Expectation Maximization (EM) for MEME Motif Discovery in Bioinformatics (Part 1 of 3)

Описание: Please note: MEME is Multiple Expectation maximizations for Motif Elicitation. In bioinformatics, motifs typically are sequence patterns that occur many times in a group of related protein or DNA sequences. Typically, motifs are associated with some biological function (e.g. Transcription Factor Binding Sites where Transcription Factors bind to regulatory elements like promoters/enhancers). Saniya goes through a detailed toy example of applying MEME algorithm to learn a Position Weight Matrix (PWM) and associated motif occurrences.

Please note this is the 1st of 3 detailed videos walking through an example of using MEME to discover motifs for TF binding.
Part 1 of 3 (current video):    • Expectation Maximization (EM) for MEME Mot...  
Part 2 of 3:    • Expectation Maximization (EM) for MEME Mot...  
Part 3 of 3:    • Expectation Maximization (EM) for MEME Mot...  

Please note PWM should actually be called the Position Weighted Matrix and not Probability Weighted Matrix. Sorry about that!

Also, Saniya made a mistake! There are 11 unique motifs that are found across all 4 sequences :)
GTC, TCA, CAG, AGG, GAG, AGA, AGT, ACG, CGG, GGA, CCA. Alas, Saniya could not put this correction into the video and mistakenly said 9 motifs, when there really are 11! :(

Please reach out with any and all questions and please subscribe to Saniya's YouTube channel for more updates.

************ Please note this toy example: ************
L = 6 bases (length of the DNA sequence)
W = 3 bases (motif bases); please note this is a parameter we selected.
N = 4 sequences

Please note these 4 DNA sequences:
1. GTCAGG
2. GAGAGT
3. ACGGAG
4. CCAGTC

Using MEME algorithm, please find the Position Weight Matrix (PWM) including background (non-motif) probabilities. Please also find the occurrences of the motifs in these 4 sequences. :)
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TIME STAMPS:
00:00 Expectation Maximization (EM) for MEME Motif Discovery in Bioinformatics (Part 1 of 3)
00:21 Transcription Factors (TFs) bind to sequence-specific motifs along DNA: to their respective Transcription Factor Binding Sites (TFBSs)
01:21 Motif Model Learning Task (Multiple Expectation maximizations for Motif Elicitation)
02:35 What is Expectation Maximization (EM)?
============================ The example problem we will work on in these next 3 videos :) ===========================
02:57 The problem: Finding Motifs of Width 3 in 4 DNA sequences of Length L = 6 Bases
04:06 Finding the possible starting positions for the motifs in the sequences (based on W and L: motif width versus sequence length): m = 4 possible starting positions
======== Finding all of the unique motifs that are possible
04:52 Finding possible motifs for Sequence 1: GTC, TCA, CAG, AGG
05:31 Finding possible motifs for Sequence 2: GAG, AGA, AGT
05:56 Finding possible motifs for Sequence 3: ACG, CGG, GGA, GAG
06:10 Finding possible motifs for Sequence 4: CCA, CAG, AGT, GTC
06:38 The 11 total unique motifs: GTC, TCA, CAG, AGG, GAG, AGA, AGT, ACG, CGG, GGA, CCA (please note 9 was a mistake; there are actually 11 motifs across all 4 sequences)
07:05 Understanding the Z matrix: probability of the motif starting in a given position in each sequence. Z matrix has N rows (1 for each sequence) and m columns (1 for each possible start position for the motif)
12:08 How to initialize the Z matrix (setting equally likely probability values): values of 1/m for each entry in Z matrix.
12:53 Using Subsequences as Starting Points for EM (Initializing a PWM for a given motif)
15:18 Assumptions about background (non-motif probabilities) in the initial PWMs: backgrounds are initially 25% for each DNA base.