Genomics, proteomics & bioinformatics最新文献

It is widely accepted that N6-methyladenosine (m6A) exhibits significant intercellular specificity, which poses challenges for its detection using existing m6A quantitative methods. In this study, we introduced Single-cell m6A Analysis (Scm6A), a machine learning-based approach for single-cell m6A quantification. Scm6A leverages input features derived from the expression levels of m6A trans regulators and cis sequence features, and offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm6A, we first applied Scm6A to single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and calculated the m6A levels in CD4+ and CD8+ T cells. We also applied a winscore-based m6A calculation method to conduct N6-methyladenosine sequencing (m6A-seq) analysis on CD4+ and CD8+ T cells isolated through magnetic-activated cell sorting (MACS) from the same samples. Notably, the m6A levels calculated by Scm6A exhibited a significant positive correlation with those quantified through m6A-seq in different cells isolated by MACS, providing compelling evidence for Scm6A's reliability. Additionally, we performed single-cell-level m6A analysis on lung cancer tissues as well as blood samples from patients with coronavirus disease 2019 (COVID-19), and demonstrated the landscape and regulatory mechanisms of m6A in different T cell subtypes from these diseases. In summary, Scm6A is a novel, dependable, and accurate method for single-cell m6A detection and has broad applications in the realm of m6A-related research.

As the most abundant messenger RNA (mRNA) modification, N6-methyladenosine (m6A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the role of the m6A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m6A methylomes across nine human tissues from 97 m6A sequencing (m6A-seq) and RNA sequencing (RNA-seq) samples. Our findings demonstrate that m6A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m6A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called "m6A-express", we predicted m6A-regulated genes and revealed that cancer type-specific m6A-regulated genes contributed to the prognosis, tumor origin, and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m6A regulators that regulate cancer-specific m6A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m6A regulator CAPRIN1 controls the m6A level of TP53. Overall, our work reveals the clinical relevance of m6A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m6A in cancer precision medicine for patients with different cancer types.

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels. This resource integrates eight robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five distinct aspects: identifying the signature genes of cancer stemness; exploring the associations with cancer hallmarks and cellular states; exploring the associations with immune response and the communications with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding downstream functional interpretation, including immune response and cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

Invasive species cause massive economic and ecological damage. Climate change has resulted in an unprecedented increase in the number and impact of invasive species; however, the mechanisms underlying these invasions are unclear. The sycamore lace bug, Corythucha ciliata, is a highly invasive species originating from North America and has expanded across the Northern Hemisphere since the 1960s. In this study, we assembled the C. ciliata genome using high-coverage Pacific Biosciences (PacBio), Illumina, and high-throughput chromosome conformation capture (Hi-C) sequencing. A total of 15,278 protein-coding genes were identified, and expansions of gene families with oxidoreductase and metabolic activities were observed. In-depth resequencing of 402 samples from native and nine invaded countries across three continents revealed 2.74 million single nucleotide polymorphisms. Two major invasion routes of C. ciliata were identified from North America to Europe and Japan, with a contact zone forming in East Asia. Genomic signatures of selection associated with invasion and long-term balancing selection in native ranges were identified. These genomic signatures overlapped with expanded genes, suggesting improvements in the oxidative stress and thermal tolerance of C. ciliata. These findings offer valuable insights into the genomic architecture and adaptive evolution underlying the invasive capabilities of species during rapid environmental changes.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity with unprecedented resolution. However, many current methods are limited in capturing full-length transcripts and discerning strand orientation. We present RAG-seq, an innovative strand-specific total RNA sequencing technique that combines not-so-random (NSR) primers with Tn5 transposase-mediated tagmentation. RAG-seq overcomes previous limitations by delivering comprehensive transcript coverage and maintaining strand orientation, which is essential for accurate quantification of overlapping genes and detection of antisense transcripts. Through optimized reverse transcription with oligo dT primers, rRNA depletion via Depletion of Abundant Sequences by Hybridization (DASH), and linear amplification, RAG-seq enhances sensitivity and reproducibility, especially for low-input samples and single cells. Application to mouse oocytes and early embryos highlights RAG-seq's superior performance in identifying stage-specific antisense transcripts, shedding light on their regulatory roles during early development. This advancement represents a significant leap in transcriptome analysis within complex biological contexts.

Identification evaluation and result dissemination are essential components in mass spectrometry-based proteomics analysis. The visualization of fragment ions in mass spectrum provides strong evidence for peptide identification and modification localization. Here, we present an easy-to-use tool, named GP-Plotter, for ion annotation of tandem mass spectra and corresponding image output. Identification result files of common searching tools in the community and user-customized files are supported as input of GP-Plotter. Multiple display modes and parameter customization can be achieved in GP-Plotter to present annotated spectra of interest. Different image formats, especially vector graphic formats, are available for image generation which is favorable for data publication. Notably, GP-Plotter is also well-suited for the visualization and evaluation of glycopeptide spectrum assignments with comprehensive annotation of glycan fragment ions. With a user-friendly graphical interface, GP-Plotter is expected to be a universal visualization tool for the community. GP-Plotter has been implemented in the latest version of Glyco-Decipher (v1.0.4) and the standalone GP-Plotter software is also freely available at https://github.com/DICP-1809.

Whole-exome sequencing (WES) data are frequently used for cancer diagnosis and genome-wide association studies (GWAS), based on high-coverage read mapping, informative variant calling, and high-quality reference genomes. The center position of the currently used genome assembly, GRCh38, is now challenged by two newly published telomere-to-telomere (T2T) genomes, T2T-CHM13 and T2T-YAO, and it becomes urgent to have a comparative study to test population specificity using the three reference genomes based on real case WES data. Here we report our analysis along this line for 19 tumor samples collected from Chinese patients. The primary comparison of the exon regions among the three references reveals that the sequences in up to ∼ 1% target regions in T2T-YAO are widely diversified from GRCh38 and may lead to off-target in sequence capture. However, T2T-YAO still outperforms GRCh38 genomes by obtaining 7.41% more mapped reads. Due to more reliable read-mapping and closer phylogenetic relationship with the samples than GRCh38, T2T-YAO reduces half of variant calls of clinical significance which are mostly benign, while maintaining sensitivity in identifying pathogenic variants. T2T-YAO also outperforms T2T-CHM13 in reducing calls of Chinese-specific variants. Our findings highlight the critical need for employing population-specific reference genomes in genomic analysis to ensure accurate variant analysis and the significant benefits of tailoring these approaches to the unique genetic backgrounds of each ethnic group.

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell type or state. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell-type/state map for each context and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell type/state. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar genes detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.