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Abstract The helical structures of DNA and RNA were originally revealed by experimental data. Likewise, the development of programs for modeling these natural polymers was guided by known structures. These nucleic acid polymers represent only two members of a potentially vast class of polymers with similar structural features, but that differ from DNA and RNA in the backbone or nucleobases. Xeno nucleic acids (XNAs) incorporate alternative backbones that affect the conformational, chemical, and thermodynamic properties of XNAs. Given the vast chemical space of possible XNAs, computational modeling of alternative nucleic acids can accelerate the search for plausible nucleic acid analogs and guide their rational design. Additionally, a tool for the modeling of nucleic acids could help reveal what nucleic acid polymers may have existed before RNA in the early evolution of life. To aid the development of novel XNA polymers and the search for possible pre-RNA candidates, this article presents the proto-Nucleic Acid Builder (https://github.com/GT-NucleicAcids/pnab), an open-source program for modeling nucleic acid analogs with alternative backbones and nucleobases. The torsion-driven conformation search procedure implemented here predicts structures with good accuracy compared to experimental structures, and correctly demonstrates the correlation between the helical structure and the backbone conformation in DNA and RNA. 

Abstract RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world’s largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.