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| general:bioseqanalysis:intro [2019/01/21 20:04] – created ingo | general:bioseqanalysis:intro [2019/01/21 20:22] (current) – [In silico characterization] ingo | ||
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| ===== In silico characterization ===== | ===== In silico characterization ===== | ||
| - | The //in silico// way to learn about what a gene might be doing is typically considered the easier one. We use the computer to tentatively assign a certain activity to the gene product, based on the results of various algorithms for biological sequence analysis. One of the most widely used evidence is the sharing of a significant sequence similarity to either an already functionally characterized gene product, or to statistical models that represent conserved regions in aligned sequences. In both cases, this similarity is interpreted as a signal that the sequences share a common ancestry, i.e. are homologous. | + | The //in silico// way to learn about what a gene might be doing is typically considered the easier one. We use the computer to tentatively assign a certain activity to the gene product, based on the results of various algorithms for biological sequence analysis. One of the most widely used evidence is the sharing of a significant sequence similarity to either an already functionally characterized gene product, or to statistical models that represent conserved regions in aligned sequences. In both cases, this similarity is interpreted as a signal that the sequences share a common ancestry, i.e. are homologous.\\ |
| + | Basically, in silico analyses of gene function boil down to three general approaches | ||
| + | * The identification of functionally annotated sequences displaying a significant sequence similarity. The most common tool to do this is [[https:// | ||
| + | * The identification of evolutionary conserved subsequences -sometimes referred to as //domains// - in the sequence of interest. One of the most widely used tools/ | ||
| + | * The identification of short motifs in a sequence. Note, contrary to the other two approaches, a motif search typically does not rely on the inference of an evolutionary relationship((Motifs are short sub-sequences of DNA or a protein of defined length, e.g. the start codon //**ATG**// or the canonical splice donor and splice acceptor sites GT-AG. Due to their short length, it is feasible to assume that they can emerge independently more than once in the course of evolution.)) | ||
| ===== Integrative approaches ===== | ===== Integrative approaches ===== | ||
| Nowadays, experimental approaches, and those that are computer-based are far from being mutually exclusive. People rarely enter the lab without a prior idea about what a certain gene product could do. And likewise, //in silico// analyses on the computer heavily depend on the amount of existing information that can be exploited in the process annotating the function of an unknown gene product. In essence, annotating a novel gene product is equivalent to connecting it to the network of existing information about gene product function. Information is transferred, | Nowadays, experimental approaches, and those that are computer-based are far from being mutually exclusive. People rarely enter the lab without a prior idea about what a certain gene product could do. And likewise, //in silico// analyses on the computer heavily depend on the amount of existing information that can be exploited in the process annotating the function of an unknown gene product. In essence, annotating a novel gene product is equivalent to connecting it to the network of existing information about gene product function. Information is transferred, | ||