Briefings in Functional Genomics最新文献

Background: Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, but the underlying intrinsic link between Alzheimer's disease (AD) and inflammatory bowel disease (IBD) is not adequately understood.

Methods: To identify pathogenic cell types of AD and IBD and explore their shared genetic architecture, we developed Pathogenic Cell types and shared Genetic Loci (PCGL) framework, which studied AD and IBD and its two subtypes of ulcerative colitis (UC) and Crohn's disease (CD).

Results: We found that monocytes and CD8 T cells were the enriched pathogenic cell types of AD and IBDs, respectively. By PCGL framework, there was a significant global genetic correlation between AD and each of IBD, UC, and CD. Especially, local genetic correlations between AD and IBD showed strong signals in chr6. Bidirectional two-sample MR Analyses also validated these. Cross-trait meta-analysis identified two key genetic loci rs660895 (on chr6) and rs917117 (on chr7), which have not been previously reported. Two loci are located on the genes HLA-DRB1 and JAZF1, respectively. MAGMA genome-wide gene-based analysis identified six overlapping genes including HLA-DRB1. Subsequently, for one thing, SMR analyses further validated six shared genes in specific tissues and monocytes. For another, pathway enrichment analysis revealed shared genes were enriched in several natural killer cell mediated cytotoxicity and chemokine signaling pathways.

Conclusions: PCGL not only revealed the significant genetic correlations underlying AD and IBDs but also identified enriched pathogenic cell types and new shared loci and genes. We highlighted the mediation of HLA-DRB1 effects in the comorbidity mechanisms.

Deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence compressors for novel species frequently face challenges when processing wide-scale raw, FASTA, or multi-FASTA structured data. For years, molecular sequence databases have favored the widely used general-purpose Gzip and Zstd compressors. The absence of sequence-specific characteristics in these encoders results in subpar performance, and their use depends on time-consuming parameter adjustments. To address these limitations, in this article, we propose a reference-free, lossless sequence compressor called GraSS (Grammatical, Statistical, and Substitution Rule-Based). GraSS compresses sequences more effectively by taking advantage of certain characteristics seen in DNA and RNA sequences. It supports various formats, including raw, FASTA, and multi-FASTA, commonly found in GenBank DNA and RNA files. We evaluate GraSS's performance using ten benchmark DNA sequences with reduced number of repeats, two highly repetitive RNA sequences, and fifteen raw DNA sequences. Test results indicate that the weighted average compression ratios (WACR) for DNA and RNA sequences are 4.5 and 19.6, respectively. Additionally, the entire DNA sequence corpus has a total compression time (TCT) of 246.8 seconds (s). These results demonstrate that the proposed compression method performs better than several advanced algorithms specifically designed to handle various levels of sequence redundancy. The decompression times, memory usage, and CPU usage are also very competitive. Contact:  anirban@klyuniv.ac.in.

Transcription factor (TF) chromatin immunoprecipitation followed by sequencing (ChIP-seq) is essential for identifying genome-wide TF-binding sites (TFBSs), and the collected datasets offer a variety of opportunities for downstream analyses such as inference of gene regulatory network and prediction for effects of single-nucleotide polymorphisms (SNPs) on TFBSs. Although TF ChIP-seq data continue to accumulate in public databases, comprehensive coverage of biologically relevant TF-sample pairs (i.e. combination of targeted TF and cell type) remains elusive. This is due to the need for TF-specific antibodies and large cell numbers, limiting feasible TF-cell type combinations. Moreover, ChIP-seq is measurable when the TF is expressed in the target cell type. Thus, defining the full space of biologically relevant TF-sample pairs-including both measured and unmeasured-is essential to assess and improve dataset comprehensiveness. Here, we investigated publicly available human TF ChIP-seq datasets and introduced the concept of unmeasured TF-sample pairs, defined as biologically relevant TF-sample combinations for which ChIP-seq experiments have not yet been performed. Notably, many expressed TFs in specific cell types remain unmeasured by ChIP-seq, affecting the coverage of regulatory regions revealed by TF ChIP-seq and genome-wide association study-SNP analyses. Furthermore, we propose practical strategies to efficiently supplement currently unmeasured data and discuss how these approaches can significantly enhance data-driven research. The database of unmeasured human TF-sample pairs is publicly accessible at https://moccs-db.shinyapps.io/Unmeasured_shiny_v1/, facilitating the systematic expansion of TF ChIP-seq datasets and thereby enhancing our comprehension of gene regulatory mechanisms.

RNA modifications include not only methylation modifications, such as m6A, but also acetylation modifications, which constitute a complex interaction involving "writers," "readers," and "erasers" that play crucial roles in growth, genetics, and disease. N4-acetylcytidine (ac4C) is an ancient and highly conserved RNA modification that plays a profound role in the pathogenesis of a wide range of diseases. This review provides insights into the functional impact of ac4C modifications in disease and introduces new perspectives for disease treatment. These studies provide important insights into the biological functions of post-transcriptional RNA modifications and their potential roles in disease mechanisms, offering new perspectives and strategies for disease treatment.

In current bioinformatics research, spatial transcriptomics (ST) as a rapidly evolving technology is gradually receiving widespread attention from researchers. Spatial domains are regions where gene expression and histology are consistent in space, and detecting spatial domains can better understand the organization and functional distribution of tissues. Spatial domain recognition is a fundamental step in the process of ST data interpretation, which is also a major challenge in ST analysis. Therefore, developing more accurate, efficient, and general spatial domain recognition methods has become an important and urgent research direction. This article aims to review the current status and progress of spatial domain recognition research, explore the advantages and limitations of existing methods, and provide suggestions and directions for future tool development.

Acute myeloid leukemia (AML) is one of the leading leukemic malignancies in adults. The heterogeneity of the disease makes the diagnosis and treatment extremely difficult. With the advent of next-generation sequencing (NGS) technologies, exploration at the molecular level for the identification of biomarkers and drug targets has been the focus for the researchers to come up with novel therapies for better prognosis and survival outcomes of AML patients. However, the huge amount of data from NGS platforms requires a comprehensive AML platform to streamline literature mining efforts and save time. To facilitate this, we developed AMLdb, an interactive multi-omics platform that allows users to query, visualize, retrieve, and analyse AML related multi-omics data. AMLdb contains 86 datasets for gene expression profiles, 15 datasets for methylation profiles, CRISPR-Cas9 knockout screens of 26 AML cell lines, sensitivity of 26 AML cell lines to 288 drugs, mutations in 41 unique genes in 23 AML cell lines, and information on 41 experimentally validated biomarkers. In this study, we have reported five genes, i.e. CBFB, ENO1, IMPDH2, SEPHS2, and MYH9 identified via our analysis using AMLdb. ENO1 is uniquely identified gene which requires further investigation as a novel potential target while other reported genes have been previously confirmed as targets through experimental studies. Top of form we believe that these findings utilizing AMLdb can make it an invaluable resource to accelerate the development of effective therapies for AML and assisting the research community in advancing their understanding of AML pathogenesis. AMLdb is freely available at https://project.iith.ac.in/cgntlab/amldb.

Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of feature called proportionalized split amino acid composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at both the N-terminal and the C-terminal, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, Matthews correlation coefficient (MCC), G-measure, and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package that is freely available at https://github.com/wan-mlab/SAMP.