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Time-resolved RNA sequencing methods are applications of RNA-seq that allow for observations of RNA abundances over time in a biological sample or samples. Second-Generation DNA sequencing has enabled cost effective, high throughput and unbiased analysis of the transcriptome. [1]
Monocle [121] Differential expression and time-series analysis for single-cell RNA-Seq and qPCR experiments. SCANPY [122] [123] Scalable Python-based implementation for preprocessing, visualization, clustering, trajectory inference and differential expression testing. SCell [124] integrated analysis of single-cell RNA-seq data.
Number of citations of the terms "Multiomics" and "Multi-omics" in PubMed until the 31st December 2021. Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome (i.e., a meta-genome and/or meta-transcriptome, depending ...
Contains manual curations of public transcriptome datasets, focusing on medical and plant biology data. Individual experiments are normalised across the full database to allow comparison of gene expression across diverse experiments. Full functionality requires licence purchase, with free access to a limited functionality. RefEx [172] DDBJ: All
The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription. The early stages of transcriptome annotations began with cDNA libraries published in the 1980s. Subsequently, the advent of high-throughput technology led to ...
In 1969 the analysis of sequences of transfer RNAs was used to infer residue interactions from correlated changes in the nucleotide sequences, giving rise to a model of the tRNA secondary structure. [7] In 1970, Saul B. Needleman and Christian D. Wunsch published the first computer algorithm for aligning two sequences. [8]
When selecting or generating sequence data, it is also vital to consider the tissue type, developmental stage and environmental conditions of the organisms. Since the transcriptome represents a snapshot of gene expression, minor changes to these conditions may significantly affect which transcripts are expressed. This may detrimentally affect ...
Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.