Metagenome analyses of microbial population in geotextile fabrics used in permeable reactor barriers for toluene biodegradation.

Toluene is one of the hydrocarbons that contaminate soil and groundwater, and has a high cost to remediate, which makes it an environmental pollutant of concern. This study aimed to find bacterial distribution from nonwoven geotextile (GT) fabric specimens in a pilot-scale permeable reactive barrier (PRB). Upon 167 days of incubation with the addition of toluene, the microbial community on the GT surfaces (n = 12) was investigated by the 16S rRNA metagenome sequencing approach. According to taxonomic classification, the Proteobacteria phylum dominated the metagenomes of all the geotextile samples (80-90%). Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway database search of the toluene degradation mechanism revealed the susceptible toluene-degrading species. For the toluene-to-benzoate degradation, the Cupriavidus genus, particularly C. gilardii, C. metallidurans, and C. taiwanensis, are likely to be functional. In addition to these species, the Novosphingobium genus was abundantly localized in the GTs, in particular Novosphingobium sp. ABRDHK2. The results suggested the biodegradation potential of these species in toluene remediation. Overall, this work sheds light on the variety of microorganisms found in the geotextile fabrics used in PRBs and the species involved in the biodegradation of toluene from several sources, including soil, sediment, and groundwater. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03460-y.

hCoronavirusesDB: an integrated bioinformatics resource for human coronaviruses.

In the twenty-first century, three new human coronaviruses have been identified with known zoonotic origins: severe acute respiratory syndrome coronavirus (SARS-CoV), SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV). SARS-CoV-2 was identified in November 2019 and is associated with an ongoing pandemic. Molecular surveillance and monitoring studies are essential for containing viral outbreaks, epidemics, and pandemics. In addition, the development and deployment of bioinformatics resources for highly pathogenic human coronaviruses are crucial for understanding the genetic and immunogenic landscape associated with these viruses. Here, we introduce an open-access, integrated resource for SARS-CoV, SARS-CoV-2, and MERS-CoV: the Human Coronaviruses Database and Analysis Resource (hCoronavirusesDB; http://hcoronaviruses.net/), which include nucleotide and protein sequence data obtained for these viruses. The database also offers a user-friendly search interface coupled with bioinformatics analytics and visualization tools. In addition, hCoronavirusesDB contains curated, experimentally validated B cell and T cell epitope data for these viruses. This resource can assist with the molecular surveillance necessary to trace virus circulation and contribute to microevolutionary studies. This application can also serve as a valuable resource for the development of rationally designed pan-coronavirus diagnostic tools, vaccines, and therapeutic agents. Database URL:http://hcoronaviruses.net/.

DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. RESULTS: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp . CONCLUSIONS: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants.

An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Semantic prioritization of novel causative genomic variants.

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining.

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.