Editing Bioinformatics (section)

==Gene and protein expression==

===Analysis of gene expression===
The [[gene expression|expression]] of many genes can be determined by measuring [[mRNA]] levels with multiple techniques including [[DNA microarray|microarrays]], [[expressed sequence tag|expressed cDNA sequence tag]] (EST) sequencing, [[serial analysis of gene expression]] (SAGE) tag sequencing, [[massively parallel signature sequencing]] (MPSS), [[RNA-Seq]], also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate [[signal]] from [[noise]] in high-throughput gene expression studies.<ref>{{cite journal | vauthors = Grau J, Ben-Gal I, Posch S, Grosse I | title = VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees | journal = Nucleic Acids Research | volume = 34 | issue = Web Server issue | pages = W529-33 | date = July 2006 | pmid = 16845064 | pmc = 1538886 | doi = 10.1093/nar/gkl212 }}</ref> Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous [[epithelial]] cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

[[File:MIcroarray vs RNA-Seq.png|thumb|center|400px|MIcroarray vs RNA-Seq]]

===Analysis of protein expression===
[[Protein microarray]]s and high throughput (HT) [[mass spectrometry]] (MS) can provide a snapshot of the proteins present in a biological sample. The former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples when multiple incomplete peptides from each protein are detected. Cellular protein localization in a tissue context can be achieved through affinity [[proteomics]] displayed as spatial data based on [[immunohistochemistry]] and [[tissue microarray]]s.<ref>{{Cite web |url=https://www.proteinatlas.org/ |title=The Human Protein Atlas |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=4 March 2020 |archive-url=https://web.archive.org/web/20200304041657/http://www.proteinatlas.org/ |url-status=live }}</ref>

===Analysis of regulation===
[[Gene regulation]] is a complex process where a signal, such as an extracellular signal such as a [[hormone]], eventually leads to an increase or decrease in the activity of one or more [[protein]]s. Bioinformatics techniques have been applied to explore various steps in this process.

For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of [[sequence motif]]s in the DNA surrounding the protein-coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [[Enhancer (genetics)|Enhancer]] elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of [[chromosome conformation capture]] experiments.

Expression data can be used to infer gene regulation: one might compare [[microarray]] data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the [[cell cycle]], along with various stress conditions (heat shock, starvation, etc.). [[cluster analysis|Clustering algorithms]] can be then applied to expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented [[regulatory elements]]. Examples of clustering algorithms applied in gene clustering are [[k-means clustering]], [[self-organizing map]]s (SOMs), [[hierarchical clustering]], and [[consensus clustering]] methods.