Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases.

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites.

XGRm: A Web Server for Interpreting Mouse Summary-level Genomic Data.

We introduce XGR-model (or XGRm), a web server made accessible at http://www.xgrm.pro, with the aim of meeting the increasing demand for effectively interpreting summary-level genomic data in model organisms.

A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation.

Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory).

Priority index for critical Covid-19 identifies clinically actionable targets and drugs.

While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19.

Priority index for asthma (PIA): In silico discovery of shared and distinct drug targets for adult- and childhood-onset disease.

Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies.

OpenXGR: a web-server update for genomic summary data interpretation.

How to effectively convert genomic summary data into downstream knowledge discovery represents a major challenge in human genomics research. To address this challenge, we have developed efficient and effective approaches and tools. Extending our previously established software tools, we here introduce OpenXGR (http://www.

The dcGO Domain-Centric Ontology Database in 2023: New Website and Extended Annotations for Protein Structural Domains.

Protein structural domains have been less studied than full-length proteins in terms of ontology annotations. The dcGO database has filled this gap by providing mappings from protein domains to ontologies. The dcGO update in 2023 extends annotations for protein domains of multiple definitions (SCOP, Pfam, and InterPro) with commonly used ontologies that are categorised into functions, phenotypes, diseases, drugs, pathways, regulators, and hallmarks.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective.

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance.

Genomic Evidence Supports the Recognition of Endometriosis as an Inflammatory Systemic Disease and Reveals Disease-Specific Therapeutic Potentials of Targeting Neutrophil Degranulation.

Background: Endometriosis, classically viewed as a localized disease, is increasingly recognized as a systemic disease with multi-organ effects. This disease is highlighted by systemic inflammation in affected organs and by high comorbidity with immune-mediated diseases.

Results: We provide genomic evidence to support the recognition of endometriosis as an inflammatory systemic disease.

Identifying Potential Neoantigens for Cervical Cancer Immunotherapy Using Comprehensive Genomic Variation Profiling of Cervical Intraepithelial Neoplasia and Cervical Cancer.

Cervical cancer (CC) is one of the most common gynecological malignant tumors. The 5-year survival rate remains poor for the advanced and metastatic cervical cancer for the lack of effective treatments. Immunotherapy plays an important role in clinical tumor therapy.

The diagnostic and prognostic usage of circulating tumor DNA in operable hepatocellular carcinoma.

Circulating tumor DNA (ctDNA) is a promising noninvasive biomarker for hepatocellular carcinoma (HCC). In this study, we aimed to assess the diagnostic and prognostic value of ctDNA in HCC. Twenty-six operable HCC, 10 hepatitis and 10 cirrhosis patients were enrolled in this study.

An efficient genomic signature ranking method for genomic island prediction from a single genome.

Genomic islands that are associated with microbial adaptations and carry genomic signatures different from that of the host, and thus many methods have been proposed to select the informative genomic signatures from a range of organisms and discriminate genomic islands from the rest of the genome in terms of these signature biases. However, they are of limited use when closely related genomes are unavailable. In the present work, we proposed a kurtosis-based ranking method to select the informative genomic signatures from a single genome.

Comprehensive genomic variation profiling of cervical intraepithelial neoplasia and cervical cancer identifies potential targets for cervical cancer early warning.

Background: To better understand the pathogenesis of cervical cancer (CC), we systematically analysed the genomic variation and human papillomavirus (HPV) integration profiles of cervical intraepithelial neoplasia (CIN) and CC.

Methods: We performed whole-genome sequencing or whole-exome sequencing of 102 tumour-normal pairs and human papillomavirus probe capture sequencing of 45 CCs, 44 CIN samples and 25 normal cervical samples, and constructed strict integrated workflow of genomic analysis.

Results: Mutational analysis identified eight significantly mutated genes in CC including four genes (, , and ), which have not previously been reported in CC.

MTGIpick allows robust identification of genomic islands from a single genome.

Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias.