Hotte Term Paper

Hotte Term Paper-64
Removal of adapters, PCR primers and low quality bases is essential for effective analyses of NGS libraries, and a variety of user-friendly tools have been developed for this purpose. The current Illumina platforms output quality scores “Q” that fit into a 0–41 scale (Q10 corresponds to 1 expected error for every 10 sequenced bases; Q20 = 1 error for every 100 bases, and so on).

Removal of adapters, PCR primers and low quality bases is essential for effective analyses of NGS libraries, and a variety of user-friendly tools have been developed for this purpose. The current Illumina platforms output quality scores “Q” that fit into a 0–41 scale (Q10 corresponds to 1 expected error for every 10 sequenced bases; Q20 = 1 error for every 100 bases, and so on).

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This article provides a compendium of good practices for the analysis of NGS microbiome libraries sequenced with the Mi Seq platform but, for the most part, our suggestions are applicable to data generated with other NGS platforms. doi: 10.1038/nmeth.2658 Pub Med Abstract | Cross Ref Full Text | Google Scholar Qichao, T., Zhili, H., and Jizhong, Z. Strain/species identification in metagenomes using genome-specific markers.

Using gut microbiome datasets specially designed to illustrate the strengths and weaknesses of 16S or shotgun libraries, we describe several methods for performing taxonomical classification of bacterial sequences, assessment of bacterial diversity within and between samples, and inference of the metabolic capabilities associated with the bacterial microbiome. Differential abundance analysis for microbial marker-gene surveys.

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.

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.

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.

In the clinical context, the human gut microbiome has been the subject of intense investigation, which has revealed a sophisticated interplay between the microbiome and the host immune system and metabolism (Garrett et al., 2010; Brown et al., 2013; Huttenhower et al., 2014a; Martín et al., 2014; Broderick, 2015).

For instance, it is well known that bacteria aid in many important metabolic pathways, including synthesis of essential compounds like secondary bile acids and short-chain fatty acids (Flint et al., 2012; Nicholson et al., 2012).

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