Genome Biology最新文献

Ancient metagenomic studies using capture or shotgun sequencing often perform pairwise alignment of individual reads against large reference databases followed by lowest common ancestor assignment for taxonomic identification. Here, we present bamdam, a lightweight post-mapping, post lowest common ancestor toolkit for eukaryotic or microbial metagenomics. Bamdam can shrink large metagenomics bam files, often by a factor of 10x or more, while retaining all informative reads and alignments, compute a suite of authentication metrics for each taxonomic node including k-mer duplicity, postmortem damage, and mean read complexity, and generate various visualizations including multi-sample deamination plots and damage-colored interactive Krona plots.

Background: CRISPR-Cas9 genome editing enables precise genetic modifications by introducing targeted DNA double-strand breaks (DSBs). While Cas9-induced DSBs are known to cause unintended on-target mutations, their impact on the epigenetic landscape remains unexplored.

Results: Here, we investigate how Cas9-induced DSBs affect DNA methylation patterns in human embryonic stem cells (hESCs). We induce DSBs at differentially methylated regions of imprinted genomic loci and perform high-coverage, long-read native DNA sequencing to simultaneously obtain genetic variant and base-resolution methylation data in a haplotype-resolved manner. Our findings reveal that DSBs cause significant changes in DNA methylation at target sites through mechanisms including homologous recombination, large structural variations, or defective methylation maintenance during DNA repair. Notably, these epigenetic changes can occur either together with or independently of genetic alterations. Beyond imprinted loci, Cas9-induced DSBs significantly disrupt DNA methylation patterns of the MLH1 epimutation alleles in colorectal cancer cells, and hypermethylated heterochromatin loci in hESCs. Clonal analysis indicates that the aberrant methylation changes are stable during in vitro passaging. Intriguingly, significant changes in DNA methylation levels are also detected around endogenous deletions in unedited genomic regions, suggesting that methylation alterations are not unique to Cas9 nuclease activity but represent a general outcome of DSB repair in human cells.

Conclusions: This study underscores the importance of assessing and mitigating unintended epigenetic consequences in genome editing applications, as such changes can profoundly affect gene regulation and cellular function.

Background: Next-generation sequencing (NGS) has become an indispensable diagnostic tool across various diseases. However, sequencing and analysis errors remain major barriers to clinical implementation. In cancer diagnostics, detecting low-level somatic variants is particularly challenging due to tumor heterogeneity and contamination from normal cells.

Results: We assess targeted next-generation sequencing (T-NGS) performance using reference-standard DNA mixtures of homozygote hydatidiform mole and heterozygote blood DNA at varying ratios, analyzed by certified NGS providers. Analytical sensitivity differs by up to 13.9-fold, and false positive (FP) error rates vary up to 615-fold, depending on provider and pipeline. For identical raw data, DRAGEN and the in-house pipeline differ by up to 36.3-fold in FP error rates. Moderately recurrent FP-prone alleles, although representing only 5.37% of all FP sites, contribute to 36.7% of total FP errors in the Geninus in-house result. Among 22 discordant variant calls between DRAGEN and in-house analyses, more than half of them are not confirmed by single base extension assays, indicating likely false positives. Compared to DRAGEN, a conventional BWA + GATK Mutect2 pipeline maintains equivalent sensitivity but produces a 4-fold increase in FP errors, along with a notable enrichment of recurrent FP-prone alleles.

Conclusions: T-NGS results from certified providers exhibit substantial variability in both sensitivity and FP error rates. Conventional pipelines not only increase FP errors but also accumulate recurrent FP-prone alleles. These findings underscore the urgent need for standardized pipelines and rigorous quality control measures to ensure the reliability of T-NGS in clinical diagnostics.

Background: Protein function research helps in understanding the complex biological processes that occur within cells. However, the intricate nature of protein structures and functions, along with the rapid growth of protein sequence data, presents a pressing challenge to develop efficient computational methods for accurate protein annotation.

Results: In this study, we propose ENGINE, a multi-channel deep learning framework designed for robust protein function prediction. ENGINE integrates an equivariant graph convolutional network model to capture geometric features from protein 3D structures, leverages the large language model ESM-C to encode evolutionary and sequence-derived information, and combines an innovative 3D sequence representation that unifies spatial and sequential signals. We demonstrate that ENGINE consistently surpasses current state-of-the-art methods across diverse protein function prediction benchmarks, demonstrating robust generalisation and high predictive accuracy. Beyond performance, ENGINE provides interpretable insights into key sequence features and structural motifs, enabling the identification of functionally critical residues and substructures within proteins. This facilitates a deeper mechanistic understanding of protein function annotation outcomes and supports hypothesis generation for downstream biological studies.

Conclusion: By offering reliable predictions with biological interpretability, ENGINE contributes to advancing research into cellular processes and disease mechanisms. The model is available at GitHub ( https://github.com/ABILiLab/ENGINE ) and Zenodo ( https://doi.org/10.5281/zenodo.17221153 ), serving as a valuable tool for the broader scientific community.

Background: Mitochondrial DNA is a key marker for assessing genetic diversity, critical for the conservation of endangered species. This study investigates the mitochondrial diversity of the Bwindi Impenetrable National Park (BINP) mountain gorilla population (Gorilla beringei beringei), one of the most endangered primate subspecies.

Results: Using pooled sequencing of 200 faecal samples collected from both habituated and wild gorillas, we identify ten mtDNA variants exceeding a 20% threshold across the population mitogenome. Comparisons with previously sequenced individual BINP gorilla mitogenomes corroborates these findings and reveals additional putative haplotypes, potential heteroplasmy and nuclear mitochondrial DNA segments. Our approach overcomes challenges associated with pooled samples, distinguishing sequencing noise from biological variation. The observed diversity suggests that mitochondrial variability in mountain gorillas is comparable to the higher levels reported in the closely related Grauer's gorilla (G. beringei graueri).

Conclusions: This study demonstrates the utility of non-invasive faecal sampling and pooled sequencing for assessing genetic diversity in challenging field conditions, highlighting its potential for population-level genetic monitoring of non-human primates. Our findings provide valuable insights into the genetic makeup of this critically endangered population, contributing to future conservation efforts, and supporting the recovery of mountain gorillas.