A Comparison of Two DNA Metagenomic Bioinformatic Pipelines while evaluating the Microbial Diversity in feces of Tanzanian small holder dairy cattle

Background: Analysis of shotgun metagenomic data generated from next generation sequencing platforms can be done through a variety of bioinformatic pipelines. These pipelines employ different sets of sophisticated bioinformatics algorithms which may affect the results of this analysis. Furthermore, no conventional assessment technique for estimating the precision of each pipeline exists and few studies have been carried out to compare the characteristics, benefits and disadvantages of each pipeline. In this study we compared two commonly used pipelines for shotgun metagenomic analysis: MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Kraken, in terms of taxonomic classification, diversity analysis and usability using their primarily default parameters Results: : Overall, the two pipelines detected similar abundance distributions in the three most abundant taxa Proteobacteria, Firmicutes and Bacteroidetes. Within bacterial domain, 497 genera were identified by both pipelines, while an additional 694 and 98 genera were solely identified by Kraken and MG-RAST respectively. 933 species were detected by the two algorithms. Kraken solely detected 3550 species, while MG-RAST identified 557 species uniquely. For archaea, Kraken generated 105 and 236 genera and species respectively while MG-RAST detected 60 genera and 88 species. 54 genera and 72 species were commonly detected by the two methods. Kraken had a quicker analysis time (~4 hours) while MG-RAST took approximately 2 days per sample. Conclusions: : This study revealed that Kraken and MG-RAST generate comparable results and that a reliable high-level overview of sample is generated irrespective of the pipeline selected. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. Specifically, the pipelines could have been limited because some rumen microbes lack reference genomes. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of combined pipelines.