Methods for identifying sequence motifs, and applications thereof

A method for identifying one or more sequence motifs that are over-represented or under-represented in a real genome or real genome portion as compared to the frequency of those sequence motifs that would be expected to occur by chance, the method comprising:

• (i) selecting a real genome or real genome portion in which to identify over- or under-represented sequence motifs,
• (ii) generating a background genome that encodes the same amino acids, and has the same codon usage as the real genome, but is otherwise random,
• (iii) identifying, and counting the number of occurrences of, each word of a given length in the background genome,
• (iv) converting the number of occurrences of each word in the background genome to a probability of occurrence of each word in the background genome,
• (v) counting the number of occurrences of, each word identified in part (iii) in the real genome or real genome portion,
• (vi) converting the number of occurrences of each word in the real genome or real genome portion to a probability of occurrence of each word in the real genome,
• (v) performing an iterative algorithm to identify words contributing to the difference between the background genome probability distribution and the real genome probability distribution, wherein the iterative algorithm comprises performing the following the steps:
• (a) identifying the word most significantly contributing to the difference between the real genome probability distribution and the background genome probability distribution,
• (b) resealing the background genome to factor out the difference between the real genome probability distribution and the background genome probability distribution that was due to the word identified in step (a), and
• (c) optionally repeating steps (a) and (b) to identify additional words contributing to the difference between the real genome probability distribution and the background genome probability distribution,
• wherein the words identified in each iteration of step (a) are sequence motifs that are over- or under-represented in the real genome as compared to the frequency of those sequences that would be expected to occur by chance.

A method for identifying one or more sequence motifs that are over-represented or under-represented in a real genome or real genome portion as compared to the frequency of those sequence motifs that would be expected to occur by chance, the method comprising:

• (i) selecting a real genome or real genome portion in which to identify over- or under-represented sequence motifs,
• (ii) generating multiple background genomes, each of which encodes the same amino acids, and has the same codon usage as the real genome or real genome portion, but is otherwise random,
• (iii) identifying, and counting the number of occurrences of, each word of a given length in each background genome,
• (iv) calculating the average number of occurrences of each word identified in step (iii) across each of the background genomes generated in step (ii)
• (iv) converting the average number of occurrences of each word in the background genomes to an average probability of occurrence of each word,
• (v) counting the number of occurrences of, each word identified in part (iii) in the real genome or real genome portion,
• (vi) converting the number of occurrences of each word in the real genome or real genome portion to a probability of occurrence of each word in the real genome or real genome portion,
• (v) performing an iterative algorithm to identify words contributing to the difference between the background genome probability distribution and the real genome probability distribution, wherein the iterative algorithm comprises performing the following the steps:
• (a) identifying the word most significantly contributing to the difference between the real genome probability distribution and the background genome probability distribution,
• (b) resealing the background genome to factor out the difference between the real genome probability distribution and the background genome probability distribution that was due to the word identified in step (a), and
• (c) optionally repeating steps (a) and (b) to identify additional words contributing to the difference between the real genome probability distribution and the background genome probability distribution,
• wherein the words identified in each iteration of step (a) are sequence motifs that are over- or under-represented in the real genome or real genome portion as compared to the frequency of those sequences that would be expected to occur by chance.

A method for optimizing the production of a protein in a host, the method comprising:

• (a) identifying one or more sequence motifs that are either under-represented or over-represented in a host genome or genome portion, as compared to the frequency of those sequences that would be expected to occur by chance,
• (b) obtaining a nucleotide sequence encoding a protein to be expressed in the host,
• (c) mutating the nucleotide sequence encoding the protein to reduce the number of those sequence motifs that are under-represented in the host genome or genome portion, or to increase the number of those sequence motifs that are over-represented in the host genome or genome portion, or both,
• wherein the mutations result in improved production of the protein in the host.

A method for optimizing the production of a protein in a host, the method comprising:

• (a) identifying one or more sequence motifs that are either under-represented or over-represented in a host genome, as compared to the frequency of those sequences that would be expected to occur by chance, by performing the following steps:
• (i) obtaining the nucleotide sequence of the host genome,
• (ii) generating a background genome that encodes the same amino acids, and has the same codon usage as the host genome, but is otherwise random,
• (iii) identifying, and counting the number of occurrences of each word of a given length in the background genome,
• (iv) counting the number of occurrences of, each word identified in part (iii) in the host genome,
• (v) identifying the word most significantly contributing to the difference between the host genome and the background genome,
• (vi) resealing the background genome to factor out the difference between the host genome and the background genome that was due to the word identified in step (v), and
• (vii) optionally repeating steps (v) and (vi) to identify additional words contributing to the difference between the host genome and the background genome,
• wherein the words identified in each repetition of step (v) are sequence motifs that are over-represented or under-represented in the real genome as compared to the frequency of those sequences that would be expected to occur by chance,
• (b) obtaining a nucleotide sequence encoding a protein to be expressed in the host,
• (c) mutating the nucleotide sequence encoding the protein to either remove or disrupt one or more of sequence motifs that are under-represented in the host, or to add one or more sequence motifs that are over-represented in the host, or both,
• wherein the mutations result in improved production of the protein in the host.

A method for comparing a first sequence, S1, to second sequence, S2, the method comprising:

• (a) identifying one or more words that are either under-represented or over-represented in a first sequence, S1, as compared to the frequency of those words that would be expected to occur by chance,
• (b) determining whether any of the words identified in step (a) are either under-represented or over-represented in a second sequence, S2, as compared to the frequency of those words that would be expected to occur by chance,
• (c) generating a score calculating for the similarity between S1 and S2 based on the number of words, out of the total number of words identified in step (a), that are either over-represented in both S1 and S2, or are under-represented in both S1 and S2,
• wherein the higher the score the greater the similarity between sequence S1 and sequence S2.

A method for comparing a first sequence S1 of length s1, to a second sequence S2 of length s2, the method comprising:

• (a) generating a list of words that are either under-represented of over-represented in a sequence S1 of length s1, as compared to the frequency of those words that occur in a background genome, BS1, that encodes the same amino acids, and has the same codon usage as S1, but is otherwise random,
• (b) generating a list L of words W, wherein each of the words W is a word identified in step (a), whose under- or over-representation would be statistically significant in a coding sequence of length s2,
• (c) generating a background sequence BS2 that encodes the same amino acids, and has the same codon usage as the sequence S2, but is otherwise random,
• (d) performing an iterative algorithm comprising the following steps:
• (i) taking a word W from the list L,
• (ii) adding a numerical, score of “one” for that word only if the word is over-represented in both S1 and S2 compared to their respective backgrounds BS1 and BS2, or if the word is under-represented in both S1 and S2 compared to their respective backgrounds BS1 and BS2,
• (iii) resealing the background BS2 to factor out the effects of W, and
• (iv) repeating steps (i) to (iii) for each word Win the list L, to produce a list of Y words having a score of one or more out of X possible words in the list W,
• (e) calculating a final score based on the number of sequence motifs having a score of one or more out of the total number of sequence motifs identified in step (a),
• wherein the higher the final score the greater the similarity between sequence S1 and sequence S2.

A method for comparing a first sequence S1 of length s1, to a second sequence S2 of length s2, the method comprising:

• (a) generating a list of words that are either under-represented of over-represented in a sequence S1 of length s1, as compared to the frequency of those words that occur in a background genome, BS1, that encodes the same amino acids, and has the same codon usage as S1, but is otherwise random,
• (b) generating a list L of words W, wherein each of the words W is a word identified in step (a), whose under- or over-representation would be statistically significant in a coding sequence S2 of length s2,
• (c) generating a background sequence BS2 that encodes the same amino acids, and has the same codon usage as the sequence S2, but is otherwise random,
• (d) performing an iterative algorithm comprising the following steps:
• (i) taking a word W from the list L,
• (ii) adding a numerical score of “one” only if the word W is over-represented in both S1 and S2 compared to their respective backgrounds BS1 and BS2, or if W is under-represented in both S1 and S2 compared to their respective backgrounds BS1 and BS2,
• (iii) resealing the background BS2 to factor out the effects of W, and
• (iv) repeating steps (i) to (iii) for each word Win the list L, to produce a list of Y words having a score of one or more out of X possible words in the list W,
• (e) calculating a final score using the formula C×(X−Y/2)√Y, where C is a constant
• wherein the higher the final score the greater the similarity between sequence S1 and sequence S2.