RNA is emerging as a key regulator of a plethora of biological processes. While its study has remained elusive for decades, the recent advent of high-throughput sequencing technologies provided the unique opportunity to develop novel techniques for the study of RNA structure and post-transcriptional modifications. Nonetheless, most of the required downstream bioinformatics analyses steps are not easily reproducible, thus making the application of these techniques a prerogative of few laboratories. Here we introduce RNA Framework, an all-in-one toolkit for the analysis of most NGS-based RNA structure probing and post-transcriptional modification mapping experiments. To prove the extreme versatility of RNA Framework, we applied it to both an in-house generated DMS-MaPseq dataset, and to a series of literature available experiments. Notably, when starting from publicly available datasets, our software easily allows replicating authors' findings. Collectively, RNA Framework provides the most complete and versatile toolkit to date for a rapid and streamlined analysis of the RNA epistructurome. RNA Framework is available for download at: http://www.rnaframework.com.
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http://dx.doi.org/10.1093/nar/gky486 | DOI Listing |
Sci Adv
January 2025
Center for Physical Genomics and Engineering, Northwestern University, Evanston, IL 60208, USA.
In single cells, variably sized nanoscale chromatin structures are observed, but it is unknown whether these form a cohesive framework that regulates RNA transcription. Here, we demonstrate that the human genome is an emergent, self-assembling, reinforcement learning system. Conformationally defined heterogeneous, nanoscopic packing domains form by the interplay of transcription, nucleosome remodeling, and loop extrusion.
View Article and Find Full Text PDFNat Commun
January 2025
Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Genomic variants causing abnormal splicing play important roles in genetic disorders and cancer development. Among them, variants that cause the formation of novel splice-sites (splice-site creating variants, SSCVs) are particularly difficult to identify and often overlooked in genomic studies. Additionally, these SSCVs are frequently considered promising candidates for treatment with splice-switching antisense oligonucleotides (ASOs).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Aitia, Somerville, MA, USA
Background: Amyloid, Tau and neurodegeneration (ATN), the hallmark pathologies of Alzheimer’s Disease (AD) translating to measurable biomarkers are important for disease modifying therapeutics.
Method: AD Digital‐Twins were built using AITIA’s patented A.I.
ACS Nano
January 2025
National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610064, P. R. China.
Regeneration of diabetic bone defects remains a formidable challenge due to the chronic hyperglycemic state, which triggers the accumulation of advanced glycation end products (AGEs) and reactive oxygen species (ROS). To address this issue, we have engineered a bimetallic metal-organic framework-derived Mn@CoO@Pt nanoenzyme loaded with alendronate and Mg ions (termed MCPtA) to regulate the hyperglycemic microenvironment and recover the osteogenesis/osteoclast homeostasis. Notably, the Mn atom substitution in the CoO nanocrystalline structure could modulate the electronic structure and significantly improve the SOD/CAT catalytic activity for ROS scavenging.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
Background: The prohibitive costs of drug development for Alzheimer’s Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti‐modal information to predict cognitive improvement at the subject level for precision medicine in AD.
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