Transcriptomics technologies
RNA-Seq Microarray
Throughput 1 day to 1 week per experiment 1-2 days per experiment
Input RNA amount Low ~ 1 ng total RNA High ~ 1 μg mRNA
Labour intensity High ( sample preparation and data analysis ) Low
Prior knowledge None required , although a reference genome/transcriptome sequence is useful Reference genome/transcriptome is required for design of probes
Quantitation accuracy ~90% ( limited by sequence coverage ) > 90% ( limited by fluorescence detection accuracy )
Sequence resolution RNA-Seq can detect SNPs and splice variants ( limited by sequencing accuracy of ~99% ) Specialised arrays can detect mRNA splice variants ( limited by probe design and cross-hybridisation )
Sensitivity 1 transcript per million ( approximate , limited by sequence coverage ) 1 transcript per thousand ( approximate , limited by fluorescence detection )
Dynamic range 100,000:1 ( limited by sequence coverage ) 1,000:1 ( limited by fluorescence saturation )
Technical reproducibility > 99% > 99%
History -- Development of contemporary techniques
Transcriptomics technologies are the techniques used to study an organisms transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology lies in understanding how the same genome can give rise to different cell types and how gene expression is regulated. The first attempts to study whole transcriptomes began in the early 1990s. Subsequent technological advances since the late 1990s have repeatedly transformed the field, and made transcriptomics a widespread discipline in biological sciences. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA-Seq, which uses high-throughput sequencing to record all transcripts. As the technology improved, the volume of data produced by each transcriptome experiment increased. As a result, data analysis methods have steadily been adapted to more accurately and efficiently analyse increasingly large volumes of data. Transcriptome databases have grown and increased in utility as more transcriptomes are collected and shared by researchers.