The transcriptome is defined as a collection of all the transcript readouts present in a cell. RNA-seq data can be used to explore and/or quantify the transcriptome of an organism, which can be utilized for the following types of experiments:
- differential gene expression
- quantitative evaluation and comparison of transcript levels between conditions
- biological samples/library preparation
- sequence reads
- mapping/quantification
- DGE with R
- functional analysis with R
- quantitative evaluation and comparison of transcript levels between conditions
- transcriptome assembly: building the profile of transcribed regions of the genome, a qualitative evaluation
- refinement of gene models: building better gene models and verifying them using transcriptome assembly
- metatranscriptomics: community transcriptome analysis
omics
A high-throughput data even from a single sample is considered ‘omics data
- genomics = the study of complete set of DNA in an organism, single cells, or group of cells
- transcriptomics = RNA, proteomics = proteins, metabolomics = metabolites
NOTE
The size of human genome/DNA is 3.2 billion characters/base pairs/nucleotides (A, T, C or G in DNA).
Any two humans are 99.9% genetically identical, and differ in ~3-4 million base pairs. These differences are:
- SNPs (Single Nucleotide Polymorphisms): single letter changes
- insertions/deletions: adding or removing a few bases
- structural variants: larger chunks moved or repeated
High-throughput sequencing (HTS) data Next-generation sequencing (NGS) data