Act scale of dataset (1.2 Gb), may demonstrate an applicable practice of

Act scale of dataset (1.2 Gb), may demonstrate an applicable practice of metagenomic technology which could be referred to in common research conditions. Similar to the coverage trends illustrated in Figure 1, two coverage trends were observed in a recent study of soil Nafarelin metagenome [9]. Delmont et al. stated that Proteobacteria genomes from the lower coverage trend might be assembled more rapidly than Firmicutes and Verrucomicrobia in the higher coverage trend. Correspondingly, the phylogenetic annotation of the three coverage trends in this study revealed that genomes of Choloflexi and Euryarchaeota might be assembled more effectively than those from Firmicutes who took 85.5 of the 1126 trend (Figure 1). It was supposed by Delmont et al. the presence of regions that limit assembly (for example, insertion sequences regions) and the complexity of diversity among taxa was part of the reasons for the higher coverage MedChemExpress 76932-56-4 requirement of genomes in the high coverage trend. However to the opposite of their claim, the harder-toassembled Firmicutes showed a more uniform phylogenetic structure than Euryarchaeota in the lower trend. The major proportion of Firmicutes (72 of 3870 ORFs) was fitted into the higher coverage trend (1126), whereas Euryarchaeota evenly distributed in the 296 trend and the 86 trend. Our previous 16S rRNA gene analysis results also demonstrated the simple structure under phylum Firmicutes that Clostridium, the major cellulose degrader, had taken over 95 of the phylum [16]. Thusthe complexity of diversity among taxa might not contribute to the coverage scattering in this study, instead the clear dominance of phylum Firmicutes was in good part responsible for its high coverage and the relatively limited assembly efficiency because the velvet assembler showed a coverage saturation at around 30?06 coverage that further increasing read coverage over 30?06could not improve assembly in terms of N50 and length of longest contig [17].Phylogenetic Analysis of the Sludge Metagenome Based on Protein Coding RegionsIt is important to make function-based phylogenetic assignment in order to understand the functional contribution of different taxonomy units in the metagenome. However such approach based on short reads has a shortage of low annotation efficiency at certain annotation accuracy like in this study only less than 10 reads were annotated by MG-RAST comparison against SEED subsystem at E-value cutoff of 1E-5. In addition, reads annotation may result in overlook of some important functional information, for example, the “Co-enzyme M synthesis” which is the functional core of methanogenesis was completed undetectable by short reads annotation in the present study (Figure 3). On another hand, the annotation based on assembled results, such as contigs or ORFs, was hardly representative because the information of reads coverage of the contigs/ORFs was not counted in such quantification. For a metagenome with scattering coverage like the enriched consortia used in the present study (Figure 1),Metagenomic Mining of Cellulolytic GenesFigure 4. Similarity distribution of predicted ORFs with thermo-stable carbohydrate-active genes against NCBI nr database by BLASTp (E-value #1E-5). doi:10.1371/journal.pone.0053779.gincorrect taxa distribution would be easily resulted based on annotation of ORFs (Figure 2 insert). Such annotation inconsistency between reads and ORFs was also found in the metagenome of the grassland soil [9]. However, due to.Act scale of dataset (1.2 Gb), may demonstrate an applicable practice of metagenomic technology which could be referred to in common research conditions. Similar to the coverage trends illustrated in Figure 1, two coverage trends were observed in a recent study of soil metagenome [9]. Delmont et al. stated that Proteobacteria genomes from the lower coverage trend might be assembled more rapidly than Firmicutes and Verrucomicrobia in the higher coverage trend. Correspondingly, the phylogenetic annotation of the three coverage trends in this study revealed that genomes of Choloflexi and Euryarchaeota might be assembled more effectively than those from Firmicutes who took 85.5 of the 1126 trend (Figure 1). It was supposed by Delmont et al. the presence of regions that limit assembly (for example, insertion sequences regions) and the complexity of diversity among taxa was part of the reasons for the higher coverage requirement of genomes in the high coverage trend. However to the opposite of their claim, the harder-toassembled Firmicutes showed a more uniform phylogenetic structure than Euryarchaeota in the lower trend. The major proportion of Firmicutes (72 of 3870 ORFs) was fitted into the higher coverage trend (1126), whereas Euryarchaeota evenly distributed in the 296 trend and the 86 trend. Our previous 16S rRNA gene analysis results also demonstrated the simple structure under phylum Firmicutes that Clostridium, the major cellulose degrader, had taken over 95 of the phylum [16]. Thusthe complexity of diversity among taxa might not contribute to the coverage scattering in this study, instead the clear dominance of phylum Firmicutes was in good part responsible for its high coverage and the relatively limited assembly efficiency because the velvet assembler showed a coverage saturation at around 30?06 coverage that further increasing read coverage over 30?06could not improve assembly in terms of N50 and length of longest contig [17].Phylogenetic Analysis of the Sludge Metagenome Based on Protein Coding RegionsIt is important to make function-based phylogenetic assignment in order to understand the functional contribution of different taxonomy units in the metagenome. However such approach based on short reads has a shortage of low annotation efficiency at certain annotation accuracy like in this study only less than 10 reads were annotated by MG-RAST comparison against SEED subsystem at E-value cutoff of 1E-5. In addition, reads annotation may result in overlook of some important functional information, for example, the “Co-enzyme M synthesis” which is the functional core of methanogenesis was completed undetectable by short reads annotation in the present study (Figure 3). On another hand, the annotation based on assembled results, such as contigs or ORFs, was hardly representative because the information of reads coverage of the contigs/ORFs was not counted in such quantification. For a metagenome with scattering coverage like the enriched consortia used in the present study (Figure 1),Metagenomic Mining of Cellulolytic GenesFigure 4. Similarity distribution of predicted ORFs with thermo-stable carbohydrate-active genes against NCBI nr database by BLASTp (E-value #1E-5). doi:10.1371/journal.pone.0053779.gincorrect taxa distribution would be easily resulted based on annotation of ORFs (Figure 2 insert). Such annotation inconsistency between reads and ORFs was also found in the metagenome of the grassland soil [9]. However, due to.