Method for the identification of gene transcripts with improved efficiency in the treatment of errors

• US 20030138794A1
• Filed: 10/28/2002
• Published: 07/24/2003
• Est. Priority Date: 12/27/1999
• Status: Active Grant

• Current Assignee: Laboratoires Serono SA
• Original Assignee: Applied Research Systems ARS Holding NV (Merck & Co., Inc.)
• Inventors: Colinge, Jacques, Feger, Georg
• Granted Patent: US 7,101,665 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation

Abstract

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.

Claims

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) reducing the amount of sequencing errors by using a statistical model for sequencing errors to be applied to said isolated first tags.

2. The method of claim 1,

characterised in that said statistical model of said step f) is based on a confidence level of a base-calling method.

3. The method of claim 1 or 2,

characterised in that said step f) is applied after a step of

g) reducing the amount of sequencing errors by checking correctness of said isolated first ditags or tags through a base-calling method.

4. The method of claim 2 or 3,

characterised in that said base-calling method is performed through a Phred®

base-calling software.

5. The method according to any of the preceding claims,

characterised in that 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 any of the preceding claims,

characterised in that 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 wherein a step of

b) comparing said first tags and said second tags in order to determine relative abundance thereof is applied.

7. The method according to any of the preceding claims,

characterised in that it comprises a step of predicting an extra base-pair for said tags.

8. The method of claim 7,

characterised in that 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 any of the preceding claims,

characterised in that it comprises a step of checking correctness of said isolated ditags based on a length of said ditags.

10. The method according to any of claims 1 to 8,

characterised in that it comprises a step of checking correctness of said isolated ditags based on an occurrence of said ditags.

11. The method according to any of the preceding claims,

characterised in that said statistical model of said step f) is based on an average sequencing error rate.

12. The method of claim 11,

characterised in that said average sequencing error rate is calculated through

$\{\begin{array}{c}{k}_n=d-\left(1-{\left(1-q\right)}^n\right)\end{array}$

Nkm=d-(1-(1-q)m)

N 

the system

wherein q represents said average sequencing error rate.

13. The method according to any of the preceding claims,

characterised in that said step f) comprises a step of:

f1) calculating an estimate of the abundance of the tags not influenced by sequencing errors.

14. The method according to claim 13,

characterised in that said step f1) is performed through a sparse diagonal-dominant linear system.

15. The method according to claim 13 or 14,

characterised in that said step f) further comprises the steps of:

f2) calculating a difference between said estimate of the abundance of the tags and said counted abundance of the tags; and

f3) eliminating tags for which said difference is bigger than a given threshold.

16. The method according to any of claims 6 to 15,

characterised in that 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,

characterised in that said step h4) of estimating correctness of said difference applies a Claverie method.

18. The method according to any of claims 6 to 17 when depending on claim 7 or 8,

characterised in that 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 any of the preceding claims,

characterised in that 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,

characterised in that 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 or 20,

characterised in that said step i) 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,

characterised in that said first database is an EST database.

23. The method of claim 22,

characterised in that step b) is performed by using MmeI as a restriction enzyme.

24. The method according to any of the preceding claims,

characterised in that it comprises a step of visualizing the identified tags.

25. The method of claim 24,

characterised in that it comprises a step of providing means for accessing the information concerning said identified tags.

26. The method of claim 24 or 25,

characterised in that it comprises a step for assigning a unique number for each identified tag.

27. The method according to any of claims 24 to 26,

characterised in that it comprises a step for assigning a unique position on a screen for each identified tag.