Using cascading Bloom filters to improve the memory usage for de Brujin graphs
© Salikhov et al.; licensee BioMed Central Ltd. 2014
Received: 8 December 2013
Accepted: 17 February 2014
Published: 24 February 2014
De Brujin graphs are widely used in bioinformatics for processing next-generation sequencing data. Due to a very large size of NGS datasets, it is essential to represent de Bruijn graphs compactly, and several approaches to this problem have been proposed recently.
In this work, we show how to reduce the memory required by the data structure of Chikhi and Rizk (WABI’12) that represents de Brujin graphs using Bloom filters. Our method requires 30% to 40% less memory with respect to their method, with insignificant impact on construction time. At the same time, our experiments showed a better query time compared to the method of Chikhi and Rizk.
The proposed data structure constitutes, to our knowledge, currently the most efficient practical representation of de Bruijn graphs.
KeywordsNext-generation sequencing Genome assembly de Brujin graph Bloom filter
Modern next-generation sequencing (NGS) technologies generate huge volumes of short nucleotide sequences (reads) drawn from a DNA sample under study. The length of a read varies from 35 to about 400 base pairs (letters) and the number of reads may be hundreds of millions, thus the total volume of data may reach tens or even hundreds of Gb.
Many computational tools dealing with NGS data, especially those devoted to genome assembly, are based on the concept of a de Bruijn graph, see e.g. . Nodes of a de Bruijn grapha correspond to all distinct k-mers occurring in the given set of reads, and two k-mers are linked by an arc if they have a suffix-prefix overlap of size k−1. The value of k is an open parameter that in practice is chosen between 20 and 64. The idea of using de Bruijn graphs for genome assembly goes back to the “pre-NGS era” . Note, however, that de novo genome assembly is not the only application of those graphs when dealing with NGS data. There are several others, including: de novo transcriptome assembly  and de novo alternative splicing calling  from transcriptomic NGS data (RNA-seq); metagenome assembly  from metagenomic NGS data; and genomic variant detection  from genomic NGS data using a reference genome.
Due to a very large size of NGS datasets, it is essential to represent de Bruijn graphs as compactly as possible. This has been a very active line of research. Recently, several papers have been published that propose different approaches to compressing de Bruijn graphs [7–11].
Conway and Bromage  proposed a method based on classical succinct data structures, i.e. bitmaps with efficient rank/select operations. On the same direction, Bowe et al.  proposed an interesting succinct representation that, assuming only one string (read) is present, uses only 4E bits, where E is the number of arcs in the graph. The more realistic case where there are R reads can be easily reduced to the one string case by concatenating all R reads using a special separator character. In this case, however, the size of the structure is 4E+O(R logE) bits (, Theorem 1). Since the multiplicative constant of the second term is difficult to evaluate, it is hard to know precisely what would be the size of this structure in practice.
Ye et al.  proposed a different method based on a sparse representation of de Bruijn graphs, where only a subset of k-mers present in the dataset are stored. Using the Bloom filter data structure, Pell et al.  proposed probabilistic de Bruijn graphs as a compact approximate representation of full de Bruijn graphs. Finally, Chikhi and Rizk  improved Pell’s scheme in order to obtain an exact representation of the de Bruijn graph.
A direct application of Bloom filters to de Bruijn graphs, studied in , results in a space-efficient representation at the price of allowing one-sided errors, namely false positive nodes (k-mers). The method of  removes these errors and proposes a space-efficient data structure for the exact representation of de Bruijn graphs. The method is based on the following idea. In the genome assembly application, de Bruijn graphs are only used for traversal, and random accesses to graph nodes are never performed. If all queried nodes (k-mers) are only those which are reachable from some node known to belong to the graph, then only a fraction of all false positives can actually occur. Storing these false positives explicitly leads to an exact (false positive free) and space-efficient representation of the de Bruijn graph. This is the best practical exact representation of de Bruijn graphs for the purpose of genome assembly, now implemented in MINIA software .
Our main contribution is an improvement of the method of  by changing the representation of the set of false positives. We achieve this by iteratively applying a Bloom filter to represent the set of false positives, then the set of “false false positives” etc. We show analytically that this cascade of Bloom filters allows for a considerable further economy of memory, improving the method of . Depending on the value of k, our method requires 30% to 40% less memory with respect to the method of . Moreover, with our method, the memory grows very little as k grows. Finally, we implemented our method and tested it against  on real datasets. The tests confirm the theoretical predictions for the size of structure and show a 20% to 30% improvement in query times.
A de Bruijn graph, for a given parameter k, of a set of reads (strings) is entirely defined by the set T⊆U=Σ k of k-mers present in . The nodes of the graph are precisely the k-mers of T and for any two vertices u,v∈T, there is an arc from u to v if the suffix of u of size (k−1) is equal to the prefix of v of the same size. Thus, given a set T⊆U of k-mers, we can represent its de Bruijn graph using a Bloom filter B. This approach has the disadvantage of having false positive nodes, as a direct consequence of false positives in the Bloom filter, which can create false connections in the graph (see  for the influence of false positive nodes on the topology of the graph). The naive way to remove those false positives nodes by explicitly storing (e.g. using a hash table) the set of all false positives of B is clearly inefficient, as the expected number of elements to be explicitly stored is .
The key idea of  is to explicitly store only a small subset of all false positives of B, the so-called critical false positives. Consider a k-mer u that belongs to T, u has at most 2|Σ|=8potential neighbors, i.e. k-mers overlapping u by (k−1) letters. The set of critical false positives consists of the potential neighbors of k-mers of T that are false positives of B. This set is, in general, much smaller than the set of all false positives of B, its expected size can be upper-bounded by . On the other hand, storing the set of critical false positives is clearly sufficient to represent the de Bruijn graph if one only wants to support graph traversal, i.e. navigation from a node of the graph to its neighbors. In this case, only potential neighbors of nodes in T are queried.
Cascading Bloom filter
Let be a set of reads and T0 be the set of occurring k-mers (nodes of the de Brujin graph) that we want to store. As stated in Section “Preliminaries”, the method of  stores T0 via a bitmap B1 using a Bloom filter, together with the set T1 of critical false positives. T1 consists of potential neighbors of T0 which are stored in B1 “by mistake”, i.e. belongb to B1 but not to T0. B1 and T1 are sufficient to represent the graph provided that the only queried k-mers are those which are potential neighbors of k-mers of T0.
The idea we introduce in this work is to use this structure recursively and represent the set T1 by a new bitmap B2 and a new set T2, then represent T2 by B3 and T3, and so on. More formally, starting from B1 and T1 defined as above, we define a series of bitmaps B1,B2,… and a series of sets T1,T2,… as follows. B2 stores the set of false positives T1 using another Bloom filter, and T2 contains the critical false positives of B2, i.e. true positives from T0 that are stored in B2 “by mistake” (we call them false false positives). B3 and T3, and, generally, B i and T i are defined similarly: B i stores k-mers of Ti−1 using a Bloom filter, and T i contains k-mers stored in B i “by mistake”, i.e. those k-mers in B i that do not belong to Ti−1 but belong to Ti−2. Observe that T0∩T1=∅, T0⊇T2⊇T4… and T1⊇T3⊇T5….
The following lemma shows that the construction is correct, that is it allows one to verify whether or not a given k-mer belongs to the set T0.
Given a k-mer (node) u, consider the smallest i such that u∉Bi+1 (if u∉B1, we define i=0). Then, if i is odd, then u∈T0, and if i is even (including 0), then u∉T0.
Observe that u∉Bi+1 implies u∉T i by the basic property of Bloom filters that membership queries have one-sided error, i.e. there are no false negatives. We first check the Lemma for i=0,1.
For i=0, we have u∉B1, and then u∉T0.
For i=1, we have u∈B1 but u∉B2. The latter implies that u∉T1, and then u must be a false false positive, that is u∈T0. Note that here we use the fact that the only queried k-mers u are either nodes of T0 or their potential neighbors in the graph (see ), and therefore if u∈B1 and u∉T0 then u∈T1.
For the general case i≥2, we show by induction that u∈Ti−1. Indeed, u∈B1∩…∩B i implies u∈Ti−1∪T i (which, again, is easily seen by induction), and u∉Bi+1 implies u∉T i .
Since Ti−1⊆T0 for odd i, and Ti−1⊆T1 for even i (for T0∩T1=∅), the lemma follows.
Naturally, Lemma 1 provides an algorithm to check if a given k-mer u belongs to the graph: it suffices to check successively if it belongs to B1,B2,… until we encounter the first Bi+1 which does not contain u. Then, the answer will simply depend on whether i is even or odd: u belongs to the graph if and only if i is odd
In our reasoning so far, we assumed an infinite number of bitmaps B i . Of course, in practice we cannot store infinitely many (and even simply many) bitmaps. Therefore, we truncate the construction at some step t and store a finite set of bitmaps B1,B2,…,B t together with an explicit representation of T t . The procedure of Lemma 1 is extended in the obvious way: if for all 1≤i≤t, u∈B i , then the answer is determined by directly checking u∈T t .
Analysis of the data structure
Memory and time usage
A simple calculation shows that the minimum of this expression is achieved when r=5.464, and then the minimum memory used per k-mer is 8.45 bits.
As mentioned earlier, in practice we store only a finite number of bitmaps B1,…,B t together with an explicit representation (such as array or hash table) of T t . In this case, the memory taken by the bitmaps is a truncated sum r N+6r N c r +r N c r +.., and a data structure storing T t takes either or bits, depending on whether t is even or odd. The latter follows from the observations that we need to store (or ) k-mers, each taking 2k bits of memory. Consequently, we have to adjust the optimal value of r minimizing the total space, and re-estimate the resulting space spent on one k-mer.
1st column: k -mer size; 2nd and 4th columns: optimal value of r for Bloom filters (bitmap size per number of stored elements) for t =4 and t =6 respectively; 3rd and 5th columns: the resulting space per k -mer (for t =4 and t =6); 6th column: space per k -mer for the method of [] ( t =1)
Bits per k-mer
Bits per k-mer
Bits per k-mer
for t=1 ()
The last column of Table 1 shows the memory usage of the original method of , obtained using the estimation the authors provided. Note that according to that estimation, doubling the value of k results in a memory increment by 1.44 bits, whereas in our method the increment is of 0.11 to 0.22 bits.
Let us now comment on query and preprocessing times for our scheme. The query time can be split into two parts: the time spent on querying t Bloom filters and the time spent on querying T t . As stated in Section “Preliminaries”, each query in a Bloom filter corresponds to p=r ln2 hash functions evaluations. Clearly, the total query time for t Bloom filters is t p=Θ(t r). Thus, it is expected that using t Bloom filters, even if r decreases, the query time increases. For instance, with t=4 we have that r=6.049 (k=32) and the total number of hash function evaluations is proportional to r t≈24, whereas with t=1 we have that r=11.44 and r t≈12, a factor 2 increase in the number of hash function evaluations. On the other hand, the set T t is generally much smaller than T1, due to the above-mentioned exponential decrease. Depending on the data structure for storing T t , the time saving in querying T t vs. T1 may even dominate the time loss in querying multiple Bloom filters. Our experimental results (Section “Construction algorithm” below) confirm that this situation does indeed occur in practice. Note that even in the case when querying T t weakly depends on its size (e.g. when T t is implemented by a hash table), the query time will not increase much, due to our choice of a small value for t, as discussed earlier.
At the preprocessing step, we need to construct Bloom filters B1,…,B t and set T t . At each stage i, we need to store Ti−1 and Ti−2 (possibly on disk, if we want to save on the internal memory used by the algorithm) to construct B i and T i . A key observation is that the sizes of B i and T i decrease exponentially on i and therefore the time spent to construct the whole structure is a linear function on the size of T0. In particular, asymptotically it is only a small constant factor larger compared to the original method of . If the value of t is small (such as t=4, as in Table 1), the preprocessing time is obviously even smaller.
Using different values of r for different filters
and the minimum value of this expression becomes 7.93 (this value is achieved with r1=4.41;r i =1.44,i>1).
Estimated memory occupation for the case of different values of r vs. single value of r (shown in Table 1), for 4 Bloom filters ( t =4)
Bits per k-mer
Bits per k-mer
different values ofr
single value ofr
5.254, 3.541, 4.981, 8.653
5.383, 3.899, 5.318, 9.108
5.572, 4.452, 5.681, 9.108
5.786, 5.108, 6.109, 9.109
Query distribution among filters
The query algorithm of Lemma 1 simply queries Bloom filters B1,…,B t successively as long as the returned answer is positive. The query time then directly depends on the number of filters applied before getting a negative answer. Therefore, it is instructive to analyse how the query frequencies to different filters are distributed when performing a graph traversal. We provide such an analysis in this section.
We analyse query frequencies during an exhaustive traversal of the de Bruijn graph, when each true node is visited exactly once. We assume that each time a true node is visited, all its eight potential neighbors are queried, as there is no other way to tell which of those neighbors are real. Note however that this assumption does not take into account structural properties of the de Bruijn graph, nor any additional statistical properties of the genome (such as genomic word frequencies).
For a filter B i , we want to estimate the number of queried k-mers resolved by B i during the traversal, that is queries on which B i returns no. This number is the difference of the number of queries submitted to B i and the number of queries for which B i returns yes. Note that the queries submitted to B i are precisely those on which the previous filter Bi−1 returns yes.
If the input set T0 contains N k-mers, then the number of queries in a graph traversal is 8N, since for each true node each of its 8 potential neighbors are queried. Moreover, about 2N queries correspond to true k-mers, as we assume that most of the graph nodes have two true neighbors. Filter B1 will return yes on 2N+6c r N queries, corresponding to the number of true and false positives respectively. For an arbitrary i, filter B i returns yes precisely on the k-mers inserted to B i (i.e. k-mers B i is built on), and the k-mers which are inserted to Bi+1 (which are the critical false positives for B i ). The counts then easily follow from the analysis of Section “Memory and time usage”.
Estimations of the number of queries made to filters B 1 , B 2 , B 3 , B 4 and of the fraction of queries resolved by each filter (for the optimal value r =5 . 464), in the case of infinite number of filters
nb of queries
(2+6c r )N
(6c r +2c r )N
(2c r +6c2r)N
Queries returning yes
(2+6c r )N
(6c r +2c r )N
(2c r +6c2r)N
Queries returning no
(6−6c r )N
(2−2c r )N
(6c r −6c2r)N
(2c r −2c2r)N
Fraction of resolved queries
Estimated fractions of queries resolved by each filter and by the explicitely stored set T t for t =1,2,4, computed for k =32 and optimal value of r shown in the second column
In practice, constructing a cascading Bloom filter for a real-life read set is a computationally intensive step. To perform it on a commonly-used computer, the implementation makes an essential use of external memory. Here we give a short description of the construction algorithm for up to four Bloom filters. Extension for larger number of filters is straightforward.
We start from the input set T0 of k-mers written on disk. In this set, for each pair of k-mer and its reverse complement we keep only one of them, the lexicographically smaller, and identify the other to it. We build the Bloom filter B1 of appropriate size by inserting elements of T0 successively. Next, all possible extensions of each k-mer in T0 are queried against B1, and those which return true are written to the disk. Then, in this set only the k-mers absent from T0 are kept, i.e. we perform a set difference from T0. We cannot afford to load T0 entirely in memory, so we partition T0 and perform the set difference in several iterations, loading only one partition of T0 each time. This results in the set T1 of critical false positives, which is also kept on disk. Up to this point, the procedure is identical to that of .
Next, we insert all k-mers from T1 into B2 and to obtain T2, we check for each k-mer in T0 if a query to B2 returns true. This results in the set T2, which is directly stored on disk. Thus, at this point we have B1, B2 and, by loading T2 from the disk, a complete representation for t=2. In order to build the data structure for t=4, we continue this process, by inserting T2 in B3 and retrieving (and writing directly on disk) T3 from T1 (stored on disk). It should be noted that to obtain T i we need Ti−2, and by always directly storing it on disk we guarantee not to use more memory than the size of the final structure. The set T t (that is, T1, T2 or T4 in our experiments) is represented as a sorted array and is searched by a binary search. We found this implementation more efficient than a hash table.
Implementation and experimental setup
We implemented our method using MINIA software  and ran comparative tests for 2 and 4 Bloom filters (t=2,4). Note that since the only modified part of MINIA was the construction step and the k-mer membership queries, this allows us to precisely evaluate our method against the one of .
The first step of the implementation is to retrieve the list of k-mers that appear more than d times using DSK  – a constant memory streaming algorithm to count k-mers. Note, as a side remark, that performing counting allows us to perform off-line deletions of k-mers. That is, if at some point of the scan of the input set of k-mers (or reads) some of them should be deleted, it is done by a simple decrement of the counter.
Assessing the query time is done through the procedure of graph traversal, as it is implemented in . Since the procedure is identical and independent on the data structure, the time spent on graph traversal is a faithful estimator of the query time.
We compare three versions: t=1 (i.e. the version of ), t=2 and t=4. For convenience, we define 1 Bloom, 2 Bloom and 4 Bloom as the versions with t=1,2 and 4, respectively.
E.coli dataset, varying k
The total size of the structures in bits per stored k-mer, i.e. the size of B1 and T1 (respectively, B1,B2, T2 or B1,B2,B3,B4, T4) is shown in Figure 1(a). As expected, the space for 4 Bloom filters is the smallest for all values of k considered, showing a considerable improvement, ranging from 32% to 39%, over the version of . Even the version with just 2 Bloom filters shows an improvement of at least 20% over , for all values of k. Regarding the influence of the k-mer size on the structure size, we observe that for 4 Bloom filters the structure size is almost constant, the minimum value is 8.60 and the largest is 8.89, an increase of only 3%. For 1 and 2 Bloom the same pattern is seen: a plateau from k=16 to 32, a jump for k=33 and another plateau from k=33 to 64. The jump at k=32 is due to switching from 64-bit to 128-bit representation of k-mers in the table T t .
Figure 1(b) shows the size of table T t (number of k-mers) for t=1,2,4, depending on k. It clearly demonstrates the sharp decrease of the size of T t with growing t, in accordance with the exponential decrease estimated analytically in Section “Memory and time usage”. We also observe a decrease in the size of T t with growing k for t=1 and, to a smaller extent, for t=2, while for t=4 the decrease is not noticeable. This is explained by the increase rate of optimal r (Table 1) which is high for t=1, smaller for t=2 and yet smaller for t=4. Since the size of T t is O(N ct r/2) (Section “Memory and time usage”) for c<1 and almost invariable N, the decrease rate is exponential w.r.t. the increase rate of r.
Traversal times for each version are shown in Figure 1(c). The fastest version is 4 Bloom, showing an improvement over  of 18% to 30%, followed by 2 Bloom. This result is surprising and may seem counter-intuitive, as we have four filters to apply to the queried k-mer rather than a single filter as in . However, the size of T4 (or even T2) is much smaller than T1, as the size of T i ’s decreases exponentially. As T t is stored in an array, the time economy in searching T4 (or T2) compared to T1 dominates the time lost on querying additional Bloom filters, which explains the overall gain in query time.
As far as the construction time is concerned (Figure 1(d)), our versions yielded also a faster construction, with the 4 Bloom version being 5% to 22% faster than that of . The gain is explained by the time required for sorting the array storing T t , which is much higher for T0 than for T2 or T4. However, the gain is less significant here, and, on the other hand, was not observed for bigger datasets (see Section “Human dataset”).
E. coli dataset, varying coverage
E. coli dataset, query statistics
Results of 1, 2 and 4 Bloom filters version for 564M Human reads of 100 bp using k =23
Construction time (s)
Traversal time (s)
Bloom filters size (MB)
False positive table
Total size (MB)
Size (bits/ k -mer)
The results are in general consistent with the previous tests on E.coli datasets. There is an improvement of 34% (21%) for the 4 Bloom (2 Bloom) in the size of the structure. The graph traversal is also 26% faster in the 4 Bloom version. However, in contrast to the previous results, the graph construction time increased by 10% and 7% for 4 and 2 Bloom versions respectively, when compared to the 1 Bloom version. This is due to the fact that disk writing/reading operations now dominate the time for the graph construction, and 2 and 4 Bloom versions generate more disk accesses than 1 Bloom. As stated in Section “Construction algorithm”, when constructing the 1 Bloom structure, the only part written on the disk is T1 and it is read only once to fill an array in memory. For 4 Bloom, T1 and T2 are written to the disk, and T0 and T1 are read at least one time each to build B2 and B3. Moreover, since the size coefficient of B1 reduces, from r=11.10 in 1 Bloom to r=5.97 in 4 Bloom, the number of false positives in T1 increases.
Discussion and conclusions
Using cascading Bloom filters for storing de Bruijn graphs has clear advantages over the single-filter method of . In terms of memory consumption, which is the main parameter here, we obtained an improvement of around 30%-40% in all our experiments. Our data structure takes 8.5 to 9 bits per stored k-mer, compared to 13 to 15 bits by the method of . This confirms our analytical estimations. The above results were obtained using only four filters and are very close to the estimated optimum (around 8.4 bits/k-mer) produced by the infinite number of filters. This is consistent with both our analytical estimations and experimental data showing that over 99% of queries are resolved by the four filters, without resorting to the explicitely stored set T t . Even two filters only resolve about 95% of queries. An interesting characteristic of our method is that the memory grows insignificantly with the growth of k, even slower than with the method of . Somewhat surprisingly, we also obtained a significant decrease, of order 20%-30%, of query time. The construction time of the data structure varied from being 10% slower (for the human dataset) to 22% faster (for the bacterial dataset). Cascading Bloom filters have now been implemented by default in the MINIA software .
As stated previously, another compact encoding of de Bruijn graphs has been proposed in , however no implementation of the method was made available. For this reason, we could not experimentally compare our method with the one of . We remark, however, that the space bound of  heavily depends on the number of reads (i.e. coverage), while in our case, the data structure size is almost invariant with respect to the coverage (Section “E. coli dataset, varying coverage”).
An interesting open question is whether the Bloom filter construction can be made online, so that new k-mers (reads) can be inserted without reconstructing the whole data structure from scratch. Note that the presented construction (Section “Construction algorithm”) is inherently off-line, as all k-mers should be known before the data structure is built.
Another interesting prospect for possible further improvements of our method is offered by work , where an efficient replacement to Bloom filter was introduced. The results of  suggest that we could hope to reduce the memory to about 5 bits per k-mer. However, there exist obstacles on this way: an implementation of such a structure would probably result in a significant construction and query time increase.
a Note that this is actually a subgraph of the de Bruijn graph under its classical combinatorial definition. However, we still call it de Bruijn graph to follow the terminology common to the bioinformatics literature.
b By a slight abuse of notation, we also view B j as the set of all k-mers on which the filter B j returns the positive answer.
Part of this work has been done during the visit of KS to LIGM in France, supported by the CNRS French-Russian exchange program in Computer Science. GK has been partly supported by the ABS2NGS grant of the French gouvernement (program Investissement d’Avenir) as well as by a Marie-Curie Intra-European Fellowship for Carrier Development. GS was supported by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 10.
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