Method for the identification of gene transcripts with improved efficiency in the treatment of errors
View Patent ↗A method for identification of gene transcripts comprises the steps of: generating a set of raw sequences by sequencing of biological material; isolating ditags from the set of raw sequences; isolating tags from the ditags; determining abundance of the tags; and identifying the tags, the method providing a step of reducing the amount of sequencing errors by using a statistical model for sequencing errors.
1. A method for identification of gene transcripts comprising the steps of:
a) generating at least a first set of raw sequences by sequencing at least a first type of biological material;
b) isolating first ditags from said at least first set of raw sequences;
c) isolating first tags from said isolated at least first ditags;
d) determining abundance of said first tags; and
e) identifying said first tags, further comprising a step of
f) rejecting said isolated first tags that are wrongly sequenced by means of a statistical model for sequencing errors to be applied to said isolated first tags, said statistical model being defined by a probability function F(a,b), wherein said function F is intended to modelize the probability that a given tag a can be sequenced as b.
2. The method of claim 1 , wherein said statistical model of said step f) uses a confidence level of a base-calling method that has been converted into a probability that a base-pair is not correct.
3. The method of claim 1 or 2 , wherein said step f) is applied after a step of
g) rejecting said isolated first tags that are wrongly sequenced by checking correctness of said isolated first ditags or tags through a base-calling method.
4. The method of claim 2 , wherein said base-calling method is performed through a base-calling software.
5. The method according to claim 1 , wherein said ditags are isolated from said set of raw sequences through a method for serial analysis of gene expression (SAGE).
6. The method according to claim 3 , wherein said steps a) through f) or a) through g) are applied to at least a second set of raw sequences and to respective second ditags and second tags, and further comprising a step of
h) comparing said first tags and said second tags in order to determine relative abundance thereof.
7. The method according to claim 1 , further comprising a step of predicting an extra base-pair for said tags.
8. The method of claim 7 , wherein said step of predicting an extra base-pair for said tags comprises the steps of:
assigning an extra base-pair to each tag of a ditag each time a ditag of at least a predetermined number of base-pairs is isolated;
imposing for each of the tags to which an extra base-pair has been assigned to be as much abundant as a predetermined threshold;
choosing, for each of the remaining tags, its most dominant extra base-pair; and
imposing, for said most dominant extra base-pair, to represent at least a certain percentage of the number of occurrences of said tag.
9. The method according to claim 1 , further comprising a step of checking correctness of said isolated ditags by determining the length of said ditags and rejecting ditags smaller and/or larger than a predetermined length.
10. The method according to claim 1 , further comprising a step of checking correctness of said isolated ditags by determining an occurrence of said ditags and rejecting ditags occurring twice.
11. The method according to claim 1 , wherein said statistical model of said step f) modelizes the probability that a given tag a can be sequenced as b by applying an average sequencing error rate to said function F(a,b).
12. The method of claim 11 , wherein said average sequencing error rate is calculated through the system
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wherein q represents said average sequencing error rate.
13. The method according to claim 1 , wherein said step f) comprises a step of:
f1) calculating an estimate of the abundance of said isolated first tags not rejected by means of said statistical model.
14. The method according to claim 13 , wherein said step f1) is performed through a sparse diagonal-dominant linear system.
15. The method according to either one of claims 13 or 14 , wherein said step f) further comprises the steps of:
f2) calculating a difference between said estimate of the abundance of the tags and a counted abundance of the tags; and
f3) eliminating tags for which said difference is bigger than a given threshold.
16. The method according to claim 6 , wherein said step h) of comparing said first and at least second ditags in order to determine relative abundance thereof comprises the steps of:
h1) merging said first and second tags;
h2) normalizing the abundance of said first and second tags;
h3) determining a difference of abundance between said first and second ditags; and
h4) estimating correctness of said difference.
17. The method of claim 16 , wherein said step h4) of estimating correctness of said difference applies a Claverie method.
18. The method according to claim 16 , wherein said step h) of comparing said first and second ditags in order to determine relative abundance thereof further comprises the step of:
h5) checking consistence of the predicted extra base-pair.
19. The method according to claim 6 , wherein said step e) of identifying said at least first tags comprises the step of:
e1) performing a first identification of the tags by comparison with a first database.
20. The method of claim 19 , wherein said step e) of identifying said at least first tags further comprises the step of:
e2) assigning a score to each of the tags identified by said first identification.
21. The method of claim 19 , wherein said step e) of identifying said first and second tags further comprises the steps of:
e3) performing at least a second identification of the tags by comparison with at least a second database; and
e4) assigning a score to each of the tags identified by said at least second identification.
22. The method of claim 19 , wherein said first database is an EST database.
23. The method of claim 22 , wherein the step b) is performed by using MmeI as a restriction enzyme.
24. The method according to claim 1 , further comprising a step of visualizing the identified tags.
25. The method of claim 24 , further comprising a step of providing means for accessing the information concerning said identified tags.
26. The method of claim 24 , further comprising a step for assigning a unique number for each identified tag.
27. The method according to claim 24 , further comprising a step for assigning a unique position on a screen for each identified tag.
28. The method of claim 11 , wherein said average sequencing error rate equals 1%.
29. The method according to claim 28 , wherein, for every tag a ∈S T , S T being the set of every possible tag to be considered, the value of F(a,b), ∈ be S T , is:
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i) if b =a,
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