Article
Biochemistry & Molecular Biology
Chi-Nga Chow, Kuan-Chieh Tseng, Ping-Fu Hou, Nai-Yun Wu, Tzong-Yi Lee, Wen -Chi Chang
Summary: This study characterized the cis-regulatory regions of TFs and histone marks in Arabidopsis, revealing that promoters and regions around the transcription termination sites of protein-coding genes recruit the most TFs, with diverse histone combinations. Comparative analysis between genes with distinct functions showed substantial differences in cis-regulatory regions, histone regulation, and TAD boundary organization. Integration of multiple high-throughput sequencing datasets generated regulatory models to explain the complexity of transcriptional regulation.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Biochemical Research Methods
Hemanth Kari, Surya Manikhanta Sowri Bandi, Aditya Kumar, Venkata Rajesh Yella
Summary: This study combined deep learning with a plethora of promoter motifs to classify DNA sequences into promoters and non-promoters. The CNN-LSTM model achieved high testing accuracy for five model systems, providing an insightful update on next-generation promoter prediction tools for promoter biologists.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Multidisciplinary Sciences
Dade Rong, Xiaomin Chen, Jing Xiao, Daiyuan Liu, Xiangna Ni, Xiuzhen Tong, Haihe Wang
Summary: This study identified novel molecular subtypes of AML associated with histone methylation and established a scoring system to predict treatment response and prognosis. The M-RiskScore was found to be a useful prognostic biomarker and guide for the choice of appropriate chemotherapy strategy.
Article
Biochemistry & Molecular Biology
Zhan Qi, Christophe Jung, Peter Bandilla, Claudia Ludwig, Mark Heron, Anja Sophie Kiesel, Mariam Museridze, Julia Philippou-Massier, Miroslav Nikolov, Alessio Renna Max Schnepf, Ulrich Unnerstall, Stefano Ceolin, Bettina Muhlig, Nicolas Gompel, Johannes Soeding, Ulrike Gaul
Summary: Through a comprehensive structure-function analysis, it was found that the activity of the core promoter is influenced by different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, nucleosome positioning, and flanking sequences. A linear combination of the individual motif features largely explains the combinatorial effects on core promoter activity.
MOLECULAR SYSTEMS BIOLOGY
(2022)
Article
Genetics & Heredity
Arash Rafeeinia, Gholamreza Asadikaram, Vahid Moazed, Mehrnaz Karimi Darabi
Summary: Epigenetic changes induced by pesticides were investigated in children with acute lymphoblastic leukemia (ALL). The study found that CDKN2B and MGMT promoters were hypermethylated in ALL patients, accompanied by a decrease in the relative expression of H4K16ac and H3K4me3. Furthermore, OCP levels were significantly higher in ALL patients and were associated with increased promoter methylation of CDKN2B and MGMT, as well as a decrease in the relative expression of H4K16ac and H3K4me3.
Article
Biology
Qing Liu, Feng Jiang, Jie Zhang, Xiao Li, Le Kang
Summary: The research revealed unique genomic differences in locusts compared to fruit flies, with lower transcription initiation precision and fewer distance constraints. Additionally, stricter exon length constraints and widespread expression of distant core promoters were found in locusts. The study provides new insights into the impact of genome size expansion on distant transcription initiation.
Article
Biochemistry & Molecular Biology
Anastasia Melikhova, Anastasia A. Anashkina, Irina A. Il'icheva
Summary: The highly conserved eukaryotic and archaeal RNA polymerase II (POL II) machinery has been studied in relation to the extreme changes in promoter sequences in different organisms. The research aims to determine the cause of this conservation by analyzing representative sets of aligned promoter sequences from fifteen organisms at different evolutionary stages. The study reveals an evolutionarily stable and extremely heterogeneous secondary structure of POL II core promoters, which includes two singular regions known as hexanucleotide INR around the transcription start site (TSS) and octanucleotide TATA element approximately 28 base pairs upstream. These structures may have developed at some stage of evolution and have proven to be essential for pre-initiation complex formation and subsequent transcription initiation in POL II machinery.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemistry & Molecular Biology
Bo Bae Lee, Hyeonju Woo, Min Kyung Lee, SeoJung Youn, Sumin Lee, Jae-Seok Roe, Soo Young Lee, TaeSoo Kim
Summary: Chromatin-based regulation of internal cryptic promoters is mediated by core promoter strength as well as transcription elongation factors.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Plant Sciences
Dipinte Gupta, Nrisingha Dey, Sadhu Leelavathi, Rajiv Ranjan
Summary: Useful hybrid promoters were developed for efficient ectopic gene expression in monocot and dicot plants through incorporating important domains of previously characterized RTBV and MMV promoters, showing great potential in translational research in dicot, monocot plants and bacterial systems for efficient gene expression.
Article
Biochemistry & Molecular Biology
Sreepoorna Pramodh, Ritu Raina, Arif Hussain, Sali Abubaker Bagabir, Shafiul Haque, Syed Tasleem Raza, Mohammad Rehan Ajmal, Shalini Behl, Deepika Bhagavatula
Summary: This study analyzed the epigenetic modulatory behavior of luteolin on HeLa cells and found that luteolin can inhibit migration and colony formation in HeLa cells. It can also reactivate silenced tumor suppressor genes by modulating DNA methylation, enzyme activity, and global DNA methylation. Therefore, luteolin-targeted epigenetic alterations provide a potential approach for cancer prevention and intervention.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Plant Sciences
Yongil Yang, Yuanhua Shao, Timothy A. Chaffin, Jun Hyung Lee, Magen R. Poindexter, Amir H. Ahkami, Eduardo Blumwald, C. Neal Stewart Jr
Summary: Generating resistant crops to abiotic stresses is crucial for sustainable bioenergy crop production. This study developed synthetic drought stress-inducible promoters to control the precise expression of stress resistance genes in transgenic poplar, and found that shorter synthetic promoters can be used for versatile control of gene expression.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Biotechnology & Applied Microbiology
Nicolas Marx, Heena Dhiman, Valerie Schmieder, Catarina Martins Freire, Ly Ngoc Nguyen, Gerald Klanert, Nicole Borth
Summary: This study demonstrates the impact of DNA methylation on chromatin state and histone modifications, showing how targeted methylation can lead to the acquisition of repressive marks while targeted demethylation results in the acquisition of active promoter marks. The data suggest that DNA methylation plays a key role in directing specific histone marks associated with active, poised or silenced chromatin, ultimately influencing gene expression and cell phenotype.
METABOLIC ENGINEERING
(2021)
Article
Forestry
Shichao Xin, Jinu Udayabhanu, Xuemei Dai, Yuwei Hua, Yueting Fan, Huasun Huang, Tiandai Huang
Summary: In this study, three Hevea polyubiquitin genes and their promoters were identified, which could potentially be used to drive transgene expression in Hevea genetic engineering.
Article
Biochemistry & Molecular Biology
Silvia Ceschi, Michele Berselli, Marta Cozzaglio, Mery Giantin, Stefano Toppo, Barbara Spolaore, Claudia Sissi
Summary: The study identified Vimentin as a binder with nanomolar affinity for G4 repeats, suggesting its role in regulating gene expression at core promoters during cell development and migration by reshaping the local higher-order genome topology.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Multidisciplinary Sciences
Jose M. G. Vilar, Leonor Saiz
Summary: The prevalent one-dimensional alignment of genomic signals to a reference landmark is prone to mask potential relations among multiple DNA elements. We developed a systematic approach to align genomic signals to multiple locations simultaneously by expanding the dimensionality of the genomic-coordinate space. Our results reveal a conserved hierarchy of alternative TSS usage within a previously unrecognized level of genomic organization and provide a general methodology to analyze complex functional relationships among multiple types of DNA elements.
SCIENTIFIC REPORTS
(2023)
Article
Biochemical Research Methods
Ronghui You, Yuxuan Liu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: With the rapid increase in biomedical articles, the need for large-scale automatic Medical Subject Headings (MeSH) indexing has grown significantly. This study proposes a computationally lighter method, BERTMeSH, for MeSH indexing using full text and deep learning, outperforming existing methods like FullMeSH in terms of efficiency and flexibility. BERTMeSH utilizes the state-of-the-art BERT model and a transfer learning strategy to achieve superior performance in indexing.
Article
Computer Science, Artificial Intelligence
Kishan Wimalawarne, Hiroshi Mamitsuka
Summary: We investigate optimal conditions for inducing low-rankness of higher order tensors using convex tensor norms with reshaped tensors. Proposed reshaped tensor nuclear norm and reshaped latent tensor nuclear norm for regularization and combining multiple tensors, respectively. Through generalization bounds and experiments, the novel reshaping norms are shown to lead to lower complexities, favorably compared to existing tensor norms.
Article
Multidisciplinary Sciences
Hiroto Kaneko, Romain Blanc-Mathieu, Hisashi Endo, Samuel Chaffron, Tom O. Delmont, Morgan Gaia, Nicolas Henry, Rodrigo Hernandez-Velazquez, Canh Hao Nguyen, Hiroshi Mamitsuka, Patrick Forterre, Olivier Jaillon, Colomban de Vargas, Matthew B. Sullivan, Curtis A. Suttle, Lionel Guidi, Hiroyuki Ogata
Summary: There is a significant association between viral community composition and carbon export efficiency on a global scale, with viruses predicted to infect ecologically important hosts playing a crucial role in this process. These findings suggest that viruses likely act in the carbon pump process at a large scale in a manner dependent on their hosts and ecosystem dynamics.
Article
Biochemical Research Methods
Menglan Cai, Canh Hao Nguyen, Hiroshi Mamitsuka, Limin Li
Summary: The study introduces a method called CROSS-species gene set enrichment analysis (XGSEA) to predict the enrichment significance of a given gene set in a target species based on gene expression data from a source species, using domain adaptation and regression analysis to improve accuracy. Experimental results show that XGSEA significantly outperforms three baseline methods in most cases.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: Exploring the relationship between human proteins and abnormal phenotypes is crucial for disease prevention, diagnosis and treatment. HPOFiller, a graph convolutional network-based approach, aims to predict missing HPO annotations and outperforms other state-of-the-art methods through stringent evaluations.
Article
Biochemical Research Methods
Ronghui You, Shuwei Yao, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: DeepGraphGO is a multispecies graph neural network-based method aimed at solving the problem of automated function prediction of proteins. By utilizing protein sequence and high-order protein network information, a single model can be trained for all species, providing more training samples for AFP. Experimental results demonstrate that DeepGraphGO significantly outperforms other state-of-the-art methods, including network-based GeneMANIA, deepNF, and clusDCA.
Review
Biochemical Research Methods
Betul Guvenc Paltun, Samuel Kaski, Hiroshi Mamitsuka
Summary: Drug combination therapy is a promising strategy for treating complex diseases, especially in cancer patients where knowledge is limited. Machine learning methods offer an effective way to improve therapeutic efficacy and overcome drug resistance. Data integration and experimental comparisons play a crucial role in drug combination analysis.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Summary: This paper proposes a method for learning the weights of subtree patterns within the framework of WWL kernels, demonstrating its effectiveness in graph classification tasks and showcasing its validity through experiments on synthetic and real-world datasets.
Article
Biochemical Research Methods
Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: Deciphering the relationship between human genes/proteins and abnormal phenotypes is crucial for disease prevention and treatment, requiring computational predictions. The HPODNets model, with features including multiple network input, semi-supervised learning, and deep graph convolutional network, outperforms other methods in predicting human protein-phenotype associations.
Article
Multidisciplinary Sciences
Santosh Hiremath, Samantha Wittke, Taru Palosuo, Jere Kaivosoja, Fulu Tao, Maximilian Proll, Eetu Puttonen, Pirjo Peltonen-Sainio, Pekka Marttinen, Hiroshi Mamitsuka
Summary: This study investigates the feasibility of using satellite images and machine learning models to classify agricultural field parcels into those with and without crop loss. Despite the poor quality of data, the random forest model shows promising results in identifying new crop-loss fields based on reference fields of the same year. There is potential for various applications in efficient agricultural monitoring and verifying crop-loss claims.
Article
Biochemical Research Methods
Ronghui You, Wei Qu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: This study proposes a deep learning model, DeepMHCII, based on peptide binding cores and introduces a binding interaction convolution layer to better model the biological interactions between peptides and MHC II molecules. Extensive experiments demonstrate that DeepMHCII outperforms existing methods in terms of performance and can effectively predict binding cores.
Article
Biochemical Research Methods
Duc Anh Nguyen, Canh Hao Nguyen, Peter Petschner, Hiroshi Mamitsuka
Summary: Predicting the side effects of drug-drug interactions is an important task in pharmacology. Existing methods use hypergraph neural networks to learn the relationships between drugs and side effects but cannot accurately represent the multiple mechanisms of side effects. In this paper, we propose SPARSE, a method that encodes the DDI hypergraph and drug features to learn multiple combinations of latent features of drugs and side effects. By controlling model sparsity through a sparse prior, SPARSE achieves superior predictive performance and interpretability advantage.
Article
Biochemical Research Methods
Betul Guvenc Paltun, Samuel Kaski, Hiroshi Mamitsuka
Summary: Detecting predictive biomarkers from multi-omics data is crucial for precision medicine, but choosing reliable data sources is challenging. We propose the DIVERSE framework which integrates diverse data sets to predict drug responses, and it outperforms other methods in empirical experiments.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Duc Anh Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Summary: Predicting drug-drug interactions (DDIs) involves predicting the side effects of drug pairs using drug information and known side effects. This problem can be solved by predicting labels for each drug pair in a DDI graph, where drugs are nodes and interacting drugs with known labels are edges. Graph neural networks (GNNs) are commonly used for this problem, but they may not perform well for infrequent labels and complicated label relationships. To address this, the authors propose CentSmoothie, a hypergraph neural network (HGNN) that learns representations of drugs and labels using a central-smoothing formulation. Experimental results on simulations and real datasets demonstrate the superiority of CentSmoothie.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Canh Hao Nguyen, Hiroshi Mamitsuka
Summary: Hypergraph is a generalization of graph for representing high-order relations on a set of objects. To address the issue of irrelevant or noisy data, a sparse learning framework is incorporated into learning on hypergraphs. Sparse smooth formulations are proposed to learn smooth functions and induce sparsity on both hyperedges and nodes of hypergraphs.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)