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Relevant bibliographies by topics / Ribosome Profiling Translation Translation control Machine learning

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Academic literature on the topic 'Ribosome Profiling Translation Translation control Machine learning'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Ribosome Profiling Translation Translation control Machine learning"

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Muntasir, Nimeree, Agin Ravindran, Shafi Mahmud, et al. "RNA Multi-Omics to Reveal New Targets in Mounting DLBCL and B-ALL Drug Resistance." Blood 144, Supplement 1 (2024): 6258. https://doi.org/10.1182/blood-2024-207810.

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Drug resistance remains a major challenge in treating Diffuse Large B-cell Lymphoma (DLBCL) and B-cell Acute Lymphoblastic Leukemia (B-ALL). These highly prevalent blood cancers have distinct subtypes, which often associate with different chemotherapeutic prognoses. In DLBCL, the main identified subtypes are Activated B Cell-like (ABC; usually better prognosis) and Germinal Centre B cell-like (GCB; usually worse prognosis). In B-ALL, subtypes are often associated with specific gene fusions, such as ETV6-RUNX1 (usually better prognosis) and KMT2A-MLLT1 (usually worse prognosis). While many of t

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Clauwaert, Jim, Gerben Menschaert, and John Prensner. "MDB-47. DEEP LEARNING DECODES NEW SITES OF RNA TRANSLATION IN MEDULLOBLASTOMA." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.496.

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Abstract BACKGROUND Medulloblastoma, a malignant brain cancer predominantly affecting children, possesses a high propensity for metastasis and is frequently fatal. Through studies on RNA translation, a core biological process, we aim to uncover the disease biology of cancer in order to design therapeutic targets. Yet, the exploration of canonical protein-coding genes has thus far yielded few viable targets in medulloblastoma, where non-canonical open reading frames (ORFs) are increasingly being investigated for their regulatory role. METHODS To address the significant challenge in delineating

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Kathirvel, Iyappan, and Neela Gayathri Ganesan. "Computational Strategies to Enhance Cell-Free Protein Synthesis Efficiency." BioMedInformatics 4, no. 3 (2024): 2022–42. http://dx.doi.org/10.3390/biomedinformatics4030110.

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Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introd

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Shuja, Naveed. "The Impact of Omics Technologies on Advancing Developmental Medicine." DEVELOPMENTAL MEDICO-LIFE-SCIENCES 1, no. 5 (2024): 1–4. https://doi.org/10.69750/dmls.01.05.082.

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Omics technologies represent the emergence of a new paradigm in biomedical research and clinical practice and now enable us to understand human development and the molecular basis of developmental disorders[1]. Technologies such as genomics, transcriptomics, proteomics, metabolomics, and emerging fields such as epigenomics and microbiomics are transforming the revolution in our ability to decode the mysteries of health and disease in developmental medicine (a field of study that examines and treats children with congenital and developmental conditions)[2]. Genomics: Genomics is at the core of

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Wu, Winton, Chi Nam Ignatius Pang, Daniel G. Mediati, and Jai Justin Tree. "The functional small RNA interactome reveals targets for the vancomycin-responsive sRNA RsaOI in vancomycin-tolerant Staphylococcus aureus." mSystems, March 27, 2024. http://dx.doi.org/10.1128/msystems.00971-23.

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ABSTRACT Small RNAs have been found to control a broad range of bacterial phenotypes including tolerance to antibiotics. Vancomycin tolerance in multidrug resistance Staphylococcus aureus is correlated with dysregulation of small RNAs although their contribution to antibiotic tolerance is poorly understood. RNA-RNA interactome profiling techniques are expanding our understanding of sRNA-mRNA interactions in bacteria; however, determining the function of these interactions for hundreds of sRNA-mRNA pairs is a major challenge. At steady-state, protein and mRNA abundances are often highly correla

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Wu, Ting-Ying, Ya-Ru Li, Kai-Jyun Chang, Jhen-Cheng Fang, Daisuke Urano, and Ming-Jung Liu. "Modeling alternative translation initiation sites in plants reveals evolutionarily conservedcis-regulatory codes in eukaryotes." Genome Research, March 13, 2024. http://dx.doi.org/10.1101/gr.278100.123.

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mRNA translation relies on identifying translation initiation sites (TISs) in mRNAs. Alternative TISs are prevalent across plant transcriptomes, but the mechanisms for their recognition are unclear. Using ribosome profiling and machine learning, we developed models for predicting alternative TISs in the tomato (Solanum lycopersicum). Distinct feature sets were predictive of AUG and nonAUG TISs in 5′ untranslated regions and coding sequences, including a novel CU-rich sequence that promoted plant TIS activity, a translational enhancer found across dicots and monocots, and humans and viruses. Ou

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Kwasniak-Owczarek, Malgorzata, Urszula Kazmierczak, Artur Tomal, Pawel Mackiewicz, and Hanna Janska. "Deficiency of mitoribosomal S10 protein affects translation and splicing in Arabidopsis mitochondria." Nucleic Acids Research, November 16, 2019. http://dx.doi.org/10.1093/nar/gkz1069.

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Abstract The ribosome is not only a protein-making machine, but also a regulatory element in protein synthesis. This view is supported by our earlier data showing that Arabidopsis mitoribosomes altered due to the silencing of the nuclear RPS10 gene encoding mitochondrial ribosomal protein S10 differentially translate mitochondrial transcripts compared with the wild-type. Here, we used ribosome profiling to determine the contribution of transcriptional and translational control in the regulation of protein synthesis in rps10 mitochondria compared with the wild-type ones. Oxidative phosphorylati

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Cetnar, Daniel P., Ayaan Hossain, Grace E. Vezeau, and Howard M. Salis. "Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-54059-7.

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AbstractmRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH acti

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Hoskins, Ian, Shilpa Rao, Charisma Tante, and Can Cenik. "Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression." Molecular Systems Biology, February 14, 2024. http://dx.doi.org/10.1038/s44320-024-00018-9.

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AbstractMultiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regul

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May, Gemma E., Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, and Joel McManus. "Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning." eLife 12 (May 25, 2023). http://dx.doi.org/10.7554/elife.69611.

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Upstream open reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on ex

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Dissertations / Theses on the topic "Ribosome Profiling Translation Translation control Machine learning"

1

Giorgia, Giacomini. "Machine learning methods for the prediction of translation speed." Doctoral thesis, Università di Siena, 2021. http://hdl.handle.net/11365/1148962.

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Ribosomes carry out protein synthesis from mRNA templates by a highly regulated process called translation. Translational control plays a key role in the regulation of gene expression, under physiological and pathological conditions. How translation is regulated under different conditions and what factors greatly influence the translation speed remains open questions in molecular biology. In recent years, Ribosome profiling technique (Ribo-seq) has emerged as a powerful method for globally monitoring the translation process in vivo at single nucleotide resolution [1]. Ribo-seq is based

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