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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.
The word "transcriptome" was first used in the 1990s. [19] [20] In 1995, one of the earliest sequencing-based transcriptomic methods was developed, serial analysis of gene expression (SAGE), which worked by Sanger sequencing of concatenated random transcript fragments. [21] Transcripts were quantified by matching the fragments to known genes.
Paired-end and long-read sequencing of the same sample can mitigate the deficits in short read sequencing by serving as a template or skeleton. Metrics to assess the quality of a de novo assembly include median contig length, number of contigs and N50. [66] RNA-Seq alignment with intron-split short reads.
The word transcriptome is a portmanteau of the words transcript and genome.It appeared along with other neologisms formed using the suffixes -ome and -omics to denote all studies conducted on a genome-wide scale in the fields of life sciences and technology.
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The SAM format consists of a header and an alignment section. [1] The binary equivalent of a SAM file is a Binary Alignment Map (BAM) file, which stores the same data in a compressed binary representation. [4]
An example algorithm is the Monocle algorithm [26] that carries out dimensionality reduction of the data, builds a minimal spanning tree using the transformed data, orders cells in pseudo-time by following the longest connected path of the tree and consequently labels cells by type.
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