
© 2017 Lowe et al. Transcriptomics technologies are the techniques used to study an organism’s 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 noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences. Measuring the expression of an organism’s genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism’s biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.
Biochemistry & Molecular Biology, Bioinformatics, QH301-705.5, Image Processing, 612, MINIMUM INFORMATION, DNA MICROARRAY, Biochemical Research Methods, MICROARRAY EXPERIMENT MIAME, Mice, Computer-Assisted, Image Processing, Computer-Assisted, Animals, Cluster Analysis, Humans, Biology (General), GENE-EXPRESSION, Oligonucleotide Array Sequence Analysis, Uncategorized, Science & Technology, Topic Page, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, SINGLE-STEP METHOD, Rats, DIFFERENTIAL EXPRESSION ANALYSES, NONCODING RNAS, Organ Specificity, RNA, CELL RNA-SEQ, Mathematical & Computational Biology, RIBOSOMAL-RNA, Life Sciences & Biomedicine, Sequence Analysis, SEQUENCE TAGS
Biochemistry & Molecular Biology, Bioinformatics, QH301-705.5, Image Processing, 612, MINIMUM INFORMATION, DNA MICROARRAY, Biochemical Research Methods, MICROARRAY EXPERIMENT MIAME, Mice, Computer-Assisted, Image Processing, Computer-Assisted, Animals, Cluster Analysis, Humans, Biology (General), GENE-EXPRESSION, Oligonucleotide Array Sequence Analysis, Uncategorized, Science & Technology, Topic Page, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, SINGLE-STEP METHOD, Rats, DIFFERENTIAL EXPRESSION ANALYSES, NONCODING RNAS, Organ Specificity, RNA, CELL RNA-SEQ, Mathematical & Computational Biology, RIBOSOMAL-RNA, Life Sciences & Biomedicine, Sequence Analysis, SEQUENCE TAGS
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 940 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 0.01% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 0.1% |
