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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.
Orthology or paralogy inference requires an assessment of sequence homology, usually via sequence alignment. Phylogenetic analyses and sequence alignment are often considered jointly, as phylogenetic analyses using DNA or RNA require sequence alignment and alignments themselves often represent some hypothesis of homology.
For example, a database of SNPs used in Douglas fir breeding programs was created by de novo transcriptome analysis in the absence of a sequenced genome. [159] Similarly, genes that function in the development of cardiac, muscle, and nervous tissue in lobsters were identified by comparing the transcriptomes of the various tissue types without ...
Transcriptome analysis has proven to be beneficial in identifying disease processes as well as regulatory elements in disease progressions, has aided drug development through identification of disease processes, offers insight into therapeutic strategies, and has improved identification of genes that are able to respond to both biotic and ...
ADT data analysis [2] [7] [10] [11] (based on the developer's guidelines): CITE-seq-Count is a Python package from CITE-Seq developers that can be used to obtain raw counts. Seurat package from Satija lab further allows combining of the protein and RNA counts and performing clustering on both measurements, as well as doing differential ...
The transcriptome can be seen as a subset of the proteome, that is, the entire set of proteins expressed by a genome. However, the analysis of relative mRNA expression levels can be complicated by the fact that relatively small changes in mRNA expression can produce large changes in the total amount of the corresponding protein present in the cell.
Serial Analysis of Gene Expression (SAGE) is a transcriptomic technique used by molecular biologists to produce a snapshot of the messenger RNA population in a sample of interest in the form of small tags that correspond to fragments of those transcripts.
Insights based on single-cell data analysis assume that the input is a matrix of normalised gene expression counts, generated by the approaches outlined above, and can provide opportunities that are not obtainable by bulk. Three main insights provided: [18] Identification and characterization of cell types and their spatial organisation in time