Mining Motifs from Biosequences

1. Mining Motifs from Biosequences Computer Science Department National Chiao Tung University Yuh-Jyh Hu

2. Outline • Introduction to DNA Sequence Motif Prediction • Characteristics of DNA Motif-finding Problem • Issues of DNA Motif-finding Algorithms • Examples and current research directions • Introduction to RNA Structure Motif Prediction • RNA Secondary Structures • RNA Secondary Structure Prediction Basics • Prediction Methods

3. What is a sequence motif ? • A subsequence that occurs in multiple sequences with important biological meanings. • Motifs can be totally constants or have variable characters. • Protein motifs often result from structural features, e.g. binding groups in globins. • DNA motifs provide signals for protein binding or nucleic acid bindings. • TRANSFAC database • Holds information of experimentally verified transcription factors.

4. Characteristics of DNA Motif-finding Problem • Chemical reactions determine gene regulation • Shape of molecules involved • Physicochemical properties of molecules involved • e.g. interaction between regulatory proteins and their target binding sites, expecting local shapes can be primarily determined by the bases involved

5. Characteristics of DNA Motif-finding Problem • Some evidence supported by the structure of known motifs • Patterns are relatively short — define a local shape • Patterns not defined by an exact sequence of bases • Pattern location may vary in different sequences • Pattern multiplicity is important • Common to most of the sequences in a given family • Motif-finding problem is ill-defined • “motif”, “pattern”, “most”, etc. • Computationally difficult

6. Issues of DNA Motif-finding Algorithms • Objective function • To approximate the correlation between patterns and biological meanings • Heuristics derived from domain knowledge, e.g., secondary structure of homologous proteins, relation between energetic interactions among bases and base frequencies, etc. • Some proposed objective functions: • Information content • Statistical significance • Generative model, e.g., HMM

7. Issues of DNA Motif-finding Algorithms • Objective function • Time for using objective functions may vary in different approaches • Some use objective function as heuristics to guide the search for motifs (heuristics applied along with the entire search process) • Some use objective function as a measure to rank the motifs found in the end (heuristics applied only in the end, not during the search) • Many objective functions currently used, but a fact worth notice: • They are all heuristics providing no guarantee. • Statistical significance ≠ biological significance

8. Issues of DNA Motif-finding Algorithms • Representation • Basic/Simplest: Primary biosequences are described by a double- or single-stranded string of alphabet (nucleotides or amino acids) • Lack flexibility • Motifs can rarely be described by exact strings due to complexity of motif binding mechanism. • IUPAC-IUB code extends expressiveness by including degenerate nucleotides, e.g., R={A,G}. • Capable of presenting unions of nucleotides • Lack base preference information

9. Issues of DNA Motif-finding Algorithms • Representation • Position weight matrices(PWM) provide base preferences • Each element of the matrix represents a particular base’s occurrence frequency/probability in a specific position of the motif. • Cannot model correlations between bases • Cannot model insertions or deletions 1 2 3 4 5 A 0.4 0.0 0.6 0.1 0.5 G 0.3 0.8 0.4 0.6 0.0 C 0.3 0.1 0.0 0.3 0.0 T 0.0 0.1 0.0 0.0 0.5

10. Issues of DNA Motif-finding Algorithms • Representation • HMM: a probabilistic model defined over a set of states and transition probabilities. • More expressive than PWM • Can model correlation between bases • Can model insertions and deletions • Require a lot more data to train HMM than other representations

11. Issues of DNA Motif-finding Algorithms • Representation • Sequence Logos provide graphical summary of conservation of elements in a motif. • Relative heights of letters reflect their frequencies in an alignment. • Entropy-based measurements of conservation

12. Issues of DNA Motif-finding Algorithms • Representation • Spectrum more efficient less efficient base string IUPAC-IUB PWM HMM less expressive more expressive

13. Issues of DNA Motif-finding Algorithms

14. Issues of DNA Motif-finding Algorithms • Search Strategy • Closely related to local multiple alignment • To base strings or IUPAC-IUB codes, exhaustive search is applicable. • Limited data set size • Limited motif length • Stochastic approaches • Random sampling • Iterative improvement • No guarantee for optimal solutions

15. Gibbs Sampling • How Gibbs captures a motif • Probabilistic matrix of a motif with length w • The goal of Gibbs sampling is to maximize the difference between motif base composition and background base distribution.

16. Gibbs Sampling • Actual locations of motif are unknown beforehand

17. Gibbs Sampling • First randomly pick motif locations in each sequence

18. Gibbs Sampling • Take out one sequence at a time with its segment. • Form the motif without a1’ segment.

19. Gibbs Sampling • Score each segment (in the left-out seq) with the current motif.

20. Gibbs Sampling • Scoring Gibbs is aimed at optimizing the ratio of motif base composition to background base composition. Maximizing S is equivalent to maximizing F. where Sx: score of motif x W: width of motif ci,j : the count of nucleic base j in position i qi,j : the probability of nucleic base j in position i pi,j:the background probability of nucleic base j in position i pj:the background probability of nucleic base j, which is equal to pi,j

21. Gibbs Sampling • Score each segment (in the left-out seq) with the current motif.

22. Gibbs Sampling • Sample a new segment for sequence 1’s motif occurrence according to scores. • Put Sequence 1 back and derive a modified motif.

23. Gibbs Sampling • Repeat the same process till convergence.

24. BioProspector • A C program using Gibbs sampling strategy finds DNA sequence motifs with 1-2 blocks. • Challenges • Variable sites per sequence • Motifs may not be highly conserved • Motifs conserved only in a cluster, not in the entire genome • Motifs may have two blocks separated by a gap in variable length. • Sample motif x1 from its marginal distribution • Sample x2 from the conditional distribution on x1

25. RNA Biological Roles • Like DNA, RNA has 4 bases (AGCU). Less stable than DNA, so is not mainly storage media. • The DNA code of a gene is copied to mRNA. • mRNA is the version of the genetic codes translated at the ribosome. • The ribosome is made up by rRNA. • The individual amino acids are brought to the ribosome, as it reads the mRNA by the molecule called tRNA.

26. RNA Biological Roles

27. Biological Significance of RNA Folding • RNA takes on 3D structure, and this may affect • Stability within cell • Speed of translation • Frequency of translation • Interactions with other molecules, e.g., regulation of other mRNA.

28. RNA Secondary Structures • G-C and A-U form hydrogen bonded base pairs and are said to be complementary. • Base pairs are approximately coplanar and are almost always stacked onto other base pairs in an RNA structure. Contiguous base pairs are called stems. • Unlike DNA, RNA is typically produced as a single stranded molecule which then folds intramolecularly to form a number of short base-paired stems. This base-paired structure is called RNA secondary structure.

29. RNA Secondary Structures • Single stranded subsequences bounded by base pairs are called loops. A loop at the end of a stem is called a hairpin loop. Simple substructures consisting of a simple stem and loop are called stem loops or hairpins. • Single stranded bases within a stem are called a bulge or bulge loop if the single stranded bases are on only one side of the stem. • If single stranded bases interrupt both sides of a stem, they are called an internal (interior) loop. • There are multibranched loops from which three or more stems radiate.

30. RNA Secondary Structures • Sequences variations in RNA sequences maintain basepairing patterns that give rise to double-stranded regions (secondary structures) in molecules. • Alignments of RNA sequences will show covariation at interacting base-pair positions, see figure below.

31. RNA Secondary Structures • In addition to secondary structural interactions in RNA, there are also tertiary interactions, illustrated in figure below. These include A. pseudoknots, B. kissing hairpins and C. hairpin-bulge contact. • These complicated structures are usually not predictable by secondary structure prediction tools.

32. RNA Secondary Structure Prediction Basics • Like protein secondary structure, RNA secondary structure can be viewed as an intermediate step in the formation of a 3D structure. • In predicting RNA secondary structure, several simplifying assumptions are usually made. • The most likely structure is similar to the energetically most stable structure. • The energy associated with any position in the structure is only influenced by local sequence and structure. — most reliable when used for standard Watson-Crick base pairs and single G/U pairs surrounded by Watson-Crick pairs. • The structure is assumed to be formed by folding of the chain back on itself in a manner that does not produce any knots.

33. Type of RNA Secondary Structure Prediction Methods • Based on objective functions • Free energy minimization • Covariance analysis from sequence comparison • Based on number of RNA sequences for which to predict • Single-sequence prediction • To find the possible folding of a single RNA sequence • Multiple-sequence prediction • To find a global structure alignment for a set of RNA sequences • To find common structure elements within a set of RNA sequences

34. Prediction Methods • Prediction Based on Self-Complementary Regions • Dot matrix sequence comparison for self-complementary regions • The sequence is listed in the 5’3’ direction across the top of the page, and the complementary strand is listed down the side of the page, also in the 5’3’ direction. The matrix is checked for identities. Self-complementary regions are recognized as diagonal rows of dots, e.g., seq = 5’-CGAAUUUUUCG-3’ seq = 3’-GCUUAAAAAGC-5’ CGAAAUUUUUCG C G A A A A A U U C G

36. Prediction Methods • Prediction Based on Minimum Free Energy • Based on the observation that the stability of an RNA fold can be decomposed into the contributions of individual energies. • Favorable contributions include: • Hydrogen bonds of basepairs • Stacking interactions of bases • Some ad hoc basepairs created in irregular structures, e.g., loops of 4 bases (i.e. tetraloop) • Unfavorable contributions include: • Symmetric bulges in stems • Asymmetric bulges in stems • Increasing size of loop at the end of stem • Multi-branches from a single loop

37. Prediction Methods • Prediction Based on Minimum Free Energy • To predict RNA secondary structure, every base is first compared to every other base. The energy of each predicted structure is estimated by the summing the negative base-stacking energies for each pair of bases in double-stranded regions and by adding the estimated positive energies of destabilizing regions such as loops at the end of hairpins, bulges within hairpins, internal bulges, and other unpaired regions. • To evaluate all the different possible structures, a dynamic programming algorithm similar to that used in sequence alignment is applied.

39. Prediction Methods • Prediction Based on Minimum Free Energy • An example

40. Prediction Methods • Prediction Based on Sequence Covariation • This method examines columns of a multiple sequence alignment that co-vary to produce base-pairs, i.e., to look for sequence positions at which covariation maintains the base-pairing property. • The justification for this method is that covaritions are actually found to occur during evolution, e.g., using covariation analysis to decipher base-pair interaction in tRNA.

42. Prediction Methods • COVE (a formal covariance model) • The model is an ordered tree, e.g., (A) SCFG (B) RNA structure (C) parse tree • Successfully identified tRNA genes. • Extremely slow.

43. Prediction Methods • COVE (a formal covariance model) • To model two RNA hairpins with 3 basepairs and a GGCA or UGCC loop would be: S -> aW1u | uW1a | cW1g | gW1c W1 -> aW2u | uW2a | cW2g | gW2c W2 -> aW3u | uW3a | cW3g | gW3c W3 -> ggca | ugcc • This approach is similar to training a HMM for proteins to recognize a family of protein sequences. In the case of RNA, a tree model is trained by the RNA sequences, and the model is used to predict the most probable secondary structure.

44. Prediction Methods • GPRM: Genetic Programming for RNA Motifs • What we are dealing with is: • An important but less studied problem: post-transcriptional regulation • Unlike DNA-binding proteins • Sequence conservation v.s. Structure conservation • A set of post-transcriptionally coregulated RNAs • Characterized by basepair interactions • Finding common structural motifs in a family of coregulated RNA sequences

45. Motif Prediction v.s. Concept Learning • Target concept: common motifs • Training examples: biosequences • Motif prediction as supervised learning: • Positive examples: • a given set of coregulated RNAs • Negative examples: • the same number of sequences randomly generated based on the observedfrequencies of sequence alphabet in positive examples. • Target concept: • The common structural motifs that can be used to distinguish the given coregulated RNAs from the random sequences.

46. GPRM: Genetic Programming for RNA Motifs • Focus on finding Watson-Crick complementary basepairs • C-G and A-U • RNA secondary structures are typically formed by basepairing interactions. • Three components of GPRM • Population of putative structural motifs • Fitness function of motifs • Genetic operators that simulate the natural evolution process of motifs

47. Representing Individuals in A Population • Each individual in a population is a putative motif • Structural motif description: • Watson-Crick complementary segments • Non-pairing segments

48. Fitness Function • Interested in those motifs that can reflect the characteristics conserved in a family of coregulated RNAs • Assign higher values to those motifs commonly shared by the given family of RNAs, and rarely contained in random RNA sequences. • We define the fitness function as:

49. Genetic Operators • Reproduction • Pass the better half of the population to the next generation • Accelerate the reproduction process • Mutation • If a complementary segment is picked, its segment length and corresponding pairing segment are both randomly changed. • If a non-pairing segment is selected, then only its length is randomly modified. • Crossover • Exchange segment configuration between two putative motifs. • Either a pair of complementary segments or a non-pairing segment is randomly chosen for exchange.