 Research
 Open Access
 Published:
Efficient computation of spaced seed hashing with block indexing
BMC Bioinformatics volume 19, Article number: 441 (2018)
Abstract
Background
Spacedseeds, i.e. patterns in which some fixed positions are allowed to be wildcards, play a crucial role in several bioinformatics applications involving substrings counting and indexing, by often providing better sensitivity with respect to kmers based approaches. Kmers based approaches are usually fast, being based on efficient hashing and indexing that exploits the large overlap between consecutive kmers. Spacedseeds hashing is not as straightforward, and it is usually computed from scratch for each position in the input sequence. Recently, the FSH (Fast Spaced seed Hashing) approach was proposed to improve the time required for computation of the spaced seed hashing of DNA sequences with a speedup of about 1.5 with respect to standard hashing computation.
Results
In this work we propose a novel algorithm, Fast Indexing for Spaced seed Hashing (FISH), based on the indexing of small blocks that can be combined to obtain the hashing of spacedseeds of any length. The method exploits the fast computation of the hashing of runs of consecutive 1 in the spaced seeds, that basically correspond to kmer of the length of the run.
Conclusions
We run several experiments, on NGS data from simulated and synthetic metagenomic experiments, to assess the time required for the computation of the hashing for each position in each read with respect to several spaced seeds. In our experiments, FISH can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.9x to 6.03x, depending on the structure of the spaced seeds.
Background
kmers counting, indexing and searching are fundamental operations at the very basis of many bioinformatics tools. A most notable example is their exploitation on sequence similarity search for which the “hitandextend” method introduced by BLAST [1] led to a revolutionary fast and sensitive approach for local alignment. In the “hit” step exact matches of kmers (k=11 for DNA) between two sequences are detected. Next, potential candidates are extended to obtain a local alignment with high statistical significance. BLAST has long been one of the most used tools for the analysis of omics sequences.
kmers profiles are also widely used in alignmentfree techniques [2] for the definition of statistical scores for sequence comparison [3, 4], finding application on a broad range of bioinformatics problems (e.g. [5–13]), and pushing the development and usage of time and space efficient algorithms and data structures for kmer counting and indexing (e.g. [14–18]).
Although the matching of contiguous kmers is largely used in sequence analysis, the use of not consecutive matches, i.e. spaced seeds, can lead in principle to more sensitive results [19]. This is because spaced seeds offer the advantage, with respect to kmers, of considering positions that are not consecutive, hence statistically less dependent. On the other side, the problem of maximizing the spaced seeds sensitivity is known to be NPhard [20]. The design of effective spaced seeds has been addressed in several studies [21–24]. Nowadays, spaced seeds have replaced traditional kmers based approaches in the design of stateoftheart solutions to several problems that involve sequence comparison. Among others we can enlist: phylogenetic tree reconstruction [25], protein classification [26], mapping of reads [27], multiple sequence alignment [28], metagenomics binning and classification [29–31]. The literature on spaced seeds is vast, and we refer the interest reader to [32] for a survey.
Several routine operations on large scale sequence analysis, including building and querying indexes, and searching for similarity among sequences, are based on kmers counting. In order to speedup kmers counting, hashing is often used. In fact, hashing consecutive kmers is fast and simple, since the hash of a kmer starting at position i can be computed from the hash of the kmer at position i−1 with few operations, since they share k−1 symbols [33].
Unfortunately, this property no longer holds for spaced seeds, due to the presence of “don’t care” positions, leading to a slowdown of the whole analysis. A good example of this effect is the metagenomic read classifier Clark [10]. Its spaced seed counterpart, ClarkS [31], has a better classification quality, but a drop from 3.5M to 200k reads per minute on classification rate with respect to Clark. Slow downs when using spaced seeds has also been shown in [26, 27, 29].
The problem of speeding up the computation of spaced seed hashing for each position in a given sequence was recently addressed in [34, 35] where FSH, an approach based on spaced seed selfcorrelation, was proposed reporting a speedup of 1.5x, on average, with respect to the standard way to compute spaced seed hashing. In this paper we address the same problem, considering the RabinKarp rolling hash.
The novel approach we present here, FISH, is based on the decomposition of the spaced seed mask into blocks of consecutive 1s. These blocks represent contiguous matches, i.e. kmers of the specified length. Since the hashing of kmers is a very fast operation, we reduced the problem of spaced seed hashing to the problem of hashing its kmer components and then combined them in order to obtain the hashing of the complete spaced seed. We performed a wide set of experiments, using several spaced seeds, varying in terms of length and weight, and NGS datasets with different read lengths. Our approach proved to be faster than the standard approach, and also of FSH. We extended our algorithm and experiments also to the multiple spaced seed hashing framework, obtaining an average speedup with respect to standard indexing of 6x.
In the next sections we will present our approach and the results of our experiments, discussing the performances of our approach under different settings.
Methods
In this section we start by recalling some formal definitions about spaced seeds through the notation introduced in [36], and then we will describe our algorithm to compute the spaced seed hashing of each position in a given input string, a fundamental step in many applications [25–29, 31].
Fundamental concepts on spaced seeds
Definition 1
(Spaced seed.) A spacedseed S (or just a seed) is a binary string of length k, where the symbol ‘1’ requires a match in that position, while a symbol ‘0’ allows for “don’t care”. A spaced seed is characterized by its length k and by its weight W<k, which is the number of 1s in the string. A spaced seed always begins and ends with a 1.
Definition 2
(The shape Q of a spaced seed.) The shape Q of a spaced seed is the set of non negative integers that correspond to the positions of the spaced seed where there is a 1. The shape Q can describe a spaced seed completely: the weight W is equal to Q, and its span (or length) s(Q) is given by maxQ+1.
Definition 3
(The positioned shape i+Q.) Given any integer i and shape Q, we define the positioned shape i+Q as the set {i+k,k∈Q}.
Definition 4
(Qgram.) For any position i in the string x=x_{0}x_{1}…x_{n−1}, with 0≤i≤n−s(Q), let us consider the positioned shape i+Q={i_{0},i_{1},…,i_{W−1}}, where i_{0}<i_{1}<...<i_{W−1}. The Qgram x[i+Q], starting at position i in x, is the string of length Q described by \(x_{i_{0}} x_{i_{1}} \dots x_{i_{W1}}\).
Example Let us consider the string x=ACTGACTGGATTGAC, and a spaced seed 1101110011111. Then the shape of the spaced seed is Q={0,1,3,4,5,8,9,10,11,12}, its weight is Q=10 and its span is s(Q)=13. The Qgram x[0+Q] is given by the concatenation of the symbols that occur at positions 0+Q={0,1,3,4,5,8,9,10,11,12}, x[ 0+Q]=ACGACGATTG:
Similarly the other Qgrams are given by the concatenations of the symbols at positions 1+Q={1,2,4,5,6,9,10,11,12,13}: x[1+Q]=CTACTATTGA; and 2+Q={2,3,5,6,7,10,11,12,13,14}: x[2+Q]=TGCTGTTGAC.
Now we can formally state our problem such as:
Problem 1
Let x=x_{0}x_{1}…x_{i}…x_{n−1} be a string of length n, Q be a spaced seed, and h be a hash function that maps a string into a binary codeword. Compute the hash \(\mathcal {H}(x,Q)\) for each Qgram of the string x, following in the natural order from the first position 0 to the last position n−s(Q).
Spaced seed hashing
The first step when computing the hash of a string defined over an alphabet \(\mathcal {A}\) is to encode it into a binary string. For genomic sequences the simplest encoding consists in the definition of a function encode which maps the four nucleotides as follows: encode(A)=00,encode(C)=01,encode(G)=10,encode(T)=11. Given this mapping, we can compute the encodings of all symbols of the Qgram x[0+Q]:
Here we focus on the efficient computation of the RabinKarp rolling hash. In the case of DNA sequences since \(\mathcal {A}=4\) is a power of 2, the multiplications can be implemented with a shift operation. More formally, for any given position i of the string x=x_{0}x_{1}…x_{n−1}, we define the hashing h(x[ i+Q]) of the Qgram x[i+Q] as:
where m(k)={i∈Q, such thati<k}, i.e. given a position k in the spaced seed, m(k) holds the number of 1s to the left of k. Since each symbol is encoded with 2 bits, \(m(k)*{log}_{2}\mathcal {A}\) gives the number of shifts to set the encoding of the kth symbol in the right position.
In Table 1 we report a stepbystep computation of hashing value for the Qgram x[0+Q] (up to length 6 just for page width limits constrains). With respect to the above example, the hashing value associated to the Qgram ACGACGATTG simply corresponds to the list of encodings in Littleendian: 10111100100100100100. The hashing values for the others Qgrams can be determined through the function h(x[ i+Q]) with a similar procedure. Following the above example the hashing values for the Qgrams x[ 1+Q]=CTACTATTGA and x[ 2+Q]=TGCTGTTGAC are, respectively, 00101111001101001101 and 10001011111011011011.
The RabinKarp rolling hash is very intuitive. However, other hashing functions, that can be more appropriate because they have some properties such as universality, uniform distribution in the output space, and higherorder independence [33], can be computed in a similar way. For example, one could use the cyclic polynomial rolling hash by replacing: shifts with rotations, OR with XOR, and the function encode(·) in Eq. (1) with a seed table where the letters of the DNA alphabet are assigned different random 64bit integers.
Equation (1) can be directly used to address Problem 1 by applying it at each position in x. However, for each position the computation of the hashing function h(x[i+Q]) requires to extract and encode a number of symbols that is equal to the weight of the seed Q or, in other words, each symbol of x is read and encoded into the hash Q times. Therefore this solution can be very time consuming.
Computing spaced seed hashing with block indexing
In the following we describe our contribution for the computation of hashing values through Fast Indexing of Spaced seeds Hashings (FISH). Let Q={i_{1},i_{2},…i_{k}} be a spaced seed. It can be viewed as a series of runs of 1s, or unit blocks, interspersed with runs of 0s. First, we disassemble Q into its constituents unit blocks and we define the set B of starting positions of the unit blocks as:
Given B={b_{1},b_{2},…,b_{t}}, let B_{L}={l_{1},l_{2},…,l_{t}} be the (ordered) set of the lengths corresponding to each unit block. To compute the hashing of a spaced seed on a sequence x of length n, the FISH algorithm will scan x for fast hashing of lmers whose lengths are in B_{L}. For each length l∈B_{L} an array T_{l} of length n−l+1 is built where at position i the hash of the lmer x[ i,i+l−1] is stored. This preprocessing is very fast, as it can exploit the large overlap (l−1 symbols) between consecutive lmers in order to compute the hashing of consecutive positions in constant time.
Then, to compute the hash of the Qgram identified by the position shape i+Q, we proceed as follows. For each unit block b_{j} of length l_{j} we look up at the array \(T_{l_{j}}\), and specifically to the value stored at position i+b_{j}. Let h_{j} be such value. The hashing of the Qgram is then computed by shifting h_{j} of 2×m(b_{j}) positions to the left. This process is repeated for all unit blocks and the contributions of each block are summed (bitwise OR).
Example 1
Let us consider again the string x=ACTGACTGGATTGACTCC and the spaced seed S=1101 110011111, with associated shape Q={0,1,3,4,5,8,9,10,11,12},m={0,1,2,2,3,4,4,5,5,6,7,8,9,10}, and blocks with starting positions B={0,3,8}, and lengths B_{L}={2,3,5}. To compute the hashing of the Qgram x[0+Q] we must look up at T_{2}[0] to retrieve the value of h_{1}=0100, at T_{3}[3] to retrieve the value of h_{2}=010010, and at T_{5} to retrieve h_{3}=1011110010 (see Fig. 1).
Then the hashings need to be combined to obtain the final hash value of x[ 0+Q]:
Computing multiple spaced seed hashing with block indexing
In some applications (for example [25, 29–31, 37]) using several spaced seeds increases the sensitivity of the results. In such a context, the FISH algorithm can be further exploited to improve the speed up with respect to the computation of the Qgrams hashing of each spaced seed separately. In fact, if two spaced seeds share a unit block of the same length l, we will need to compute the hashing of the lmers of the input string just once, and then access the corresponding array T_{l} when computing the full hash of Qgrams for the two different spaced seeds.
More formally, let Q_{1},Q_{2},…Q_{n} be n spaced seeds. Let \(B_{L}^{Q_{i}} =\{l_{1}^{Q_{i}}, l_{2}^{Q_{i}}, {,} \dots, l_{t_{i}}^{Q_{i}} \}\) be the set of lengths of the unit blocks of the spaced seed with shape Q_{i}, for i=1,…,n. Let \(\tilde {B}_{L} =\cup _{i=1}^{n} B_{L}^{Q_{i}}\) be the superset of all different unit block lengths among the spaced seeds we are considering. We will compute the hashing tables of each lmer, with \(l \in \tilde {B}_{L}\), in the input string x just once. These tables will be used for all spaced seeds so that if two spaced seeds share a unit block, the corresponding table will be computed only once. When we need to reconstruct the hash for the Qgram intercepted by the spaced seed Q_{i} at position j in x, i.e. x[j+Q_{i}], FISH will proceed as before by looking up at the T_{l} corresponding to the lengths of the blocks in the spaced seed Q_{i}.
Results
In this section we will discuss the time performance of the block indexing based approach FISH, presented here, and the FSH approach [35]. The speed ups are computed with respect to the time needed for the standard computation of spaced seeds hashing, where the hashing of each kmer intercepted by the spaced seed is computed separately for each position in the input string as in Eq. (1).
Spaced seeds and datasets description
In order to evaluate the performance of FISH we design a series of tests with different type of spaced seeds and various reads datasets. For our experiments we used the same spaced seeds and datasets used in [34] covering three types of spaced seeds: i) maximizing the hit probability [31]; ii) minimizing the overlap complexity [23]; and iii) maximizing the sensitivity [21].
In line with previous studies, we evaluate nine spaced seeds, three for each category. The spaced seeds used for this test are shown in Table 2. All spaced seeds Q1−Q9 (see Table 2) have the same weight Qi=22 and length L=31.
In order to evaluate FISH under different conditions, we build several sets of spaced seeds with rashbari, with different lengths from 16 to 45 and weights from 11 to 32. A complete list of spaced seeds is reported in the Additional file 1: Tables S1–S5.
As for the reads data to be scanned and hashed, we consider a series of datasets of metagenomic reads already used for classification and binning [9, 38]. We use synthetic metagenomic datasets (MiSeq, HiSeq, MK_a1, MK_a2, and simBA5) as well as simulated metagenomic datasets (S,L,R). The datasets (R_{x}) simulate singleend long reads from Roche 454, with length 700 bp, and sequencing error of 1%. While the datasets (S_{x} and L_{x}) are pairedend reads of short length (80 bp) following Illumina error profile. The synthetic metagenomic datasets are built from real shotgun reads of different species to mimic various microbiome communities. Furthermore, for the comparison of spaced seeds with different weights and lengths, we generated datasets of increasing read length of 100, 200, and 400 bp with Mason simulator [39] according to Illumina error profile. A summary of the datasets used in this study is reported in Table 3. All methods have been tested on a laptop with 16 GB RAM and Intel i74510U cpu at 2GHz.
Analysis of speed up
In the first test we compare the performance of FISH with FSH in terms of speed up with respect to the standard hashing computation. In Fig. 2 we report the average speed ups on all datasets, for each spaced seed, obtainable with FISH and FSH approaches.
We can observe that FISH is faster than FSH independently on the spaced seed considered. As a reference, the standard approach (Eq. (1)), requires about 17 minutes to perform the hashing of a seed on all datasets. The two methods FISH and FSH can compute the hashings in 8.5 and 12 minutes respectively, with a speed up of 2 (FISH) and 1.46 (FSH). We noticed that the speed up can vary between spaced seeds, in fact FSH obtains speed ups in the range [1.181.58] and FISH in the interval [1.892.16]. As expected, the speed up depends on the structure of spaced seed to be hashed, however FSH seems to be highly dependent on the structure with a variation of 0.4 between minimum and maximum speed up, instead FISH variation is only 0.27. In summary, in this first experiments FISH in not only faster, but also less dependent of the spaced seed.
To have a better understanding of the behavior of FISH on all datasets, Fig. 3 reports the performance of FISH for each datasets.
We noticed that the seeds with the best performance are Q2 and Q3, the top two lines in Fig. 3. However, all spaced seeds show a similar behavior across different datasets. The maximum difference between the best seed, top line, and the worse seed, bottom line, remains constant for each datasets confirming the robustness of FISH. Another interesting observation is that the speed up tends to increase with the reads length and it reaches the maximum performance on the long read (see R7, R8 and R9). A possible reason for this behavior is that these datasets contain long reads, and the impact of the initial transient is reduced.
In Fig. 4 we report the performance of FISH and FSH for spaced seed Q7 in details over all datasets.
The results are in line with the above observations and FISH has better speed up across all datasets. However, for FISH the improvement on long reads datasets is substantial with respect to FSH.
Multiple spaced seed hashing
Several tools exploit the power of spaced seeds by using a combination of such patterns, in order to further improve their performances in terms of quality. Therefore, the simultaneous computation of the hashing of several spaced seeds at once can come very useful in such contexts.
Figure 5 reports the speed up of FISH and FSH when computing the hash of spaced seed independently (light blu and light green), and simultaneously as multiple spaced seeds (dark blu and dark green).
The use of multiple spaced seeds simultaneously increases the speed up of both methods. However, FSH improves from 1.45 to 1.49 whereas FISH from 2.48 to 6.03. On this experiment the advantage of FISH is gain substantial, where it can hash multiple spaced seeds 4 times faster than FSH. A detailed analysis of the performance on different datasets can be found in Fig. 6. Similarly to Fig. 3 we can observe that the speed up increases on long reads datasets.
The impact of reads length and spaced seeds weight
These experiments aim at posing in evidence the impact on the speed up of reads length and spaced seeds density. We generated with rasbhari [22] different sets of nine spaced seeds with lengths from 16 to 45 and weights in the range from 11 to 32, see the Additional file 1: Tables S1S5.
In Fig. 7 we compare the speedup of FISH and FSH on spaced seeds with the same length L=31, while varying the weight W. It can be observed that the speed up of both FISH and FSH increases as the weight W increases. A possible explanation is the following. If a spaced seed has an higher weight, then the ability of FISH to use the partial hashes computed in the kmers tables increases, and this will results in a better speed up. This behavior is consistent for both FISH and FSH, with the only exception of the speedup of FISH on multiple spaced seeds with W =22 and L =31. These are the seeds used in the first experiments and reported in Table 2. As opposed to the other set of seeds that have been created all with same tool and minimizing overlap complexity, these seeds have been created with different methods and thus they might expose more overlaps, allowing for a better speedup. On the other hand if the density W/L of spaced seeds weight with respect to the length is low, than both FISH and FSH will have poor performance. For example, if W/L is below 0.3 than the standard hashing computation is in general faster. On extreme cases, like the spaced seeds reported in [40], with W=12 and L=112 FISH and FSH might not be of help.
In Fig. 8 we compare the speedup of FISH while varying the reads length, as a function of spaced seeds density (fixed lenght L=31). We can note that the speedup grows with the reads length, a behavior observed also in Figs. 3 and 4.
Discussion
In this paper, we address the problem of hashing genomic sequences through the lens of spaced seeds. Spaced seeds are widely used in many tasks related to sequence alignment and comparison. In fact, on the problem of sequence similarity detection spaced seeds have shown better performance than contiguous matches [19]. While the hashing of contiguous matches can be efficiently performed, for spaced seed this was not the case.
We have already propose a method, called FSH [35], to address this problem, but in this paper we introduce a new tool, FISH, based on different strategies. FSH is based on spaced seed autocorrelation and dynamic programming, while FISH builds an index of partial common hashings that can be reused multiple times.
In the results section, we have shown that FISH can improve substantially the performance in terms of speed up w.r.t. to FSH and the traditional hashing of spaced seeds. This advantage is demonstrated on a number of different settings, varying spaced seeds density and reads length.
The speed up of FISH increases as the length of the reads grows. This is a desirable property if we consider that modern sequencing technologies can produce longer reads. Also, if spaced seeds with high density are required, FISH indexing strategy outperforms the other methods. One interesting direction of investigation is the use of long and sparse spaced seed, i.e. with very low density, for which FISH and FSH are not suited. It remains an open problem if an alternative hashing method can further improve the hashing computation, closing the gap with the fast hashing of kmers.
Conclusions
In this study we presented FISH, an indexingbased approach for speeding up the computation of rolling hash for spaced seeds. In our experiments FISH was able to compute the hashing values of spaced seeds with a speedup, on average and with respect to the traditional approach, between 1.9× (single) to 6.03× (multi), depending on the structure of the spaced seeds and on the reads length.
Abbreviations
 NGS:

Next generation sequencing
 FSH:

Fast spaced seed hashing
 FISH:

Fast indexing for spaced seed hashing
References
 1
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990; 215(3):403–10.
 2
Zielezinski A, Vinga S, Almeida J, Karlowski WM. Alignmentfree sequence comparison: benefits, applications, and tools. Genome Biol. 2017; 18:186.
 3
Reinert G, Chew D, Sun F, Waterman M. Alignmentfree sequence comparison (i): Statistics and power. J Comput Biol. 2009; 16(12):1615–34.
 4
Song K, Ren J, Reinert G, Deng M, Waterman MS, Sun F. New developments of alignmentfree sequence comparison: measures, statistics and nextgeneration sequencing. Brief Bioinform. 2014; 15(3):343–53.
 5
Comin M, Antonello M. Fast entropic profiler: An information theoretic approach for the discovery of patterns in genomes. IEEE/ACM Trans Comput Biol Bioinforma. 2014; 11(3):500–9.
 6
Pizzi C, Ornamenti M, Spangaro S, Rombo SE, Parida L. Efficient algorithms for sequence analysis with entropic profiles. IEEE/ACM Trans Comput Biol Bioinforma. 2018; 15(1):117–28.
 7
Comin M, Leoni A, Schimd M. Clustering of reads with alignmentfree measures and quality values. Algorithm Mol Biol. 2015; 10:4.
 8
Leslie C, Eskin E, Noble W. The spectrum kernel: a string kernel for SVM protein classification. In: Proceedings of Pac Symp Biocomput.Singapore: World Scientific Publishing: 2002. p. 564–75.
 9
Girotto S, Pizzi C, Comin M. MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures. Bioinformatics. 2016; 32(17):567–75.
 10
Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative kmers. BMC Genomics. 2015; 16:236.
 11
Pizzi C, Rastas P, Ukkonen E. Finding significant matches of position weight matrices in linear time. IEEE/ACM Trans Comput Biol Bioinforma. 2011; 8(1):69–79.
 12
Parida L, Pizzi C, Rombo SE. Irredundant tandem motifs. Theor Comput Sci. 2014; 525:89–102. Advances in Stringology.
 13
Shajii A, Yorukoglu D, William Yu Y, Berger B. Fast genotyping of known SNPs through approximate k mer matching. Bioinformatics. 2016; 32(17):538–44.
 14
Marcais G, Kingsford C. A fast, lockfree approach for efficient parallel counting of occurrences of kmers. Bioinformatics. 2011; 27(6):764–70.
 15
Van Dongen S, AbreuGoodger C, Enright A. Detecting microrna binding and sirna offtarget effects from expression data. Nat Methods. 2008; 5(12):1023–5.
 16
Deorowicz S, Kokot M, Grabowski S, DebudajGrabysz A. Kmc 2: fast and resourcefrugal kmer counting. Bioinformatics. 2015; 31(10):1569–76.
 17
Ferragina P, Manzini G. Opportunistic data structures with applications. In: Proceedings 41st Annual Symposium on Foundations of Computer Science. Piscataway: IEEE: 2000. p. 390–8.
 18
Belazzougui D, Cunial F. A framework for spaceefficient string kernels. Algorithmica. 2017; 79(3):857–83.
 19
Buhler J. Efficient largescale sequence comparison by localitysensitive hashing. Bioinformatics. 2001; 17(5):419–28.
 20
Ma B, Li M. On the complexity of the spaced seeds. J Comput Syst Sci. 2007; 73(7):1024–34. Bioinformatics {III}.
 21
Ma B, Tromp J, Li M. Patternhunter: faster and more sensitive homology search. Bioinformatics. 2002; 18(3):440–5.
 22
Hahn L, Leimeister CA, Ounit R, Lonardi S, Morgenstern B. Rasbhari: Optimizing spaced seeds for database searching, read mapping and alignmentfree sequence comparison. PLoS Comput Biol. 2016; 12(10):1005107.
 23
Ilie L, Ilie S, Mansouri Bigvand A. SpEED: fast computation of sensitive spaced seeds. Bioinformatics. 2011; 27(17):2433–4.
 24
Noé L. Best hits of 11110110111: modelfree selection and parameterfree sensitivity calculation of spaced seeds. Algorithm Mol Biol. 2017; 12:1.
 25
Leimeister CA, Boden M, Horwege S, Lindner S, Morgenstern B. Fast alignmentfree sequence comparison using spacedword frequencies. Bioinformatics. 2014; 30(14):1991–9.
 26
Onodera T, Shibuya T. The gapped spectrum kernel for support vector machines. In: Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM’13. Berlin, Heidelberg: Springer: 2013. p. 1–15.
 27
Rumble SM, Lacroute P, Dalca AV, Fiume M, Sidow A, Brudno M. SHRiMP: Accurate mapping of short colorspace reads. PLOS Comput Biol. 2009; 5(5):1000386.
 28
Darling AE, Treangen TJ, Zhang L, Kuiken C, Messeguer X, Perna NT. In: Bücher P, Moret BME, (eds).Procrastination Leads to Efficient Filtration for Local Multiple Alignment. Berlin, Heidelberg: Springer; 2006, pp. 126–37.
 29
Břinda K, Sykulski M, Kucherov G. Spaced seeds improve kmerbased metagenomic classification. Bioinformatics. 2015; 31(22):3584–92.
 30
Girotto S, Comin M, Pizzi C. Metagenomic reads binning with spaced seeds. Theor Comput Sci. 2017; 698:88–99. Algorithms, Strings and Theoretical Approaches in the Big Data Era (In Honor of the 60th Birthday of Professor Raffaele Giancarlo).
 31
Ounit R, Lonardi S. Higher classification sensitivity of short metagenomic reads with CLARKS. Bioinformatics. 2016; 32(24):3823–5.
 32
Brown DG, Li M, Ma B. A tutorial of recent developments in the seeding of local alignment. J Bioinforma Comput Biol. 2004; 02(04):819–42.
 33
Mohamadi H, Chu J, Vandervalk BP, Birol I. ntHash: recursive nucleotide hashing. Bioinformatics. 2016; 32(22):3492–4.
 34
Girotto S, Comin M, Pizzi C. Fast Spaced Seed Hashing In: Schwartz R, Reinert K, editors. 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), vol. 88. Dagstuhl, Germany: Schloss Dagstuhl–LeibnizZentrum fuer Informatik: 2017. p. 1–14.
 35
Girotto S, Comin M, Pizzi C. FSH: fast spaced seed hashing exploiting adjacent hashes. Algorithm Mol Biol. 2018; 13:8.
 36
Keich U, Li M, Ma B, Tromp J. On spaced seeds for similarity search. Discret Appl Math. 2004; 138(3):253–63.
 37
Girotto S, Comin M, Pizzi C. Binning metagenomic reads with probabilistic sequence signatures based on spaced seeds. In: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).Piscataway: IEEE: 2017. p. 1–8.
 38
Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014; 15:46.
 39
M H. Mason: a read simulator for second generation sequencing data. Technical report, FU Berlin. 2010. http://publications.mi.fuberlin.de/962 Accessed 09 Jan 2017.
 40
Leimeister CA, SohrabiJahromi S, Morgenstern B. Fast and accurate phylogeny reconstruction using filtered spacedword matches. Bioinformatics. 2017; 33(7):971–9.
Funding
Publication costs for this article were sponsored by the Italian MIUR project “Compositional Approaches for the Characterization and Mining of Omics Data” (PRIN20122F87B2).
Availability of data and materials
The software is freely available for academic use at: https://bitbucket.org/samu661/fish/overview.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 19 Supplement 15, 2018: Proceedings of the 12th International BBCC conference. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume19supplement15.
Author information
Affiliations
Contributions
All authors contributed to the design of the approach and to the analysis of the results. SG implemented the FISH software tool and performed the experiments. CP and MC conceived the study and drafted the manuscript. CP coordinated and supervised the work. All authors have read and approved the manuscript for publication.
Corresponding authors
Correspondence to Matteo Comin or Cinzia Pizzi.
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional information
Authors’ information
Dipartimento di Ingegneria dell’Informazione  Università degli Studi di Padova
via Gradenigo 6/A, 35131 Padova  Italy
Email addresses: SG (samuele.girotto@gmail.com), MC (comin@dei.unipd.it), CP (cinzia.pizzi@dei.unipd.it)
Additional file
Additional file 1
Supplementary Tables. (PDF 45.9 kb)
Rights and permissions
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.
About this article
Cite this article
Girotto, S., Comin, M. & Pizzi, C. Efficient computation of spaced seed hashing with block indexing. BMC Bioinformatics 19, 441 (2018). https://doi.org/10.1186/s1285901824158
Published:
Keywords
 Spaced seeds
 kmers
 Efficient computation of hashing