15Next generation sequencing technologies have become increasingly used to describe microbial communities. 16 Metagenomics characterization of microbiomes is associated with minimal manipulation during sample 17 processing, which includes sampling, storage, DNA isolation, library preparation and sequencing, before the 18 raw data are obtained. Here we assess the effect of library preparation using a kit with a polymerase chain 19 reaction (PCR) step (Nextera) and two PCR-free kits (NEXTflex and KAPA), and the effect of sequencing 20 platform (HiSeq and NextSeq) on the description of microbial communities in pig feces and sewage. Two pig 21 fecal samples were obtained from different farms and two sewage samples were collected as inlet water at 22 a local wastewater treatment facility. Samples were processed to both perform DNA-isolation immediately 23 upon arrival in the lab and after storage for 64 hours at -80°C, DNA isolation was performed in duplicate. 24We find that both library preparation and sequencing platform had systematic effects on the microbial 25 community description. The effects were at a level that made differentiating between the two pig fecal 26 samples difficult. The sewage samples represented two very different communities and were at all times 27 distinguishable from each other. We find that library preparation and sequencing platform introduced more 28 variation than freezing the samples. The community changes did not seem associated with contamination 29 during processing and distinct patterns connected specific types of organisms with a processing method, but 30it was difficult to generalize between samples. This highlights the need for continuous validation of the effect 31 of sample processing in different types of samples and that all processing steps need to be considered when 32 comparing between studies. We believe standardization of sample processing is key to generate comparable 33data within a study and that comparisons of differently generated data, e.g. in a meta-analysis, should be 34 performed cautiously.35 36
14Microbial metagenomics utilising next generation sequencing is a powerful experimental approach 15 enabling detailed and potentially complete descriptions of the microbial world around and within us. 16Selecting how to perform feature data normalization, transformation and calculate ß-diversity is a 17 critical step in the analysis of metagenomic data, but also a step for which a multitude of methods are 18 available. Researchers need to have a broad overview and understand the many methods that exist in 19 the field and the consequences from applying them. In this perspectives article, some of the most widely 20 used metagenomic feature data normalizations, transformations and ß-diversity metrics are discussed 21in the context of multivariate visualizations. We provide a framework that other researchers can utilize 22to evaluate how robust their test data are when applying different normalizations, transformations and 23 ß-diversity metrics, and visually compare the results of the methods. We constructed an in silico test 24 dataset to evaluate the setup and clarify how the theoretical discussion is transferable to this data. We 25 urge other researchers to implement their own test data, normalization, transformation, ß-diversity 26 metric and visualization methods, in the hope that it will advance better decision making both in study 27 design and analysis strategy. 28 29 1The lack of consensus on how to perform data normalization, transformation and 30 calculate ß -diversity 31Next generation sequencing (NGS) is applied heavily in microbiome research, enabling both 32 taxonomic and functional descriptions of microbiomes (1,2). Metagenomic data need to be processed 33
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