Transcription factor binding sites are notoriously difficult to accurately predi

Question

Transcription factor binding sites are notoriously difficult to accurately predict using any one approach. Describe a multi-part strategy that uses multiple approaches to identify transcription factor binding sites in the human genome. In your multipronged approach, you should consider how you might use available comparative genomic data, together with various other experimental approaches that leverage next-generation sequencing to come up with good hypotheses for where these sites exist in the genome.

Explanation / Answer

Ans.) Nowadays, gene prediction is widely used to predict that which DNA segment codes for a certain gene. It is the process of identifying the parts of genomic DNA that encode for a particular gene. For example, protein-coding gene, RNA gene and few of the regulatory region also. Gene finding is very crucial to determine the genome of a species after its sequencing. There are several methods of Gene prediction; here Ab initio method is described to understand the methodology of gene prediction.

In, Ab initio gene prediction of eukaryotes, for organisms like humans, is very challenging due to following reasons

·         The promoter and other regulatory signal in human genome are more complex and less understood than the prokaryotes. Characteristic examples of signal recognized by eukaryotic gene prediction are CpG islands and binding sites for a poly (A) tail.

·         Splicing mechanism that is applied by eukaryotic cells to join together the protein-coding sequence, interrupted by non-coding sequence. A typical protein-coding gene in humans have many exons, each less than two hundred base pairs in length or lesser than that also. That is why, it is very difficult to predict their gene product and other known characters of protein-coding DNA in eukaryotes.

The advanced version of gene predictions method for   eukaryotic genomes typically employ complex probabilistic models, like hidden Markov models (HMMs) to merge the information from a variety of different signal and content determinants.

Eukaryotic ab initio gene finders, have several successful examples; the GENSCAN and geneid programs. These programmes are more functional to solve the problems related to using a gene finder on a genome sequence that it was not trained against. It construct a detailed model by applying hidden Markov support vector machines  to understand the exact gene prediction scoring function.

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