It is well known that fewer than 1% of species in the environment can be isolated and cultured, limiting the ability to identify rare and difficult-to-cultivate members of the community ( Bodor et al., 2020 Cho and Giovannoni, 2004 Ferguson et al., 1984). Next-generation sequencing (NGS) has revolutionized the understanding of environmental systems through the characterization of microbial communities and their function by examining DNA collected from samples that contain mixed assemblages of organisms ( Bartram et al., 2011 Hugerth and Andersson, 2017 Shokralla et al., 2012). Specifically, it evaluates which data might have been obtained if a particular sample’s library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the source microbial community. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. Here, repeated rarefying is proposed as a tool for diversity analyses to normalize library sizes. Notably, the superiority of rarefying relative to many other normalization approaches has been argued in diversity analysis. Nonetheless, it remains prevalent in practice. Rarefying is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. Groups of samples typically have different library sizes that are not representative of biological variation library size normalization is required to meaningfully compare diversity between them. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |