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      RNA-Chrom: a manually curated analytical database of RNA–chromatin interactome

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          Abstract

          Every year there is more and more evidence that non-coding RNAs play an important role in biological processes affecting various levels of organization of living systems: from the cellular (regulation of gene expression, remodeling and maintenance of chromatin structure, co-transcriptional suppression of transposons, splicing, post-transcriptional RNA modifications, etc.) to cell populations and even organismal ones (development, aging, cancer, cardiovascular and many other diseases). The development and creation of mutually complementary databases that will aggregate, unify and structure different types of data can help to reach the system level of studying non-coding RNAs. Here we present the RNA-Chrom manually curated analytical database, which contains the coordinates of billions of contacts of thousands of human and mouse RNAs with chromatin. Through the user-friendly web interface ( https://rnachrom2.bioinf.fbb.msu.ru/), two approaches to the analysis of the RNA–chromatin interactome were implemented. Firstly, to find out whether the RNA of interest to a user contacts with chromatin, and if so, with which genes or DNA loci? Secondly, to find out which RNAs are in contact with the DNA locus of interest to a user (and probably participate in its regulation), and if there are such, what is the nature of their interaction? For a more detailed study of contact maps and their comparison with other data, the web interface allows a user to view them in the UCSC Genome Browser.

          Database URL https://genome.ucsc.edu/

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

            Rapid advances in next-generation sequencing technologies have dramatically changed our ability to perform genome-scale analyses. The human reference genome used for most genomic analyses represents only a small number of individuals, limiting its usefulness for genotyping. We designed a novel method, HISAT2, for representing and searching an expanded model of the human reference genome, in which a large catalogue of known genomic variants and haplotypes is incorporated into the data structure used for searching and alignment. This strategy for representing a population of genomes, along with a fast and memory-efficient search algorithm, enables more detailed and accurate variant analyses than previous methods. We demonstrate two initial applications of HISAT2: HLA typing, a critical need in human organ transplantation, and DNA fingerprinting, widely used in forensics. These applications are part of HISAT-genotype, with performance not only surpassing earlier computational methods, but matching or exceeding the accuracy of laboratory-based assays.
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

                Contributors
                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press (UK )
                1758-0463
                2023
                22 April 2023
                22 April 2023
                : 2023
                : baad025
                Affiliations
                departmentFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University , Leninskiye Gory, Moscow 119234, Russia
                departmentKharkevich Institute for Information Transmission Problems RAS , Bolshoy Karetny per., Moscow 127051, Russia
                departmentFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University , Leninskiye Gory, Moscow 119234, Russia
                departmentKharkevich Institute for Information Transmission Problems RAS , Bolshoy Karetny per., Moscow 127051, Russia
                departmentFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University , Leninskiye Gory, Moscow 119234, Russia
                departmentFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University , Leninskiye Gory, Moscow 119234, Russia
                departmentKharkevich Institute for Information Transmission Problems RAS , Bolshoy Karetny per., Moscow 127051, Russia
                departmentNational Medical Research Center for Therapy and Preventive Medicine , Petroverigsky per., Moscow, 101000, Russia
                departmentFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University , Leninskiye Gory, Moscow 119234, Russia
                departmentKharkevich Institute for Information Transmission Problems RAS , Bolshoy Karetny per., Moscow 127051, Russia
                Author notes
                *Corresponding author: Tel: +7 977 4748980; Email: ryabykhgrigory@ 123456gmail.com
                Author information
                https://orcid.org/0000-0002-2225-156X
                Article
                baad025
                10.1093/database/baad025
                10205464
                37221043
                30523634-c603-4412-9766-2d262ce8e9a3
                © The Author(s) 2023. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 March 2023
                : 22 December 2022
                : 01 April 2023
                : 20 March 2023
                : 24 April 2023
                Page count
                Pages: 10
                Categories
                Original Article
                AcademicSubjects/SCI00960

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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