Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters!

By John Vollmers, S. Wiegand, Anne-Kristin Kaster. 2017

208 citations recorded by the source corpus.

With the constant improvement in cost-efficiency and quality of Next Generation Sequencing technologies, shotgun-sequencing approaches -such as metagenomics- have nowadays become the methods of choice for studying and classifying microorganisms from various habitats. The production of data has dramatically increased over the past years and processing and analysis steps are becoming more and more of a bottleneck. Limiting factors are partly the availability of computational resources, but mainly the bioinformatics expertise in establishing and applying appropriate processing and analysis pipelines. Fortunately, a large diversity of specialized software tools is nowadays available. Nevertheless, choosing the most appropriate methods for answering specific biological questions can be rather challenging, especially for non-bioinformaticians. In order to provide a comprehensive overview and guide for the microbiological scientific community, we assessed the most common and freely available metagenome assembly tools with respect to their output statistics, their sensitivity for low abundant community members and variability in resulting community profiles as well as their ease-of-use. In contrast to the highly anticipated "Critical Assessment of Metagenomic Interpretation" (CAMI) challenge, which uses general mock community-based assembler comparison we here tested assemblers on real Illumina metagenome sequencing data from natural communities of varying complexity sampled from forest soil and algal biofilms. Our observations clearly demonstrate that different assembly tools can prove optimal, depending on the sample type, available computational resources and, most importantly, the specific research goal. In addition, we present detailed descriptions of the underlying principles and pitfalls of publically available assembly tools from a microbiologist’s perspective, and provide guidance regarding the user-friendliness, sensitivity and reliability of the resulting phylogenetic profiles.

Subfields: Ecology, Molecular Biology

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Citations in the corpus

MGnify: the microbiome analysis resource in 2020 (2019, background): “Assembly is a computationally expensive process, frequently requiring terabytes of memory to accomplish when using metaS-PAdes (20), which is our primary assembly algorithm, as the ensuing assemblies are typically of better quality than those produced using other assemblers (21).”
PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures (2018, methodology): “We chose to train our neural networks using fragments of 10 kb length, since we hypothesized that their length is similar to the average contig length of typical metagenomic assembly (55) and they are long enough to assure proper kmer coverage, even for hexamers or heptamers.”
MitoFinder: Efficient automated large‐scale extraction of mitogenomic data in target enrichment phylogenomics (2019, unknown): “Comparisons based on empirical bulk data sets of known composition (Vollmers, Wiegand, & Kaster, 2017) have identified iDbA-UD (Peng, Leung, Yiu, & Chin, 2012), MEGAHiT (Li et al., 2016) and M ETA SPA DES (Nurk, Meleshko, Korobeynikov, & Pevzner, 2017) as the most efficient current metagenomic…”
Current challenges and best-practice protocols for microbiome analysis (2019, background): “difficult due to numerous comparative reports on these different assemblers [88, 89].”