A Comparison of Two DNA Metagenomic Bioinformatic Pipelines While Evaluating the Microbial Diversity in Feces of Tanzanian Small Holder Dairy Cattle

Abstract

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. In this study, we compared two commonly used pipelines for shotgun metagenomic analysis: MG-RAST and Kraken 2, in terms of taxonomic classification, diversity analysis, and usability using their primarily default parameters. 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 2 and MG-RAST, respectively. 933 species were detected by the two algorithms. Kraken 2 solely detected 3550 species, while MG-RAST identified 557 species uniquely. For archaea, Kraken 2 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 2 had a quicker analysis time (~4 hours) while MG-RAST took approximately 2 days per sample. This study revealed that Kraken 2 and MG-RAST generate comparable results and that a reliable high-level overview of sample is generated irrespective of the pipeline selected. However, Kraken 2 generated a more accurate taxonomic identification given the higher number of “Unclassified” reads in MG-RAST. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of the combined pipelines.

Evidence for the Widespread Occurrence of Bacteria Implicated in Acute Oak Decline from Incidental Genetic Sampling

“…The developers of Kraken tested the original Kraken1 program against a simulated Hi-seq metagenome and found that precision at genus level, defined as the proportion of correctly classified reads from all the classification attempts, was 99.2% [66]. Although this precision is high, and Kraken and Kraken2 have been found by other authors to be precise and accurate and in line with other metagenomic pipelines [67,68], the low numbers of total AOD-associated bacterial reads classified may put our results within the potential margin of error for false positives. The trends we detected in the species level comparisons should therefore be verified, for example using qPCR studies on the oak phyllosphere.…”

Whole genome sequences from non-invasively collected caribou faecal samples

“…1 Composition of the raw reads for each caribou as per Kraken2, showing the proportions of caribou, human, fungi, green plants, sar (an eukaryotic clade which includes many parasites of animals), bacteria, archaea, viruses, other eukaryotes, and the unclassified reads. Proportions of caribou reads are smaller and non-host and unclassified reads larger for faecal samples (a-d) compared to tissue (e-f) samples ◂ which could not be classified in the faecal samples which could be due to several factors, including database incompleteness or biases (Pignatelli et al 2008;Kibegwa et al 2020), or potentially low quality reads (Fig. 1).…”

Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes

“…Of the known tools, we selected Kraken2, which uses a kmer counting approach for elucidation of the microbial populations from RNA-sequencing data. As previously described, Kraken2 generated accurate taxonomic identification for bacteria with very fast speed [37,38]. However, only a few studies have used Kraken2 for fungal and viral microbiome studies [39,40].…”