Briefings in Functional Genomics最新文献

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.

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.

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.

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.

40 years ago, organelle genomes were assumed to be streamlined and, perhaps, unexciting remnants of their prokaryotic past. However, the field of organelle genomics has exposed an unparallel diversity in genome architecture (i.e. genome size, structure, and content). The transcription of these eccentric genomes can be just as elaborate - organelle genomes are pervasively transcribed into a plethora of RNA types. However, while organelle protein-coding genes are known to produce polycistronic transcripts that undergo heavy posttranscriptional processing, the nature of organelle noncoding transcriptomes is still poorly resolved. Here, we review how wet-lab experiments and second-generation sequencing data (i.e. short reads) have been useful to determine certain types of organelle RNAs, particularly noncoding RNAs. We then explain how third-generation (long-read) RNA-Seq data represent the new frontier in organelle transcriptomics. We show that public repositories (e.g. NCBI SRA) already contain enough data for inter-phyla comparative studies and argue that organelle biologists can benefit from such data. We discuss the prospects of using publicly available sequencing data for organelle-focused studies and examine the challenges of such an approach. We highlight that the lack of a comprehensive database dedicated to organelle genomics/transcriptomics is a major impediment to the development of a field with implications in basic and applied science.

Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein-protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix-loop-helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.

Next-generation sequencing and other sequencing approaches have made significant progress in DNA analysis. However, there are indispensable advantages in the nonsequencing methods. They have their justifications such as being speedy, cost-effective, multi-applicable, and straightforward. Among the nonsequencing methods, the genome profiling method is worthy of reviewing because of its high potential. This article first reviews its basic properties, highlights the key concept of species identification dots (spiddos), and then summarizes its various applications.

At present, public databases house an extensive repository of transcriptome data, with the volume continuing to grow at an accelerated pace. Utilizing these data effectively is a shared interest within the scientific community. In this study, we introduced a novel strategy that harnesses SNPs and InDels identified from transcriptome data, combined with sample metadata from databases, to effectively screen for molecular markers correlated with traits. We utilized 228 transcriptome datasets of Eriocheir sinensis from the NCBI database and employed the Genome Analysis Toolkit software to identify 96 388 SNPs and 20 645 InDels. Employing the genome-wide association study analysis, in conjunction with the gender information from databases, we identified 3456 sex-biased SNPs and 639 sex-biased InDels. The KOG and KEGG annotations of the sex-biased SNPs and InDels revealed that these genes were primarily involved in the metabolic processes of E. sinensis. Combined with SnpEff annotation and PCR experimental validation, a highly sex-biased SNP located in the Kelch domain containing 4 (Klhdc4) gene, CHR67-6415071, was found to alter the splicing sites of Klhdc4, generating two splice variants, Klhdc4_a and Klhdc4_b. Additionally, Klhdc4 exhibited robust expression across the ovaries, testes, and accessory glands. The sex-biased SNPs and InDels identified in this study are conducive to the development of unisexual cultivation methods for E. sinensis, and the alternative splicing event caused by the sex-biased SNP in Klhdc4 may serve as a potential mechanism for sex regulation in E. sinensis. The analysis strategy employed in this study represents a new direction for the rational exploitation and utilization of transcriptome data in public databases.

Gene regulatory networks (GRNs) contribute toward understanding the function of genes and the development of cancer or the impact of key genes on diseases. Hence, this study proposes an ensemble method based on 13 basic classification methods and a flexible neural tree (FNT) to improve GRN identification accuracy. The primary classification methods contain ridge classification, stochastic gradient descent, Gaussian process classification, Bernoulli Naive Bayes, adaptive boosting, gradient boosting decision tree, hist gradient boosting classification, eXtreme gradient boosting (XGBoost), multilayer perceptron, light gradient boosting machine, random forest, support vector machine, and k-nearest neighbor algorithm, which are regarded as the input variable set of FNT model. Additionally, a hybrid evolutionary algorithm based on a gene programming variant and particle swarm optimization is developed to search for the optimal FNT model. Experiments on three simulation datasets and three real single-cell RNA-seq datasets demonstrate that the proposed ensemble feature outperforms 13 supervised algorithms, seven unsupervised algorithms (ARACNE, CLR, GENIE3, MRNET, PCACMI, GENECI, and EPCACMI) and four single cell-specific methods (SCODE, BiRGRN, LEAP, and BiGBoost) based on the area under the receiver operating characteristic curve, area under the precision-recall curve, and F1 metrics.