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Inchworm assembles the RNA-Seq data into transcript sequences, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts. Chrysalis clusters the Inchworm contigs and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full ...
An RNA timestamp is a technology that enables the age of any given RNA transcript to be inferred by exploiting RNA editing. [1] In this technique, the RNA of interest is tagged to an adenosine rich reporter motif that consists of multiple MS2 binding sites. These MS2 binding sites recruit a complex composed of ADAR2 (adenosine deaminase acting ...
Currently RNA-Seq relies on copying RNA molecules into cDNA molecules prior to sequencing; therefore, the subsequent platforms are the same for transcriptomic and genomic data. Consequently, the development of DNA sequencing technologies has been a defining feature of RNA-Seq. [ 78 ] [ 80 ] [ 81 ] Direct sequencing of RNA using nanopore ...
RNA-Seq [1] [2] [3] is a technique [4] that allows transcriptome studies (see also Transcriptomics technologies) based on next-generation sequencing technologies. This technique is largely dependent on bioinformatics tools developed to support the different steps of the process. Here are listed some of the principal tools commonly employed and ...
Trajectory inference as implemented in Slingshot for (a) a simulated two-dimensional dataset and (b) a single-cell RNA-seq dataset of the olfactory epithelium.. Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic process experienced by cells and then arrange cells based on their progression ...
Both methods attempt to generate biologically representative isoform-level constructs from RNA-seq data and generally attempt to associate isoforms with a gene-level construct. However, proper identification of gene-level constructs may be complicated by recent duplications, paralogs, alternative splicing or gene fusions. These complications ...
The emergence and need for the analysis of different types of data generated through biological research has given rise to the field of bioinformatics. [2] Molecular sequence and structure data of DNA, RNA, and proteins, gene expression profiles or microarray data, metabolic pathway data are some of the major types of data being analysed in ...
Data management: A single RNA-Seq experiment in humans is usually 1-5 Gb (compressed), or more when including intermediate files. [59] This large volume of data can pose storage issues. One solution is compressing the data using multi-purpose computational schemas (e.g., gzip) or genomics-specific schemas. The latter can be based on reference ...