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Electronic Journal of Biotechnology

versión On-line ISSN 0717-3458

Electron. J. Biotechnol. v.3 n.3 Valparaíso dic. 2000

 

Molecular Biology and Genetics
EJB Electronic Journal of Biotechnology ISSN: 0717-3458 Vol.3 No. 3, Issue of December 15, 2000.
© 2000 by Universidad Católica de Valparaíso -- Chile Received September 11, 2000 / Accepted November 17, 2000
RESEARCH ARTICLE

Predicting regulatory elements in repetitive sequences using transcription factor binding sites

Jorng-Tzong Horng*
Department of Computer Science and Information Engineering
National Central University
Taiwan
Tel: +886-3-4227151 Ext. 4519
Fax: +886-3-4222681

E-mail: horng@db.csie.ncu.edu.tw

Wen-Fu Cho
Applied Research Lab., Telecommunications Labs.
Chunghwa Telecom Co., Ltd.
Yang-Mei, Taoyuan, Taiwan
Tel: +886-3-4244197
Fax: +886-3-4244167

*Corresponding author

Keywords: binding sites, data mining, genomes, regulatory elements, transcription factors.


Abstract Reprint (PDF)
Abstract
Article
References

Repeat sequences are the most abundant ones in the extragenic region of genomes. Biologists have already found a large number of regulatory elements in this region. These elements may profoundly impact the chromatin structure formation in nucleus and also contain important clues in genetic evolution and phylogenic study. This study attempts to mine rules on how combinations of individual binding sites are distributed repeat sequences. The association rules mined would facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Herein, the combinations of transcription factor binding sites in the repeat sequences are obtained and, then, data mining techniques are applied to mine the association rules from the combinations of binding sites. In addition, the discovered associations are further pruned to remove those insignificant associations and obtain a set of discovered associations. Finally, the discovered association rules are used to partially classify the repeat sequences in our repeat database. Experiments on several genomes include C. elegans, human chromosome 22 and yeast.

Article
Abstract
Article

Background
  • The proposed approach
    Results
  • Figure 1
  • Figure 2
  • Figure 3
  • Figure 4
  • Figure 5
  • Table 1
  • Table 2
  • Table 3
  • Table 4
  • Appendix A
  • Appendix B
    Discussion
    Concluding Remarks
    Acknowledgements
    References
  • An increasing number of genomes sequenced has ushered in the study of sequences. In this area, repetitive sequences have received considerable interest (Moyzis et al. 1989; Williams and Robbins, 1992; Alford and Caskey, 1994; Bennatzen, 1996; Warren, 1996; Mitas, 1997; Primrose, 1998; Brown, 1999). A large amount of the subsequences continuously appears in a sequence. Repetitive sequences are the most abundant ones in extragenic region of genome, in which a large number of regulatory elements are located. These repeats may significantly affect the chromatin structure formation in nucleus and also provide valuable insight into genetic evolution and phylogeny. This study considers the repetitive sequences whose length extends from twenty to several thousands in the genomes. A database is also constructed for repetitive sequences. (http://dblab5.csie.ncu.edu.tw/RSDB/)

    Many transcription factor binding sites have been collected in databases. TRANSFAC (Heinemeyer et al. 1998; Heinemeyer et al. 1999) is the most complete and well maintained database for transcription factor binding sites. Notably, consensus patterns or nucleotide distribution matrices can be used to describe transcription factor binding sites. While describing binding sites, Brazma et al. (Brazma et al. 1997) stated "The matrix representation is generally considered as the best available means for representing the consensus, however, at present most consensus descriptions are unreliable in the sense that they tend to give many false positives when compared against the genome sequences of even modest length". Therefore, this study describes the binding sites using consensus patterns.

    Brazma (Brazma et al. 1997) developed a general software tool to find and analyze combinations of transcription factor binding sites that occur often in gene upstream regions in the yeast genome. In addition to analyze the association rules in the combinations, their work focused on upstream and random regions, in which their ratio appears. Their tool can find all the combinations satisfying the given parameters with respect to the given set of upstream regions, its counterset, and the chosen set of sites. However, the tool is only used in yeast genome.

    To face a large among of repeat sequences, data mining plays a prominent role in knowledge extraction. Agrawal (Agrawal et al. 1993) introduced the problem of mining association rules over basket data. An example of an association rule is given below. The work stated ‘50% of transactions that contain beer also contain diapers; 5% of all transactions contain both of these items’. Where 50% is called the confidence of the rule, and 5% is the support of the rule. Data mining is crucial for extracting knowledge in a database. Frequently used data mining approaches, include association rules, statistical, neural network and genetic algorithms.

    In statistics, Chi-square test statistics (c 2) is extensively applied for testing independence and correlation. Chi-square is based on comparing observed frequencies with the corresponding expected frequencies. The closer that observed frequencies are to expected frequencies, implies a greater weight in favor of independence. Let f0 be an observed frequency, and f is an expected frequency, Chi-square is used to test the significance of the deviation from the expected values. The c 2 value is defined as follows:

    Where c 2 value of 0 implies the sites that are statistically independent. If it is higher than a certain threshold value, e.g., 4.12 at the 97% significance level, we reject the independent assumption. We say that it is correlated.

    Research of partial classification using association rules introduces two case studies for partial classification (Ali et al. 1997). The two case studies are medical diagnosis and telecommunications. Instead of attempting to predict future values, such research focuses on identifying characteristics of some of the data classes.

    This study initially identifies the combinations of transcription factor binding sites in repeat sequences. Data mining techniques are then applied to mine the associations from the combinations of transcription factor binding sites that occur in repeat sequences. The data mining technique can mine an enormous number of associations. The enormous number of associations makes it extremely difficult for a human user to identify those useful or interesting ones. Next, the associations are used to remove insignificant ones and find a set of useful associations. In addition, the discovered associations are used to partially classify the repeat sequences in our repeat database. Our experimental genome sequences include C. elegans, human chromosome 22, yeast and several bacteria.

    Background

    TRANSFAC database (release 4.0) contains 4965 site sequences, and 2837 factor entries. Most sites are also consensus patterns. The data in TRANSFAC has the following features. A transcription factor binding site accession number may have different consensus sequences. Different binding site accession numbers may have a same consensus sequence. Wild characters such as ‘M’ or ‘W’ used in TRANSFAC make the sequences cover other sequences. Small consensus sequences may appear in larger ones. Our approach needs a preprocessing feature because complex characteristics of the transcription factor binding sites are encountered in TRANSFAC.

    (a) Properties of repeat sequences in the repeat database

    Repeat sequences in the repeat database can be categorized as belonging to the following three types:

    • Minisatellite repeats: variable number tandem repeat (VNTR). Each repeat sequence of this type has a length ranging from ten to sixty base pairs. It repeatedly appears from five to fifty times in a sequence.

    • Microsatellite repeats: each repeat of this type has a length ranging from one to four base pairs unit repeated 10-20 times.

    • Interspersed genome-wide repeats.
      • Short Interspersed Nuclear Elements (SINEs). The length of each repeat is less than 280 base pairs. Repeats repeatedly appeared in genes.

      • Long Interspersed Nuclear Elements (LINEs). The length of each repeat ranges from 6 to 8k base pairs. They repeatedly appear from 50,000 to 100,000 times.

    • Inverted repeats: Repeat sequences invert each other. For example, the following two repeat sequences are inverted.

    5’ GATTC---GAATC 3’

    3’ CTAAG---CTTAG 5’

    The repeat sequences in our experiments include direct and inverted repeats whose length is equal or larger than twenty base pairs.

    (b) Properties of the data in TRANSFAC

    Genome sequences are a string of A, C, G or T. The symbols used in addition to A, C, G, or T also include the following:

    W: A or T S: C or G

    R: A or G Y: C or T

    K: G or T M: A or C

    B: C, G, or T D: A, G, or T

    H: A, C, or T V: A, C, or G

    N: A, C, G, or T

    Characteristics of the data in TRANSFAC are introduced as follows:

    Example 1:

    MATWAAT R04327

    The illustrative example indicates that AATAAAT, CATAAAT, AATTAAT, CATTAAT are all matched to a same site identification.

    Example 2:

    R00018 TGCCCTAA

    R00018 TGCCCTTG

    R00018 TGCCTGG

    R00018 TGGCAAAC

    Example 2 indicates that site R00018 has four different binding site consensus sequences. In TRANSFAC, 71 site IDs belong to this type.

    Example 3:

    R01372 GGGGC

    R01241 GGGGC

    R01243 GGGGC

    Example 3 indicates different binding sites having the same consensus sequence.

    Example 4:

    R02248 MAMAG

    R08440 AAAG

    The binding site R08440 is covered by the other R02248. In TRANSFAC, 3906 binding sites belong to this type. Each site may or may not have transcription factor names. 3006 accession numbers have transcription factor names.

    Example 5 shows another situation. Different binding sites contain the same set of transcription factor names. For example, the binding sites R00303, R00304, R00305, R00306 have the same transcription factor names, i.e., Oct-1C Oct-1B Oct-4 Oct-1A.

    Example 5:

    R00001 ISGF-3

    R00002 ICSBP

    R00003 ISGF-3

    R00303 Oct-1C Oct-1B Oct-4 Oct-1A

    R00304 Oct-4 Oct-1A Oct-1B Oct-1C

    R00305 Oct-4 Oct-1A Oct-1B Oct-1C

    R00306 Oct-1B Oct-1C Oct-4 Oct-1A

    (c) Significance level

    The significant measurement with correlated and independent is defined herein as follows (Liu et al. 1999):

    Definition 1 (correlated): Where ‘s’ is a minimum support, ‘t’ is a significant level, A is a set of items and B is an item. Assume that the rule A=>B is correlated if it satisfies the following two conditions:

    • The support exceeds ‘s’.
    • The significant level exceeds ‘t’.

    Definition 2 (independent): Let ‘s’ be a minimum support, ‘t’ be a significance level, A be a set of items, and B be an item. Assume that the rule A=>B is independent if it satisfies the following two conditions.

    • The support exceeds ‘s’.
    • The significant level does not exceed ‘t’.

    The proposed approach

    Figure 1 illustrates the proposed approach. The first component is a preprocessing and a mapping between the transcription factor binding sites in TRANSFAC and the repeat sequences in the Repeat Database. Next, apriori and aprioriTid (Agrawal and Srikant, 1994) are applied to mine the association rules by combining the transcription factor binding sites in repeat sequences. Then, Chi-square is used to select certain rules. Finally, the redundant rules are pruned and structured.

    Summarized steps of the proposed approach:

    • Determine the number of item sets of the transcription factor binding sites in TRANSFAC.
    • For categorical binding sites, identification of a binding site is mapped to a set of transcription factor names.
    • Find the combinations of transcription factors in repeat sequences.
    • Apply the data mining approach to generate association rules.
    • Determine the interesting rules using Chi-square significance measure.
    • Prune redundant rules (Toivonen et al. 1995; Klemettinen et al. 1994).
    • Classify rules to cover and non-cover sets.
    • Partially classify repeat sequences using association rules mined.

    Results

    (a) Preprocessing and mapping between the data in the Repeat Database and in TRANSFAC

    The transcription factor binding sites in TRANSFAC above are first prepared due to the complicated situations described previously. This accounts for why the proposed approach requires preprocessing. Combinations of the transcription factor binding sites in the repeat sequences in our Repeat Database are then found. This work focuses mainly on the repeat sequences of the genomes C. elegans, human chromosome 22, yeast and several bacteria. Table 1 summarizes the results of the preprocessing. The abbreviations of the organisms in Table 1 are given in Appendix A.

    In Table 1, Each row refers to a genome or bacteria that is experimented with. The column ‘Average Factors’ represents the average transcription factor binding sites found in a repeat sequence. As mentioned above, we find the combinations of transcription factors in repeat sequences. The ‘Average Factors’ is defined to be the sum of the transcription factor binding sites for all repetitive sequences over the sum of the repetitive sequences. The last column ‘Ratio’ denotes the number of repetitive sequences containing more than one binding site over the total repetitive sequences in a genome. For example, the ratio 77.17% in C. elegans indicates 77.17% repeat sequences, i.e., 351,084 ones that will be used to mine associations.

    Exactly how to mine associations from the combinations of the transcription factor binding sites found above is discussed as follows. Consider a large database with transactions, where each transaction consists of a set of items. An association rule is an expression as A=>B, where A and B are the sets of items. The mining of an association rule is that a transaction in the database that contains A also tends to contain B. For example, 90% of the people who purchase beer also purchase diapers. So, 90% is called the confidence of the rule. The support of the rule A=>B given herein is the percentage of transactions that contain both A and B.

    The formal statement of the problem is described below. Let I={i1, i2, ... ,im} be a set of sites, called ‘item set’. Let D be a set of repeat sequences, where each repeat sequence S corresponding to a transaction contains a set of items such that S Í I. Figure 2 presents an example of a mapping between the repeat sequences and the transcription factor binding sites, where TID is a number of a repetitive sequences and RID is a set of IDs of binding sites. In the proposed approach, only consider repetitive sequences that contain more than one binding site.

    Example 6 is used to illustrate the mapping between a repeat sequence and the transcription factor binding sites.

    Example 6:

    >IDI0000000013

    AGTTATTCAAACACGTATAA

    TTCAAA R02749

    TATAA R00046 R00705 R00706 R03054

    TATA R00671 R00689 R00938 R01128 R01129 R01191 R04293

    In Example 6, ‘AGTTATTCAAACACGTATAA’ is a repeat sequence in the Repeat Database. We map it to a transaction whose id is IDI0000000013. The repeat sequence has three consensus patterns, i.e., ‘TTCAAA’, ‘TATAA’ and ‘TATA’. The consensus pattern ‘TTCAAA’ has an accession number R02749. However, the other two consensus patterns ‘TATAA’ and ‘TATA’ have many accession numbers. The situation must be preprocessed. Example 6 is another case. Similarly, IDI0000000737 is a transaction ID mapped from a repeat sequence ‘TTGAAATTTTGAAATTTAAA’. The repeat sequence has four consensus patterns.

    Example 7:

    >IDI0000000737

    TTGAAATTTTGAAATTTAAA

    TTGAA R04347 R04360 R04369

    ATTTNNNNATTT R02171

    TKNNGNAAK R02216

    TTTAAA R01598

    Example 7 presents the results after the mapping. Each list shows the factor name, consensus sequences and the identification of the binding site.

    Example 8:

    >IDI0000000737

    TTGAAATTTTGAAATTTAAA

    DE unknown=TTTAAA>R01598

    DE unknown=TTGAA>R04347\R04360\R04369

    DE HiNF-A=ATTTNNNNATTT>R02171

    DE C/EBPbeta\C/EBPdelta=TKNNGNAAK>R02216

    In Example 8, the repeat sequence (transaction) ‘TTGAAATTTTGAAATTTAAA’ contains four consensus patterns (items), i.e., TTTAAA, TTGAA, ATTTNNNNATTT, and TKNNGNAAK.

    Example 8 contains many interesting observations:

    • One site and no factor: they resemble R01598.
    • One site and one factor: they resemble R02171 with the factor HiNF-A.
    • One site with many accession numbers: it is like R04347, R04360, and R04369 with the same consensus sequence TTGAA.
    • One site and many factors: they resemble R02216 with factors ‘C/EBPbeta’ and ‘C/EBPdelta’. Different factors or bindings are separated by the symbol ‘\’.

    A transaction and the items it contains are represented in Example 9.

    Example 9:

    >IDI0000000737 R04347\R04360\R04369 HiNF-A C/EBPdelta\C/EBPbeta R01598

    In Example 9, the transaction IDI0000000737 contains four items that are denoted R04347\R04360\R04369, HiNF-A, C/EBPdelta\C/EBPbeta, and R01598, respectively.

    Assume that a repeat sequence ‘S’ contains ‘A’, a set of items of ‘I’, if A Í S. An association rule is an implicate of the form A=>B, where A Ì I, B Ì I, and AB=0.

    The rule A=>B holds in the repetitive sequence set ‘D’ with confidence conf if c% of transactions in ‘D’ contains ‘A’ and also ‘B’. The rule A=>B has support sup in the repetitive sequence set ‘D’ if s% of repeat sequences in ‘D’ contained A È B. In our experiments, the minimum support is set to 10%. The association rules are generated if the rule has a higher support and confidence than user specified. Apriori and aprioriTid (Agrawal and Srikant, 1994) are then applied to mine association rules.

    An enormous number of association rules are generated. The enormous number of association rules makes extremely difficult for human users to identify those interesting and useful ones. Therefore, Chi-square is applied to prune the discovered association rules to remove those insignificant association rules.

    (b) Pruning and structuring association results

    Herein, rules are generated using Chi-square significance test. The discovered rules are still large and unreadable after applying the process of Chi-square significance test. The redundant rules are pruned and the rules are structured to cover set and non-cover set.

    Figure 3 presents the conceptual flow of the pruning and structuring, summarized as follows:

    • Discovered rules may be not interesting for several reasons (Klemettinen et al. 1994). Rules can correspond either to prior biology knowledge or expectations.
    • Rules can refer to uninteresting sites or sites combinations such as transcription factor binding sites on protein to C. elegans.
    • Rules can be redundant.

    Three operations are used to process a large collection of rules.

      • Pruning: the operation may reduce the insignificant rules.

      • Structuring: the operation divides the rules into cover and non-cover sets.

      • Sorting: rank the rules by the use of confidence.

    Chi-square significance is not hindered by simple redundancy and strict redundancy. For example, the rule AB=>C is redundant to A=>BC. The rule AB=>C is tested, while A=>BC is not. The strict rule A=>B is redundant of A=>BC, and A=>B is tested. The redundancy in our rules is similar to A=>B and AC=>B. The rule A=>B is kept and the rule AC=>B is pruned because AC=>B is covered by the rule A=>B. For example, consider the rule MAMAG=>AAAG. Obviously, the binding site on the right-hand side is covered by that on the left-hand side because ‘M’ may be ‘A’ or ‘C’.

    Next, the rule is put into the cover set. Tables 2 and 3 present the association rules mined after applying Chi-square from Table 1. In Table 3, the significance level is set to 95%. In Table 2, the ‘MiniSup’ column refers to the minimum support used. The ‘Cover Rules’ and ‘Non Cover Rules’ denote the number of rules in the cover and non-cover sets, respectively, after they are mined, pruned, and structured. The ‘Total Rules’ denotes the sum the rules in the cover and non-cover sets. The ‘Ratio of Partial Classification’ represents the ratio of the repeat sequences and are classified by the ‘Total Rules’. For example, 47% repeat sequences of C. elegans are partially classified by the ten rules mined. Conversely, the situation also indicates that other 53% repeat sequences can’t be classified by the rules. Therefore, the ratio can also be used to measure whether the rules mined are representative. Similarly, Table 3 summarizes the data for archaea, bacteria and virus. The minimum support is set to 10% and those with the ‘*’ symbol in the precedence of the genome name is set to 20%.

    Figures 4 and 5 present partial classification rules for the human chromosome 22 and C. elegans genome, respectively. These rules can be used to find genes in complete genomes and cluster repeat sequences once they are verified. Biologists at National Ynag-Ming University in Taiwan are verifying these results.

    Discussion

    To verify the association rules found in repetitive sequences also appear in their genomes. We experiment on several archaea and bacteria. This is because their sizes are shorter. The experimental results are shown in Table 4. The column ‘Occurrences in Repeats’ denotes how many copies of a repetitive sequence are found in a genome. The column ‘Occurrences in Genome’ represents how many associations are found in a genome. The ‘Window’ column indicates the offset of the transcription factors binding site, e.g., the difference of the transcription factors binding site. For example, two of the rules YY1=R00231\R00232\R00335\R00668\R00669\R00761\R01081\R01345\ R01445\R01446\R02955\R02957 and YY1=>R00388 are found in a repetitive sequence of the organism Pyrococcus abyssi. For more details of the two rules, the reader may refer to Appendix B. The repetitive copies of the repetitive sequence are 39. We then go back to its genome scale and find the association YY1= R00388 also exist 48 different positions when the window is set 5. The result seems to be reasonable. The larger of the window is, the more associations are found. However, a huge amount of associations are found in a genome scale such as Thermotoga maritima even the occurrences of the repetitive sequence is not large. We can’t conclude from these observations. We will further study the phenomenon in the future.

    Concluding Remarks

    This study finds combinations of transcription factor binding sites in the repeat sequences of the Repeat Database. Each repeat sequence is mapped to a transaction, and combinations of transcription factor binding sites are mapped to items of a transaction. The transcription factor binding sites in TRANSFAC are first preprocessed due to their complex characteristics. The apriori and aprioriTid (Agrawal and Srikant, 1994) approaches are then applied to mine the associations from the combinations of transcription factor binding sites in repeat sequences. An enormous number of association rules are generated. The enormous number of association rules makes it extremely difficult for a human user to identify those interesting and useful ones. In addition, Chi-square significance level is used to remove those insignificant rules. Finally, the redundant rules are pruned and then the remaining rules are classified into cover and non-cover sets. Moreover, experiments are conducted on many genomes including C. elegans, human chromosome 22, yeast and bacteria. Biologists at National Yang-Ming University in Taiwan have verified and found the rules mined to be interesting. The rules mined can also be used to find useful genes in complete genomes as well as partially cluster the repeat sequences in Repeat Database. Biologists are experimenting and verifying now.

    Acknowledgements

    The authors would like to thank the National Science Council of the Republic of China for financially support this research under Contract No. NSC 89-2213-E-008-061. In addition, We would like to thank Professors Cheng-Yen Kao at National Taiwan University and Ueng-Cheng Yang and Dr. Yu-Chung Chang at Yang-Ming University for their helpful suggestions.

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