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VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data.

Laczny CC, Sternal T, Plugaru V, Gawron P, Atashpendar A, Margossian HH, Coronado S, der Maaten Lv, Vlassis N, Wilmes P - Microbiome (2015)

Bottom Line: The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation.We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium.The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.

View Article: PubMed Central - PubMed

Affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, 4362 Luxembourg.

ABSTRACT

Background: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge.

Results: We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.

Conclusions: VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.

No MeSH data available.


Visualization and polygonal selection of clusters from a cellulolytic microbial consortium metagenomic dataset 37B [16]. Points highlighted in red according to contig assignment in MaxBin: (A) bin 37B.out.024 and (B) bin 37B.out.026, respectively. Individual subclusters (37B.out.024.001, 37B.out.024.002, 37B.out.026.001, and 37B.out.026.002) are highlighted with inserts showing closeups. Minimal fragment length: 1,000 nt.
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Fig3: Visualization and polygonal selection of clusters from a cellulolytic microbial consortium metagenomic dataset 37B [16]. Points highlighted in red according to contig assignment in MaxBin: (A) bin 37B.out.024 and (B) bin 37B.out.026, respectively. Individual subclusters (37B.out.024.001, 37B.out.024.002, 37B.out.026.001, and 37B.out.026.002) are highlighted with inserts showing closeups. Minimal fragment length: 1,000 nt.

Mentions: Using the example of the 37B dataset, bins 37B.out.024 and 37B.out.026 exhibit each two pronounced and wellseparated subclusters (Additional file 1: Figure S3A,B). This suggests that these bins should each be subdivided. Additional prominent examples from the other datasets include 37A.out.014, 37A.out.018, SRS013705.out.004, SRS013705.out.026, and SRS013705.out.029 (Additional file 1: Figure S3C-G). Using all originally binned contigs from 37B (17,622 in total), we coloured the contigs originally assigned to bins 37B.out.24 and 37B.out.26, respectively (Figure 3). We then applied the polygonal selection tool in VizBin to delineate and export the sequences for each apparent subcluster (per individual MaxBin-based bin) for further inspection of their homogeneity and completeness. The resulting subclusters in 37B.out.024 (defined herein as 37B.out.024.001, 37B.out.024.002; Figure 3A) and 37B.out.026 (37B.out.026.001, 37B.out.026.002; Figure 3B) exhibit increased homogeneity as well as similar or increased completeness (Table 1). The increased homogeneity results from the separation of originally mixed metagenomic fragments. The increased completeness, in turn, is due to the recruitment of new metagenomic fragments (as compared to the original, automated binning) to the respective subclusters, which were likely incorrectly binned by MaxBin.Figure 3


VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data.

Laczny CC, Sternal T, Plugaru V, Gawron P, Atashpendar A, Margossian HH, Coronado S, der Maaten Lv, Vlassis N, Wilmes P - Microbiome (2015)

Visualization and polygonal selection of clusters from a cellulolytic microbial consortium metagenomic dataset 37B [16]. Points highlighted in red according to contig assignment in MaxBin: (A) bin 37B.out.024 and (B) bin 37B.out.026, respectively. Individual subclusters (37B.out.024.001, 37B.out.024.002, 37B.out.026.001, and 37B.out.026.002) are highlighted with inserts showing closeups. Minimal fragment length: 1,000 nt.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4305225&req=5

Fig3: Visualization and polygonal selection of clusters from a cellulolytic microbial consortium metagenomic dataset 37B [16]. Points highlighted in red according to contig assignment in MaxBin: (A) bin 37B.out.024 and (B) bin 37B.out.026, respectively. Individual subclusters (37B.out.024.001, 37B.out.024.002, 37B.out.026.001, and 37B.out.026.002) are highlighted with inserts showing closeups. Minimal fragment length: 1,000 nt.
Mentions: Using the example of the 37B dataset, bins 37B.out.024 and 37B.out.026 exhibit each two pronounced and wellseparated subclusters (Additional file 1: Figure S3A,B). This suggests that these bins should each be subdivided. Additional prominent examples from the other datasets include 37A.out.014, 37A.out.018, SRS013705.out.004, SRS013705.out.026, and SRS013705.out.029 (Additional file 1: Figure S3C-G). Using all originally binned contigs from 37B (17,622 in total), we coloured the contigs originally assigned to bins 37B.out.24 and 37B.out.26, respectively (Figure 3). We then applied the polygonal selection tool in VizBin to delineate and export the sequences for each apparent subcluster (per individual MaxBin-based bin) for further inspection of their homogeneity and completeness. The resulting subclusters in 37B.out.024 (defined herein as 37B.out.024.001, 37B.out.024.002; Figure 3A) and 37B.out.026 (37B.out.026.001, 37B.out.026.002; Figure 3B) exhibit increased homogeneity as well as similar or increased completeness (Table 1). The increased homogeneity results from the separation of originally mixed metagenomic fragments. The increased completeness, in turn, is due to the recruitment of new metagenomic fragments (as compared to the original, automated binning) to the respective subclusters, which were likely incorrectly binned by MaxBin.Figure 3

Bottom Line: The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation.We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium.The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.

View Article: PubMed Central - PubMed

Affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, 4362 Luxembourg.

ABSTRACT

Background: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge.

Results: We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.

Conclusions: VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.

No MeSH data available.