Genome Biology最新文献

Background: The fields of ancient DNA research and forensic genetics share both methodological similarities and common challenges, particularly in the analysis of degraded DNA. Leveraging these overlaps, this study evaluates three single nucleotide polymorphisms (SNP)-based genotyping assays for analyzing challenging forensic samples: the FORCE-QIAseq SNP panel, the Twist ancient DNA hybridization capture panel, and whole-genome sequencing.

Results: We analyze twenty skeletal bone and tooth samples from authentic missing person cases, where almost all samples are severely degraded and contain exceptionally low amounts of endogenous DNA, reflected by both reduced quantifiable DNA concentrations and lower proportions of human DNA reads than typically obtained from high-quality forensic samples. Despite these challenging sample characteristics, both the FORCE and Twist assays successfully generate a substantial number of genotypes across many samples, while whole-genome sequencing yields fewer SNP calls. However, techniques like probabilistic genotyping, increase sequencing depth or genotype imputation can further enhance the utility of WGS for forensic use.

Conclusions: This study highlights the effectiveness of incorporating ancient DNA methods into forensic genetics for the analysis of degraded samples. The findings are broadly applicable to both forensic and ancient DNA research disciplines, offering valuable insights into assay selection based on sample condition and investigative goals.

Determining tumor-specificity of MHC-bound peptides is crucial for cancer immunotherapy development, yet current methods struggle with class II peptides and non-reference sequences. We introduce PepQueryMHC, an ultra-fast tool that integrates MHC-bound peptide sequences with translated RNA-seq reads for efficient tumor antigen prioritization. We demonstrate its versatility in prioritizing class I and II tumor antigens, mapping the cellular origins of presented peptides, and resolving uncertainties surrounding the prevalence of proteasome-spliced peptides.

Background: The capacity of cells to retain a memory of previous signals enables acquisition of unique fates and adaptation to their environment. The underlying gene expression memory can arise from mutual repression of two genes, forming a toggle switch. Mutual repression can occur at antisense loci, where convergent genes repress each other in cis. The conditions for generating expression memory via antisense transcription remain poorly understood. To address this question, we combine mathematical modeling, genomics and a synthetic biology approach.

Results: Simulations demonstrate stable memory emergence when both genes in an antisense pair transcribe via the convergent promoter and induce a stable repressive chromatin state. Genome-wide analysis of nascent transcription supports antisense-mediated promoter repression, since promoter-overlapping antisense gene pairs exhibit mutually exclusive expression. Through constructing a synthetic antisense locus in mESCs, we demonstrate that antisense transcription can induce stable repression, a key prerequisite for memory. Repression stability increases during mESC differentiation, highlighting cell type-specific epigenetic memory.

Conclusions: Our work establishes a quantitative framework which predicts that antisense-mediated cis-memory can arise within physiologically relevant conditions, and shows that a biological phenomenon with kinetics in the range of weeks can emerge from the interplay of multiple faster molecular processes. This framework, combined with our experimental findings, demonstrates how antisense transcription can encode stable gene expression states. Our discovery that stem cells adjust their memory capacity during differentiation may clarify mechanisms underlying stemness maintenance.

Merging multiple slices into a unified 3D atlas is a significant challenge in spatial transcriptomics. Here, we introduce STAIR, an end-to-end solution for alignment, integration, and 3D reconstruction. STAIR employs a heterogeneous graph attention network with spot-level and slice-level attention mechanisms to achieve a unified embedding space and guide unsupervised 3D reconstruction. We demonstrate STAIR's marked improvements in feature integration and 2D alignment across samples and platforms compared to previous methods. Furthermore, STAIR shows first-of-its-kind performance in z-axis reconstruction of parallel slices and seamlessly integrates new slices into existing 3D atlases, providing novel biological insights from a 3D perspective.