TSNAdb v2.0:The Updated Version of Tumor-specific Neoantigen Database

In recent years,neoantigens have been recognized as ideal targets for tumor immunotherapy.With the development of neoantigen-based tumor immunotherapy,comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies.We have built the tumor-specific neoantigen database(TSNAdb)previously,which has attracted much attention.In this study,we provide TSNAdb v2.0,an updated version of the TSNAdb.TSNAdb v2.0 offers several new features,including(1)adopting more stringent criteria for neoantigen identification,(2)providing predicted neoantigens derived from three types of somatic mutations,and(3)collecting experimentally validated neoantigens and dividing them according to the experimental level.

Comparative Analysis of Codon Usage Patterns Among Mitochondrion, Chloroplast and Nuclear Genes

In many organisms, the difference in codon usage patterns among genes reflects variation in local base compositional biases and the intensity of natural selection. In this study, a comparative analysis was performed to investigate the characteristics of codon bias and factors in shaping the codon usage patterns among mitochondrion, chloroplast and nuclear genes in common wheat （Triticum aestivum L.）. GC contents in nuclear genes were higher than that in mitochondrion and chloroplast genes. The neutrality and correspondence analyses indicated that the codon usage in nuclear genes would be a result of relative strong mutational bias, while the codon usage patterns of mitochondrion and chloroplast genes were more conserved in GC content and influenced by translation level. The Parity Rule 2 （PR2） plot analysis showed that pyrimidines were used more frequently than purines at the third codon position in the three genomes. In addition, using a new alterative strategy, 11, 12, and 24 triplets were defined as preferred codons in the mitochondrion, chloroplast and nuclear genes, respectively. These findings suggested that the mitochondrion, chloroplast and nuclear genes shared particularly different features of codon usage and evolutionary constraints.

TIR-Learner, a New Ensemble Method for TIR Transposable Element Annotation, Provides Evidenee for Abundant New Transposable Elements in the Maize Genome

Transposable elements (TEs) make up a large and rapidly evolving proportion of plant genomes. Among Class II DNA TEs, TIR elements are flanked by characteristic terminal inverted repeat sequences (TIRs). TIR TEs may play important roles in genome evolution, including generating allelic diversity, inducing structural variation, and regulating gene expression. However, TIR TE identification and annotation has been hampered by the lack of effective tools, resulting in erroneous TE annotations and a significant underestimation of the proportion of TIR elements in the maize genome. This problem has largely limited our understanding of the impact of TIR elements on plant genome structure and evolution. In this paper, we propose a new method of TIR element detection and annotation. This new pipeline combines the advantages of current homology-based annotation methods with powerful de novo machine-learning approaches, resulting in greatly increased efficiency and accuracy of TIR element annotation. The results show that the copy number and genome proportion of TIR elements in maize is much larger than that of current annotations. In addition, the distribution of some TIR superfamily elements is reduced in centromeric and pericentromeric positions, while others do not show a similar bias. Finally, the incorporation of machine-learning techniques has enabled the identification of large numbers of new DTA (hAT) family elements, which have all the hallmarks of bona fide TEs yet which lack high homology with currently known DTA elements. Together, these results provide new tools forTE research and new insight into the impact ofTIR elements on maize genome diversity .

TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database （TSNAdb v1.0）, based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen （HLA） allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas （TCGA） and The Cancer Immunome Atlas （TCIA）. We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients,which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy.TSNAdb is freely available at http：//biopharm.zju.edu.cn/tsnadb.

Paralog-divergent Features May Help Reduce Off-target Effects of Drugs:Hints from Glucagon Subfamily Analysis

Side effects from targeted drugs remain a serious conccrn. One reason is the nonselective binding of a drug to unintended proteins such as its paralogs, which arc highly homologous in sequences and have similar structures and drug-binding pockets. To identify targctablc differences between paralogs, we analyzed two types （type-I and type-ll） of functional divergence between two paralogs in the known target protein receptor family G-protein coupled receptors （GPCRs） at the amino acid level. Paralogous protein receptors in glucagon-like subfamily, glucagon receptor （GCGR） and glucagon-like peptide-I receptor （GLP-I R）, exhibit divergence in ligands and are clinically validated drug targets for type 2 diabetes. Our data showed that type-ll alnino acids were significantly enriched in the binding sites of antagonist MK-0893 to GCGR. which had a radical shift in physicochemical properties between GCGR and GLP-1R. We also examined the role of type-I amino acids between GCGR and GLP-IR. The divergent features between GCGR and GLP-I R paralogs may be helpful in their discrimination, thus enabling the identification of binding sites to reduce undesirable side effects and increase the target specificity of drugs.

Random Penetrance of Mutations Among Individuals:A New Type of Genetic Drift in Molecular Evolution

The determinative view of mutation penetrance is a fundamental assumption for the building of molecular evolutionary theory:individuals in the population with the same genotype have the same fitness effect.Since this view has been constantly challenged by experimental evidence,it is desirable to examine to what extent violation of this view could affect our under-standing of molecular evolution.To this end,the author formulated a new theory of molecular evolution under a random model of penetrance:for any individual with the same mutational genotype,the coefficient of selection is a random variable.It follows that,in addition to the conventional Ne-genetic drift(Ne is the effective population size),the variance of penetrance among individuals(ε^(2))represents a new type of genetic drift,coined by theε^(2)-genetic drift.It has been demonstrated that these two genetic drifts together provided new insights on the nearly neutral evolution:the evolutionary rate is inversely related to the log-of-Ne when theε^(2)-genetic drift is nontrivial.This log-of-Ne feature ofε^(2)-genetic drift did explain well why the dN/dS ratio(the nonsynonymous rate to the synonymous rate)in humans is only as twofold as that in mice,while the effective population size(Ne)of mice is about two-magnitude larger than that of humans.It was estimated that,for the first time,the variance of random penetrance in mammalian genes was approximatelyε^(2)≈5.89×10^(-3).