Given a sequence, local alignment algorithms such as BLAST, FASTA and Smith-Waterman look for regions of similarity between the target sequence and possible candidate matches. Once candidate DNA sequences have been determined, it is a relatively straightforward algorithmic problem to efficiently search a target genome for matches, complete or partial, and exact or inexact.
Given a protein sequence, a family of possible coding DNA sequences can be derived by reverse translation of the genetic code. Given an mRNA sequence, it is trivial to derive a unique genomic DNA sequence from which it had to have been transcribed. In empirical (similarity, homology or evidence-based) gene finding systems, the target genome is searched for sequences that are similar to extrinsic evidence in the form of the known expressed sequence tags, messenger RNA (mRNA), protein products, and homologous or orthologous sequences. Many aspects of structural gene prediction are based on current understanding of underlying biochemical processes in the cell such as gene transcription, translation, protein–protein interactions and regulation processes, which are subject of active research in the various omics fields such as transcriptomics, proteomics, metabolomics, and more generally structural and functional genomics. Gene prediction is closely related to the so-called 'target search problem' investigating how DNA-binding proteins ( transcription factors) locate specific binding sites within the genome. Gene prediction is one of the key steps in genome annotation, following sequence assembly, the filtering of non-coding regions and repeat masking. Predicting the function of a gene and confirming that the gene prediction is accurate still demands in vivo experimentation through gene knockout and other assays, although frontiers of bioinformatics research are making it increasingly possible to predict the function of a gene based on its sequence alone. Today, with comprehensive genome sequence and powerful computational resources at the disposal of the research community, gene finding has been redefined as a largely computational problem.ĭetermining that a sequence is functional should be distinguished from determining the function of the gene or its product.
Statistical analysis of the rates of homologous recombination of several different genes could determine their order on a certain chromosome, and information from many such experiments could be combined to create a genetic map specifying the rough location of known genes relative to each other. In its earliest days, "gene finding" was based on painstaking experimentation on living cells and organisms. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes.
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