Contreras-Moreira, B. | Search bioRxiv

bioRxiv2018

GET_PHYLOMARKERS, a software package to select optimal orthologous clusters for phylogenomics and inferring pan-genome phylogenies, used for a critical geno-taxonomic revision of the genus Stenotrophomonas

The massive accumulation of genome-sequences in public databases promoted the proliferation of genome-level phylogenetic analyses in many areas of biological research. However, due to diverse evolutionary and genetic processes, many loci have undesirable properties for phylogenetic reconstruction. These, if undetected, can result in erroneous or biased estimates, particularly when estimating species trees from concatenated datasets. To deal with these problems, we developed GET_PHYLOMARKERS, a pipeline designed to identify high-quality markers to estimate robust genome phylogenies from the orthologous clusters, or the pan-genome matrix (PGM), computed by GET_HOMOLOGUES. In the first context, a set of sequential filters are applied to exclude recombinant alignments and those producing anomalous or poorly resolved trees. Multiple sequence alignments and maximum likelihood (ML) phylogenies are computed in parallel on multi-core computers. A ML species tree is estimated from the concatenated set of top-ranking alignments at the DNA or protein levels, using either FastTree or IQ-TREE (IQT). The latter is used by default due to its superior performance revealed in an extensive benchmark analysis. In addition, parsimony and ML phylogenies can be estimated from the PGM.\n\nWe demonstrate the practical utility of the software by analyzing 170 Stenotrophomonas genome sequences available in RefSeq and 10 new complete genomes of environmental S. maltophilia complex (Smc) isolates reported herein. A combination of core-genome and PGM analyses was used to revise the molecular systematics of the genus. An unsupervised learning approach that uses a goodness of clustering statistic identified 20 groups within the Smc at a core-genome average nucleotide identity of 95.9% that are perfectly consistent with strongly supported clades on the core- and pan-genome trees. In addition, we identified 14 misclassified RefSeq genome sequences, 12 of them labeled as S. maltophilia, demonstrating the broad utility of the software for phylogenomics and geno-taxonomic studies. The code, a detailed manual and tutorials are freely available for Linux/UNIX servers under the GNU GPLv3 license at https://github.com/vinuesa/get_phylomarkers. A docker image bundling GET_PHYLOMARKERS with GET_HOMOLOGUES is available at https://hub.docker.com/r/csicunam/get_homologues/, which can be easily run on any platform.

genomics↗

bioRxiv2017

A new multi-genomic approach for the study of biogeochemical cycles at global scale: the molecular reconstruction of the sulfur cycle

Despite the great advances in microbial ecology and the explosion of high throughput sequencing, our ability to understand and integrate the global biogeochemical cycles is still limited. Here we propose a novel approach to summarize the complexity of the Sulfur cycle based on the minimum ecosystem concept, the microbial mat model and the relative entropy of protein domains involved in S-metabolism. This methodology produces a single value, called the Sulfur Score (SS), which informs about the specific S-related molecular machinery. After curating an inventory of microorganisms, pathways and genes taking part in this cycle, we benchmark the performance of the SS on a collection of 2,107 non-redundant RefSeq genomes, 900 metagenomes from MG-RAST and 35 metagenomes analyzed for the first time. We find that the SS is able to correctly classify microorganisms known to be involved in the S-cycle, yielding an Area Under the ROC Curve of 0.985. Moreover, when sorting environments the top-scoring metagenomes were hydrothermal vents, microbial mats and deep-sea sediments, among others. This methodology can be generalized to the analysis of other biogeochemical cycles or processes. Provided that an inventory of relevant pathways and microorganisms can be compiled, entropy-based scores could be used to detect environmental patterns and informative samples in multi-genomic scale.

bioinformatics↗