Figure 1: A historical timeline of transcriptomics. | Nature Reviews Genetics

Figure 1: A historical timeline of transcriptomics.

From: Cancer transcriptome profiling at the juncture of clinical translation

Figure 1

Illustrated is the lockstep development of experimental and computational aspects of transcriptomics. Advances in the experimental protocols for the high-throughput profiling of RNA necessitate the development of databases to catalogue the results and trigger curation efforts to define reference transcriptomes. However, these endeavours depend on the development of accurate and scalable computational methods to search, quantify and assemble RNA molecules. Within each field, the most influential, seminal or unique references were selected. AceView, a gene annotation resource267; ArrayDB, a database of microarray gene expression data268; ArrayExpress, a public repository for microarray gene expression data78; BLAST, Basic Local Alignment Search Tool73; CAGE, cap analysis of gene expression269; CEL-seq, cell expression by linear amplification and sequencing49; CGAP, Cancer Genome Anatomy Project270; CIBERSORT, a tool for estimating the abundances of cell types in a mixed cell population260; dbEST, a database for expressed sequence tags68; EdgeR, a package for differential expression analysis170; EMBL, European Molecular Biology Laboratory; Ensembl, a genome browser for vertebrate genomes74; ESTs, expressed sequence tags28; FANTOM5, Functional Annotation of the Mammalian Genome 5 (Ref. 271); FASTA, a text format for representing nucleotide or peptide sequences72; GenBank, the US National Institutes of Health (NIH) genetic sequence database; GENCODE, the genome annotation project of the Encyclopedia of DNA Elements (ENCODE)272; GenomeSpace, a cloud-based resource for integrative genomics analyses83; GEO, Gene Expression Omnibus77; GSEA, gene set enrichment analysis273; InsilicoDB, a database of microarray and RNA-seq data82; Known Genes, a resource of RNA and protein data274; Limma, Linear Models for Microarray Data167; MiTranscriptome, a human RNA-seq database76; Mitelman, Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer275; MPSS, massively parallel signature sequencing42; Oncomine, a cancer microarray database and integrated data mining platform180; qPCR, quantitative PCR29; RACE, rapid amplification of cDNA ends276; Refseq, NCBI Reference Sequence Database75; RNAscope, an in situ hybridization assay for RNA detection277; RNA-seq, RNA sequencing; RNA-seq 454, RNA sequencing using the 454 (Roche) pyrosequencing platform44; RNA-seq SBS, RNA sequencing using sequencing-by-synthesis platforms278; RT-qPCR, reverse transcription quantitative PCR30,31,32; SAGE, serial analysis of gene expression36; SAGEmap, SAGE tag to gene mapping279; Sailfish, a transcript isoform quantification tool280; SAM, Significance Analysis of Microarrays281; Smith–Waterman, a local sequence alignment algorithm70; STAR, Spliced Transcripts Alignment to a Reference282; Symatlas, gene expression and annotation resource, now superseded by BioGPS283; TACO, Transcriptome Assemblies Combined into One (a consensus transcriptome tool)284; TopHat and Cufflinks, software tools for RNA-seq alignment and transcriptome assembly5; Trans-ABySS, Transcript Assembly By Short Sequences285; Trinity, a tool for de novo assembly of RNA-seq data286; UMI, unique molecular identifier48; Xena, a genomic data mining and analysis portal287.

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