 Research
 Open Access
Efficient sequential and parallel algorithms for finding edit distance based motifs
 Soumitra Pal^{1},
 Peng Xiao^{1} and
 Sanguthevar Rajasekaran^{2}Email author
 Published: 18 August 2016
Abstract
Background
Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Editdistancebased Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion.
Methods
One popular technique to solve the problem is to explore for each input string the set of all possible lmers that belong to the dneighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multicore shared memory setting uses arrays for storing and a novel modification of radixsort for sorting the candidate motifs.
Results
The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads.
Conclusions
Our algorithms have pushed up the stateoftheart of EMS solvers and we believe that the techniques introduced in this paper are also applicable to other motif search problems such as Planted Motif Search (PMS) and Simple Motif Search (SMS).
Keywords
 Motif
 Edit distance
 Trie
 Radix sort
Background
Motif search has applications in solving such crucial problems as identification of alternative splicing sites, determination of open reading frames, identification of promoter elements of genes, identification of transcription factors and their binding sites, etc. (see e.g., Nicolae and Rajasekaran [1]). There are many formulations of the motif search problem. A widely studied formulation is known as (l,d)motif search or Planted Motif Search (PMS) [2]. Given two integers l,d and n biological strings the problem is to find all strings of length l that appear in each of the n input strings with atmost d mismatches. There is a significant amount of work in the literature on PMS (see e.g., [1, 3–5], and so on).
PMS considers only point mutations as events of divergence in biological sequences. However, insertions and deletions also play important roles in divergence [2, 6]. Therefore, researchers have also considered a formulation in which the Levenshtein distance (or edit distance), instead of mismatches, is used for measuring the degree of divergence [7, 8]. Given n strings S ^{(1)},S ^{(2)},…,S ^{(n)}, each of length m from a fixed alphabet Σ, and integers l,d, the Editdistancebased Motif Search (EMS) problem is to find all patterns M of length l that occur in atleast one position in each S ^{(i)} with an edit distance of atmost d. More formally, M is a motif if and only if ∀i, there exist k∈ [ l−d,l+d],j∈ [ 1,m−k+1] such that for the substring \(S^{(i)}_{j,k}\) of length k at position j of S ^{(i)}, \(ED\left (S^{(i)}_{j,k},M\right) \le d\). Here E D(X,Y) stands for the edit distance between two strings X and Y.
EMS is also NPhard since PMS is a special case of EMS and PMS is known to be NPhard [9]. As a result, any exact algorithm for EMS that finds all the motifs for a given input can be expected to have an exponential (in some of the parameters) worst case runtime. One of the earliest EMS algorithms is due to Rocke and Tompa [7] and is based on Gibbs Sampling which requires repeated searching of the motifs in a constantly evolving collection of aligned strings, and each search pass requires O(n l) time. This is an approximate algorithm. Sagot [8] gave a suffix tree based exact algorithm that takes O(n ^{2} m l ^{ d }Σ^{ d }) time and O(n ^{2} m/w) space where w is the word length of the computer. Adebiyi and Kaufmann [10] proposed an exact algorithm with an expected runtime of O(n m+d(n m)^{(1+p o w(ε))} logn m) where ε=d/l and p o w(ε) is an increasing concave function. The value of p o w(ε) is roughly 0.9 for protein and DNA sequences. Wang and Miao [11] gave an expectation minimization based heuristic genetic algorithm.
Rajasekaran et al. [12] proposed a simpler Deterministic Motif Search (DMS) that has the same worst case time complexity as the algorithm by Sagot [8]. The algorithm generates and stores the neighborhood of every substring of length in the range [l−d,l+d] of every input string and using a radix sort based method, outputs the neighbors that are common to atleast one substring of each input string. This algorithm was implemented by Pathak et al. [13].
Following a useful practice for PMS algorithms, Pathak et al. [13] evaluated their algorithm on certain instances that are considered challenging for PMS: (9,2), (11,3), (13,4) and so on [1], and are generated as follows: n=20 random DNA/protein strings of length m=600, and a short random string M of length l are generated according to the independent identically distributed (i.i.d) model. A separate random dhamming distance neighbor of M is “planted” in each of the n input strings. Such an (l,d) instance is defined to be a challenging instance if l is the largest integer for which the expected number of spurious motifs, i.e., the motifs that would occur in the input by random chance, is atleast 1.
Expected number of spurious motifs in random instances for n=20,m=600. Here, ∞ represents value ≥1.0e+7
l  d=0  1  2  3  4  5  6  7  8  9  10  11  12  13 

5  0.0  1024.0  1024.0  ∞  
6  0.0  4096.0  4096.0  ∞  ∞  
7  0.0  14141.8  16384.0  ∞  ∞  ∞  
8  0.0  225.8  65536.0  65536.0  ∞  ∞  ∞  
9  0.0  0.0  262144.0  262144.0  ∞  ∞  ∞  ∞  
10  0.0  0.0  1047003.6  1048576.0  ∞  ∞  ∞  ∞  ∞  
11  0.0  0.0  1332519.5  4194304.0  ∞  ∞  ∞  ∞  ∞  ∞  
12  0.0  0.0  294.7  1.678e+07  1.678e+07  ∞  ∞  ∞  ∞  ∞  ∞  
13  0.0  0.0  0.0  6.711e+07  6.711e+07  ∞  ∞  ∞  ∞  ∞  ∞  ∞  
14  0.0  0.0  0.0  2.517e+08  2.684e+08  ∞  ∞  ∞  ∞  ∞  ∞  ∞  ∞  
15  0.0  0.0  0.0  2.749e+07  1.074e+09  ∞  ∞  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
16  0.0  0.0  0.0  139.1  4.295e+09  4.295e+09  ∞  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
17  0.0  0.0  0.0  0.0  1.718e+10  1.718e+10  ∞  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
18  0.0  0.0  0.0  0.0  3.965e+10  6.872e+10  ∞  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
19  0.0  0.0  0.0  0.0  1.226e+08  2.749e+11  2.749e+11  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
20  0.0  0.0  0.0  0.0  35.8  1.100e+12  1.100e+12  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
21  0.0  0.0  0.0  0.0  0.0  4.333e+12  4.398e+12  ∞  ∞  ∞  ∞  ∞  ∞  ∞ 
The sequential algorithm by Pathak et al. [13] solves the moderately hard instance (11,3) in a few hours and does not solve the next difficult instance (13,4) even after 3 days. A key timeconsuming part of the algorithm is in the generation of the edit distance neighborhood of all substrings as there are many common neighbors.
Contributions
In this paper we present several improved algorithms for EMS that solve instance (11,3) in less than a couple of minutes and instance (13,4) in less than a couple of hours. On (16,3) our algorithm is more than 20 times faster than EMS1. Our algorithm uses an efficient technique (introduced in this paper) to generate the edit distance neighborhood of length l with distance atmost d of all substrings of an input string. Our parallel algorithm in the multicore shared memory setting has more than 600 % scaling performance on 16 threads. Our approach uses following five ideas which can be applied to other motif search problems as well:
Efficient neighborhood generation: We show that it is enough to consider the neighbors which are at a distance exactly d from all possible substrings of the input strings. This works because the neighbors at a lesser distance are also included in the neighborhood of some other substrings.
Compact representation using wildcard characters: We represent all possible neighbors which are due to an insertion or a substitution at a position by a single neighbor using a wildcard character at the same position. This compact representation of the candidate motifs in the neighborhood requires less space.
Avoiding duplication of candidate motifs: Our algorithm uses several rules to avoid duplication in candidate motifs and we prove that our technique generates neighborhood that is nearly duplication free. In other words, our neighborhood generation technique does not spend a lot of time generating neighbors that have already been generated.
Trie based data structure for storing compact motifs: We use a trie based data structure to efficiently store the neighborhood. This not only simplifies the removal of duplicate neighbors but also helps in outputting the final motifs in sorted order using a depth first search traversal of the trie.
Modified radixsort for compact motifs: Our parallel algorithm stores the compact motifs in an array and uses a modified radixsort algorithm to sort them. Use of arrays instead of tries simplifies updating the set of candidate motifs by multiple threads.
Methods
In this section we introduce some notations and observations.
Using Eqs. (1), (2), (3) and (4), Pathak et al. [13] gave an algorithm that stores all possible candidate motifs in an array of size Σ^{ l }. However the algorithm is inefficient in generating the neighborhood as the same candidate motif is generated by several combinations of the basic edit operations. Also, the O(Σ^{ l }) memory requirement makes the algorithm inapplicable for larger instances. In this paper we mitigate these two limitations.
Efficient neighborhood generation
We now give a more efficient algorithm to generate the (l,d)neighborhood of all possible kmers of a string. Instead of computing (l,t)friendhood for all 0≤t≤d, we compute only the exact (l,d)friendhood.
Lemma 1.
\(M_{l,d}(S) = \cup _{k=ld}^{l+d} \cup _{j=1}^{mk+1} F_{l,d}(S_{j,k})\).
Proof.
Applying (6) repeatedly for k=l−d,l−d+1,…,l+d−1, along with (5) in (1) and (2) gives the result of the lemma.
We generate F _{ l,d }(S _{ j,k }) in three phases: we apply δ deletions in the first phase, β substitutions in the second phase, and α insertions in the final phase, where d=δ+α+β and l=k−δ+α. Solving for α,β,δ gives max{0,q}≤δ≤(d+q)/2, α=δ−q and β=d−2δ+q where q=k−l. In each of the phases, the neighborhood is grown by one edit operation at a time.
Compact motifs
Trie for storing compact motifs
We store the compact motifs in a trie based data structure which we call a motif trie. This helps implement the intersection defined in (7). Each node in the motif trie has atmost Σ children. The edges from a node u to its children v are labeled with mutually exclusive subsets l a b e l(u,v)⊆Σ. An empty set of compact motifs is represented by a single root node. A nonempty trie has l+1 levels of nodes, the root being at level 0. The trie stores the lmer σ _{1} σ _{2}…σ _{ l }, all σ _{ j }∈Σ, if there is a path from the root to a leaf where σ _{ j } appears in the label of the edge from level j−1 to level j.
The final set of common motifs is obtained by a depthfirst traversal of \(\mathcal {M}\) outputting the label of the path from the root whenever a leaf is traversed. An edge (u,v) is traversed separately for each σ∈l a b e l(u,v).
Efficient compact neighborhood generation
A significant part of the time taken by our algorithm is in inserting compact neighbors into the motif trie as it is executed for each neighbor in the friendhood. Our efficient neighborhood generation technique and the use of compact neighbors reduce duplication in neighborhood but do not guarantee completely duplication free neighborhood. In this section, we design few simple rules to reduce duplication further. Later we will see that these rules are quite close to the ideal as we will prove that the compact motif generated after skipping using the rules, are distinct if all the characters in the input string are distinct.
To differentiate multiple copies of the same compact neighbor, we augment it with the information about how it is generated. This information is required only in the proof and is not used in the actual algorithm. Formally, each compact neighbor L of a kmer S _{ j,k } is represented as an ordered tuple 〈S _{ j,k },T〉 where T denotes the sequence of edit operations applied to S _{ j,k }. Each edit operation in T is represented as a tuple 〈p,o〉 where p denotes the position (as in S) where the edit operation is applied and o∈{D,R,I} denotes the type of the operation – deletion, substitution and insertion, respectively. At each position there can be one deletion or one substitution but one or more insertions. The tuples in T are sorted lexicographically with the natural order for p and for o, D<R<I.
Conditions for skipping motif L=〈M,S _{ j,k },T〉
Rule  Conditions (in all rules t≥0) 

1  (j+k≤m)∧〈j,D〉∈T 
2  {〈j+t,D〉,〈j+t+1,R〉}⊆T 
3  (j+k≤m)∧{〈j,R〉,〈j+1,R〉,…,〈j+t,R〉,〈j+t+1,D〉}⊆T 
4  {〈j+t,D〉,〈j+t,I〉}⊆T 
5  {〈j+t,D〉,〈j+t+1,I〉}⊆T 
6  {〈j+t,R〉,〈j+t,I〉}⊆T 
7  (j>1)∧〈j,I〉∈T 
8  (j>1)∧{〈j,R〉,〈j+1,R〉,…,〈j+t,R〉,〈j+t+1,I〉}⊆T 
9  (j+k≤m)∧〈j+k,I〉∈T 
Let \(\bar {M}_{l,\,d}(S)\) denote the multiset of tuples for the compact motifs of S that were not skipped by our algorithm using the rules in Table 2 and M _{ l, d }(S) be the set of compact motifs generated by (3). Let Γ(〈S _{ j, k },T〉) be the resulting string when the operations in T are applied to S _{ j, k } and Γ(Z)=∪_{ L∈Z } Γ(L).
Lemma 2.
\(\Gamma (\bar {M}_{l,d}(S)) = M_{l,d}(S)\).
Proof.
By construction, \(\Gamma (\bar {M}_{l,d}(S)) \subseteq M_{l,d}(S)\). We show \(M_{l,d}(S) \subseteq \Gamma (\bar {M}_{l,d}(S))\) by giving a contradiction when \(M_{l,d}(S) \setminus \Gamma (\bar {M}_{l,d}(S)) \ne \emptyset \).
We define an order on the compact neighbors \(\phantom {\dot {i}\!}L_{1} = \langle {S_{j_{1},k_{1}}, T_{1}}\rangle \) and \(\phantom {\dot {i}\!}L_{2} = \langle {S_{j_{2},k_{2}}, T_{2}}\rangle \) as follows: L _{1}<L _{2} if Γ(L _{1})<Γ(L _{2}) and L _{2}<L _{1} if Γ(L _{2})<Γ(L _{1}). When Γ(L _{1})=Γ(L _{2}) we have L _{1}<L _{2} if and only if (k _{1}<k _{2})∨((k _{1}=k _{2})∧(p _{1}<p _{2}))∨((k _{1}=k _{2})∧(p _{1}=p _{2})∧(o _{1}<o _{2})) where 〈p _{1},o _{1}〉∈T _{1},〈p _{2},o _{2}〉∈T _{2} are the leftmost edit operations where T _{1},T _{2} differ.
Suppose \(M \in M_{l,d}(S) \setminus \Gamma (\bar {M}_{l,d}(S))\). Let L=〈S _{ j, k },T〉 be the largest (in the order defined above) tuple skipped by our algorithm such that Γ(L)=M. For each r=1,…,9 we show a contradiction that if L is skipped by Rule r then there is another \(\phantom {\dot {i}\!}L'=\langle {S_{j',\,k'},T'}\rangle \) with the same number of edit operations and Γ(L ^{′})=M but L<L ^{′}. Figure 3 illustrates the choice of L ^{′} under different rules.
Rule 1. Here j+k≤m and 〈j,D〉∈T. Consider T ^{′}=(T∖{〈j,D〉})∪{j+k,D}, and j ^{′}=j+1,k ^{′}=k.
Rule 2. Consider T ^{′}=T∖{〈j+t,D〉,〈j+t+1,R〉}∪{〈j+t,R〉,〈j+t+1,D〉}, and j ^{′}=j,k ^{′}=k.
Rule 3. T ^{′}=T∖{〈j,R〉,〈j+t+1,D〉}∪{〈j+t+1,R〉,〈j+k,D〉}, j ^{′}=j+1,k ^{′}=k.
Rule 4. For this and subsequent rules k<l+d as there is atleast one insertion and hence k ^{′} could possibly be equal to k+1. We consider two cases. Case (i) j+k≤m: T ^{′}=T∖{〈j+t,D〉,〈j+t,I〉}∪{〈j+t,R〉,〈j+k,D〉}, j ^{′}=j,k ^{′}=k+1. Case (ii) j+k=m+1: Here deletion of S _{ j } is allowed by Rule 1. T ^{′}=T∖{〈j+t,D〉,〈j+t,I〉}∪{〈j−1,D〉,〈j+t,R〉}, j ^{′}=j−1,k ^{′}=k+1.
Rule 5. The same argument for Rule 4 applies considering 〈j+t+1,I〉 instead of 〈j+t,I〉.
Rule 6. T ^{′}=T∖{〈j+t,I〉}∪{〈j+t+1,I〉}, and j ^{′}=j,k ^{′}=k.
Rule 7. T ^{′}=T∖{〈j,I〉}∪{〈j−1,R〉}, j ^{′}=j−1,k ^{′}=k+1.
Rule 8. T ^{′}=T∖{〈j+t,I〉}∪{〈j−1,R〉}, j ^{′}=j−1,k ^{′}=k+1.
Rule 9. T ^{′}=T∖{〈j+k,I〉}∪{〈j+k,R〉}, j ^{′}=j,k ^{′}=k+1.
Consider two compact motifs \(\phantom {\dot {i}\!}M_{1} = \langle {S_{j_{1},k_{1}}, T_{1}}\rangle \) and \(\phantom {\dot {i}\!}M_{2} = \langle {S_{j_{2},k_{2}}, T_{2}}\rangle \) in \(\bar {M}_{l,d}(S)\). For q∈{1,2}, let \(\left \langle {p_{q}^{(1)}, o_{q}^{(1)}}\right \rangle, \left \langle {p_{q}^{(2)}, o_{q}^{(2)}}\right \rangle, \ldots, \left \langle {p_{q}^{(d)}, o_{q}^{(d)}}\right \rangle \) be the sequence of edit operations in T _{ q } arranged in the order as the neighbors are generated by our algorithm, and the intermediate neighbors be \(L_{q}^{(h)} = \left \langle S_{j_{q},k_{q}}, \left \{\left \langle {p_{q}^{(1)}, o_{q}^{(1)}}\right \rangle,\right.\right.\left.\left. \left \langle {p_{q}^{(2)}, o_{q}^{(2)}}\right \rangle, \ldots, \left \langle {p_{q}^{(h)}, o_{q}^{(h)}}\right \rangle \right \} \right \rangle \) for all h=1,2,…,d. We also denote the initial kmer as a neighbor \(L_{q}^{(0)} = \langle {S_{j_{q},k_{q}}, \emptyset }\rangle \).
Lemma 3.
If S _{ j }s are all distinct and \(\Gamma \left (L_{1}^{(h)}\right) = \Gamma \left (L_{2}^{(h)}\right)\) for 1≤h≤d then \(\left \langle {p_{1}^{(h)}, o_{1}^{(h)}}\right \rangle = \left \langle {p_{2}^{(h)}, o_{2}^{(h)}}\right \rangle \) and \(\Gamma \left (L_{1}^{(h1)}\right) = \Gamma \left (L_{2}^{(h1)}\right)\).
Proof.
To simplify the proof, we use p _{ q },o _{ q },L _{ q } to denote \(p_{q}^{(h)}, o_{q}^{(h)}, L_{q}^{(h)}\), respectively, for all q∈{1,2}. Without loss of generality we assume p _{1}≤p _{2}.
As p _{1},p _{2} are positions in S, it would be enough to prove 〈p _{1},o _{1}〉=〈p _{2},o _{2}〉 because that would imply \(\Gamma \left (L_{1}^{(h1)}\right) = \Gamma \left (L_{2}^{(h1)}\right)\).
Case  o _{ 1 }  o _{ 2 }  cond.  Case  o _{ 1 }  o _{ 2 }  cond.  Case  o _{ 1 }  o _{ 2 }  cond. 

1  D  D  p _{1}<p _{2}  4  R  D  p _{1}≤p _{2}  7  I  D  p _{1}≤p _{2} 
2  D  R  p _{1}≤p _{2}  5  R  R  p _{1}<p _{2}  8  I  R  p _{1}≤p _{2} 
3  D  I  p _{1}≤p _{2}  6  R  I  p _{1}≤p _{2}  9  I  I  p _{1}<p _{2} 
Cases 2, 3, 4, 7
Our algorithm applies edit operations in phases: first deletions, followed by substitutions and finally insertions. In all these cases, one of Γ(L _{1}),Γ(L _{2}) does not have any ∗ because only deletions have been applied so far and the other has at least one ∗ because a substitution or an insertion has been applied. This implies Γ(L _{1})≠Γ(L _{2}), a contradiction.
Case 1
L _{2} has \(S_{p_{2}}\) deleted. As Γ(L _{1})=Γ(L _{2}), \(S_{p_{2}}\) must have been deleted in some operation prior to reaching L _{1}. As the deletions are applied in order, left to right, we must have p _{1}=p _{2} which is a contradiction.
Case 5
Now for each t<p _{1}, right to left, and y=t+p _{2}−p _{1}, we have the following: S _{ y } is either deleted or substituted in L _{1}, which implies that S _{ y } must be substituted in L _{2} as the deletion of S _{ y } in L _{2} is prohibited by Rule 2, and finally to match this ∗ in L _{2}, S _{ t } must be substituted in L _{1} as S _{ t } cannot be deleted in L _{1}, again by Rule 2.
But this makes Rule 3 applicable to L _{1} and L _{1} must have been skipped. This is a contradiction.
Case 6
By Rule 9 the insertion in L _{2} cannot be at the rightmost position and hence L _{2} must have at least one character after the insertion. By Rules 4 and 6, \(S_{p_{2}}\) must not be deleted or substituted in L _{2} and hence it must not be deleted or substituted in L _{1} either. Thus p _{1}<p _{2}. There cannot be any insertion or substitution at a position >p _{1} in L _{1}. Thus the ∗ due to the insertion at p _{2} in L _{2} must be matched by the ∗ due to the substitution at p _{1} in L _{1} and all of \(S_{p_{1}+1},\ldots,S_{p_{2}1}\) must be deleted in L _{1}.
By Rule 7, \(S_{p_{2}}\) cannot be the leftmost in \(S_{j_{2},k_{2}}\). So we consider \(S_{p_{2}1}\) in L _{1},L _{2}. It is either deleted or substituted in L _{1} and hence by Rule 5, it must be substituted in \(S_{p_{2}}\) (there can be multiple insertions at p _{2} in L _{2} but that does not affect this argument). To match this ∗, \(S_{p_{1}1}\) must be substituted in L _{1}.
Using a similar argument as in Case 5, S _{ t } must be substituted in L _{1} for each t<p _{1}−1. But this again makes Rule 3 applicable to L _{1} and L _{1} must have been skipped, which is not possible. This case has been illustrated in Fig. 4 b.
Case 8
Due to Rules 4, 6 and 9, \(S_{p_{1}}\) must not be deleted or substituted in L _{1} and hence it must not be deleted or substituted in L _{2} either. Thus p _{1}<p _{2}. The ∗ due to the insertion in L _{1} must be matched by a substitution at p _{3}<p _{1} such that all of \(S_{p_{3}+1}, \dots, S_{p_{1}1}\) are deleted in L _{2}.
By Rule 7, p _{1} cannot be the leftmost in L _{1}. For each t<p _{1}, right to left, and y=t+p _{3}−p _{1}, we have the following: S _{ y } is substituted in L _{1} because as the deletion of S _{ y } in L _{1} is prohibited by Rules 2 and 5, S _{ y } must be substituted in L _{2} again by Rule 2, and to match this ∗, S _{ t } must be substituted in L _{1}.
But this makes Rule 8 applicable to L _{1} and L _{1} must have been skipped which is not possible. This case has been illustrated in Fig. 4 c.
Case 9
This case has been illustrated in Fig. 4 d. Due to Rules 4, 6 and 9, \(S_{p_{1}},S_{p_{2}}\) must not be deleted or substituted in L _{1},L _{2}. The insertion at p _{2} in L _{2} must be matched by a substitution at a position p _{3} in L _{1} such that p _{1}<p _{3}<p _{2} and all of \(S_{p_{3}+1},\ldots,S_{p_{2}1}\) must be deleted in L _{1}.
Now for each position y, from right to left, where p _{1}<y<p _{2}, S _{ y } is either deleted or substituted in S _{1}, S _{ y } cannot be deleted in L _{2} by Rules 2 and 5 and hence must be substituted in L _{2}, which again must be matched by a substitution at a position t in L _{1} such that p _{1}<t<p _{3}. However this is impossible as the number of possible ys is larger than the number of possible ts.
If all S _{ j }s are distinct and Γ(M _{1})=Γ(M _{2}) then applying Lemma 3 repeatedly for h=d,d−1,…,0 gives us the fact that starting kmers \(S_{j_{1},k_{1}},S_{j_{2},k_{2}}\) as well as the corresponding edit operations in T _{1},T _{2} for M _{1},M _{2} must be the same. This is another way of stating the following theorem.
Theorem 1.
If S _{ j }s are all distinct then \(\bar {M}_{l,d}(S)\) is duplication free.
Implementation To track the deleted characters, instead of actually deleting we substitute them by a new symbol − not in Σ ^{′}. We populate the motif trie M ^{(i)} by calling g e n A l l(S ^{(i)}) given in Algorithm 2. Rules 1–8 are incorporated in G(L,j,δ,β,α), H(L,j,β,α) and I(L,j,α) which are shown in Algorithms 3, 4, and 5, respectively where s u b(L,j,σ) substitutes L _{ j } by σ and i n s(L,j,σ) inserts σ just before L _{ j }.
Modified radixsort for compact motifs
A simpler data structure alternative to tries for storing compact motifs could be an array. However, it becomes difficult to compute the intersection in (3) as defined in (7) when the compact motifs are stored in arrays. One straightforward solution is to first expand the ∗s in the compact motifs, then sort the expanded motifs and finally compute the intersection by scanning through the two sorted arrays. This, to a great extent, wipes out the advantage using the ∗s in the compact motifs. However, we salvage execution time by executing a modified radixsort that simultaneously expands and sorts the array of compact motifs: CompactRadixSort(A,l) where the first parameter A represents the array of compact motifs and the second parameter represents the number of digits of the elements in A which is equal to the number of base positions l in a motif.
As in the standard radixsort, our algorithm uses l phases, one for each base position in the motif. In the ith phase it sorts the motifs using bucket sort on the ith base of the motifs. However, in case of compact motifs, for each ∗ at a base position, the bucket counters for all σ∈Σ are incremented. While reordering the motifs as per the bucket counts, if there is a ∗ at ith base position of a motif, Σ copies of the motif are created and they are placed at appropriate locations in the array after finalizing the correct σ for the ∗. The details are given in Algorithm 6. In each phase a bucket counter B and a cumulative counter C are used. The temporary array T stores the partially expanded motifs from the current phase.
Discussion We did an experiment to compare the time taken by the two approaches – (i) using the expanded motifs, i.e., without using the wildcard character, and (ii) using compact motifs and sorting them using CompactRadixSort. For a single input string of instance (16,3), the first approach generated in 24.4 s 198,991,822 expanded motifs in which 53,965,581 are unique. The second approach generated in 13.7 s 11,474,938 compact motifs with the same number of unique expanded motifs. This shows the effectiveness of the second approach.
Parallel algorithm
We now give our parallel algorithm in the multicore shared memory setting. To process each input sequence S ^{(i)} the algorithm uses p+1 threads. The main thread first prepares the workload for other p threads. A workload involves the generation of the neighborhood for a kmer of S ^{(i)}, where l−d≤k≤l+d. There are total \(\sum _{k=ld}^{l+d} (mk+1) = (2d+1)(ml+1)\) workloads. The number of neighbors generated in the workloads are not the same due to the skipping of some neighbors using rules 1–9. For load balancing, we randomly and evenly distribute workloads to threads. Each thread first generates all the compact motifs in its workloads and then sort them using CompactRadixSort. If i>2 then it removes all neighbors not present in M ^{(i−1)} which is the set of common motifs of S ^{(1)},S ^{(2)},…,S ^{(i−1)}. The master thread then merges the residue candidate motifs from all the p threads to compute M ^{(i)}. The merging takes place in log2p phases in a binary tree fashion where the jth phase uses \(2^{\log _{2}{p}  j}\) threads each merging two sorted arrays of motifs.
Results and discussion

EMS1: A modified implementation of the algorithm in [13] which considered the neighborhood of only lmers whereas the modified version considers the neighborhood of all kmers where l−d≤k≤l+d.

EMS2: A faster implementation of our sequential algorithm which uses tries for storing candidate motifs where each node of the trie stores an array of pointers to each children of the node. However, this makes the space required to store a tree node dependent on the size of the alphabet Σ.

EMS2M: A slightly slower but memory efficient implementation of our sequential algorithm where each node of the trie keeps two pointers: one to the leftmost child and the other to the immediate right sibling. Access to the other children are simulated using the sibling pointers.

EMS2P: Our parallel algorithm which uses arrays for storing motifs. We experimented with p=1,2,4,8,16 threads.
Comparison between EMS1 and three implementations of EMS2
Instance  Metric  EMS1  EMS2  EMS2M  EMS2P threads  

1  2  4  8  16  
(8,1)  time  0.11 s  0.13 s  0.12 s  0.09 s  0.08 s  0.06 s  0.05 s  0.06 s 
memory  2.69 MB  4.25 MB  3.17 MB  2.67 MB  3.20 MB  3.55 MB  6.02 MB  7.99 MB  
(12,2)  time  19.87 s  15.60 s  16.62 s  2.71 s  1.94 s  1.44 s  0.89 s  0.55 s 
memory  34.28 MB  210.47 MB  126.91 MB  84.98 MB  104.60 MB  125.18 MB  142.82 MB  150.23 MB  
(16,3)  time  1.74 h  23.73 m  26.79 m  3.73 m  2.32 m  1.38 m  48.58 s  36.93 s 
memory  8.39 GB  11.62 GB  6.97 GB  8.55 GB  10.21 GB  10.53 GB  9.84 GB  9.91 GB  
(9,2)  time  10.84 s  1.72 s  3.02 s  1.12 s  0.96 s  0.78 s  0.49 s  0.35 s 
memory  3.44 MB  26.67 MB  17.04 MB  42.86 MB  57.76 MB  54.77 MB  59.85 MB  66.53 MB  
(11,3)  time  33.48 m  1.91 m  3.57 m  45.85 s  30.78 s  19.68 s  13.49 s  9.78 s 
memory  92.86 MB  477.12 MB  313.33 MB  2.27 GB  2.63 GB  2.65 GB  2.55 GB  2.60 GB  
(13,4)  time    1.08 h  1.76 h  44.03 m  26.16 m  14.51 m  8.62 m  6.82 m 
memory    8.26 GB  5.58 GB  149.60 GB  179.66 GB  180.13 GB  168.80 GB  172.74 GB 
Our efficient neighborhood generation enables our algorithm to solve instance (13,4) in less than two hours which EMS1 could not solve even in 3 days. The factor by which EMS2 takes more memory compared to EMS1 gradually decreases as instances become harder. As EMS2 stores 4 child pointers for A,C,G,T in each node of the motif trie whereas EMS2M simulates access to children using only 2 pointers, EMS2 is faster. Memory reduction in EMS2M is not exactly by a factor 2(=4/2) because we also keep a bit vector in each node to represent the subset of {A,C,G,T} a child corresponds to. The memory reduction would be significant for protein strings.
Conclusions
We presented several efficient sequential and parallel algorithms for the EMS problem. Our algorithms use some novel and elegant rules to explore the candidate motifs in such a way that only a small fraction of the candidate motifs are explored twice or more. In fact, we also proved that these rules are close to ideal in the sense that no candidate motif is explored twice if the characters in the input string are all distinct. This condition may not be practical and ideas from [14] can be used when the characters in the input string are repeated. Nevertheless, the rules help because the instances are randomly generated and the kmers in the input string are not much frequent. The second reason for the efficiency of our sequential algorithms is the use of a trie based data structure to compactly store the motifs. Our parallel algorithm stores candidate motifs in an array and uses a modified radixsort based method for filtering out invalid candidate motifs.
Our algorithms pushed up the stateoftheart of EMS solvers to a state where the challenging instance (16,3) is solved in slightly more than half a minute using 16 threads. Future work could be to solve harder instances, including those involving protein strings, and possibly using manycore distributed algorithms.
Declarations
Acknowledgments
This work has been supported in part by the NIH grant R01LM010101 and NSF grant 1447711.
Declarations
Publication of this article was funded by the NIH grant R01LM010101 and NSF grant 1447711. This article has been published as part of BMC Genomics Vol 17 Suppl 4 2016: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2015: genomics. The full contents of the supplement are available online at https://github.com/soumitrakp/ems2.git.
Availability
A C++ based implementation of our algorithm can be found at the following github public repository:
Authors’ contributions
SP and SR conceived the study. SP implemented the algorithms and PX carried out the experiments. SP and SR analyzed the results and wrote the paper. All authors reviewed the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
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