Book ChapterDOI
An Informatics Pipeline for Profiling and Annotating RNA Modifications
TLDR
In this paper, the authors demonstrate how to identify and annotate RNA modifications based on the informatic analysis of methylated RNA immunoprecipitation and sequencing (MeRIP-seq) data.Abstract:
While over 150 distinct types of chemical modifications are known to occur on various cellular RNAs and can be dynamically controlled, the function of most of these modifications remains poorly defined Collectively, these RNA modifications have been recently termed the "epitranscriptome" Identification and annotation of individual RNA modifications throughout the transcriptome are key for studying the role of the epitranscriptome in the regulation of gene expression and for elucidating the functional relevance of particular RNA modifications in diverse physiological and disease processes In this protocol, we demonstrate how to identify and annotate RNA modifications based on the informatic analysis of methylated RNA immunoprecipitation and sequencing (MeRIP-seq) data, using RNAmod, a convenient one-stop online interactive platform for the annotation, analysis, and visualization of mRNA modificationsread more
Citations
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Journal ArticleDOI
Bioinformatic tools for epitranscriptomics.
TL;DR: A review of recent developments in epitranscriptome spatial detection and data analysis can be found in this article , where the authors summarized recent developments and discussed their progression in the field.
Journal ArticleDOI
Therapeutic Potential of Long Non-Coding RNAs of HIV-1, SARS-CoV-2, and Endogenous Retroviruses
TL;DR: It is expected that lncRNA drugs will be able to modulate human and viral transcription in an unprecedented way but still effectively maintain homeostasis by deploying functionality below the pathogenic threshold.
References
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Journal ArticleDOI
STAR: ultrafast universal RNA-seq aligner
Alexander Dobin,Carrie A. Davis,Felix Schlesinger,Jorg Drenkow,Chris Zaleski,Sonali Jha,Philippe Batut,Mark Chaisson,Thomas R. Gingeras +8 more
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Journal ArticleDOI
Model-based Analysis of ChIP-Seq (MACS)
Yong Zhang,Tao Liu,Clifford A. Meyer,Jérôme Eeckhoute,David S. Johnson,Bradley E. Bernstein,Bradley E. Bernstein,Chad Nusbaum,Richard M. Myers,Myles Brown,Wei Li,X. Shirley Liu +11 more
TL;DR: This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
Journal ArticleDOI
TopHat: discovering splice junctions with RNA-Seq
TL;DR: The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer.
Journal ArticleDOI
Integrative genomics viewer
James T. Robinson,Helga Thorvaldsdottir,Wendy Winckler,Mitchell Guttman,Eric S. Lander,Eric S. Lander,Gad Getz,Jill P. Mesirov +7 more
TL;DR: In this article, the authors present an approach for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Journal ArticleDOI
Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities
Sven Heinz,Christopher Benner,Nathanael J. Spann,Eric Bertolino,Yin C. Lin,Peter Laslo,Jason X. Cheng,Cornelis Murre,Harinder Singh,Harinder Singh,Christopher K. Glass +10 more
TL;DR: It is demonstrated in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions.