Abstract The adaptive immune response depends on T cell receptor (TCR) diversity to counter cancers, autoimmunity, and infections. This diversity is encoded within CDR3, which serves as a unique barcode for each T cell clone. Characterizing CDR3 sequences is essential for tracking clonal dynamics, identifying neoantigen-reactive T cells, and designing personalized immunotherapy. While TCR-seq remains the benchmark, existing RNA-seq datasets offer a cost-effective retrospective alternative. However, tools like MiXCR and TRUST4, optimized for TCR-enriched samples, are limited in bulk and single-cell RNA-seq due to low transcript abundance and sparse coverage. In this review, we synthesize quantitative evidence from published head-to-head benchmarking studies that performed matched RNA-seq and TCR-seq on the same biological samples. These studies demonstrate that standard RNA-seq typically recovers only 3–5% of unique clonotypes identified by TCR-seq, with substantial underrepresentation of low-frequency clones; however, in T cell-rich tissues and when high sequencing depth is combined with optimized computational pipelines, RNA-seq can capture the majority of high-frequency clonotypes, albeit with limited accuracy in clonotype frequency estimation and poor α/β pairing recovery. We review computational efforts to recover CDR3 sequences from RNA-seq data, highlighting recurrent obstacles: transcript scarcity, fragmentation, short reads, and assembly errors. We critically assess whether artificial intelligence can overcome these barriers . We also examine emerging wet-lab strategies including probe-based enrichment, FUME-TCRseq, and rhTCRseq. We conclude that RNA-seq cannot replace TCR-seq, but AI-enhanced tools with targeted library improvements could expand the utility of existing datasets and degraded specimens.
Abstract Laboratories that move from GRCh38 to T2T-CHM13 take on a new callset as well as a new assembly. I asked whether the move changes sex-chromosome genotype summaries, and whether it changes how well classifiers trained on those summaries serve males and females. I compared the published 1000 Genomes GRCh38 and T2T-CHM13 callsets for 3,202 people. The two callsets come from different alignment, calling, phasing and filtering workflows, so every difference below belongs to the reference and its pipeline together. In women, the fraction of non-reference chrX genotypes that were heterozygous was 0.605 on GRCh38 and 0.644 on T2T. It fell in African-ancestry women and rose in the other four superpopulations. The rise came from fewer non-reference homozygous genotypes (− 7,757 per woman) and not from more heterozygous ones (− 1,504). Five classifier families were trained on six chrX summaries to predict superpopulation, and the male–female gap in balanced accuracy was compared between callsets. Women were classified better than men under both callsets. Logistic regression, random forest and XGBoost widened that gap on T2T, and the support vector machine and the perceptron narrowed it. After correction across all 20 tests, random forest, XGBoost and the support vector machine remained significant. Separating relatives did not remove the disagreement. Classifiers built from the first 500 common SNPs per chromosome in file order showed no change after correction. The gap measures model performance, not biological sex bias. Resolving the assembly from its callset requires processing the same reads through one pipeline against both references.
Abstract Background Soil bacterial communities are essential for regulating soil fertility, nutrient cycling, and agricultural sustainability. However, their responses to different fertilization regimes, especially in intensively managed agroecosystems, remain inadequately characterized in Chattogram. Methods This study assessed the effects of prolong applications of chemical and organic fertilizers on soil bacterial community structure using culture-independent techniques (Oxford Nanopore long-read 16S rRNA gene sequencing). Result In subsequent taxonomic composition analyses, beta diversity revealed significant differences in bacterial community composition among the fertilization regimes ( p < 0.05), suggesting that long-term differences in fertilization practices may be associated with variation in bacterial community structure. The R 2 value (0.84) indicates that the fertilization regime was associated with a substantial proportion of the observed variation in bacterial community composition. However, organic amendment was associated with significantly higher abundances of several bacterial taxa linked to plant growth promotion and nutrient cycling than chemical fertilization. Among the predominant taxa, Bacillus was represented by 8.44 ± 1.255% under chemical fertilization and 14.77 ± 0.790% under organic amendment, while Gemmataceae accounted for 7.65 ± 0.689% and 11.60 ± 0.441%, respectively. A similar upward trend was observed among the less abundant taxa, with Xanthobacteraceae comprising 2.88 ± 0.197% and 4.15 ± 0.204%, and Tumebacillus 1.35 ± 0.171% and 2.73 ± 0.183%, under chemical fertilization and organic amendment, respectively (FDR < 0.001). Bradyrhizobium and Paenibacillus showed relatively similar abundances between the two fertilization treatments. Bradyrhizobium accounted for 3.33 ± 0.240% under chemical fertilization and 2.90 ± 0.216% under organic amendment (FDR = 0.055), whereas Paenibacillus represented 2.02 ± 0.173% and 2.50 ± 0.155%, respectively (FDR < 0.001). METAGENassist predictions and multivariate functional profiling suggest that organic amendments may enhance microbial capacity for the decomposition of organic matter and nutrient turnover. Conclusion These current findings suggest that the effects of fertilization on soil bacterial communities may extend beyond overall richness and may involve changes in community structure and relative abundance. However, the effects of fertilization on soil fertility and long-term agricultural sustainability should be assessed in the context of broader soil attributes and microbial community features.
Abstract Puumala virus (PUUV), the principal causative agent of nephropathia epidemica (NE), poses a significant public health threat due to its zoonotic transmission, renal complications, and the lack of an approved vaccine. In this study, an immunoinformatics-driven mRNA multi-epitope vaccine, PV-GNProtecta, was designed using conserved regions of the viral glycoprotein (G) and nucleoprotein (N). Highly antigenic, non-allergenic, and non-toxic T-cell and B-cell epitopes were identified and assembled with appropriate adjuvants to enhance immunogenicity. Population coverage analysis suggested broad HLA responsiveness across diverse populations. The vaccine construct exhibited favorable physicochemical properties, including stability, solubility, and antigenicity. Structural validation predicted high model quality, with 95.5% of residues located in favored Ramachandran regions and an ERRAT score of 91.4. Molecular docking and MM-GBSA analyses predicted favorable computational interactions between PV-GNProtecta and TLR4, while a 100 ns molecular dynamics simulation provided computational insights into the dynamic behavior of the vaccine-TLR4 complex. Codon optimization suggested compatibility of the designed sequence with an Escherichia coli -based expression framework. Immune simulation predicted potential humoral and cellular immune response patterns, including changes in IgG, IFN-γ, and IL-2 levels. The designed mRNA sequence exhibited favorable predicted secondary structural characteristics; however, further experimental evaluation is required to assess translational efficiency and stability. Although this study is limited to computational analyses and requires experimental validation, the findings suggest that PV-GNProtecta may represent a potential candidate for further investigation against PUUV infection.
Abstract Roy et al. reported an extensive computational analysis of nonsynonymous single-nucleotide variants (nsSNPs) in the human CYLD gene and characterized several variants as potentially disease- or cancer-associated. Although such analyses can be valuable for generating hypotheses, computational predictions should be clearly distinguished from clinical or functional evidence. Among the 18 “high-risk nsSNPs” prioritized by the authors, only p.Glu747Gly (E747G) has established clinical evidence supporting pathogenicity, whereas the remaining variants lack sufficient evidence for pathogenic classification and should be considered variants of uncertain significance pending further evidence. We highlight three major concerns: the lack of independence among some prediction tools used for variant prioritization; the interpretation of somatic mutation clustering as evidence for germline disease association; and the presentation of variants with no established clinical or experimental evidence as disease-associated. Under the ACMG/AMP framework, computational evidence can provide supporting evidence but cannot substitute for clinical, population, segregation, or functional evidence. We suggest that the terminology and conclusions of the original article be revised to distinguish computationally predicted effects from demonstrated disease associations.
