Blanke, M. | Search bioRxiv

bioRxiv2024

CoCoPyE: feature engineering for learning and prediction of genome quality indices

The exploration of the microbial world has been greatly advanced by the reconstruction of genomes from metagenomic sequence data. However, the rapidly increasing number of metagenome-assembled genomes has also resulted in a wide variation in data quality. It is therefore essential to quantify the achieved completeness and possible contamination of a reconstructed genome before it is used in subsequent analyses. The classical approach for the estimation of quality indices solely relies on a relatively small number of universal single copy genes. Recent tools try to extend the genomic coverage of estimates for an increased accuracy. CoCoPyE is a fast tool based on a novel two-stage feature extraction and transformation scheme. First it identifies genomic markers and then refines the marker-based estimates with a machine learning approach. In our simulation studies, CoCoPyE showed a more accurate prediction of quality indices than the existing tools.

bioinformatics↗

bioRxiv2020

Phylogenetic placement of short reads without sequence alignment

Phylogenetic placement is the task of placing a query sequence of unknown taxonomic origin into a given phylogenetic tree of a set of reference sequences. Several approaches to phylogenetic placement have been proposed in recent years. The most accurate of them need a multiple alignment of the reference sequences as input. Most of them also need alignments of the query sequences to the multiple alignment of the reference sequences. A major field of application of phylogenetic placement is taxonomic read assignment in metagenomics. Herein, we propose App-SpaM, an efficient alignment-free algorithm for phylogenetic placement of short sequencing reads on a tree of a set of reference genomes. App-SpaM is based on the Filtered Spaced Word Matches approach that we previously developed. Unlike other methods, our approach neither requires a multiple alignment of the reference genomes, nor alignments of the queries to the reference sequences. Moreover, App-SpaM works not only on assembled reference genomes, but can also take reference taxa as input for which only unassembled read sequences are available. The quality of the results achieved with App-SpaM is comparable to the best available approaches to phylogenetic placement. However, since App-SpaM is not based on sequence alignment, it is between one and two orders of magnitude faster than those existing methods.

bioinformatics↗