PhylteR: Efficient Identification of Outlier Sequences in Phylogenomic Datasets

Discovering Fragile Clades and Causal Sequences in Phylogenomics by Evolutionary Sparse Learning

Phylogenomic analyses of long sequences, consisting of many genes and genomic segments, reconstruct organismal relationships with high statistical confidence. But, inferred relationships can be sensitive to excluding just a few sequences. Currently, there is no direct way to identify fragile relationships and the associated individual gene sequences in species. Here, we introduce novel metrics for gene-species sequence concordance and clade probability derived from evolutionary sparse learning models. We validated these metrics using fungi, plant, and animal phylogenomic datasets, highlighting the ability of the new metrics to pinpoint fragile clades and the sequences responsible. The new approach does not necessitate the investigation of alternative phylogenetic hypotheses, substitution models, or repeated data subset analyses. Our methodology offers a streamlined approach to evaluating major inferred clades and identifying sequences that may distort reconstructed phylogenies using large datasets.

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Classifying the flow subsequences of sensor networks is an effective way for fault detection in the Industrial Internet of Things (IIoT). Traditional fault detection algorithms identify exceptions by a single abnormal dataset and do not pay attention to the factors such as electromagnetic interference, network delay, sensor sample delay, and so on. This paper focuses on fault detection by continuous abnormal points. We proposed a fault detection algorithm within the module of sequence state generated by unsupervised learning (SSGBUL) and the module of integrated encoding sequence classification (IESC). Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the different network cards in the IIoT gateway, and then combined the multiple code sequences into one integrated sequence. Next, we classified the integrated sequence by comparing the integrated sequence with the encoding fault type. The results obtained from the three IIoT datasets of a sewage treatment plant show that the accuracy of the SSGBUL-IESC algorithm exceeds 90% with subsequence length 10, which is significantly higher than the accuracies of the dynamic time warping (DTW) algorithm and the time series forest (TSF) algorithm. The proposed algorithm reaches the classification requirements for fault detection for the IIoT.

Origin of eukaryotic-like Vps23 shapes an ancient functional interplay between ESCRT and ubiquitin system in Asgard archaea

Functional interplay between the endosomal sorting complexes required for transport (ESCRT) and the ubiquitin system underlies the ubiquitin-dependent cargo-sorting pathway of the eukaryotic endomembrane system, yet its evolutionary origin remains unclear. Here, we show that a UEV-Vps23 protein family, which contains UEV and Vps23 domains, mediates an ancient ESCRT and ubiquitin system interplay in Asgard archaea. The UEV binds ubiquitin with high affinity, making the UEV-Vps23 a sensor for sorting ubiquitinated cargo. A steadiness box in the Vps23 domain undergoes ubiquitination through an Asgard E1, E2, and RING E3 cascade. The UEV-Vps23 switches between autoinhibited and active forms, regulating the ESCRT and ubiquitin system interplay. Furthermore, the shared sequence and structural homology among the UEV-Vps23, eukaryotic Vps23, and archaeal CdvA suggest a common evolutionary origin. Together, this work expands our understanding of the ancient ESCRT and ubiquitin system interplay that likely arose antedating divergent evolution between Asgard archaea and eukaryotes.

Forty Years of Inferential Methods in the Journals of the Society for Molecular Biology and Evolution

We are launching a series to celebrate the 40th anniversary of the first issue of Molecular Biology and Evolution. In 2024, we will publish virtual issues containing selected papers published in the Society for Molecular Biology and Evolution journals, Molecular Biology and Evolution and Genome Biology and Evolution. Each virtual issue will be accompanied by a perspective that highlights the historic and contemporary contributions of our journals to a specific topic in molecular evolution. This perspective, the first in the series, presents an account of the broad array of methods that have been published in the Society for Molecular Biology and Evolution journals, including methods to infer phylogenies, to test hypotheses in a phylogenetic framework, and to infer population genetic processes. We also mention many of the software implementations that make methods tractable for empiricists. In short, the Society for Molecular Biology and Evolution community has much to celebrate after four decades of publishing high-quality science including numerous important inferential methods.