Transcriptomique

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 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 is to understand how a single genome gives rise to a variety of cells. Another is 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 getting bigger and more useful as transcriptomes continue to be collected and shared by researchers. It would be almost impossible to interpret the information contained in a transcriptome without the knowledge of previous experiments. Measuring the expression of an organism's genes in different tissues or conditions, or at different times, gives information on how genes are regulated and reveals details of an organism's biology. It can also be used to infer the functions of previously unannotated genes. Transcriptome analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease.

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

Multimodal transcriptomic atlases of mouse spinal cord injury

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Multimodal transcriptomic atlases of mouse spinal cord injury