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Each DNA (RNA) sequence has 6 possible Open Reading Frames(ORF). My question is: What are the theoretical bases of in vitro or in silico tries to find correct reading frame of a sequence?

Is it just distance between Start and Stop codons, or are there some other factors with more important impacts in this subject?

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  • $\begingroup$ I have edited your question a bit. Please feel free to roll back if this does not suit you $\endgroup$ – One Face Feb 3 '15 at 14:43
  • $\begingroup$ Homology, ESTs, codon usage, length of ORF $\endgroup$ – canadianer Feb 3 '15 at 15:14
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    $\begingroup$ The start and stop codons depend on the ORF selected. If one of the six ORFs has nicely paired start and stop codons and the other five do not, then that is a pretty good hint. There are other ways to tell (shine-dalgarno sequences in prokaryotes, Kozak consensus sequences in eukaryotes, etc). $\endgroup$ – Resonating Feb 3 '15 at 18:37
  • $\begingroup$ canadianer please explain more $\endgroup$ – MySky Feb 4 '15 at 12:48
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TransDecoder is a commonly used program for extracting likely coding regions from transcriptome assemblies, which does the following to make a call:

TransDecoder identifies likely coding sequences based on the following criteria:

  • a minimum length open reading frame (ORF) is found in a transcript sequence
  • a log-likelihood score similar to what is computed by the GeneID software is > 0.
  • the above coding score is greatest when the ORF is scored in the 1st reading frame as compared to scores in the other 5 reading frames.
  • if a candidate ORF is found fully encapsulated by the coordinates of another candidate ORF, the longer one is reported. However, a single transcript can report multiple ORFs (allowing for operons, chimeras, etc).
  • optional the putative peptide has a match to a Pfam domain above the noise cutoff score.

So in essence, look for the longest ORF, and then use some secondary metric (hidden Markov model, position weight array, database query, etc) to refine your prediction.

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