T genome, but the gene prediction algorithms fail to identify it. The number of missing genes in Illumina-based assemblies is similar to that for Sanger-based assemblies (Figure 4B). Closer inspection revealed that the greater number of genes unrecognized with the ab initio gene predictors was due to the extend of fragmentation in the draft genome. The larger number of contigs resulted in many fragmented genes, frequently at the ends ofFigure 2. Assembly quality as assessed by the number of scaffolds in draft assemblies. Data is shown for the six sequencing methods with more than 5 projects. Indicated are the range from upper to lower quartile (boxes), the median (thick black line), and the minimum/maximum values. doi:10.1371/journal.pone.0048837.gFigure 3. Assembly quality for the draft TA01 site genomes included in this analysis. Assembly quality is assessed by (a) the number of gaps in the draft assemblies, and (b) gap size expressed as a percentage of genome length. Data is shown for the six sequencing methods with more than 5 projects. doi:10.1371/journal.pone.0048837.gDraft vs Finished GenomesNotably, assembly of reads generated by Illumina alone yielded more gene discrepancies (Figure 6), indicating that the assembled sequence contains either misassemblies (resulting in genes with low identity and truncated genes) or short contigs that contain gene fragments (resulting in truncated genes). To address this issue, short genes located at the end of draft contigs were Lixisenatide web excluded from these analyses.Effect of genome properties on assemblyThe effect of three genome properties (GC , number of repeats and genome size) on the quality of assembly was investigated using the number of draft contigs as a proxy for assembly quality (Table 2). Unexpectedly, the number of draft contigs shows no correlation with genome GC . This can be attributed to the use of public draft assemblies in the analysis which often included multiple libraries or alternate chemistries to compensate for the poor quality of the initial assembly due to GC biases. It is known that a large number of repeats poses a problem during assembly, especially when the 1655472 repeats are longer than the reads or inserts used [12?4]. As expected a correlation between the repeat content and the number of contigs was observed here, mostly with NGS-based sequencing, although weaker than expected. Similarly, there was only a weak correlation between genome size and the number of contigs. Here, too, the absence of bias in the public draft assemblies reflects the implementation of compensatory steps taken during sequencing or analysis.Figure 4. Genes missed in draft assemblies. Data is shown for the sequencing methods with more than 5 projects. (a) Missed gene sequences, i.e., the number of genes in the finished genome whose nucleotide sequence is absent from the draft assembly. (b) Unrecognized genes, i.e., the number of genes whose nucleotide sequence is present in the draft assembly but that were not predicted by Prodigal (v2.5). doi:10.1371/journal.pone.0048837.gConclusionsOur analyses show that the use of Illumina-based sequencing technologies for microbial genome projects is not only cost effective but can generate the entire sequence without significant loss of information, similarly to what other studies have shown [15]. Even when the genome is fragmented into multiple scaffolds, the amount of missing sequence is minimal, thus very few genes are actually missed. Furthermore, these sequencing technolo.T genome, but the gene prediction algorithms fail to identify it. The number of missing genes in Illumina-based assemblies is similar to that for Sanger-based assemblies (Figure 4B). Closer inspection revealed that the greater number of genes unrecognized with the ab initio gene predictors was due to the extend of fragmentation in the draft genome. The larger number of contigs resulted in many fragmented genes, frequently at the ends ofFigure 2. Assembly quality as assessed by the number of scaffolds in draft assemblies. Data is shown for the six sequencing methods with more than 5 projects. Indicated are the range from upper to lower quartile (boxes), the median (thick black line), and the minimum/maximum values. doi:10.1371/journal.pone.0048837.gFigure 3. Assembly quality for the draft genomes included in this analysis. Assembly quality is assessed by (a) the number of gaps in the draft assemblies, and (b) gap size expressed as a percentage of genome length. Data is shown for the six sequencing methods with more than 5 projects. doi:10.1371/journal.pone.0048837.gDraft vs Finished GenomesNotably, assembly of reads generated by Illumina alone yielded more gene discrepancies (Figure 6), indicating that the assembled sequence contains either misassemblies (resulting in genes with low identity and truncated genes) or short contigs that contain gene fragments (resulting in truncated genes). To address this issue, short genes located at the end of draft contigs were excluded from these analyses.Effect of genome properties on assemblyThe effect of three genome properties (GC , number of repeats and genome size) on the quality of assembly was investigated using the number of draft contigs as a proxy for assembly quality (Table 2). Unexpectedly, the number of draft contigs shows no correlation with genome GC . This can be attributed to the use of public draft assemblies in the analysis which often included multiple libraries or alternate chemistries to compensate for the poor quality of the initial assembly due to GC biases. It is known that a large number of repeats poses a problem during assembly, especially when the 1655472 repeats are longer than the reads or inserts used [12?4]. As expected a correlation between the repeat content and the number of contigs was observed here, mostly with NGS-based sequencing, although weaker than expected. Similarly, there was only a weak correlation between genome size and the number of contigs. Here, too, the absence of bias in the public draft assemblies reflects the implementation of compensatory steps taken during sequencing or analysis.Figure 4. Genes missed in draft assemblies. Data is shown for the sequencing methods with more than 5 projects. (a) Missed gene sequences, i.e., the number of genes in the finished genome whose nucleotide sequence is absent from the draft assembly. (b) Unrecognized genes, i.e., the number of genes whose nucleotide sequence is present in the draft assembly but that were not predicted by Prodigal (v2.5). doi:10.1371/journal.pone.0048837.gConclusionsOur analyses show that the use of Illumina-based sequencing technologies for microbial genome projects is not only cost effective but can generate the entire sequence without significant loss of information, similarly to what other studies have shown [15]. Even when the genome is fragmented into multiple scaffolds, the amount of missing sequence is minimal, thus very few genes are actually missed. Furthermore, these sequencing technolo.
