However, our recent application of machine learning approaches to the identification of modification sites has uncovered an unmet need for particular features in said tools. In principle, a variety of so-called alignment viewers like IGV, Tablet, Savant, UGENE and Persephone provide more or less detailed graphical representations of mapping results, typically resolving the base composition and orientation of reads covering a reference sequence. Before engaging in such an endeavor, the experimentalist, and potential user of the software presented here, may want to assess the significance of an identification event, and visually inspect parameters at a given site. Because of these vast numbers, experimental verification of candidate sites by independent methods must typically be restricted to a small subset. Collection and curation of RNA sequences containing modifications underline a central problem in the field, arising from the vast number of candidate sites in large datasets. Also, chemical treatments that selectively alter the properties of a given modification may therefore be exploited as an additional layer of information in single RNA species or in transcriptome-wide mapping. Detailed analysis showed correlation between modification type and the relative composition of misincorporated nucleotides. The combined appearance of both RT arrest and misincorporation at modification sites was analyzed in early work. This misincorporation has led to the first transcriptome-wide mapping of an RNA modification. A model modification for misincorporation, inosine, the product of an A-to-I deamination, is reliably reverse transcribed into a cytidine rather than a thymidine residue in the resulting cDNA. Before the advent of methods that are nowadays subsumed as deep sequencing, RT reverse transcription arrest was traditionally analyzed by gel or capillary electrophoresis. Here, two important features for detection are reverse transcription (RT) arrest and misincorporation during complementary DNA (cDNA) synthesis. Despite this high diversity, some common denominators apply to both function and detection. Furthermore, there is evidence that the diversity may yet increase with the discovery of more modifications. RNA modifications are structurally highly diverse, and among the approximately 150 chemically different structures in the Modomics database, all major classes of natural product compounds can be found. Coupled to new detection methods came new insights into the function of RNA modifications in the regulation of RNA stability, regulation of gene expression, and immunity. The detection of RNA modifications has recently re-emerged as a very timely topic of current research. With SAM file format as standard input, CAn is an intuitive and user-friendly tool that is generally applicable to the large community of biomedical users, starting from simple visualization of RNA sequencing (RNA-Seq) data, up to sophisticated modification analysis with significance-based modification candidate calling. It is freely available for all three main operating systems. CoverageAnalyzer (CAn) was developed in response to the demand for a powerful inspection tool. Consequently, the community will profit from a platform seamlessly connecting detailed visual inspection of RT signatures and automated screening for modification candidates. Common alignment viewers lack specialized functionality, such as filtering, tailored visualization, image export and differential analysis. Recent studies yielded high-resolution RT signatures of modified ribonucleotides relying on both sequence-dependent mismatch patterns and reverse transcription arrests. Combination of reverse transcription (RT) and deep sequencing has emerged as a powerful instrument for the detection of RNA modifications, a field that has seen a recent surge in activity because of its importance in gene regulation.
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