Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of
new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).
The methods for gut microbiota analysis
Both target gene and metagenomic sequencing approaches are key to decipher a plethora of roles which are played by environmental microorganisms. However, both sequencing and computational methods still suffer from many biases that are due to errors in sample handling, experimental errors
and downstream bioinformatics analysis. Thus, improvements in sequencing technologies and the development of new computational tools and algorithms should always be based on prior knowledge, e.g. known caveats at each sample processing step. Factors that potentially influence preprocessing, as well as downstream analysis of both short-read and long-read data including sample preparation, sequencing, binning, assembly and functional annotations, should be catalogued precisely.
Herein, we have attempted to list challenges and best-practice protocols utilized during microbiome acquisition using 16SrRNA and metagenomic sequencing. This is important due to the large and expanding paradigms of computational tools that have been developed in recent years for analyzing long and short-read sequencing data. Here, we provide a workflow of optimally tested tools available for processing sequencing samples, estimating microbial abundances, and classification, assembly and functional annotations. In addition, we also discussed the experimental challenges with a systematic review of steps involved in 16S rRNA and shotgun metagenomics.
The experimental challenges mainly account for factors responsible for contamination in isolated microbial genomes and resulting variations in microbial profiles. Although gradual improvisation of these factors has been implemented, extensive and multilayered, sequencing data remain prone to errors at various levels. Hence, we believe that utilization and awareness of integrated methods described here will not just help to improve the reliability of sequencing outcomes but would also reduce variability in the data generation and processing steps.
Key words
microbiome; amplicon sequencing; 16S rRNA sequencing; metagenomics
Reference
Bharti R, Grimm DG. Current challenges and best-practice protocols for microbiome analysis. Briefings in bioinformatics. 2021 Jan;22(1):178-93. doi: 10.1093/bib/bbz155
