Towards precision medicine:Omics approach for COVID-19

The coronavirus disease 2019(COVID-19)pandemic had a devastating impact on human society.Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19.Here,we reviewed how omics,including genomics,proteomics,single-cell multi-omics,and clinical phenomics,play roles in answering biological and clinical questions about COVID-19.Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification.Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement.Furthermore,decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune system.Characterization of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and immunotherapy.Although TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced analyses.Therefore,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and visualization.TCRosetta combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as possible.The TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 min.TCRosetta is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://guolab.wchscu.cn/TCRosetta/.

Platelet RNA signature independently predicts ovarian cancer prognosis by deep learning neural network model

Dear Editor,Platelets are circulating anucleate cytoplasmic fragments of megakaryocytes and characterized by their functions in wound healing and vascular integrity maintenance.Increasing evidence highlights the extensive reciprocal signaling interactions between platelets and tumor cells(Haemmerle et al.,2018).Tumor cells activate and aggregate platelets to sustain proliferation(Cho et al.,2012),resist apoptosis,and promote metastasis(Haemmerle et al.,2017).

Platelet RNA enables accurate detection of ovarian cancer:an intercontinental,biomarker identification study

Platelets are reprogrammed by cancer via a process called education,which favors cancer development.The transcriptional profile of tumor-educated platelets(TEPs)is skewed and therefore practicable for cancer detection.This intercontinental,hospital-based,diagnostic study included 761 treatment-naive inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers(China,n=3;Netherlands,n=5;Poland,n=1)between September 2016 and May 2019.The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese(VC1 and VC2)and the European(VC3)validation cohorts collectively and independently.Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets.The AUCs for TEPs in the combined validation cohort,VC1,VC2,and VC3 were 0.918(95%CI 0.889-0.948),0.923(0.855-0.990),0.918(0.872-0.963),and 0.887(0.813-0.960),respectively.Combination of TEPs and CA125 demonstrated an AUC of 0.922(0.889-0.955)in the combined validation cohort;0.955(0.912-0.997)in VC1;0.939(0.901-0.977)in VC2;0.917(0.824-1.000)in VC3.For subgroup analysis,TEPs exhibited an AUC of o.858,0.859,and 0.920 to detect early-stage,borderline,non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis.TEPs had robustness,compatibility,and universality for preop.erative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities,heterogeneous histoiogical subtypes,and early-stage ovarian cancer.However,these observations warrant prospective validations in a larger population beforeclinicalutilities.