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  2. RNA-Seq - Wikipedia

    en.wikipedia.org/wiki/RNA-Seq

    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.

  3. Time-resolved RNA sequencing - Wikipedia

    en.wikipedia.org/wiki/Time-resolved_RNA_sequencing

    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 ]

  4. 3' mRNA-seq - Wikipedia

    en.wikipedia.org/wiki/3'_mRNA-seq

    3' mRNA-seq methods are generally cheaper per sample than standard bulk RNA-seq methods. [2] [7] [8] [9] This is because of the lower sequencing depth required due to only the 3' end of mRNA molecules being sequenced instead of the whole length of entire transcripts. Read depths of between one million and five million reads are recommended in ...

  5. List of RNA-Seq bioinformatics tools - Wikipedia

    en.wikipedia.org/wiki/List_of_RNA-Seq...

    On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build."

  6. Transcriptomics technologies - Wikipedia

    en.wikipedia.org/wiki/Transcriptomics_technologies

    RNA-Seq methodology has constantly improved, primarily through the development of DNA sequencing technologies to increase throughput, accuracy, and read length. [61] Since the first descriptions in 2006 and 2008, [ 40 ] [ 62 ] RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015.

  7. Single-cell transcriptomics - Wikipedia

    en.wikipedia.org/wiki/Single-cell_transcriptomics

    RNA Seq Experiment. The single-cell RNA-seq technique converts a population of RNAs to a library of cDNA fragments. These fragments are sequenced by high-throughput next generation sequencing techniques and the reads are mapped back to the reference genome, providing a count of the number of reads associated with each gene. [13]

  8. TopHat (bioinformatics) - Wikipedia

    en.wikipedia.org/wiki/TopHat_(bioinformatics)

    It mapped more than 2.2 million reads per CPU hour. That speed allowed the user to process and entire RNA-Seq experiment in less than a day, even on a standard desktop computer. [1] Tophat uses Bowtie in the beginning to analyze the reads, but then does more to analyze the reads that span exon-exon junctions.

  9. RNA timestamp - Wikipedia

    en.wikipedia.org/wiki/RNA_timestamp

    The binding of the ADAR2 enzyme to the RNA timestamp initiates the gradual conversion of adenosine to inosine molecules. Over time, these edits accumulate and are then read through RNA-seq. This technology allows us to glean cell-type specific temporal information associated with RNA-seq data, that until now, has not been accessible. [1]