This document introduces probabilistic logic programming and its applications to biological sequence analysis. It discusses using probabilistic logic programming to build models for tasks like gene finding in DNA sequences. These models can represent relationships between sequence features and embed domain constraints while reasoning under uncertainty. The document outlines the author's research questions around using this approach for biological sequence analysis and their approach of building applications, abstractions, and optimizations to evaluate it. It provides background on prokaryotic gene finding tasks and probabilistic logic programming languages like PRISM.

1. INTRODUCTION Background A peek into my work Conclusions
Efficient
Probabilistic Logic Programming
for
Biological Sequence Analysis
Christian Theil Have
Research group PLIS: Programming, Logic and Intelligent Systems
Department of Communication, Business and Information Technologies
Roskilde University

2. INTRODUCTION Background A peek into my work Conclusions
OUTLINE
INTRODUCTION
Domain
Research questions
Background
Gene finding
Probabilistic Logic Programming
A peek into my work
Overview of papers
The trouble with tabling of structured data
Constrained HMMs
Applications: Genome models
Conclusions

3. INTRODUCTION Background A peek into my work Conclusions
INTRODUCTION

4. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics

5. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences

6. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA

7. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA
RNA

8. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA
RNA
Proteins

9. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA
RNA
Proteins
to understand

10. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA
RNA
Proteins
to understand
Features

11. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA
RNA
Proteins
to understand
Features
Functions

12. INTRODUCTION Background A peek into my work Conclusions
BIOLOGICAL SEQUENCE ANALYSIS
Subfield of bioinformatics
Analyze biological
sequences
DNA
RNA
Proteins
to understand
Features
Functions
Evolutionary
relationships

13. INTRODUCTION Background A peek into my work Conclusions
PROBABILISTIC LOGIC PROGRAMMING
Declarative programming paradigm
Ability to express common and complex models used in
biological sequence analysis
Concise expression of complex models
Separation between logic and control
Generic inference algorithms
Transformations

14. INTRODUCTION Background A peek into my work Conclusions
MODELS FOR BIOLOGICAL SEQUENCE ANALYSIS
Reflect relationships between features of sequence data
Embody constraints – assumptions about data
Infer information from data
Reasoning about uncertainty → probabilities

15. INTRODUCTION Background A peek into my work Conclusions
THE LOST PROJECT
. . . seeks to improve ease of modeling, accuracy and reliability of
sequence analysis by using logic-statistical models . . .
Key focus areas:
The PRISM system
Prokaryotic gene finding
My Ph.D. project is part of the LoSt project and share these
focus areas.

16. INTRODUCTION Background A peek into my work Conclusions
RESEARCH QUESTIONS
1. To what extent is it possible to use probabilistic logic
programming for biological sequence analysis?
2. How can constraints relevant to the domain of biological
sequence analysis be combined with probabilistic logic
programming?
3. What are the limitations with regard to efficiency and how can
these be dealt with?
I believe that these are the central questions that need be
addressed in order to be able to construct useful tools for
biological sequence analysis using probabilistic logic
programming.

17. INTRODUCTION Background A peek into my work Conclusions
RELATIONS BETWEEN RESEARCH QUESTIONS
1. To what extent is it possible to use
probabilistic logic programming for
biological sequence analysis?
2. How can constraints relevant to the
domain of biological sequence analysis
be combined with probabilistic logic
programming?
3. What are the limitations with regard to
efficiency and how can these be dealt
with?

18. INTRODUCTION Background A peek into my work Conclusions
APPROACH
To build and evaluate
Applications
Abstractions
Optimizations
for biological sequence analysis using probabilistic logic
programming.

19. INTRODUCTION Background A peek into my work Conclusions
APPROACH
Applications
Deal with relevant biological sequence analysis problems
Potentially to contribute new knowledge to biology or
bioinformatics
Direct substantiation with regard to research question 1
Abstractions
Ease modeling
Language for incorporating constraints from the domain
A higher level of declarativity;
Focus on problem rather than implementation (model)
details
Optimizations
Deal with limitations of probabilistic logic programming
that may hinder its use in biological sequence analysis.
Efficient inference is a precondition for practical use.

20. INTRODUCTION Background A peek into my work Conclusions
BACKGROUND
Prokaryotic gene finding
Probabilistic logic programming

21. INTRODUCTION Background A peek into my work Conclusions
PROKARYOTIC GENE FINDING
Identify regions of DNA which encode proteins:
A (prokaryotic) gene is a consecutive stretch of DNA which,
is transcribed as part of an RNA
is translated to a complete protein and
has a length which is a multiple of three (codons)
starts with a “start” codon
last codon is a “stop” codon

22. INTRODUCTION Background A peek into my work Conclusions
GENES AND OPEN READING FRAMES
The identification of prokaryotic genes may be decomposed
into two distinct problems:
1. Identification of ORFs which contain protein coding genes.
2. Identification of the correct start codon within an ORF.
ORF ::= start not-stop * stop
start ::= TTG | CTG | ATT | ATC | ATA | ATG | GTG
stop ::= TAA | TAG | TGA
not-stop ::= AAA | ... | TTT //all codons except those in stop

23. INTRODUCTION Background A peek into my work Conclusions
SIGNALS FOR PROKARYOTIC GENE FINDING
Open reading frames
Length
Nucleotide sequence composition
Conservation (sequence similarity in other organisms)
Local context
Promoters
Ribosomal binding site
Termination signal
GB
-35
PB
-10 +1
tss
SD
≈ +10
Gene
≈ +15-20
Terminator

24. INTRODUCTION Background A peek into my work Conclusions
READING FRAMES AND OVERLAPPING GENES
RNA can be transcribed from either strand
Genes may start in different “reading frames”
Genes can overlap
in the same and in different reading frames
on opposite strands

25. INTRODUCTION Background A peek into my work Conclusions
PROBABILISTIC LOGIC PROGRAMMING
Logic programming and Prolog
Probabilistic logic programming and PRISM

26. INTRODUCTION Background A peek into my work Conclusions
LOGIC PROGRAMMING AND PROLOG
A Prolog program consist of a finite sequence of rules,
B:-A1, . . . , An.
These rules define implications, i.e.,
B if A1 and . . . and An

27. INTRODUCTION Background A peek into my work Conclusions
TERMS, LITERALS AND VARIABLES
Literals can consist of (possibly) structured terms, that may
include variables.
number(0).
number(s(X)) :- number(X).

28. INTRODUCTION Background A peek into my work Conclusions
TERMS, LITERALS AND VARIABLES
Literals can consist of (possibly) structured terms, that may
include variables.
fact
number(0).
number(s(X)) :- number(X).

29. INTRODUCTION Background A peek into my work Conclusions
TERMS, LITERALS AND VARIABLES
Literals can consist of (possibly) structured terms, that may
include variables.
constant
number(0).
number(s(X)) :- number(X).

30. INTRODUCTION Background A peek into my work Conclusions
TERMS, LITERALS AND VARIABLES
Literals can consist of (possibly) structured terms, that may
include variables.
number(0).
number(s(X)) :- number(X).
term

31. INTRODUCTION Background A peek into my work Conclusions
TERMS, LITERALS AND VARIABLES
Literals can consist of (possibly) structured terms, that may
include variables.
number(0).
number(s(X)) :- number(X).
variables

32. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation

33. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(0)

34. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →

35. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →

36. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →
number(s(0))

37. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →

38. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →

39. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →
number(s(s(X))) →

40. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →
number(s(s(X))) →

41. INTRODUCTION Background A peek into my work Conclusions
RESOLUTION
Problems are stated as theorems (goals) to be proved, e.g.,
number(X)
To prove a consequent, we recursively need to prove the
antecedents by using rules where these appear as consequents,
number(0).
number(s(X)) :-
number(X).
Solutions
number(X) → X = 0
number(X) → X = s(0)
number(X) → X = s(s(0))
. . .
Derivation
number(X) →
number(s(X)) →
number(s(s(X))) →
number(s(s(0)))

42. INTRODUCTION Background A peek into my work Conclusions
DERIVATIONS AND EXPLANATION GRAPHS
Consider the following
program which adds natural
numbers:
add(0+0,0).
add(A+s(B),s(C)) :-
add(A+B,C).
add(s(A)+B,s(C)) :-
add(A+B,C).
And suppose we call the goal,
add(s(s(0))+s(s(0)),R)
We now have two alternative
applicable clauses,
alternatives
Resulting in either,
add(s(0)+s(s(0)),s(R))
or
add(s(s(0))+s(0),s(R))

43. INTRODUCTION Background A peek into my work Conclusions
DERIVATIONS TREE
s(s(0))+s(s(0))
s(0)+s(s(0))
0+s(s(0))
0+s(0)
0+0
s(0)+s(0)
0+s(0)
0+0
s(0)+0
0+0
s(s(0))+s(0)
s(0)+s(0)
0+s(0)
0+0
s(0)+0
0+0
s(s(0))+0
s(0)+0
0+0

44. INTRODUCTION Background A peek into my work Conclusions
DERIVATIONS TREE
s(s(0))+s(s(0))
s(0)+s(s(0))
0+s(s(0))
0+s(0)
0+0
s(0)+s(0)
0+s(0)
0+0
s(0)+0
0+0
s(s(0))+s(0)
s(0)+s(0)
0+s(0)
0+0
s(0)+0
0+0
s(s(0))+0
s(0)+0
0+0
Exponential!

45. INTRODUCTION Background A peek into my work Conclusions
EXPLANATION GRAPH
Polynomial1
1
O(n ∗ m), but would be O(n + m) if arguments were ordered by size

46. INTRODUCTION Background A peek into my work Conclusions
TABLING
Idea
The system maintains a table of calls and their answers.
when a new call is entered, check if it is stored in the table
if so, use previously found solution
Consequence:
Explanation graph representation.
Significant speed-up of program execution.

47. INTRODUCTION Background A peek into my work Conclusions
PROBABILISTIC LOGIC PROGRAMMING
Probabilistic logic programming is a form of logic
programming which deals with uncertainty.
Assign probability to each possible derivation in a logic
program.
Probabilistic inference, e.g.,
derive the probability of a goal
Infer the most probable derivation of a goal
Learn the affinities for different derivations from data

48. INTRODUCTION Background A peek into my work Conclusions
PRISM
PRogramming In Statistical Modelling is a framework for
probabilistic logic programming
Developed by collaboration partners of the Lost project:
Yoshitaka Kameya, Taisuke Sato, and Neng-Fa Zhou.
An extension of Prolog with random variables, called MSWs
Provides efficient generalized inference algorithms
PRISM program = probabilistic model

49. INTRODUCTION Background A peek into my work Conclusions
HIDDEN MARKOV MODEL EXAMPLE
Postcard
Greetings from wherever, where I am having
a great time. Here is what I have been doing:
The first two days, I stayed at the hotel
reading a good book. Then, on the third day I
decided to go shopping. The next three days I
did nothing but lie on the beach. On my last
day, I went shopping for some gifts to bring
home and wrote you this postcard.
Sincerely, Some friend of yours
Observation sequence

50. INTRODUCTION Background A peek into my work Conclusions
HIDDEN MARKOV MODEL run
Definition
A run of an HMM as a pair consisting of a sequence of states
s(0)
s(1)
. . . s(n)
, called a path and a corresponding sequence of
emissions e(1)
. . . e(n)
, called an observation, such that
s(0) = s0;
∀i, 0 ≤ i ≤ n − 1, p(s(i); s(i+1)) > 0
(probability to transit from s(i) to s(i+1));
∀i, 0 < i ≤ n, p(s(i); e(i)) > 0
(probability to emit e(i) from s(i)).
Definition
The probability of such a run is defined as
i=1..n p(s(i−1); s(i)) · p(s(i); e(i))

51. INTRODUCTION Background A peek into my work Conclusions
DECODING WITH HIDDEN MARKOV MODELS
Infer the hidden path given the observation sequence.
argmaxpathP(path|observation)
source: wikipedia
→The Viterbi algorithm

52. INTRODUCTION Background A peek into my work Conclusions
EXAMPLE HMM IN PRISM
values/2
declares the
outcomes of
random variables
msw/2
simulates a
random variable,
stochastically
selecting one of
the outcomes
Model in Prolog
Specifies relation
between variables
Example HMM in PRISM
values(trans(_), [sunny,rainy]).
values(emit(_), [shop,beach,read]).
hmm(L):- run_length(T),hmm(T,start,L).
hmm(0,_,[]).
hmm(T,State,[Emit|EmitRest]) :-
T > 0,
msw(trans(State),NextState),
msw(emit(NextState),Emit),
T1 is T-1,
hmm(T1,NextState,EmitRest).
run_length(7).

53. INTRODUCTION Background A peek into my work Conclusions
EXAMPLE HMM IN PRISM
values/2
declares the
outcomes of
random variables
msw/2
simulates a
random variable,
stochastically
selecting one of
the outcomes
Model in Prolog
Specifies relation
between variables
Example HMM in PRISM
values(trans(_), [sunny,rainy]).
values(emit(_), [shop,beach,read]).
hmm(L):- run_length(T),hmm(T,start,L).
hmm(0,_,[]).
hmm(T,State,[Emit|EmitRest]) :-
T > 0,
msw(trans(State),NextState),
msw(emit(NextState),Emit),
T1 is T-1,
hmm(T1,NextState,EmitRest).
run_length(7).

54. INTRODUCTION Background A peek into my work Conclusions
EXAMPLE HMM IN PRISM
values/2
declares the
outcomes of
random variables
msw/2
simulates a
random variable,
stochastically
selecting one of
the outcomes
Model in Prolog
Specifies relation
between variables
Example HMM in PRISM
values(trans(_), [sunny,rainy]).
values(emit(_), [shop,beach,read]).
hmm(L):- run_length(T),hmm(T,start,L).
hmm(0,_,[]).
hmm(T,State,[Emit|EmitRest]) :-
T > 0,
msw(trans(State),NextState),
msw(emit(NextState),Emit),
T1 is T-1,
hmm(T1,NextState,EmitRest).
run_length(7).

55. INTRODUCTION Background A peek into my work Conclusions
A PEEK INTO MY WORK
Overview of papers
A few selected cases:
An abstraction: Constrained HMMs (also an optimization)
An optimization: Regarding tabling of structured data
A couple of applications: Genome models
Using constrained probabilistic models for gene finding with
overlapping genes
Gene finding with a probabilistic model for
genome-sequence of reading

56. INTRODUCTION Background A peek into my work Conclusions
PAPERS 1
1. Henning Christiansen, Christian Theil Have, Ole Torp Lassen
and Matthieu Petit
Taming the Zoo of Discrete HMM Subspecies & some of their Relatives
Frontiers in Artificial Intelligence and Applications, 2011
2. Henning Christiansen, Christian Theil Have, Ole Torp Lassen
and Matthieu Petit
Inference with constrained hidden Markov models in PRISM
Theory and Practice of Logic Programming, 2010
3. Christian Theil Have
Constraints and Global Optimization for Gene Prediction Overlap
Resolution
Workshop on Constraint Based Methods for Bioinformatics, 2011

57. INTRODUCTION Background A peek into my work Conclusions
PAPERS 2
4. Henning Christiansen, Christian Theil Have, Ole Torp Lassen
and Matthieu Petit
The Viterbi Algorithm expressed in Constraint Handling Rules
7th International Workshop on Constraint Handling Rules, 2010
5. Christian Theil Have and Henning Christiansen
Modeling Repeats in DNA Using Probabilistic Extended Regular
Expressions
Frontiers in Artificial Intelligence and Applications, 2011
6. Henning Christiansen, Christian Theil Have, Ole Torp Lassen
and Matthieu Petit
Bayesian Annotation Networks for Complex Sequence Analysis
Technical Communications of the 27th International Conference
on Logic Programming (ICLP’11)

58. INTRODUCTION Background A peek into my work Conclusions
PAPERS 3
7. Henning Christiansen, Christian Theil Have, Ole Torp Lassen
and Matthieu Petit
A declarative pipeline language for big data analysis
Presented at LOPSTR, 2012
8. Christian Theil Have and Henning Christiansen
Efficient Tabling of Structured Data Using Indexing and Program
Transformation
Practical Aspects of Declarative Languages, 2012
9. Neng-Fa Zhou and Christian Theil Have
Efficient tabling of structured data with enhanced hash-consing
Theory and Practice of Logic Programming, 2012

59. INTRODUCTION Background A peek into my work Conclusions
PAPERS 4
10. Christian Theil Have and Søren Mørk
A Probabilistic Genome-Wide Gene Reading Frame Sequence Model
Submitted to PLOS One, 2012
11. Christian Theil Have, Sine Zambach and Henning
Christiansen
Effects of using Coding Potential, Sequence Conservation and mRNA
Structure Conservation for Predicting Pyrrolysine Containing Genes
Submitted to BMC Bionformatics, 2012

60. INTRODUCTION Background A peek into my work Conclusions
THE TROUBLE WITH TABLING OF STRUCTURED DATA

61. INTRODUCTION Background A peek into my work Conclusions
THE TROUBLE WITH TABLING OF STRUCTURED DATA
An innocent looking
predicate: last/2
last([X],X).
last([_|L],X) :-
last(L,X).
Traverses a list to
find the last element.
Time/space
complexity: O(n).
If we table last/2:
n + n − 1 + n − 2 . . . 1
≈ O(n2) !
call:
last([1,2,3,4,5],X)
last([1,2,3,4,5],X)
last([1,2,3,4],X)
last([1,2,3],X)
last([1,2],X)
last([1],X)
call table
last([1,2,3,4,5],X).
last([1,2,3,4],X).
last([1,2,3],X).
last([1,2],X).
last([1],X).

62. INTRODUCTION Background A peek into my work Conclusions
A WORKAROUND IMPLEMENTED IN PROLOG
We describe a workaround giving O(1) time and space
complexity for table lookups for programs with arbitrarily
large ground structured data as input arguments.
A term is represented as a set of facts.
A subterm is referenced by a unique integer serving as an
abstract pointer.
Matching related to tabling is done solely by comparison
of such pointers.

63. INTRODUCTION Background A peek into my work Conclusions
AN ABSTRACT DATA TYPE
store_term( +ground-term, pointer)
The ground-term is any ground term, and the
pointer returned is a unique reference (an integer)
for that term.
retrieve_term( +pointer, ?functor, ?arg-pointers-list)
Returns the functor and a list of pointers to
representations of the substructures of the term
represented by pointer.

64. INTRODUCTION Background A peek into my work Conclusions
ADT EXAMPLE
The following call converts the term f(a,g(b)) into its
internal representation and returns a pointer value in the
variable P.
store_term(f(a,g(b)),P).
Implementation with assert, e.g.,
retrieve_term(100,f,[101,102]).
retrieve_term(101,a,[]).
retrieve_term(102,g,[103]).
retrieve_term(103,b,[]).

65. INTRODUCTION Background A peek into my work Conclusions
AN AUTOMATIC PROGRAM TRANSFORMATION
We introduce an automatic transformation:
Structured terms are moved from the head of clauses to calls in
the body to retrieve_term/2.

66. INTRODUCTION Background A peek into my work Conclusions
TRANSFORMED HIDDEN MARKOV MODEL
original version
hmm(_,[]).
hmm(S,[Ob|Y]) :-
msw(out(S),Ob),
msw(tr(S),Next),
hmm(Next,Y).
transformed version
hmm(S,ObsPtr):-
retrieve_term(ObsPtr,[]).
hmm(S,ObsPtr) :-
retrieve_term(ObsPtr,[Ob,Y]),
msw(out(S),Ob),
msw(tr(S),Next),
hmm(Next,Y).

67. INTRODUCTION Background A peek into my work Conclusions
BENCHMARKING RESULTS
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sequence length
Runningtime(seconds)

68. INTRODUCTION Background A peek into my work Conclusions
THE NEXT STEP
Integration at the Prolog engine implementation level.
Neng-Fa Zhou and Christian Theil Have
Efficient tabling of structured data with enhanced hash-consing
Theory and Practice of Logic Programming, 2012
Full sharing between tables (call and answer)
Sharing with structured data in call stack

69. INTRODUCTION Background A peek into my work Conclusions
CONSTRAINED HMMS
Definition
A constrained HMM (CHMM)
is an HMM
extended with a set of constraints C, each of which is a
mapping from HMM runs into {true, false}.

70. INTRODUCTION Background A peek into my work Conclusions
CONSTRAINED HMMS
Why extend an HMM with side-constraints?
Convenient to express knowledge in terms of constraints
Reuse underlying model with different assumptions
Some constraints are not feasible as model structure (e.g.
all_different)
fewer paths to consider for any given sequence
→ decreased running time (under certain conditions)

71. INTRODUCTION Background A peek into my work Conclusions
ALIGNMENT WITH A CONSTRAINED PAIR HMM
In a biological context, we may want to
only consider alignments with a limited
number of insertions and deletions given
the assumption that the two sequences
are closely related.

72. INTRODUCTION Background A peek into my work Conclusions
ALIGNMENT WITH CONSTRAINTS

73. INTRODUCTION Background A peek into my work Conclusions
ADDING CONSTRAINT CHECKING TO THE HMM
HMM with constraint checking
hmm(T,State,[Emit|EmitRest],StoreIn) :-
T > 0,
msw(trans(State),NxtState),
msw(emit(NxtState),Emit),
check_constraints([NxtState,Emit],StoreIn,StoreOut),
T1 is T-1,
hmm(T1,NxtState,EmitRest,StoreOut).
Call to check_constraints/3 after each distinct
sequence of msw applications
Side-constaints: The constraints are assumed to be
declared elsewhere and not interleaved with model
specification
Extra Store argument in the probabilistic predicate

74. INTRODUCTION Background A peek into my work Conclusions
A LIBRARY OF GLOBAL CONSTRAINTS FOR HIDDEN
MARKOV MODELS
Our implementation contains a few well-known global
constraints adapted to Hidden Markov Models.
Global constraints
cardinality lock_to_sequence
all_different lock_to_set
In addition, the implementation provides operators which may
be used to apply constraints to a limited set of variables.
Constraint operators
state_specific
emission_specific
forall_subseq (sliding window operator)
for_range (time step range operator)

75. INTRODUCTION Background A peek into my work Conclusions
TABLING ISSUES
Problem: The extra Store argument makes PRISM table
multiple goals (for different constraint stores) when it should
only store one.
hmm(T,State,[Emit|EmitRest],Store)
To get rid of the extra argument, check_constraints
dynamically maintains it as a stack using assert/retract.
Note: This is not sound solution for all types of constraints
(some need tabling).

76. INTRODUCTION Background A peek into my work Conclusions
IMPACT OF USING A SEPARATE CONSTRAINT STORE
STACK

77. INTRODUCTION Background A peek into my work Conclusions
GENOME MODELS
Gene finding in a genomic context
What are the constraints between adjacent genes in the
genome?
Extent of (possible) overlap
Modeled as hard constraints
Gene reading frames, i.e., due to leading strand bias,
operons etc.
Modeled as (probabilistic) soft constraints

78. INTRODUCTION Background A peek into my work Conclusions
AN APPLICATION OF CONSTRAINED MARKOV
MODELS
We wish to incorporate overlapping gene constraints into gene
finding.
Divide and conquer two step approach to gene finding:
1. Gene prediction: A gene finder supplies a set of candidate
predictions p1 . . . pn, called the initial set.
2. Pruning: The initial set is pruned according to certain rules
or constraints. We call the pruned set the final set.

79. INTRODUCTION Background A peek into my work Conclusions
PRUNING STEP AS A CONSTRAINT OPTIMIZATION
PROBLEM
CSP formulation
We introduce variables X = xi . . . xn corresponding to each
prediction p1 . . . pn in the initial set (sorted by position in
genome) All variables have boolean domains,
∀xi ∈ X, D(xi) = {true, false} and xi = true ⇔ pi ∈ final set.
Multiple solutions
We want the “best” solution
Optimize for prediction confidence scores
Constraint Optimization Problem (COP)
COP formulation
Let the scores of p1 . . . pn be s1 . . . sn and si ∈ R+.
Maximize n
i=1 si, subject to C.

80. INTRODUCTION Background A peek into my work Conclusions
COP IMPLEMENTATION WITH MARKOV CHAIN (1)
We propose to use a (constrained) Markov chain for the COP.
The Markov chain has a begin state, an end state and two
states for each variable xi corresponding to its boolean
domain D(xi).
The state corresponding to D(xi) = true is denoted αi and
the state corresponding to D(xi) = false is denoted βi.
In this model, a path from the begin state to the end state
corresponds to a potential solution of the CSP.

81. INTRODUCTION Background A peek into my work Conclusions
FROM CONFIDENCE SCORES TO TRANSITION
PROBABILITIES
P(α1|begin) = σ1
P(β1|begin) = 1 − σ1
P(end|αn) = P(end|βn) = 1.
P(αi|αi−1) = P(αi|βi−1) = σi
P(βi|αi−1) = P(βi|βi−1) = 1 − σi
σi = 0.5 + λ +
(0.5 − λ) × (si − min(s1 . . . sn))
max(s1 . . . sn) − min(s1 . . . sn)

82. INTRODUCTION Background A peek into my work Conclusions
ENCODING CONSTRAINTS WITH CONSTRAINT
HANDLING RULES
Constraints: alpha/2 and beta/2 ≈ visited states.
Example: Genemark inconsistency rules
alpha(Left1,Right1), alpha(Left2,Right2) <=>
Left1 =< Left2, Right1 >= Right2 | fail.
beta(Left1,Right1), alpha(Left2,Right2) <=>
Left1 =< Left2, Right1 >= Right2 | fail.
The most probable consistent path is found using PRISMs
generic adaptation of the Viterbi algorithm
Each step adds either a alpha or beta (active) constraint
Incremental Pruning: For each step we only apply
constraints which may be transitively involved in rules
with the active constraint

83. INTRODUCTION Background A peek into my work Conclusions
EXPERIMENTAL RESULTS
Prediction on E.coli. using simplistic codon frequency
based gene finder.
Pruning using our global optimization approach (with all
inconsistency rules) versus local heuristic rules2.
Method #predictions Sensitivity Specificity Time (seconds)
initial set 10799 0.7625 0.2926 na
Genemark rules 5823 0.7558 0.5379 1.4
ECOGENE rules 4981 0.7148 0.5947 1.7
global optimization 5222 0.7201 0.5714 75
Sensitivity = fraction of known reference genes predicted.
Specificity = fraction of predicted genes that are correct.
2
Note that the results for the ECOGENE heuristic may vary depending on
execution strategy - in case of above results, predictions with lower left
position are considered first.

84. INTRODUCTION Background A peek into my work Conclusions
A MODEL FOR THE GENOME-WIDE SEQUENCE OF
READING FRAMES
We wish to incorporate gene reading frame constraints into
gene finding.
Divide and conquer two step approach to gene finding (again):
1. Gene prediction: A gene finder supplies a set of candidate
predictions p1 . . . pn, called the initial set.
2. Pruning: The initial set is pruned according to gene finder
confidence scores and the the probabilities adjacent gene
reading frames. We call the pruned set the final set.

85. INTRODUCTION Background A peek into my work Conclusions
METHODOLOGY
Genes predictions are
sorted by stop codon
position.
Gene finder scores are
discretized into symbolic
values.
A type of Hidden Markov
Model which we call a
delete-HMM:
A state for each of the
six possible reading
frames and
one delete state
F1
F2
F3
F4
F5
F6
delete

86. INTRODUCTION Background A peek into my work Conclusions
MODEL
F1 . . . F6 Emissions: Finite
set of i symbols δ1 . . . δn
corresponding to ranges of
prediction scores
Delete state emission:
P(δi|state = delete) =
FPδi
FP
Frame state transitions:
Relative frequency of
"observed" adjacent gene
reading frame pairs
(normalized)
Transition to delete:
P(delete) = 1 − TP
TP+FP
(tunable)
F1
F2
F3
F4
F5
F6
delete

87. INTRODUCTION Background A peek into my work Conclusions
RESULTS
1−Specificity (FPR)
Sensitivity(TPR)
0.0 0.1 0.2 0.3 0.4 0.5
0.50.60.70.80.91.0
threshold
frameseq, trained on Escherichia
frameseq, trained on Salmonella
frameseq, trained on Legionella
frameseq, trained on Bacillus
frameseq, trained on Thermoplasma

88. INTRODUCTION Background A peek into my work Conclusions
CONCLUSIONS
1. To what extent is it possible to use
probabilistic logic programming for
biological sequence analysis?
2. How can constraints relevant to the
domain of biological sequence analysis
be combined with probabilistic logic
programming?
3. What are the limitations with regard to
efficiency and how can these be dealt
with?

89. INTRODUCTION Background A peek into my work Conclusions
CONCLUSIONS
Commonly used models for biological analysis can
conveniently expressed using probabilistic logic
programming
Probabilistic logic programming is also a powerful tool for
experimenting with new kinds of models
It can support integration of constraints in a variety of
ways
Efficiency is an issue, but with suitable optimizations it is
efficient enough for many interesting problems
It is not merely a powerful abstraction
A valuable and practical tool for biological sequence
analysis

90. INTRODUCTION Background A peek into my work Conclusions
THANKS