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ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-02-29 Cristina Moral-Turón, Gualberto Asencio-Cortés, Francesc Rodriguez-Diaz, Alejandro Rubio, Alberto G Navarro, Ana M Brokate-Llanos, Andrés Garzón, Manuel J Muñoz, Antonio J Pérez-Pulido
Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes
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GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-02-23 Zhecheng Zhou, Zhenya Du, Xin Jiang, Linlin Zhuo, Yixin Xu, Xiangzheng Fu, Mingzhe Liu, Quan Zou
MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA–drug resistance (MDR) is crucial for the success
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Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-02-20 Xiao-Wei Liu, Han-Lin Li, Cai-Yi Ma, Tian-Yu Shi, Tian-Yu Wang, Dan Yan, Hua Tang, Hao Lin, Ke-Jun Deng
Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant
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A comprehensive review of deep learning-based variant calling methods Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-02-17 Ren Junjun, Zhang Zhengqian, Wu Ying, Wang Jialiang, Liu Yongzhuang
Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning–based approaches for variation detection have gained
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DeepPRMS: advanced deep learning model to predict protein arginine methylation sites Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-01-25 Monika Khandelwal, Ranjeet Kumar Rout
Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for
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Integration tools for scRNA-seq data and spatial transcriptomics sequencing data Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-01-25 Chaorui Yan, Yanxu Zhu, Miao Chen, Kainan Yang, Feifei Cui, Quan Zou, Zilong Zhang
Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application
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Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-01-20 Ibrahim Alsaggaf, Daniel Buchan, Cen Wan
Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations
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Personalized differential expression analysis in triple-negative breast cancer Brief. Funct. Genomics (IF 4.0) Pub Date : 2024-01-10 Hao Cai, Liangbo Chen, Shuxin Yang, Ronghong Jiang, You Guo, Ming He, Yun Luo, Guini Hong, Hongdong Li, Kai Song
Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs
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Genomics in Clinical trials for Breast Cancer Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-12-26 David Enoma
Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress
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Integrating multi-omics data to analyze the potential pathogenic mechanism of CTSH gene involved in type 1 diabetes in the exocrine pancreas Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-12-05 Zerun Song, Shuai Li, Zhenwei Shang, Wenhua Lv, Xiangshu Cheng, Xin Meng, Rui Chen, Shuhao Zhang, Ruijie Zhang
Type 1 diabetes (T1D) is an autoimmune disease caused by the destruction of insulin-producing pancreatic islet beta cells. Despite significant advancements, the precise pathogenesis of the disease remains unknown. This work integrated data from expression quantitative trait locus (eQTL) studies with Genome wide association study (GWAS) summary data of T1D and single-cell transcriptome data to investigate
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Emerging questions on the mechanisms and dynamics of 3D genome evolution in spiralians Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-10-10 Thea F Rogers, Oleg Simakov
Information on how 3D genome topology emerged in animal evolution, how stable it is during development, its role in the evolution of phenotypic novelties and how exactly it affects gene expression is highly debated. So far, data to address these questions are lacking with the exception of a few key model species. Several gene regulatory mechanisms have been proposed, including scenarios where genome
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STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing K mRNA Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-10-06 Emanuele Monteleone, Paola Corrieri, Paolo Provero, Daniele Viavattene, Lorenzo Pulvirenti, Laura Raggi, Elena Carbognin, Marco E Bianchi, Graziano Martello, Salvatore Oliviero, Pier Paolo Pandolfi, Valeria Poli
Embryonic stem cells (ESCs) preserve the unique ability to differentiate into any somatic cell lineage while maintaining their self-renewal potential, relying on a complex interplay of extracellular signals regulating the expression/activity of pluripotency transcription factors and their targets. Leukemia inhibitory factor (LIF)-activated STAT3 drives ESCs’ stemness by a number of mechanisms, including
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DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-31 Upendra K Pradhan, Prabina K Meher, Sanchita Naha, Nitesh K Sharma, Aarushi Agarwal, Ajit Gupta, Rajender Parsad
DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these
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EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-29 Huixiang Peng, Jing Xu, Kangchen Liu, Fang Liu, Aidi Zhang, Xiujun Zhang
Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships
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Prediction of drug–protein interaction based on dual channel neural networks with attention mechanism Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-29 Dayu Tan, Haijun Jiang, Haitao Li, Ying Xie, Yansen Su
The precise identification of drug–protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data
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Single-cell transcriptomics refuels the exploration of spiralian biology Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-23 Laura Piovani, Ferdinand Marlétaz
Spiralians represent the least studied superclade of bilaterian animals, despite exhibiting the widest diversity of organisms. Although spiralians include iconic organisms, such as octopus, earthworms and clams, a lot remains to be discovered regarding their phylogeny and biology. Here, we review recent attempts to apply single-cell transcriptomics, a new pioneering technology enabling the classification
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Cell type and gene regulatory network approaches in the evolution of spiralian biomineralisation Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-18 Victoria A Sleight
Biomineralisation is the process by which living organisms produce hard structures such as shells and bone. There are multiple independent origins of biomineralised skeletons across the tree of life. This review gives a glimpse into the diversity of spiralian biominerals and what they can teach us about the evolution of novelty. It discusses different levels of biological organisation that may be informative
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High-level RNA editing diversifies the coleoid cephalopod brain proteome Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-10 Gjendine Voss, Joshua J C Rosenthal
Coleoid cephalopods (octopus, squid and cuttlefish) have unusually complex nervous systems. The coleoid nervous system is also the only one currently known to recode the majority of expressed proteins through A-to-I RNA editing. The deamination of adenosine by adenosine deaminase acting on RNA (ADAR) enzymes produces inosine, which is interpreted as guanosine during translation. If this occurs in an
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Molecular insights on the origin and development of waxy genotypes in major crop plants Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-10 Vikram S Gaur, Salej Sood, Carlos Guzmán, Kenneth M Olsen
Starch is a significant ingredient of the seed endosperm with commercial importance in food and industry. Crop varieties with glutinous (waxy) grain characteristics, i.e. starch with high amylopectin and low amylose, hold longstanding cultural importance in some world regions and unique properties for industrial manufacture. The waxy character in many crop species is regulated by a single gene known
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Functional genomics in Spiralia Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-10 Francisco M Martín-Zamora, Billie E Davies, Rory D Donnellan, Kero Guynes, José M Martín-Durán
Our understanding of the mechanisms that modulate gene expression in animals is strongly biased by studying a handful of model species that mainly belong to three groups: Insecta, Nematoda and Vertebrata. However, over half of the animal phyla belong to Spiralia, a morphologically and ecologically diverse animal clade with many species of economic and biomedical importance. Therefore, investigating
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Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-08-01 Jing Li, Haiyan Liu, Wei Liu, Peijun Zong, Kaimei Huang, Zibo Li, Haigang Li, Ting Xiong, Geng Tian, Chun Li, Jialiang Yang
Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data
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Mapping of long stretches of highly conserved sequences in over 6 million SARS-CoV-2 genomes Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-07-18 Akhil Kumar, Rishika Kaushal, Himanshi Sharma, Khushboo Sharma, Manoj B Menon, Vivekanandan P
We identified 11 conserved stretches in over 6.3 million SARS-CoV-2 genomes including all the major variants of concerns. Each conserved stretch is ≥100 nucleotides in length with ≥99.9% conservation at each nucleotide position. Interestingly, six of the eight conserved stretches in ORF1ab overlapped significantly with well-folded experimentally verified RNA secondary structures. Furthermore, two of
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Integration of hybrid and self-correction method improves the quality of long-read sequencing data Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-06-21 Tao Tang, Yiping Liu, Binshuang Zheng, Rong Li, Xiaocai Zhang, Yuansheng Liu
Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction
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Integrating single-cell RNA sequencing data to genome-wide association analysis data identifies significant cell types in influenza A virus infection and COVID-19 Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-06-21 Yixin Zou, Xifang Sun, Yifan Wang, Yidi Wang, Xiangyu Ye, Junlan Tu, Rongbin Yu, Peng Huang
With the global pandemic of COVID-19, the research on influenza virus has entered a new stage, but it is difficult to elucidate the pathogenesis of influenza disease. Genome-wide association studies (GWASs) have greatly shed light on the role of host genetic background in influenza pathogenesis and prognosis, whereas single-cell RNA sequencing (scRNA-seq) has enabled unprecedented resolution of cellular
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Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-06-09 Dianshuang Zhou, Shiwei Guo, Yangyang Wang, Jiyun Zhao, Honghao Liu, Feiyang Zhou, Yan Huang, Yue Gu, Gang Jin, Yan Zhang
Abnormalities of DNA modifications are closely related to the pathogenesis and prognosis of pancreatic cancer. The development of third-generation sequencing technology has brought opportunities for the study of new epigenetic modification in cancer. Here, we screened the N6-methyladenine (6mA) and 5-methylcytosine (5mC) modification in pancreatic cancer based on Oxford Nanopore Technologies sequencing
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Widespread transcriptomic alterations of transient receptor potential channel genes in cancer Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-06-08 Tao Pan, Yueying Gao, Gang Xu, Lei Yu, Qi Xu, Jinyang Yu, Meng Liu, Can Zhang, Yanlin Ma, Yongsheng Li
Ion channels, in particular transient–receptor potential (TRP) channels, are essential genes that play important roles in many physiological processes. Emerging evidence has demonstrated that TRP genes are involved in a number of diseases, including various cancer types. However, we still lack knowledge about the expression alterations landscape of TRP genes across cancer types. In this review, we
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Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-31 Daniela Felício, Miguel Alves-Ferreira, Mariana Santos, Marlene Quintas, Alexandra M Lopes, Carolina Lemos, Nádia Pinto, Sandra Martins
Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability
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Quantifying transcriptome diversity: a review Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-25 Emma F Jones, Anisha Haldar, Vishal H Oza, Brittany N Lasseigne
Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information
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Single-cell RNA-seq data clustering by deep information fusion Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-20 Liangrui Ren, Jun Wang, Wei Li, Maozu Guo, Guoxian Yu
Determining cell types by single-cell transcriptomics data is fundamental for downstream analysis. However, cell clustering and data imputation still face the computation challenges, due to the high dropout rate, sparsity and dimensionality of single-cell data. Although some deep learning based solutions have been proposed to handle these challenges, they still can not leverage gene attribute information
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RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-10 Upendra K Pradhan, Prabina K Meher, Sanchita Naha, Soumen Pal, Sagar Gupta, Ajit Gupta, Rajender Parsad
RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic
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Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-03 Hao Wu, Bing Zhou, Haoru Zhou, Pengyu Zhang, Meili Wang
The chromatin loops in the three-dimensional (3D) structure of chromosomes are essential for the regulation of gene expression. Despite the fact that high-throughput chromatin capture techniques can identify the 3D structure of chromosomes, chromatin loop detection utilizing biological experiments is arduous and time-consuming. Therefore, a computational method is required to detect chromatin loops
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ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-01 Tao Bai, Bin Liu
Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second
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AAFL: automatic association feature learning for gene signature identification of cancer subtypes in single-cell RNA-seq data Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-05-01 Meng Huang, Changzhou Long, Jiangtao Ma
Single-cell RNA-sequencing (scRNA-seq) technologies have enabled the study of human cancers in individual cells, which explores the cellular heterogeneity and the genotypic status of tumors. Gene signature identification plays an important role in the precise classification of cancer subtypes. However, most existing gene selection methods only select the same informative genes for each subtype. In
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Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-29 Harold Brayan Arteaga-Arteaga, Mariana S Candamil-Cortés, Brian Breaux, Pablo Guillen-Rondon, Simon Orozco-Arias, Reinel Tabares-Soto
Artificial intelligence is revolutionizing all fields that affect people’s lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity
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Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-28 Hui Guo, Xiang Lv, Yizhou Li, Menglong Li
Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based
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Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-27 Dongqing Su, Haoxin Zhang, Yuqiang Xiong, Haodong Wei, Yao Yu, Honghao Li, Tao Wang, Yongchun Zuo, Lei Yang
The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy.
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Molecular language models: RNNs or transformer? Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-20 Yangyang Chen, Zixu Wang, Xiangxiang Zeng, Yayang Li, Pengyong Li, Xiucai Ye, Tetsuya Sakurai
Language models have shown the capacity to learn complex molecular distributions. In the field of molecular generation, they are designed to explore the distribution of molecules, and previous studies have demonstrated their ability to learn molecule sequences. In the early times, recurrent neural networks (RNNs) were widely used for feature extraction from sequence data and have been used for various
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Single-cell multi-omics sequencing and its application in tumor heterogeneity Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-20 Yuqing Sun, Zhiyu Liu, Yue Fu, Yuwei Yang, Junru Lu, Min Pan, Tian Wen, Xueying Xie, Yunfei Bai, Qinyu Ge
In recent years, the emergence and development of single-cell sequencing technologies have provided unprecedented opportunities to analyze deoxyribonucleic acid, ribonucleic acid and proteins at single-cell resolution. The advancements and reduced costs of high-throughput technologies allow for parallel sequencing of multiple molecular layers from a single cell, providing a comprehensive insight into
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Adaptive deep propagation graph neural network for predicting miRNA–disease associations Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-20 Hua Hu, Huan Zhao, Tangbo Zhong, Xishang Dong, Lei Wang, Pengyong Han, Zhengwei Li
Background A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the
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Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-04-06 Xiya Guo, Jin Ning, Yuanze Chen, Guoliang Liu, Liyan Zhao, Yue Fan, Shiquan Sun
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes
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Multi-omics studies in interpreting the evolving standard model for immune functions Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-03-11 Dipyaman Ganguly
A standard model that is able to generalize data on myriad involvement of the immune system in organismal physio-pathology and to provide a unified evolutionary teleology for immune functions in multicellular organisms remains elusive. A number of such ‘general theories of immunity’ have been proposed based on contemporaneously available data, starting with the usual description of self–nonself discrimination
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An integrated complete-genome sequencing and systems biology approach to predict antimicrobial resistance genes in the virulent bacterial strains of Moraxella catarrhalis Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-03-07 Sadia Afrin Bristy, Md Arju Hossain, Md Imran Hasan, S M Hasan Mahmud, Mohammad Ali Moni, Md Habibur Rahman
Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful; therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance
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SARS-CoV-2 ORF8 dimerization and binding mode analysis with class I MHC: computational approaches to identify COVID-19 inhibitors Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-24 Chandrabose Selvaraj, Dhurvas Chandrasekaran Dinesh, Emilia Maria Pedone, Abdulaziz S Alothaim, Rajendran Vijayakumar, Ondippili Rudhra, Sanjeev Kumar Singh
SARS-CoV-2 encodes eight accessory proteins, one of which, ORF8, has a poorly conserved sequence with SARS-CoV and its role in viral pathogenicity has recently been identified. ORF8 in SARS-CoV-2 has a unique functional feature that allows it to form a dimer structure linked by a disulfide bridge between Cys20 and Cys20 (S-S). This study provides structural characterization of natural mutant variants
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Network-medicine approach for the identification of genetic association of parathyroid adenoma with cardiovascular disease and type-2 diabetes Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-15 Nikhat Imam, Aftab Alam, Mohd Faizan Siddiqui, Akhtar Veg, Sadik Bay, Md Jawed Ikbal Khan, Romana Ishrat
Primary hyperparathyroidism is caused by solitary parathyroid adenomas (PTAs) in most cases (⁓85%), and it has been previously reported that PTAs are associated with cardiovascular disease (CVD) and type-2 diabetes (T2D). To understand the molecular basis of PTAs, we have investigated the genetic association amongst PTAs, CVD and T2D through an integrative network-based approach and observed a remarkable
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Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-14 Yanhao Huo, Chuchu Li, Yujie Li, Xianbin Li, Peng Xu, Zhenshen Bao, Wenbin Liu
As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show
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MiRNA–gene network embedding for predicting cancer driver genes Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-08 Wei Peng, Rong Wu, Wei Dai, Yu Ning, Xiaodong Fu, Li Liu, Lijun Liu
The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene–gene networks by assuming that driver genes tend to work together, form protein complexes and enrich
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An improved hierarchical variational autoencoder for cell–cell communication estimation using single-cell RNA-seq data Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-08 Shuhui Liu, Yupei Zhang, Jiajie Peng, Xuequn Shang
Analysis of cell–cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell–cell communication based on the known
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Experimental and computational methods for studying the dynamics of RNA–RNA interactions in SARS-COV2 genomes Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-08 Mansi Srivastava, Matthew R Dukeshire, Quoseena Mir, Okiemute Beatrice Omoru, Amirhossein Manzourolajdad, Sarath Chandra Janga
Long-range ribonucleic acid (RNA)–RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA–RNA hybrids have shown that these long-range structures exist in severe acute
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The evolution of the spike protein and hACE2 interface of SARS-CoV-2 omicron variants determined by hydrogen bond formation Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-02 Yu-Yuan Yang, Yufeng Jane Tseng
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in December 2019. As of mid-2021, the delta variant was the primary type; however, in January 2022, the omicron (BA.1) variant rapidly spread and became the dominant type in the United States. In June 2022, its subvariants surpassed previous variants in different temporal and spatial situations. To investigate the high
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Promoter–motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-02 Anuraj Nayarisseri, Anushka Bhrdwaj, Arshiya Khan, Khushboo Sharma, Uzma Shaheen, Chandrabose Selvaraj, Mohammad Aqueel Khan, Rajaram Abhirami, Muthuraja Arun Pravin, Gurunathan Rubha Shri, Dhanjay Raje, Sanjeev Kumar Singh
Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier
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Significance of understanding the genomics of host–pathogen interaction in limiting antibiotic resistance development: lessons from COVID-19 pandemic Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-01 Vikas Yadav, Srividhya Ravichandran
The entire world is facing the stiff challenge of COVID-19 pandemic. To overcome the spread of this highly infectious disease, several short-sighted strategies were adopted such as the use of broad-spectrum antibiotics and antifungals. However, the misuse and/or overuse of antibiotics have accentuated the emergence of the next pandemic: antimicrobial resistance (AMR). It is believed that pathogens
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The regulatory role of non-coding RNAs and their interactions with phytochemicals in neurodegenerative diseases: a systematic review Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-02-01 Sajad Fakhri, Ebrahim Darvish, Fatemeh Narimani, Seyed Zachariah Moradi, Fatemeh Abbaszadeh, Haroon Khan
Neurodegenerative diseases (NDDs) are on the rise in the world. Therefore, it is a critical issue to reveal the precise pathophysiological mechanisms and novel therapeutic strategies to deal with such conditions. Passing through different mechanisms, non-coding RNAs (ncRNAs) play a pivotal role in NDDs through various mechanisms, by changing the expression of some genes, interference with protein translation
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iEnhancer-SKNN: a stacking ensemble learning-based method for enhancer identification and classification using sequence information Brief. Funct. Genomics (IF 4.0) Pub Date : 2023-01-30 Hao Wu, Mengdi Liu, Pengyu Zhang, Hongming Zhang
Enhancers, a class of distal cis-regulatory elements located in the non-coding region of DNA, play a key role in gene regulation. It is difficult to identify enhancers from DNA sequence data because enhancers are freely distributed in the non-coding region, with no specific sequence features, and having a long distance with the targeted promoters. Therefore, this study presents a stacking ensemble
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Genomic islands and their role in fitness traits of two key sepsis-causing bacterial pathogens Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-12-18 Mohd Ilyas, Dyuti Purkait, Krishnamohan Atmakuri
To survive and establish a niche for themselves, bacteria constantly evolve. Toward that, they not only insert point mutations and promote illegitimate recombinations within their genomes but also insert pieces of ‘foreign’ deoxyribonucleic acid, which are commonly referred to as ‘genomic islands’ (GEIs). The GEIs come in several forms, structures and types, often providing a fitness advantage to the
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COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-12-18 Yuanyuan Bu, Cangzhi Jia, Xudong Guo, Fuyi Li, Jiangning Song
Antiviral defenses are one of the significant roles of RNA interference (RNAi) in plants. It has been reported that the host RNAi mechanism machinery can target viral RNAs for destruction because virus-derived small interfering RNAs (vsiRNAs) are found in infected host cells. Therefore, the recognition of plant vsiRNAs is the key to understanding the functional mechanisms of vsiRNAs and developing
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Advancing disease genomics beyond COVID-19 and reducing health disparities: what does the future hold for Africa? Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-11-25 Chibuike Ibe, Akaninyene Asuquo Otu, Nicholaus P Mnyambwa
The COVID-19 pandemic has ushered in high-throughput sequencing technology as an essential public health tool. Scaling up and operationalizing genomics in Africa is crucial as enhanced capacity for genome sequencing could address key health problems relevant to African populations. High-quality genomics research can be leveraged to improve diagnosis, understand the aetiology of unexplained illnesses
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Identifying magnetosome-associated genes in the extended CtrA regulon in Magnetospirillum magneticum AMB-1 using a combinational approach Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-11-25 Yizi Yang, Chen Cao, Ning Gu
Magnetotactic bacteria (MTB) are worth studying because of magnetosome biomineralization. Magnetosome biogenesis in MTB is controlled by multiple genes known as magnetosome-associated genes. Recent advances in bioinformatics provide a unique opportunity for studying functions of magnetosome-associated genes and networks that they are involved in. Furthermore, various types of bioinformatics analyses
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Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-11-22 Aswathi Viswanathan, Kavita Kundal, Avik Sengupta, Ambuj Kumar, Keerthana Vinod Kumar, Antony B Holmes, Rahul Kumar
Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an
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Neoepitopes prediction strategies: an integration of cancer genomics and immunoinformatics approaches Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-11-18 Sandeep Kumar Dhanda, Swapnil Mahajan, Malini Manoharan
A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome
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Multifactorial feature extraction and site prognosis model for protein methylation data Brief. Funct. Genomics (IF 4.0) Pub Date : 2022-10-31 Monika Khandelwal, Ranjeet Kumar Rout, Saiyed Umer, Saurav Mallik, Aimin Li
Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However