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Profile Hidden Markov Models For Sequence Alignment And Parameter Estimation

Get this Assignment Solution on sequence alignment in bioinformatics.

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Profile Hidden Markov Models For Sequence Alignment And Parameter Estimation - Page 1 preview imageProfile Hidden Markov Models for Sequence Alignment and Parameter EstimationHomework 31. Profile HMMs for sequence familiesFigure 1. Multiple sequence alignment of five DNA sequencesT C-C T--C A C T GT- -C T AT G-G C AC C-T T Ca)Define matching (M), insert (I) and delete (D) states of the multiple sequence alignment(MSA) shown in figure 1Answer:Matching (M) state: These are positions where all sequences align to a commoncharacter. For example:oIn column 1:Tin all sequences (M state)oIn column 2:Cin all sequences (M state)oIn column 5:T(in all sequences except the second sequence, but the column isconsistent across the majority, so it's considered an M state)Insert (I) state: These represent columns where some sequences have an insertion (a gapis present in other sequences).oFor example, the gap (-) in column 3 means one sequence has an insertion.oColumn 4 also has a gap (-) in sequence 1.Delete (D) state: These states are used when a sequence has a deletion (represented by a-), and other sequences have aligned bases. This corresponds to the positions in thealignment where a sequence is "missing" a base, relative to others.So, based on the provided MSA:M state: Columns 1, 2, 5 (most of the time, but column 2 is more consistent).I state: Columns where sequences have gaps (Columns 3, 4).D state: The column(s) where a base is missing compared to other sequences (the gap incolumn 3 and 4 in some sequences).
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