blast or Mummer.

Introduction

Next generation sequencing (NGS) platforms have reduced sequencing costs and increased the amount of data generated, resulting in a greater number of complete genomes for eukaryotes and prokaryotes, which are subsequently deposited in public databases [1,2].

Several computational tools have been developed for processing reads, such as error correction and quality filters, as well as additional programs and pipelines that perform genome assemblies of reads generated by NGS platforms, producing complete genomes or scaffolds [3,4]. As a result of assembly reads, many contigs are produced. These reads or reference genomes can be used to order the contigs to produce a scaffold. Some regions in the scaffold have no assigned bases (A,C,T or G) due to the limitations of sequencing technology or assembly algorithms; these regions are called gaps and are usually represented by Ns [57].

Beyond commercial programs, such as CLC Genomic Workbench and Lasergene Suite, which have available options for finishing genome assemblies, including steps that fill gaps, open source programs are available. For example the open source programs G4ALL [8], GapCloser [3], GapFiller [6], and FGAP [9] use different approaches, such as paired reads or results of assemblies obtained with different software, to fill gap regions. The FGAP program was implemented in Matlab language and uses a draft of the assembly and a set of contigs that are mapped against genome draft to close gaps using BLAST algorithms. Both a fasta and a log file that report the filled gaps are generated at the end of the process. However, FGAP has no graphical interface [9].

G4ALL was implemented via JAVA programming language. The software has a graphical interface that allows the user to perform gap closure through manual curation of the scaffolds by comparing the BLAST results of the assembled contigs to the assembled scaffolds, similar to the GapBlaster method. G4ALL is useful for extending the contigs based on the overlap between them; however, it does not use contigs to close the gap regions [8].

GapCloser uses the information from paired reads to extend the sequences of contigs between gaps. Thus, the gaps can be closed or reduced [3]. Similar to GapCloser, the GapFiller program uses paired reads and is able to use data from different sequencing rounds simultaneously [6]. It is one of the available tools for closing gaps in prokaryotic and eukaryotic genomes of sizes up to ~100 Mb [10].

Genomes that have gaps may impair further studies because they may only partially represent an organism’s gene repertoire. Incomplete genomes can affect downstream analyses of genomic plasticity and comparative genomics [11].

Therefore, it is important to use complete genomes for comparative studies to properly characterize genome structure variations and gene content. This characterization allows the identification of genes that are 1) shared among all isolates and are thus useful for applied issues, such as vaccine and drug design [12]; 2) shared by some organisms, but not all studied organisms, and are thus useful for studying the reference lab activities for pathogenic bacteria [13,14]; and 3) present in a single isolate providing information regarding bacteria lifestyle [15].

Thus, this study presents a computational tool with a graphical user interface that helps reduce gaps through manual curation to increase the completion of genome assembly, rather than relying on the complete automation of this task.

Materials and Methods

Implementation

The GapBlaster was developed via JAVA programming language (http://java.sun.com/) using the paradigm of object orientation and the Swing library to create the visual resources (http://java.sun.com/docs/books/tutorial/uiswing).

Through the main interface of GapBlaster (S1 Fig), the user can input the scaffold and the contig files in FASTA format. After processing, another screen (S2 Fig) shows the alignment results. The user is then able to perform manual curation and select alignments that fill gaps confidently, as when the user finds a contig aligned in the gap flanks, closing the gap completely, as shown in S3 Fig.

GapBlaster performs five steps to identify possible gaps to be filled. All of the contigs obtained in the assembly are aligned against the draft genome or scaffold using BLAST Legacy [16], Blast+ or Mummer [17] based on user choice, and the alignment result is converted to the GapBlaster format. The contigs are subsequently ordered according to the mapping position in the scaffold. The program searches the alignments of the same contig that flank gap regions. A new ordination of the alignments is performed to determine the best option for gap closure. All identified alignments that fill gaps are presented to the user for evaluation (accepted or rejected) through the GapBlaster interface, and a log of changes made is generated. The selection of the alignment and the parameters can be defined by the user through the GapBlaster interface.

Test data

To evaluate the GapBlaster program, analyses were conducted using two datasets: the first used sequencing data of Corynebacterium pseudotuberculosis, and the second was obtained from the GAGE (Genome Assembly Gold-Standard Evaluation) assembly of genomes [18].

C. pseudotuberculosis is a facultative intracellular gram-positive bacterium that causes caseous lymphadenitis (CLA), an infectious disease that affects small ruminants and belongs to CMNR group (Corynebacterium, Mycobacterium, Nocardia, and Rhodococcus) [19].

The sequencing of C. pseudotuberculosis was performed by an Ion Torrent PGM platform (Table 1). The reads (available in SRA database: SRR3312980) were assembled by a de novo strategy using SPADES version 3.1.0, with default parameters for Ion Torrent PGM data [20]. The scaffolds and contigs files produced in the assembly (available in https://sourceforge.net/projects/gapblaster2015/files/test_dataset/) were used as inputs in GapBlaster.

The GAGE dataset had the assemblies of the Staphylococcus aureus and Rhodobacter sphaeroides genomes, containing contigs and scaffolds generated by the following assemblers: Abyss, ABySS2, AllPaths-LG, Bambus2, MSR-CA, SGA, SOAPdenovo and Velvet for both organisms, whereas the CABOG was used for only Rhodobacter sphaeroides [18]. The data are available at http://gage.cbcb.umd.edu/, and the genome sequencing information can be seen in Table 1.

GapBlaster

All contigs and scaffolds of the datasets were manually evaluated with GapBlaster version 1.1.1 to close gaps. In our analysis, we used one scaffold and one contig file for each organism/assembly, with the parameter Flank Length = 11 and the aligner Blast+ (the parameters in the GapBlaster should be set to reproduce our results). To close gaps, regions flanking the gaps (represented by Ns) were considered only when they had high identity (the threshold should be defined by the user).

Gap closure comparison

To compare gap filling performance, GapBlaster, GapFiller and FGAP software were used in a gap closure analysis of the GAGE dataset and C. pseudotuberculosis.

The GAGE dataset with the mate-pair reads was analyzed with GapFiller [6] and FGAP [9] based on gap closure performance. Both types of software were used under default parameters, and the results were subsequently compared to GapBlaster.

The C. pseudotuberculosis genome was analyzed with FGAP only as GapFiller software requires paired-end libraries, and C. pseudotuberculosis was sequenced using fragmented libraries. The results of FGAP were compared to GapBlaster.

Additionally, GapBlaster was used to reduce gaps in the output files of FGAP and GapFiller software.

Results evaluation

To validate the gap filling analysis, an in-house script was developed to evaluate the amount of gaps and Ns for each of the tests. The FASTA file (original scaffolds and the results of GapBlaster, FGAP, and GapFiller) was used as an input to count the number of gaps and their respective sizes. This script and a brief manual are available at https://sourceforge.net/projects/gapblaster2015/upload/scripts/.

To confirm if the gaps were correctly closed, the validation script of GAGE was used (http://gage.cbcb.umd.edu/results/gage-paper-validation.tar.gz). The input of this script was the reference genome (Table 2) and the original scaffold or gap-filled scaffold file.

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Table 2. Information of the reference genomes used to validate the filled-in gaps.

https://doi.org/10.1371/journal.pone.0155327.t002

Results and Discussion

The assembly results (number of bases and scaffolds) of the C. pseudotuberculosis genome produced by SPADES and the information concerning several assemblies of S. aureus and R. sphaeroides produced by various types of assemblers are shown in Table 3.

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Table 3. Genome assembly information for C. pseudotuberculosis 262, S. aureus and R. sphaeroides.

https://doi.org/10.1371/journal.pone.0155327.t003

The results of the gap closure process for the Corynebacterium data assembled by SPADES are shown in Table 4.

For C. pseudotuberculosis the amount of gaps was reduced from 24 to 11 with GapBlaster, and from 24 to 5, with FGAP. Gap length was also reduced for the Corynebacterium genome, as shown in Table 4. The C. pseudotuberculosis genome was sequenced using fragment libraries; thus, they could not be submitted to GapFiller.

The GAGE data of S. aureus and R. sphaeroides were assembled by several assemblers, and the results (contigs and scaffolds) were submitted to GapBlaster, FGAP and GapFiller. All assemblies of S. aureus revealed reductions in gaps and Ns when analyzed by GapBlaster. For R. sphaeroides, only the data for SGA did not show a reduction in gaps by GapBlaster (Table 5). It is important to highlight that GapBlaster allows manual curation; it allows less stringent criteria with careful manual evaluation, which is able to produce better results.

The FGAP and GapFiller programs were used to perform the gap closure step, and these results were compared with those obtained by GapBlaster (Table 5). GapFiller increased the numbers of gaps in most of the analyzed assemblies due the insert length, which was used to align against the reference sequences. In other cases, any gap that was closed had its length (the amount of Ns) increased, which occurred for the assemblies from SGA and SOAPdenovo for S. aureus and for the assemblies from SOAPdenovo for R. sphaeroides. Other results showed that GapFiller reduced the amount of gaps but increased their length (amount of Ns), which was observed for MSR-CA for S. aureus and CABOG, MSR-CA, SGA and for Velvet for R. sphaeroides (Table 5). Despite GapFiller having closed more gaps than GapBlaster for CABOG, MSR-CA, SGA and Velvet for R. sphaeroides, GapBlaster was superior to GapFiller. GapBlaster was able to fill more gaps and reduce the number of Ns in the sequences for nearly all GAGE assemblies, although it did not use paired reads.

FGAP filled more gaps than GapBlaster for all assemblies of the GAGE dataset. Nevertheless, GapBlaster filled more Ns than FGAP for ABySS and SGA for S. aureus and CABOG, MSR-CA and Velvet for R. sphaeroides (Table 5).

Despite FGAP performing the gap filling analysis automatically while GapBlaster performed the analysis manually, they achieved very similar results with respect to the number of gaps and N reductions for SOAPdenovo for S. aureus and Bambus2, CABOG, MSR-CA and SOAPdenovo for R. sphaeroides (Table 5).

FGAP showed better results for both the C. pseudotuberculosis and the GAGE datasets. We performed the gap filling analysis of the FGAP results with the original contigs of each organism and assembly through GapBlaster to determine whether GapBlaster could improve the results produced by FGAP. The results are shown in Table 6. Compared with the FGAP results, GapBlaster improved 55.55% of all assemblies of the GAGE dataset and C. pseudotuberculosis. GapFiller was not used for this comparison of Corynebacterium data because only a fragment library was available for this organism.

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Table 6. Comparison of the original results of FGAP and after manual curation with GapBlaster.

https://doi.org/10.1371/journal.pone.0155327.t006

GapBlaster improved the results of FGAP for C. pseudotuberculosis in that it reduced the number of gaps from 5 to 3. Therefore, GapBlaster improved the gap filling results for several assemblies for S. aureus and R. sphaeroides, as shown in Table 6. This analysis shows that despite its usefulness for closing gaps through its GUI, GapBlaster is also useful for gap filling when used in combination with another tools.

Similar to the analysis of the FGAP results, we conducted an evaluation of the GapFiller output files and the original contigs of each organism/assembler of the GAGE dataset via GapBlaster. Compared with the GapFiller results, GapBlaster improved 70.58% of all assemblies of the GAGE dataset (Table 7).

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Table 7. Comparison of the original results of GapFiller and after manual curation with GapBlaster.

https://doi.org/10.1371/journal.pone.0155327.t007

GapBlaster improved the results of GapFiller for almost all of the CAGE data (Table 7). The best gap filling results were ABySS2 and SGA for S. aureus, where the gaps decreased from 35 to 30 and 654 to 646, respectively (Table 7). Beyond being a very useful tool with an interface for manual curation, GapBlaster is a valuable open source program that can be used with other tools in the gap filling analysis to produce more complete genome drafts.

To evaluate the accuracy of the closed gaps, all results produced by GapBlaster, FGAP, GapFiller and the original files (scaffolds) were aligned against their respective genome reference (Table 2). The results show that all of the files produced in the gap filling analysis showed similar alignment percentages with the original files, which confirms that the bases introduced in the filled gaps were correct (S1 Table).

Despite the three methods (Blast+, Blast Legacy, and Mummer) implemented in GapBlaster, we used only Blast+ to fill gaps as this method is the same used for FGAP software. However, we tested all of the algorithms for GAGE data, and Blast Legacy and Blast+ presented similar results (S2 Table). The comparisons of the features of GapBlaster, FGAP and GapFiller helped to identify the main advantage of GapBlaster, the graphical interface, which uses contigs to fill gaps and allows manual curation (Table 8).

Conclusions

Despite the efficiency of tools such as FGAP and GapFiller, the gap closure process continues to be a step that requires manual curation for the acquisition of high quality results, such as those presented by GapBlaster, the use of which is simplified by the graphical interface.

GapBlaster revealed improved gap filling performance using contigs compared to GapFiller for nearly all data evaluated despite the use of paired reads in GapFiller.

In addition to presenting better results, the GapBlaster program has the advantage of introducing fewer errors, based on the ability of the interface to allow the user to decide if a gap is filled properly. As an alternative, GapBlaster can be used in addition to other gap closer programs to facilitate genome completion through manual manipulation, as was shown in the analysis of the GapBlaster program to improve the results of FGAP and GapFiller.

Supporting Information

S1 Fig. GapBlaster main interface.

The main graphical interface through which the user can input the contigs and scaffold files and set the alignment preferences.

https://doi.org/10.1371/journal.pone.0155327.s001

(TIF)

S2 Fig. Alignment interface.

The screen shows the results of the alignment of a contig against a scaffold. All alignments produced are listed. The user can check if the alignments are correct and select them.

https://doi.org/10.1371/journal.pone.0155327.s002

(TIF)

S3 Fig. Selected Alignment.

The aligned contig filled the gap with high accuracy due to the high identity found in the gap flanks.

https://doi.org/10.1371/journal.pone.0155327.s003

(TIF)

S1 Table. GapBlaster, FGAP and GapFiller accuracy.

This table shows information about the percentage of bases aligned against the GapBlaster, FGAP, GapFiller results and the original scaffolds to the reference genome to evaluate if the filled gaps introduced the correct bases in the analysis.

https://doi.org/10.1371/journal.pone.0155327.s004

(XLS)

S2 Table. GapBlaster algorithm comparison.

Comparison of the three alignment algorithms implemented in GapBlaster (Blast+, Blast Legacy, Mummer) to evaluate the number of alignments identified, closed gaps and N removed after the gap filling process.

https://doi.org/10.1371/journal.pone.0155327.s005

(XLS)

Acknowledgments

This work was part of the Rede Paraense de Genômica e Proteômica and was supported by Fundação de Amparo a Pesquisa do Estado do Pará. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Author Contributions

Conceived and designed the experiments: PHCGS FM RTJR. Performed the experiments: PHCGS FM KP RTJR. Analyzed the data: PHCGS AV DMM SS KP LG RTJR. Contributed reagents/materials/analysis tools: RTJR AS VA. Wrote the paper: PHCGS AV DMM SS KP LG VA AS RTJR.

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