MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Understanding T-cell receptor (TCR) specificity is not only essential for fundamental research, but could open up novel avenues for diagnostics, cancer immunotherapy, and the targeted treatment of autoimmune diseases. The immune system responds to challenges through groups of T-cells with similar TCR sequences. In recent years, searching for TCRs with an enrichment of similar sequences - neighbors - in a TCR repertoire has become a standard procedure for antigen-specific TCR identification. This study provides a systematic comparison of computational algorithms-ALICE, TCRNET, GLIPH2, and tcrdist3-that leverage neighborhood enrichment for antigen-specific TCR identification. Using published murine datasets from Lymphocytic choriomeningitis virus (LCMV) infection and novel datasets from Sputnik V vaccination and Mycobacterium tuberculosis (Mtb) infection, we evaluated the performance of these algorithms. To facilitate reproducible analysis, we developed TCRgrapher, an R library that integrates these pipelines into a user-friendly framework. TCRgrapher enables efficient identification of antigen-specific TCRs from single repertoire snapshots and supports flexible parameter customization. Our comparative analysis revealed that ALICE and TCRNET consistently outperformed GLIPH2 and tcrdist3 across most datasets, achieving higher area under precision-recall curve. While murine datasets provide valuable insights into algorithm performance, caution is advised when extrapolating these results to other species or different experimental conditions. TCRgrapher is freely available on GitHub (https://github.com/KseniaMIPT/tcrgrapher), offering researchers a robust tool for investigating TCR specificity and advancing immunological studies.

Single-cell RNA sequencing (scRNA-Seq) provides valuable insights into cell biology. However, current scRNA-Seq analytic approaches do not distinguish between spliced and unspliced mRNA at the level of dimensionality reduction. RNA velocity paradigm suggests that the presence of unspliced mRNA reflects transitional cell states, informative for studies of dynamic processes such as embryogenesis or tissue regeneration. Alternatively, stable cell subsets may also maintain translationally repressed spliced mRNA (e.g., in P-bodies) and/or unspliced mRNA reservoirs for prompt initiation of transcription-independent expression. Thus, functional cell subsets may differ not only in the current levels of actively produced mRNAs, but also in which mRNAs and in what forms are stored in the nucleus and cytoplasm. To enable splicing-aware analysis of scRNA-Seq data, we developed a method called SANSARA (Splicing-Aware scrNa-Seq AppRoAch). We employed SANSARA to characterize peripheral blood regulatory T cell (Treg) subsets, revealing a complementary interplay between the FOXP3 and Helios master transcription factors and high levels of spliced IL10RA, LGALS3, FCRL3, CD38, ITGAL, and LEF1 mRNAs in effector Tregs. Among Th1 and cytotoxic CD4+ T cell subsets, SANSARA also revealed substantial splicing heterogeneity across subset-specific genes. SANSARA is straightforward to implement in current data analysis pipelines and opens new dimensions for scRNA-Seq-based discoveries.

Transcriptome sequencing has become common in cancer research, resulting in the generation of a substantial volume of RNA sequencing (RNA-Seq) data. The ability to extract immune repertoires from these data is crucial for obtaining information on infiltrating T- and B-lymphocyte clones when dedicated amplicon T-cell/B-cell receptors sequencing (TCR-Seq/BCR-Seq) methods are unavailable. In response to this demand, several dedicated computational methods have been developed, including MiXCR, TRUST and ImRep. In the recent publication in Briefings in Bioinformatics, Peng et al. have conducted an intensive, systematic comparison of the three previously mentioned tools. Although their effort is commendable, we do have a few constructive critiques regarding technical elements of their analysis.

The diversity of T-cell receptors recognizing foreign pathogens is generated through a highly stochastic recombination process, making the independent production of the same sequence rare. Yet unrelated individuals do share receptors, which together constitute a "public" repertoire of abundant clonotypes. The TCR repertoire is initially formed prenatally, when the enzyme inserting random nucleotides is downregulated, producing a limited diversity subset. By statistically analyzing deep sequencing T-cell repertoire data from twins, unrelated individuals of various ages, and cord blood, we show that T-cell clones generated before birth persist and maintain high abundances in adult organisms for decades, slowly decaying with age. Our results suggest that large, low-diversity public clones are created during pre-natal life, and survive over long periods, providing the basis of the public repertoire.

Unique molecular identifiers (UMIs) show outstanding performance in targeted high-throughput resequencing, being the most promising approach for the accurate identification of rare variants in complex DNA samples. This approach has application in multiple areas, including cancer diagnostics, thus demanding dedicated software and algorithms. Here we introduce MAGERI, a computational pipeline that efficiently handles all caveats of UMI-based analysis to obtain high-fidelity mutation profiles and call ultra-rare variants. Using an extensive set of benchmark datasets including gold-standard biological samples with known variant frequencies, cell-free DNA from tumor patient blood samples and publicly available UMI-encoded datasets we demonstrate that our method is both robust and efficient in calling rare variants. The versatility of our software is supported by accurate results obtained for both tumor DNA and viral RNA samples in datasets prepared using three different UMI-based protocols.

BACKGROUND: The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing. RESULTS: Here we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. The tool has proven its utility in recent research studies. CONCLUSIONS: tcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The stable version can be directly installed from The Comprehensive R Archive Network ( http://cran.r-project.org/mirrors.html ). The source code and development version are available at tcR GitHub ( http://imminfo.github.io/tcr/ ) along with the full documentation and typical usage examples.

Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.