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This poses a significant challenge for microbiologists and clinical researchers interested in diverse aspects of the microbiota. A human gut microbial gene catalogue established by metagenomic sequencing. doi: 10.1038/nature08821 Pub Med Abstract | Cross Ref Full Text | Google Scholar Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. doi: 10.1038/nature11450 Pub Med Abstract | Cross Ref Full Text | Google Scholar Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Fortunately, the open-source software community has been diligent in developing user-friendly bioinformatics tools required for the analyses of bacterial NGS datasets. doi: 10.1093/nar/gku138 Pub Med Abstract | Cross Ref Full Text | Google Scholar Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. For example, both IBD and obesity are associated with enrichment of enzymes in the nitrate reductase pathway, the metabolism of choline and p-cresol, as well as the phosphotransferase system, required for assimilation of dietary carbohydrates (Greenblum et al., 2012; Levy and Borenstein, 2014). Improved taxonomic assignment of human intestinal 16S r RNA sequences by a dedicated reference database. doi: 10.1186/s12864-015-2265-y Pub Med Abstract | Cross Ref Full Text | Google Scholar Salter, S.
The two main approaches for analyzing the microbiome, 16S ribosomal RNA (r RNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses.
Several methods for taxonomic classification of bacterial sequences are discussed. A statistical toolbox for metagenomics: assessing functional diversity in microbial communities. doi: 10.1186/1471-2105-9-34 Pub Med Abstract | Cross Ref Full Text | Google Scholar Schloss, P.
For the most part, microbiome studies have focussed primarily on the structure and function of bacterial communities, fungi and viruses have received less attention thus far, but are starting to gain momentum (Reyes et al., 2010; Norman et al., 2014, 2015; Wang et al., 2015).
There is also renewed interest in better understanding gaseous products from the gut microbiome, including carbon dioxide, hydrogen, methane and hydrogen sulfide (Pimentel et al., 2013).
Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses.
High-throughput comparative metagenomics enabled by development of next-generation sequencing (NGS) platforms (Mardis, 2008; Novais and Thorstenson, 2011) has led to an outburst of research endeavors that have rapidly advanced our understanding of the composition and function of bacterial populations in very diverse environments (Ley et al., 2006; Garrett et al., 2010; Caporaso et al., 2011; Bolhuis et al., 2014; Huttenhower et al., 2014a; Norman et al., 2014; Yoon et al., 2015). For shotgun data it is recommended to use trimming software that remove low-quality bases from both termini of each sequence, like cutadapt (Martin), sickle (Joshi and Fass, 2011), or fastq Mcf (Aronesty, 2011). For 16S r RNA gene sequences, it is advisable to trim sequences along the entire length, starting from the 5′ end and using a quality threshold as high as possible, while leaving sufficient sequences to perform the analyses. doi: 10.1016/j.mimet.20 Pub Med Abstract | Cross Ref Full Text | Google Scholar Oksanen, J., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O&Hara, R., et al. Setting a quality threshold remains at the researcher's discretion; however, it is good practice to use only those sequences with the highest possible quality. doi: 10.1093/nar/gki866 Pub Med Abstract | Cross Ref Full Text | Google Scholar Paulson, J. In our experience, sacrificing sequences with low quality scores often improves the accuracy of the analyses by a significant margin. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Moreover, reduced diversity and/or imbalances in the gut microbiome have been associated with a variety of phenotypes, including obesity (Turnbaugh et al., 2009; Turnbaugh and Gordon, 2009), inflammatory bowel diseases (IBD) (Knights et al., 2013; Huttenhower et al., 2014b; Kostic et al., 2014; Norman et al., 2015), type II diabetes (T2D) (Qin et al., 2012; Hartstra et al., 2015), fatty liver disease (Arslan, 2014), and numerous additional disorders (Bhattacharjee and Lukiw, 2013; Dinan et al., 2014; Bajaj et al., 2015; Dash et al., 2015). The mechanisms whereby bacteria affect the host physiology are also well appreciated from a gene content/functional perspective. This means that both ends (end1 and end2) of the library insert are sequenced separately. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. doi: 10.1186/s12864-015-1419-2 Pub Med Abstract | Cross Ref Full Text | Google Scholar Overbeek, R., Begley, T., Butler, R. End1 and end2 may or may not overlap and together are referred to as a “read.” With Illumina chemistry, bases at the front (5′ end) of each sequence generally exhibit higher quality than those at the back (3′ end) (Supplemental Figure 1); however, in the case of 16S libraries, the primers used for amplification can also generate regions of low quality at the front of each sequence. Available online at: https://cran.r-project.org/web/packages/vegan/Ounit, R., Wanamaker, S., Close, T. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. Assessing and improving methods used in operational taxonomic unit-based approaches for 16S r RNA gene sequence analysis.