UMGAP: the Unipept MetaGenomics Analysis Pipeline.

BACKGROUND: Shotgun metagenomics yields ever richer and larger data volumes on the complex communities living in diverse environments. Extracting deep insights from the raw reads heavily depends on the availability of fast, accurate and user-friendly biodiversity analysis tools. RESULTS: Because environmental samples may contain strains and species that are not covered in reference databases and because protein sequences are more conserved than the genes encoding them, we explore the alternative route of taxonomic profiling based on protein coding regions translated from the shotgun metagenomics reads, instead of directly processing the DNA reads. We therefore developed the Unipept MetaGenomics Analysis Pipeline (UMGAP), a highly versatile suite of open source tools that are implemented in Rust and support parallelization to achieve optimal performance. Six preconfigured pipelines with different performance trade-offs were carefully selected, and benchmarked against a selection of state-of-the-art shotgun metagenomics taxonomic profiling tools. CONCLUSIONS: UMGAP's protein space detour for taxonomic profiling makes it competitive with state-of-the-art shotgun metagenomics tools. Despite our design choices of an extra protein translation step, a broad spectrum index that can identify both archaea, bacteria, eukaryotes and viruses, and a highly configurable non-monolithic design, UMGAP achieves low runtime, manageable memory footprint and high accuracy. Its interactive visualizations allow for easy exploration and comparison of complex communities.

Accurate determination of node and arc multiplicities in de bruijn graphs using conditional random fields.

BACKGROUND: De Bruijn graphs are key data structures for the analysis of next-generation sequencing data. They efficiently represent the overlap between reads and hence, also the underlying genome sequence. However, sequencing errors and repeated subsequences render the identification of the true underlying sequence difficult. A key step in this process is the inference of the multiplicities of nodes and arcs in the graph. These multiplicities correspond to the number of times each k-mer (resp. k+1-mer) implied by a node (resp. arc) is present in the genomic sequence. Determining multiplicities thus reveals the repeat structure and presence of sequencing errors. Multiplicities of nodes/arcs in the de Bruijn graph are reflected in their coverage, however, coverage variability and coverage biases render their determination ambiguous. Current methods to determine node/arc multiplicities base their decisions solely on the information in nodes and arcs individually, under-utilising the information present in the sequencing data. RESULTS: To improve the accuracy with which node and arc multiplicities in a de Bruijn graph are inferred, we developed a conditional random field (CRF) model to efficiently combine the coverage information within each node/arc individually with the information of surrounding nodes and arcs. Multiplicities are thus collectively assigned in a more consistent manner. CONCLUSIONS: We demonstrate that the CRF model yields significant improvements in accuracy and a more robust expectation-maximisation parameter estimation. True k-mers can be distinguished from erroneous k-mers with a higher F1 score than existing methods. A C++11 implementation is available at https://github.com/biointec/detox under the GNU AGPL v3.0 license.

Improved Node and Arc Multiplicity Estimation in De Bruijn Graphs Using Approximate Inference in Conditional Random Fields.

In de novo genome assembly using short Illumina reads, the accurate determination of node and arc multiplicities in a de Bruijn graph has a large impact on the quality and contiguity of the assembly. The multiplicity estimates of nodes and arcs guide the cleaning of the de Bruijn graph by identifying spurious nodes and arcs that correspond to sequencing errors. Additionally, they can be used to guide repeat resolution. Here, we model the entire de Bruijn graph and the accompanying read coverage information with a single Conditional Random Field (CRF) model. We show that approximate inference using Loopy Belief Propagation (LBP) on our model improves multiplicity assignment accuracy within feasible runtimes. The order in which messages are passed has a large influence on the speed of LBP convergence. Little theoretical guarantees exist and the conditions for convergence are not easily checked as our CRF model contains higher-order interactions. Therefore, we also present an empirical evaluation of several message passing schemes that may guide future users of LBP on CRFs with higher-order interactions in their choice of message passing scheme.