Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set
© Wren et al; licensee BioMed Central Ltd. 2006
Published: 15 July 2005
There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands.
There are many software packages written for genomic analysis, most of which provide a relatively standard battery of functions: Visualization of sequence features (e.g. repetitive elements, enzyme cut sites, exons), alignment of sequences (e.g. BLAST, BLAT, FASTA), and algorithmic identification of patterns such as genes, promoters and splice sites . The journal Nucleic Acids Research publishes an annual issue for databases and web servers, and offers a good overview of what is publicly available. The underlying assumption during the development of most genomic analysis tools is that a user knows – at least to a degree – what he/she is looking for and this may not always be the case. Frequently users are simply looking for what they might describe as "something out of the ordinary". Even packages such as Helix Bioinformatics' GenoStar http://www-helix.inrialpes.fr/index.php that engage in "exploratory genomics", are still driven by user input. Most sequence analysis programs are not designed to accept sets of data as input for analysis (besides phylogenetic packages), such as a series of genes or ontology categories so that common features can be identified. Given the enormous growth in the amount of publicly available data, what is needed is a software package that can systematically examine all available information for statistically significant correlations and report them back to the user.
1.1 Data Mining
Data mining is a relatively recent development that has arisen principally as a response to the growing size of datasets, defined loosely as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". In essence, data mining implies that there are answers out there to questions that we have not thought to ask yet – data drives the generation of hypotheses rather than vice versa. As opposed to traditional hypothesis testing designed to verify a priori hypotheses about relations between variables, exploratory data analysis (EDA) is used to systematically explore the possibility of relations between variables when there are no (or incomplete) a priori expectations as to the nature of those relations. The usage of data-mining techniques in biology and medicine has grown rapidly since 1997, but has always been limited in scope by the number of different features that can be analyzed.
Sequence databases are extremely difficult to "mine" – not only are biological sequences associated with many different data types, but sequence data itself is difficult to fit neatly into any data type. Thus, data mining methods are limited by the amount of information available for analysis within the database they are used upon. The more data that can be integrated into one source, the more potential data mining methodologies have for discovery. Integrating various sources of genomic data into a common format that preserves position-specific information makes it feasible to standardize analysis approaches.
As the amount and complexity of data grows, there is an increasing need to be able to ask more open-ended questions, which has been referred to as "data-driven" research as opposed to "hypothesis driven" . It's also accurate to say that the hypothesis driving the research is that informative trends exist within the data and can be found. The goal of this report is to create a "genomic observation system" – one that is able to intelligently employ a set of defined algorithms on a set of genomic fields representing features of interest and then inform the user of what it has found. That is, to note features and/or associations that stand out as statistically significant. However, if the system is truly to be of value in discovering novel associations, it must be able not just to conduct analyses, but to iteratively build upon them. For example, it is interesting to note that some genes have CpG islands upstream and some do not , but more interesting is to understand what types of genes do or do not have CpG islands upstream and what these genes have in common that would help researchers better understand the significance of CpG islands. CpG islands are stretches of DNA rich in cytosine-guanine pairs, which can be methylated, and can regulate expression of genes. It generally is assumed that CpG islands are involved in modulating "house keeping" genes, but recent reports indicate that there are exceptions to this "rule". To address this example requires at least a two-step process – first the division must be identified, and then genes in each category must be associated or analyzed in some other terms such as function, location, or gene ontology. Because it is not obvious a priori which analysis would be best to conduct on what feature to offer insight into any given problem, the value of an exploratory (data mining) approach becomes more apparent.
1.2 A data integration format for association rule discovery
There is a strong need in bioinformatics for standardization of analyses and integration of data. At the most basic level is link federation, which is used by web-enabled databases that provide links to other web-enabled databases. Link federation, however, fails to address the issues involved with data integration since it is vulnerable to semantic differences, can fail due to link updates, and shifts the work of integration to the user . Another approach has been view integration, where a wrapper/mediator architecture middleware component is placed between the databases and the user, presenting the data as part of one large system. Examples of this are Kleisli , GECKO  and AnaBench . These, however, have somewhat failed to take hold due to the need to write a driver for each database to be accessed. View integration also fails to fully address the integration issue as it only brings the data to the user while leaving it up to the user to describe how the data fits together. Additionally, there is the option of data warehousing (e.g., Shah et al ), whereby a unified schema is made, data is obtained from heterogeneous sources and then transformed to match that schema. However, this is difficult to maintain, as updates need to be performed, making it vulnerable to frequently changing database schema . It also leaves the relationships between the data up to the user. Finally, there have been attempts to present data in a single type. One such format used has been eXtended Markup Language (XML), using wrappers to obtain the data and LSX to convert it to XML . They have even added support for automated updates to the data . Once the user obtains the data locally, he is left with only one format to read and query. The problem with XML is the same with the other integration options in that it leaves the task of fully integrating data to the user. Each of these means of integrating data has its strengths and weaknesses, but none are well suited for the type of exploratory and adaptive data mining approach we are proposing here.
The idea to develop a modular, standardized exploratory genomic analysis system came from the realization that many questions about genomic properties and correlations involve relatively straightforward correlation analyses, yet software that integrates different data sources is typically written to answer one or a few related questions, usually as a result of individual investigator interest (e.g., see references [21–23]). Data exists in many different types, in many different locations and is produced by many different software packages that run on different platforms. Currently, the number of questions that can be asked is much larger than the number that can be answered, and the more genes, genomes and features that are discovered, the more questions that can be asked. If some method is not developed to begin integrating data and methodologies of examination, then the number of possible questions that can be asked will always exceed our ability to answer them. To bridge this gap, we have developed a prototype for an automated (i.e. minimally dependant upon user guidance), extensible, modular and systematic method of searching for correlations within genomic data.
The approach described herein begins by encoding sequence-based data in the columns of a matrix format that permits position-specific information to be represented, including accommodations made for non-integer data types (e.g. the presence of a gene can be denoted with binary values, but the name of the gene must be linked to an index value). Encoding raw genomic sequence data in the columns of a position-specific matrix permits annotation associated with specific genomic regions to be represented within rows. Annotation includes many data types such as genes, intron-exon boundaries, chemically relevant features such as guanine/cytosine percent (G+C%) content, and gene ontology classifications to name a few. Each row is linked to a row index (RI) that defines the type of values within the row. To enable an exploratory approach, a sequence matrix (SM) classification tree is defined for each data type to be encoded in the rows. This classification tree both limits and guides the types of analyses that are appropriate to perform between rows based upon their type. Finally, to enable the system to build upon previous observations, new rows can be added to the SM when statistically significant correlations are found.
2. Construction of a Sequence Matrix Format
Sequence related data can be expressed as a matrix, where rows represent features and columns represent positions in the sequence. The Sequence Matrix format consists of several files. First, each row is expressed as a file. Next, there is an index file that specifies how each file is stored, what it stores, and comparison related information. Programs were written in Visual Basic 6 (SP6) and run on a Pentium 4, 3 GHz machine running Windows 2000 with 1 GB RDRAM. Genomic data was obtained from Genbank ftp://ftp.ncbi.nih.gov/genomes/H_sapiens/ and the August 2004 update was used.
Especially of interest is to link genomic features to Gene Ontologies (GO) , providing an excellent opportunity to search for functional genomic correlations. Each GO field is encoded with a numeric record ID, permitting it to be easily incorporated into the SM. GO codes have been downloaded and incorporated into a database, each GO category indexed by its ID number.
2.1 Developing a classification tree for genomic features
A classification tree of each row type enables an explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them. Here, the entities being studied are genomic features and the classification tree being developed is a means of defining how these entities relate to one another and the comparisons that can be conducted between them.
Despite the heterogeneity of data types (e.g. window-based, defined position start and end sites, variable composition), they can be roughly divided into two types: Entity and property (shown below). Entities are defined as portions of the genomic sequence with defined start and end positions that serve a specific function and are either present or not. Introns, for example, are regions that are transcribed into RNA but spliced out in mature mRNA transcripts. Properties, on the other hand, are a range of quantitative or qualitative values that can be taken by a region of sequence. C+G% content, for example, is calculated by examining a window of sequence and varies in its value as the window moves along the sequence. Properties typically have threshold values that delineate genomic features (e.g. C+G% content defines genetic "isochores").
1. Entity – A sequence region with a defined beginning and end. Column values can be of type:
a. Binary – A marker that indicates the existence of an entity, spanning all columns from its beginning to end
b. Index – A binary-like marker that indicates the existence of an entity, spanning all columns from its beginning to end. The value contained within this field points to an external database unique ID, such as the Genbank Identifier or a Gene Ontology ID number
c. Range – Possessing a continuous set of possible values
d. Category – Possessing a discrete set of values, not necessarily sequential
e. Decimal – Used for entities that are conditionally structured or ordered, such as alternative splice variants
2. Property – A genomic feature defined in relation to an entity or region. Column values can be of type:
a. Binary – Yes or no, on or off (e.g. is a nucleotide within a CpG island?)
b. Weighted – Frequency of occurrence in relation to another field (e.g. positional variation such as single nucleotide polymorphisms, or possibly exon splice variants)
c. Range – Possessing a continuous set of possible values (e.g. the number of protein-protein interactions a gene has)
d. Category – Possessing a discrete set of values, not necessarily sequential (e.g. Gene Ontology IDs)
The goal in development is to keep the classification tree as simple as possible, and as broad as possible on the top node. If a top-level classification tree has only one member, it is probably too specific and not appropriate for inclusion (low-level subcategories can have only one member). Each entry must both make sense and correspond to a specific set or type of analysis to be conducted. Subcategories, similarly, should not lead to entirely different analyses but offer clarification, refinement or exclusion of analyses defined by their parent category.
An initial list of genomic "features" that will be the subjects of analysis by the system.
Enzyme cut site
Entity, set, categorical
Entity, set, categorical
Entity, set, categorical
Entity, set, categorical
Entity, set, categorical
Entity, set, range
Entity, set, range
Entity, set, range
Property (sequence), window-based, binary
Property (sequence), window-based, range
# of interactions
Property (gene), range
Property (gene), range
Property (gene), range, conditional
Property (gene), categorical, hierarchical
Property (CpG Island), categorical (no/maternal/paternal)
Property (VNTR), range
2.2 Correlative analyses
Induction, defined as attempting to establish a general principle using specific observations, can be used to identify correlations and in data mining is typically conducted via inductive logic programming(ILP) . Induction is limited by the extent to which an induced principle holds true. For example, if all alternatively spliced exons are located within 5 kilobases of an upstream CpG island, this can be induced as a general principle by the system. Note that induction is used to find correlative principles and not strictly defined rules or "truths", because any future alternatively spliced exon in this example that does not have an upstream CpG island would negate it as a strictly defined rule. Induced properties are declared when they pass a thresholded confidence level, and may change upon the addition of new data to the system. The system will automatically cycle through a range of p-values (e.g. p < 0.01 to p < 0.10 stepping in increments of 0.01) to see how performance is affected. That is, to see whether inductions are highly sensitive to small changes in p-values or not. We anticipate they should not be, but if they are then it suggests the possibility that correlations are being formed somewhat spuriously and significance metrics should be reexamined.
Other induction algorithms such as CN2 , ID3 which eventually evolved into C4.5, and the AQ series  to name a few, are not suitable for the proposed study because they are used for different purposes, and require a training set of correct and incorrect inductions to begin learning the underlying patterns before inductive classification can begin. Induction will proceed by correlative analyses since it is not known a priori what will be found. An initial, non-exhaustive list of analyses is shown below. Here, a classification tree for analysis defines which analyses are deemed "appropriate" for each data type. Because classification trees are hierarchical, they can be expanded based upon sub-categories to exclude inappropriate analyses. Also, because branches can share parent nodes, it is possible that analyses can overlap between certain sub-categories as appropriate.
1. Proximity – Does the distance between X & Y deviate from random? (e.g. closer or farther?)
2. Co-incidence – Are X & Y co-incident with each other more or less frequently than expected?
3. Composition – Is there a bias in X for either:
a. Its frequency/presence within a distribution
b. Its relative value within a range of values
4. Distribution – Does the observed distribution of values deviate from the expected? (e.g. Exon-exon distances). The absolute value of the differences between the areas under the curve (AUC) can answer this question.
5. Sequence similarity – e.g. BLAST score (highly CPU intensive when conducting multiple comparisons)
6. Gradient – Given a spectrum of values, does the set being analyzed fall within an extreme?
The addition of new rows to the SM is a critical part of exploratory discovery, yet arguably the most difficult part for several reasons. First, it's necessary to build upon previous observations. Consider an example in which one set of genes has CpG islands upstream in their promoter and the rest do not, CpG islands being necessary for shutting off gene transcription via promoter methylation. Assume, hypothetically, that CpG islands were known as a feature of interest but their function was not known. In theory, the SM data-mining approach should be able to identify that CpG islands are located proximal to the 5' end of genes to a statistically significant degree. This is an observation, yet it is not clear what to make of it without knowing the role of CpG islands. With one additional piece of information, researchers might be able to deduce what this correlation implies and formulate a testable hypothesis for the role of CpG islands. A correlation (negative in this case) might then be identified between genes with CpG islands and an ontological category of basic metabolic processes (i.e. genes that must be expressed in every cell and therefore should not be shut off). Thus, it is necessary to be able to record statistically significant events in some manner, else the algorithm will be nothing more than an exhaustive battery of analyses run against an exhaustive set of fields. The addition of a new row marks the genomic locations of the identified correlation and the type of new row added would depend upon the type of correlation identified. In the CpG island example given above, the type of row added would be defined as a set of entities with defined start and end sites (i.e. the genes with CpG islands upstream).
Rather than ask specific questions about genomic correlations, if we can define when an event is statistically noteworthy and select genomic features that might be of potential interest (i.e. we are willing to examine in a report), then we can enable a software program to look for any and all statistically noteworthy associations and report them to us. The system should be able to identify known correlations and, optimally, the system will identify many that have not been documented within the scientific literature. A major pitfall is that the system may overfit the data – that is, it may find strong rules that hold within a given dataset but do not generalize to new datasets. One possible remedy is to warn the user against "statistical" interpretation of significance scores, and to point out significance scores are only used for ranking discovered associations. This is an approach followed in the association rule discovery literature. The traditional machine learning remedy against overfitting is to split data into a training and test set, find correlations on the training set and, for the "best" rules, report significance values computed on the test set. There are also techniques for cross-validation that would be useful for on-the-fly evaluation such as k-fold cross-validation and leave-one-out cross-validation. This type of training is different than the pattern-learning training normally associated with inductive classification – here the training and test sets would be used to define sensitivity thresholds.
2.3 Problems of significance
So far we have defined a format and means of automated exploration of row-row correlations, and even a means whereby we hope to circumvent the problems that may be encountered with different data types and distributions, but perhaps the most daunting challenge will be how to establish the significance of a correlation. Using genomic correlations already known to be true, we can first identify minimal significance cutoffs that would miss valid observations were they any more stringent. These cutoffs may well vary by data types compared, which may emerge as the system is developed beyond the current version. Optimal significance cutoffs are more difficult to determine, but the iterative observation procedure outlined in the previous section may enable us to empirically approximate this cutoff. Ultimately, any and all correlations identified by the system can be simplified to the questions "So what?" or "What does it mean?", so we would first expect that the system would not continue to add new observation rows indefinitely. Intuitively, while we expect there may be much that we do not know about the relationship between genomics and biology, there is nonetheless a finite amount. As the system is further developed, we expect to test the growth of new rows, both depth and breadth, and hope this will permit us to adjust significance cutoffs so that infinite matrix expansion is prevented. As biologists examine SM correlations, we would expect that their feedback on the utility/meaningfulness of correlations would enable better adjustment of the system as well in terms of significance cutoffs. That is, if correlations identified by the system have no apparent meaning to the biologist, then perhaps they may be statistically valid, but without biological meaning. For example, two features may coincide with one another at a statistically significant frequency, yet there may be no corresponding implication – it may just be a coincidence. Some correlations may exist in what at first appears to be a vacuum, at least given our current level of understanding, and not make sense until more information is known about the evolution of genomes. Which leads to the final aspect of why we believe the SM approach will be of great utility – examining Figure 8, we see that when a significant correlation is identified it is simply created as a new row. In this example, certain genes with CpG islands located upstream of the start site disproportionately fall within a certain classification tree category. At first, a biologist might concede that this observation seems interesting, but in terms of relevance it falls within the "so what?" category. Later in the iterative analysis, the system might then identify that the positions in this row are strongly correlated with whether or not the first exon is spliced out of a gene. Tracing this row back to its ancestral correlation, it would then be reasonable to hypothesize about the biological relevance of CpG islands located upstream of the start site – perhaps they determine whether or not the first exon is spliced out and offer a possible mechanism (demethylation) by which such an action is possible.
3. Prototype testing – finding known correlations de novo
Comparing the number of features observed together within the same genomic space against Monte Carlo stochastic placement simulations. The first 10 MB from chromosomes 3, 5, 13 and 18 were used for analysis and the averages shown above.
Avg. # of coincident features (c)
Avg. # of coincident features in MC simulation
Average MC / c
CpGs vs. Repeats
1.26 ± 0.35
CpGs vs. Genes
1.24 ± 0.18
CpGs vs. Exons
3.08 ± 1.38
Repeats vs. Genes
1.03 ± 0.05
Repeats vs. Exons
0.38 ± 0.03
Genes vs. Exons
3.02 ± 0.60
Table 2 reveals a number of stronger and weaker correlations and permits an analysis of the range of values to be expected. The control analysis, that genes and exons should always coincide, was compared with random arrangements of genes and exons. The difference in the average number of coincident features observed compared to that observed during MC simulations was 3.02 times higher on average (chromosomes vary regionally in their gene richness). Two other strong correlations stand out, the first is that repetitive elements are relatively uncommon within exons, an observation verified in other studies. The overrepresentation of CpGs in exons but not in genes makes sense given that CpG islands are generally found upstream of genes, including the 5' untranslated region, but genes are generally much larger than their exons (e.g. the first 12 genes on chromosome 6 had over 2.6 MB of sequence compared to only about 46 KB for their corresponding exons). Transcription through C-G base pairings requires separation of 3 hydrogen bonds compared to 2 for A-T. Energy conservation is seen during transcription in that frequently transcribed genes contain shorter exons, thus it also makes sense that exons would be more rich than introns in CpGs. Short repetitive elements consisting of 1–2 base pair repeats were far more common in introns than genes, so the lack of correlation here also makes sense.
Probably the greatest advantage of SM is that for data analyses of spatially close positions, SM is embarrassingly parallelizable. Data access time can be reduced greatly, by reading windows in parallel, but also windowed comparisons can easily be done in parallel. With Bioinformatic Sequence Markup Language (BSML) data, the data must be pre-processed to know how to divide the data, and the number of chunks it can be broken into while still retaining XML properties limits parallelizing.
Proposed here is a system to enable automated association rule discovery on genomic sequence data. Since genomic data is intrinsically linked to raw sequence data, we have proposed a format by which position-dependant information can be both stored and represented in the form of a sequence matrix where rows represent annotated data linked to the sequence position represented by the column. The explicit linking of row values makes the SM embarrassingly parallelizable, which means it may take advantage of the scaling power that computing clusters have to offer. The sequence matrix enables correlative data mining algorithms to be run, but by itself is static. We have thus developed a means by which data mining can be conducted through the creation of a classification tree for row data types that define how and whether or not one row data type can be analyzed in the context of another row data type. The classification tree enables a means of iteratively examining row-row relations through varying data mining methods. Furthermore, because the classification tree also defines the data type of the correlation between rows, this enables iterative analysis of genomic features by which significant correlations, when observed, can be expanded into new rows. These new rows can then be re-analyzed in the context of other known features, enabling a machine learning system that is able to build upon previous observations. One downside to the SM format is that overlapping features (e.g. genes that share the same coding region on a chromosome) are difficult to model in the current format, whereas markup languages have no problem with this.
A de novo genome-wide analysis that searches for correlations between all known fields and expands its matrix when statistically significant matches are found.
User-defined set-based correlation query that enables a user to select which features they want analyzed within the SM (e.g. a set of genes, chromosomes, or promoter regions) and run the same analysis
User-defined incorporation of a new matrix row, requiring them to enter their own matrix values and specify a classification tree structure (or select from existing options). From that point, all currently defined analyses can be performed and reported in terms of existing rows.
Unresolved problems include a means of significance testing for row-row correlations. Herein, we've proposed to use established correlations and Monte Carlo simulations to identify a lower boundary for p-values, but it's not clear yet what the distribution will be in these p-values and whether or not there will be any overlap between the p-values from correlations considered spurious and p-values from known correlations. For example, a weak yet well-established correlation could have a p-value less than 0.2, whereas another correlation that has no apparent biological meaning could have a p-value less than 0.05. It's not clear yet whether this should simply be accepted as a caveat and judgment reserved on the "meaningless" correlation until a later date, or whether p-value boundary refinements will be necessary.
For future directions, there are several functional genomics questions that we are interested in, and hope the system can eventually be used to answer. First, we know that first exons are farther away from second exons and second from third, etc. Do genes that deviate from this trend have anything in common? GO correlations may prove helpful in answering this question. Second, transcriptional regulation by methylated promoters is being increasingly recognized as a possible origin for complex, late-onset diseases. Not all genes have CpG islands upstream, distance from start site to CpG island varies, and so does the number of methylatable sites. Do the extreme values for any of these variables correlate disproportionately with any late-onset diseases? Genes annotated in OMIM titles with the words "late onset", "adult onset" and "variable onset" will be used as an associative data type. As we think to ask new questions that do not fit into any of the defined categories of analysis discussed in this paper (i.e. that would be either a row or analysis), the goal of the SM is to provide a framework to make specific questions more generalizable.
This work was funded in part by NSF-EPSCoR grant # EPS-0132534 (JDW).
- Venkatesh TV, Harlow HB: Integromics: challenges in data integration. Genome Biol 2002, 3(8):REPORTS4027. 10.1186/gb-2002-3-8-reports4027PubMed CentralView ArticlePubMedGoogle Scholar
- Searls DB: Data integration – connecting the dots. Nat Biotechnol 2003, 21(8):844–845. 10.1038/nbt0803-844View ArticlePubMedGoogle Scholar
- Searls DB: Data integration: challenges for drug discovery. Nat Rev Drug Discov 2005, 4(1):45–58. 10.1038/nrd1608View ArticlePubMedGoogle Scholar
- Davies K: The 2005 Database Explosion. Bio-IT World (online) 2005.Google Scholar
- Safran M, Solomon I, Shmueli O, Lapidot M, Shen-Orr S, Adato A, Ben-Dor U, Esterman N, Rosen N, Peter I, et al.: GeneCards 2002: towards a complete, object-oriented, human gene compendium. Bioinformatics 2002, 18(11):1542–1543. 10.1093/bioinformatics/18.11.1542View ArticlePubMedGoogle Scholar
- Womble DD: GCG: The Wisconsin Package of sequence analysis programs. Methods Mol Biol 2000, 132: 3–22.PubMedGoogle Scholar
- Frawley W, Piatetsky-Shapiro G, Matheus C: Knowledge Discovery in Databases: An Overview. AI Magazine 1992, 213–228.Google Scholar
- Krueger J: Null hypothesis significance testing. On the survival of a flawed method. Am Psychol 2001, 56(1):16–26. 10.1037/0003-066X.56.1.16View ArticlePubMedGoogle Scholar
- Wren JD, Mittelman DA, Garner HR: SIGNAL-Sequence Information and GeNomic AnaLysis. Comput Methods Programs Biomed 2002, 68(2):177–181. 10.1016/S0169-2607(01)00187-0View ArticlePubMedGoogle Scholar
- Nakai K, Vert JP: Genome informatics for data-driven biology. Genome Biol 2002, 3(4):REPORTS4010. 10.1186/gb-2002-3-4-reports4010PubMed CentralView ArticlePubMedGoogle Scholar
- Antequera F, Bird A: Number of CpG islands and genes in human and mouse. Proc Natl Acad Sci U S A 1993, 90(24):11995–11999. 10.1073/pnas.90.24.11995PubMed CentralView ArticlePubMedGoogle Scholar
- Gardiner-Garden M, Frommer M: CpG islands in vertebrate genomes. J Mol Biol 1987, 196(2):261–282. 10.1016/0022-2836(87)90689-9View ArticlePubMedGoogle Scholar
- Costello JF, Plass C: Methylation matters. J Med Genet 2001, 38(5):285–303. 10.1136/jmg.38.5.285PubMed CentralView ArticlePubMedGoogle Scholar
- Stein L: Integrating Biological Databases. Genetics 2003, 4(May 2003):337–345.PubMedGoogle Scholar
- Bajic V, Li J, Ng S, Wong L: From informatics to bioinformatics. Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003: February 01, 2003 2003; Adelaide, Australia 2003, 3–12.Google Scholar
- Theilhaber J, Ulyanov A, Malanthara A, Cole J, Xu D, Nahf R, Heuer M, Brockel C, Bushnell S: GECKO: a complete large-scale gene expression analysis platform. BMC Bioinformatics 2004, 5(1):195. 10.1186/1471-2105-5-195PubMed CentralView ArticlePubMedGoogle Scholar
- Badidi E, De Sousa C, Lang BF, Burger G: AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis. BMC Bioinformatics 2003, 4(1):63. 10.1186/1471-2105-4-63PubMed CentralView ArticlePubMedGoogle Scholar
- Shah SP, Huang Y, Xu T, Yuen MM, Ling J, Ouellette BF: Atlas – a data warehouse for integrative bioinformatics. BMC Bioinformatics 2005, 6(1):34. 10.1186/1471-2105-6-34PubMed CentralView ArticlePubMedGoogle Scholar
- Shui WM, Wong RK, Graham SC, Lee LK, Church WB: A new approach to protein structure and function analysis using semi-structured databases. Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003: February 01, 2003 2003; Adelaide, Australia 2003, 61–69.Google Scholar
- Shui WM, Wong RK: Application of XML Schema and Active Rules System in Management and Integration of Heterogeneous Biological Data. Proceedings of IEEE International Symposium on BioInformatics and BioEngineering (BIBE): March 10–12, 2003 2003; Bethesda, MD, USA 2003, 367–374.Google Scholar
- Zhang LV, Wong SL, King OD, Roth FP: Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 2004, 5(1):38. 10.1186/1471-2105-5-38PubMed CentralView ArticlePubMedGoogle Scholar
- Foissac S, Schiex T: Integrating alternative splicing detection into gene prediction. BMC Bioinformatics 2005, 6(1):25. 10.1186/1471-2105-6-25PubMed CentralView ArticlePubMedGoogle Scholar
- Liu Y, Zhao H: A computational approach for ordering signal transduction pathway components from genomics and proteomics Data. BMC Bioinformatics 2004, 5(1):158. 10.1186/1471-2105-5-158PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25(1):25–29. 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
- Claverie JM, Audic S: The statistical significance of nucleotide position-weight matrix matches. Comput Appl Biosci 1996, 12(5):431–439.PubMedGoogle Scholar
- Muggleton S: Inductive Logic Programming. London: Academic Press; 1992.Google Scholar
- Clark P, Niblett T: The CN2 Induction Algorithm. Machine Learning 1989, 3(4):261–283.Google Scholar
- Quinlan JR: C4.5: Programs for Machine Learning:. Morgan Kaufmann; 1993.Google Scholar
- Michalski RS: A Theory and Methodology of Inductive Learning: Developing Foundations for Multistrategy Learning. In Machine Learning: An Artificial Intelligence Approach. Volume 1. Edited by: Michalski RS, Carbonell JG, Mitchell TM. Palo Alto, CA: Morgan Kaufmann; 1983.View ArticleGoogle Scholar
- Manly BFJ: Randomization, Bootstrap and Monte Carlo Methods in Biology. 2nd edition. Chapman & Hall; 1997.Google Scholar
- Wren JD, Forgacs E, Fondon JW 3rd, Pertsemlidis A, Cheng SY, Gallardo T, Williams RS, Shohet RV, Minna JD, Garner HR: Repeat polymorphisms within gene regions: phenotypic and evolutionary implications. Am J Hum Genet 2000, 67(2):345–356. 10.1086/303013PubMed CentralView ArticlePubMedGoogle Scholar
- Castillo-Davis CI, Mekhedov SL, Hartl DL, Koonin EV, Kondrashov FA: Selection for short introns in highly expressed genes. Nat Genet 2002, 31(4):415–418.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.