BackgroundAdvancements in Next Generation Sequencing (NGS) technologies regarding throughput, read length and accuracy had a major impact on microbiome research by significantly improving 16S rRNA amplicon sequencing. As rapid improvements in sequencing platforms and new data analysis pipelines are introduced, it is essential to evaluate their capabilities in specific applications. The aim of this study was to assess whether the same project-specific biological conclusions regarding microbiome composition could be reached using different sequencing platforms and bioinformatics pipelines.ResultsChicken cecum microbiome was analyzed by 16S rRNA amplicon sequencing using Illumina MiSeq, Ion Torrent PGM, and Roche 454 GS FLX Titanium platforms, with standard and modified protocols for library preparation. We labeled the bioinformatics pipelines included in our analysis QIIME1 and QIIME2 (de novo OTU picking [not to be confused with QIIME version 2 commonly referred to as QIIME2]), QIIME3 and QIIME4 (open reference OTU picking), UPARSE1 and UPARSE2 (each pair differs only in the use of chimera depletion methods), and DADA2 (for Illumina data only). GS FLX+ yielded the longest reads and highest quality scores, while MiSeq generated the largest number of reads after quality filtering. Declines in quality scores were observed starting at bases 150–199 for GS FLX+ and bases 90–99 for MiSeq. Scores were stable for PGM-generated data. Overall microbiome compositional profiles were comparable between platforms; however, average relative abundance of specific taxa varied depending on sequencing platform, library preparation method, and bioinformatics analysis. Specifically, QIIME with de novo OTU picking yielded the highest number of unique species and alpha diversity was reduced with UPARSE and DADA2 compared to QIIME.ConclusionsThe three platforms compared in this study were capable of discriminating samples by treatment, despite differences in diversity and abundance, leading to similar biological conclusions. Our results demonstrate that while there were differences in depth of coverage and phylogenetic diversity, all workflows revealed comparable treatment effects on microbial diversity. To increase reproducibility and reliability and to retain consistency between similar studies, it is important to consider the impact on data quality and relative abundance of taxa when selecting NGS platforms and analysis tools for microbiome studies.Electronic supplementary materialThe online version of this article (10.1186/s12866-017-1101-8) contains supplementary material, which is available to authorized users.

Colorectal cancer (CRC) is the third most common cancer in the world and the second leading cause of cancer deaths in the US and Spain. The molecular mechanisms involved in the etiology of CRC are not yet elucidated due in part to the complexity of the human gut microbiota. In this study, we compared the microbiome composition of 90 tumor and matching adjacent tissue (adjacent) from cohorts from the US and Spain by 16S rRNA amplicon sequencing in order to determine the impact of the geographic origin on the CRC microbiome. Data showed a significantly (P < 0.05) higher Phylogenetic Diversity ( Predicted metagenome functional content from 16S rRNA surveys showed that bacterial motility proteins and proteins involved in flagellar assembly were over represented in adjacent tissues of both cohorts, while pathways involved in fatty acid biosynthesis, the MAPK signaling pathway, and bacterial toxins were over represented in tumors. Our study suggests that microbiome compositional and functional dissimilarities by geographic location should be taken in consideration when approaching CRC therapeutic options.

The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host–microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.

Large scale genomic analysis of 3067 SARS-1 CoV-2 genomes reveals a clonal geo-distribution 2 and a rich genetic variations of hotspots 3 mutations 4 Abstract 33In late December 2019, an emerging viral infection COVID-19 was identified in Wuhan, 34China, and became a global pandemic. Characterization of the genetic variants of SARS-35CoV-2 is crucial in following and evaluating it spread across countries. In this study, we 36 collected and analyzed 3,067 SARS-CoV-2 genomes isolated from 55 countries during the 37 first three months after the onset of this virus. Using comparative genomics analysis, we 38 traced the profiles of the whole-genome mutations and compared the frequency of each 39 mutation in the studied population. The accumulation of mutations during the epidemic 40 period with their geographic locations was also monitored. The results showed 782 variant 41 sites, of which 512 (65.47%) had a non-synonymous effect. Frequencies of mutated alleles 42 revealed the presence of 38 recurrent non-synonymous mutations, including ten hotspot 43 mutations with a prevalence higher than 0.10 in this population and distributed in six 44 SARS-CoV-2 genes. The distribution of these recurrent mutations on the world map 45 revealed certain genotypes specific to the geographic location. We also found co-occurring 46 mutations resulting in the presence of several haplotypes. Moreover, evolution over time 47We have also created an inclusive unified database (http://genoma.ma/covid-19/) that lists 52 all of the genetic variants of the SARS-CoV-2 genomes found in this study with 53 phylogeographic analysis around the world. 54 55 56