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Environmental community transcriptomics: strategies and struggles Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-08-26 Jeanet Mante, Kyra E Groover, Randi M Pullen
Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the
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A review: simulation tools for genome-wide interaction studies Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-08-22 Junliang Shang, Anqi Xu, Mingyuan Bi, Yuanyuan Zhang, Feng Li, Jin-Xing Liu
Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist
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Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-08-19 Kristina Santucci, Yuning Cheng, Si-Mei Xu, Michael Janitz
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
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Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-08-19 Tomas Klingström, Emelie Zonabend König, Avhashoni Agnes Zwane
Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics
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Genetic variation mining of the Chinese mitten crab (Eriocheir sinensis) based on transcriptome data from public databases Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-07-10 Yuanfeng Xu, Fan Yu, Wenrong Feng, Jia Wei, Shengyan Su, Jianlin Li, Guoan Hua, Wenjing Li, Yongkai Tang
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
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Crosstalk between genomic variants and DNA methylation in FLT3 mutant acute myeloid leukemia Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-06-29 Bac Dao, Van Ngu Trinh, Huy V Nguyen, Hoa L Nguyen, Thuc Duy Le, Phuc Loi Luu
Acute myeloid leukemia (AML) is a type of blood cancer with diverse genetic variations and DNA methylation alterations. By studying the interaction of gene mutations, expression, and DNA methylation, we aimed to gain valuable insights into the processes that lead to block differentiation in AML. We analyzed TCGA-LAML data (173 samples) with RNA sequencing and DNA methylation arrays, comparing FLT3
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Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-06-24 Han Cheng, Liping Xu, Cangzhi Jia
RNA interference (RNAi) technology is widely used in the biological prevention and control of terrestrial insects. One of the main factors with the application of RNAi in insects is the difference in RNAi efficiency, which may vary not only in different insects, but also in different genes of the same insect, and even in different double-stranded RNAs (dsRNAs) of the same gene. This work focuses on
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Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-06-14 Zhaolan Du, Yi Shi, Jianjun Tan
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
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AMLdb: a comprehensive multi-omics platform to identify biomarkers and drug targets for acute myeloid leukemia Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-06-12 Keerthana Vinod Kumar, Ambuj Kumar, Kavita Kundal, Avik Sengupta, Kunjulakshmi R, Subashani Singh, Bhanu Teja Korra, Simran Sharma, Vandana Suresh, Mayilaadumveettil Nishana, Rahul Kumar
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
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A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-06-11 Yidi Sun, Lingling Kong, Jiayi Huang, Hongyan Deng, Xinling Bian, Xingfeng Li, Feifei Cui, Lijun Dou, Chen Cao, Quan Zou, Zilong Zhang
In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization
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Microscale marvels: unveiling the macroscopic significance of micropeptides in human health Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-05-06 Deepyaman Das, Soumita Podder
Non-coding RNA encodes micropeptides from small open reading frames located within the RNA. Interestingly, these micropeptides are involved in a variety of functions within the body. They are emerging as the resolving piece of the puzzle for complex biomolecular signaling pathways within the body. Recent studies highlight the pivotal role of small peptides in regulating important biological processes
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Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-05-01 Archana Mathur, Nikhilanand Arya, Kitsuchart Pasupa, Sriparna Saha, Sudeepa Roy Dey, Snehanshu Saha
We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability
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Short-homology-mediated PCR-based method for gene introduction in the fission yeast Schizosaccharomyces pombe Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-04-26 Cai-Xia Zhang, Ying-Chun Hou
Schizosaccharomyces pombe is a commonly utilized model organism for studying various aspects of eukaryotic cell physiology. One reason for its widespread use as an experimental system is the ease of genetic manipulations, leveraging the natural homology-targeted repair mechanism to accurately modify the genome. We conducted a study to assess the feasibility and efficiency of directly introducing exogenous
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Genome structural dynamics: insights from Gaussian network analysis of Hi-C data Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-04-24 Anupam Banerjee, She Zhang, Ivet Bahar
Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data
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DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-04-07 Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, Xun Wang
Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of
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A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-04-05 Biyu Diao, Jin Luo, Yu Guo
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body’s normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration
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Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-03-31 Hollie Wilkinson, Jamie McDonald, Helen S McCarthy, Jade Perry, Karina Wright, Charlotte Hulme, Paul Cool
This project investigates if third-generation genomic sequencing can be used to identify the species of bacteria causing prosthetic joint infections (PJIs) at the time of revision surgery. Samples of prosthetic fluid were taken during revision surgery from patients with known PJIs. Samples from revision surgeries from non-infected patients acted as negative controls. Genomic sequencing was performed
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A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-03-31 Anup Kumar Halder, Abhishek Agarwal, Karolina Jodkowska, Dariusz Plewczynski
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin
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Understanding large scale sequencing datasets through changes to protein folding Brief. Funct. Genomics (IF 2.5) Pub Date : 2024-03-24 David Shorthouse, Harris Lister, Gemma S Freeman, Benjamin A Hall
The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the
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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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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 2.5) 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