GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

Large-scale phylogenetic analysis provides insights into the diversification and evolution of sessilid peritrich ciliates (Protista: Ciliophora).

The Peritrichia is a speciose and morphologically distinctive assemblage of ciliated protists that was first observed by Antonie van Leeuwenhoek over 340 years ago. In the last two decades, the phylogenetic relationships of this group have been increasingly debated as morphological and molecular analyses have generated contrasting conclusions, mainly owing to limited sampling. In the present study, we performed expanded phylogenetic analyses of 152 sessilid peritrichs collected from 14 different provinces of China and 141 SSU rDNA peritrich sequences from GenBank. The results of the analyses revealed new divergent relationships between and within major clades that challenge the morphological classification of this group including, (1) the recovery of four major phylogenetically divergent clades in the monophyletic order Sessilida, (2) aboral structures such as the stalk and spasmoneme were evolutionary labile, (3) the stalk or/and spasmoneme was lost in each divergent clade indicating that parallel evolution occurred in sessilid peritrichs and (4) the life cycle and habit drive the diversity of aboral structures as well as diversification and evolution in peritrichs.