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 parametersResults: 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.

Smallholder dairy cattle rumen microbiotas are subjected to a wide range of antimicrobials as well as sudden fluctuations in diets. As such, they develop an enormous reservoir of resistant genes, mobilome and stress response genes. However, information on metagenomic reactions to such dietary variations, especially for cattle reared in the tropics, remains largely unexplored. This meta-analysis was conducted to assess if antibiotic and toxic compound resistance genes (ARGs), stress response genes and bacterial phages, prophages and transposable element genes were present, and to what extent, in three dairy cattle genotypes (Friesian, FriesianXJersey crossbreed, Jersey) reared in a farm that practiced judicious use of antimicrobials. Potential bacterial hosts to these genes were also explored. The rumen metagenomes generated from Next Generation Sequencing (NGS) technology were analyzed using MG-RAST. According to the results stress reaction, resistance and mobilome genes were present in similar amounts in all the three genotypes. Cobalt-zinc-cadmium resistance, fluoroquinolone resistance, methicillin resistance in Staphylococci and multidrug resistance efflux pumps were the most abundant resistant genes and were spread across 20, 24, 16 and 21 bacterial classes, respectively. Bacteria in charge of phage integration and excision, phages replication and phage packaging were mostly allocated to the phyla Firmicutes, Bacteroides and Proteobacteria. Within the stress response genes, metagenomic assembly-based host-tracking analysis identified the extended heat shock dnaK gene cluster as the most abundant genes, while Bacteroides and Clostridium were the principal bacterial hosts. The results show that even with proper use of antimicrobials, the cattle rumen contained an immense distribution of responses to stress, ARGs and mobilome genes distributed in a vast assemblage of hosts. There is also a high correlation between these three functional groups.