 Methodology article
 Open access
 Published:
Querying large read collections in main memory: a versatile data structure
BMC Bioinformatics volume 12, Article number: 242 (2011)
Abstract
Background
High Throughput Sequencing (HTS) is now heavily exploited for genome (re) sequencing, metagenomics, epigenomics, and transcriptomics and requires different, but computer intensive bioinformatic analyses. When a reference genome is available, mapping reads on it is the first step of this analysis. Read mapping programs owe their efficiency to the use of involved genome indexing data structures, like the BurrowsWheeler transform. Recent solutions index both the genome, and the kmers of the reads using hashtables to further increase efficiency and accuracy. In various contexts (e.g. assembly or transcriptome analysis), read processing requires to determine the subcollection of reads that are related to a given sequence, which is done by searching for some kmers in the reads. Currently, many developments have focused on genome indexing structures for read mapping, but the question of read indexing remains broadly unexplored. However, the increase in sequence throughput urges for new algorithmic solutions to query large read collections efficiently.
Results
Here, we present a solution, named Gk arrays, to index large collections of reads, an algorithm to build the structure, and procedures to query it. Once constructed, the index structure is kept in main memory and is repeatedly accessed to answer queries like "given a kmer, get the reads containing this kmer (once/at least once)". We compared our structure to other solutions that adapt uncompressed indexing structures designed for long texts and show that it processes queries fast, while requiring much less memory. Our structure can thus handle larger read collections. We provide examples where such queries are adapted to different types of read analysis (SNP detection, assembly, RNASeq).
Conclusions
Gk arrays constitute a versatile data structure that enables fast and more accurate read analysis in various contexts. The Gk arrays provide a flexible brick to design innovative programs that mine efficiently genomics, epigenomics, metagenomics, or transcriptomics reads. The Gk arrays library is available under Cecill (GPL compliant) license from http://www.atgcmontpellier.fr/ngs/.
Background
Nextgeneration sequencing technologies are presently being used to answer key biological questions at the scale of the entire genome and with unprecedented depth. Whether determining genetic or genomic variations, cataloging transcripts and assessing their expression levels, identifying DNAprotein interactions or chromatin modifications, surveying the species diversity in an environmental sample, all these tasks are now tackled with High Throughput Sequencing (HTS) and require different, but computer intensive bioinformatic analyses. Typically, a recent RNA sequencing experiment (RNASeq) produces about 8 million reads of 75 base pairs each [1], but both the yield and read length will increase [2].
Mapping the reads against a reference genome provides the genomic positions of mapped reads. For instance with RNASeq reads, these positions allow to know whether a gene is expressed in the studied condition. The set of mapped positions represents only part of the information needed to analyze the reads, and it can be obtained only if a genome is available. Indeed, other important information are contained in the read collection itself. For instance, to determine the frequency of haplotypes at a SNP position, one needs to align the reads related to this position. These can be obtained by considering for some length k, the kmers overlapping the SNP and searching for the reads sharing this kmer. This procedure is applicable even in the absence of a reference genome, and similar ones can be designed to search for a binding motif in ChIPSeq reads, to determine with RNASeq data whether different regions of a messenger RNA sequence are susceptible to be differentially expressed, etc.
For tasks like assembly or read clustering, one needs to determine reads overlapping each other or that align partly one to another. Numerous works on similarity search algorithms have developed seedandextend strategies and shown that it can be performed efficiently by searching common kmers between two sequences [3, 4].
Surely, now and even more in the near future, we will need efficient indexing data structures to store and query large collections of reads in main memory. Up to now, a lot of computational research has been devoted to read mapping, and the most efficient tools owe their efficiency to the use of involved genome indexing data structures, like the BurrowsWheeler transform [5]. On the other hand, the question of read indexing remains quite unexplored, although the improvements in sequencing throughput suggest that such structures will become a compulsory part of future read analysis programs. A sign supporting this view: even mapping programs now start to index both the genome and the kmers of the reads to boost efficiency and accuracy [6].
Numerous works have presented data structures to index a single text, like the well known Suffix Tree (ST) or the Suffix Array (SA) [7, 8]. These enable the socalled locate query, that is to locate all occurrences of a pattern P either from its sequence or from a position j of occurrence in the text, as well as count query to obtain the number of occurrences of P. These structures can be adapted to index a set of texts, where each text differ from each other; the structures are then called generalized Suffix Tree [9], or generalized Suffix Array (gSA) [10]. This is done by concatenating all texts and adding a separator symbol that does not belong to the alphabet (e.g., a $ for the DNA alphabet) after each text [9], or directly [10]. Then it requires to store the length of each text in an additional array to correctly answer locate queries. Such algorithms have not been adapted to collections of texts, where two texts may be equal in sequence but differ in their identifier. The reads obtained from sequencers form a collection, not a set.
When the total text is too large, compressed indexes reduce the memory needed by storing not all, but only a certain proportion of the text positions. Compression is obtained by sampling the positions to be stored, while non sampled positions need to be recomputed at run time. This enables the user to control the balance between amount of memory and query time. Hence, compression has an impact on the time needed to compute a query. Ferragina et al. report in a large practical evaluation of compressed text indexes, that the query time of all tested compressed indexes are between 100 and 1,000 times slower than with a plain SA for an index that is 5 times smaller [11]. The FMindex [5] is used to index all chromosomes in mapping applications [12]. However, the scalability of neither plain nor compressed indexes to collections of millions of texts has not been investigated so far. We thus address the question of indexing large collections of reads with an uncompressed index and compare its performance to a generalized suffix array and a hash table. Our structure aims to save space compared to those indexes while globally retaining queries as fast. Thus we avoid the pitfall of compressed indexes which are less space consuming but slower by orders of magnitude.
In this work, we propose a new data structure to index reads, an algorithm to build the structure, and procedures to query it. Our structure, named Gk arrays, is kept in main memory once built and repeatedly accessed to answer different kinds of queries like "given a kmer, get the reads containing this kmer (once/at least once)". One can ask both for the kmer positions or simply for the reads containing it, which can prove useful in different applications. We focus on cases where millions of queries need to be computed; clearly, memory usage will be the key issue. An alternative solution is to adapt some uncompressed indexing structures designed for long texts (suffix tree or suffix array [9, 13]). We compare Gk arrays to such an alternative and show experimentally that they process queries fast, while requiring much less memory (between 2/3 and 1/3 of a suffix array solution). We also perform experimental comparisons against a method using hash table: it shows that while the hash table method can answer quickly to queries it does not scale to large collections of reads.
If in biology the term kmer is preferred, computer scientists rather use the equivalent words of kfactor or ksubstring; we will stick to the term kmer. The Gk arrays allow to answer queries related to an input kmer; let us call these kmer queries. Before entering the algorithms description, we list below the applications of kmer queries in the analysis of High Throughput Sequencing data. The Results section will first present our data structure, its construction algorithm and the procedures to answer kmer queries, then detail the experimental comparisons.
Finally, we discuss the advantages of our structure and conclude with future developments.
Note that this study does not tackle the question of read mapping, it focuses on read indexing.
Queries and Applications
Let us give an informal presentation of the problem. We are given a collection of q reads of length m and a length of substring k such that k ≤ m.
Suppose one is given a string f of length k; one does not know whether it appears in some of the reads or not (i.e., whether f is a substring of some read). In the Algorithm section, we describe a data structure in which all substrings of length k of the reads are ordered lexicographically. Hence, one can search for f using a dichotomic search in O(k log((m  k + 1)q))) worst case time in this structure (the dichotomic search is the standard procedure in this context [8, 9]), and determine whether at least one read contains f as a substring and at which position. If not, the answers to the queries below, which are all related to a subcollection of reads containing f, are trivially the empty set or zero. Otherwise, one knows that f occurs in some read r of the collection at position j, and wishes to get some information on the other reads where f occurs. One wants to answer the following questions:
Q1: In which reads does f occur?
Q2: In how many reads does f occur?
Q3: What are the occurrence positions of f in the reads?
Q4: What is the number of occurrences of f in the reads?
Q5: In which reads does f occur only once?
Q6: In how many reads does f occur only once?
Q7: What are the occurrence positions of f in each read where f occurs only once?
Q8: What is the number of occurrences of f in the reads where it occurs only once?
We state several remarks about the queries before dwelling on applications.

1.
The queries go by pairs: the first one computes a set of positions or read indices, while the second computes the cardinality of that set.

2.
Note the clear semantic difference between Q1/Q2 and Q3/Q4. The answer to Q1 yields the identifiers of the reads in which f occurs, while that to Q3 gives also all its positions in the read. This clearly differs since f may occur several times in a read (e.g., if f is a polyA sequence). Sometimes the positions are needed, sometimes only the reads (see below).

3.
Queries Q5Q8 are versions of Q1Q4 constrained to a single occurrence of f in the reads. Of course other variants can also be computed, e.g. where the number of occurrences is limited by a user defined threshold. Since f is constrained to occur only once in each read, Q6 and Q8 are equivalent, and we will mention only Q6 in the sequel.

4.
The data structure we propose is intended to be kept in memory and used for multiple queries.
Although this paper focuses on the data structure, its efficiency, and on the algorithms to solve these type of queries, it is important to list applications of these queries. In which context of read analysis, can one use such queries? Note that in such context, k is smaller than the read length. Theoretical and empirical investigations show that for instance, with k ≥ 19 or 20, kmers indicate in average a single genomic location in the human genome [14]. Such values of k can be computed depending on the genome length. Translated to reads or sequences: it is unlikely that two reads sharing a kmer were not sequenced from the same part of the DNA. In other words, sharing a kmer is a witness for having a common genomic origin.
Mutation detection
Putative mutations (SNP, somatic mutations, small indels) are indicated by differences between a read and a reference genome. Once the reads have been mapped to the reference genome, one analyzes the subcollection of reads that covers a genomic position to count how many reads support the variation observed in the read or that observed in the genome. If one considers the two substrings of length k centered on this mutation position, one in the read and one in the genome, answering Q2 for these substrings will give an approximate count of these two haplotypes. If one needs the corresponding reads, then Q1 is the appropriate query. If only a single, or a few reads, share this kmer, then a sequence error might be suspected [15].
Local coverage
Suppose one is given a target sequence, which can be a read or an external sequence. For each of its kmer, let us call the local coverage, the number of reads sharing this kmer (this requires a dichotomic search). The local coverage profile (i.e. a histogram of the local coverage) along the target sequence provides useful information in various contexts. For a known mRNA and an RNASeq experiment, the average local coverage on all kmers is a proxy for the expression level of the target, while the profile enables one to distinguish the target's subregions expressed at different levels [16, 17]. In another context, with a genomic library, taking reads as queries and looking at their local coverage profile may help to detect those overlapping the extremity of a repeated or transposable element. This may prove useful to study the distribution and evolution of these elements in the genome.
Clustering and assembly without a reference genome
As for Expressed Sequence Tags, it is suitable to cluster and assemble RNASeq reads to compute the various transcripts expressed in the assayed library [16, 17]. It is necessary to detect near exact alignment between pair of reads, and this is usually performed efficiently by filtration using seeds. In such case, very efficient and sensible seeds are exact shared kmers [4]. Here, the subcollection of reads sharing a kmer with a given read, as well as the kmer positions, can be obtained using query Q3. The answer to Q4 can help guiding the clustering process.
Similar needs of query occur in the assembly of genomic reads [18, 19]. To know with which reads one can assemble a given read without ambiguity, one may perform query Q7 using kmers at the 5' or 3' extremities of the read. The obtained occurrences together with their positions will indicate the matching reads and the relative positions of read pairs for assembly.
Our application list provides examples and is by no means exhaustive. We could also mention for instance the estimation of the target genome length in assembly context, which uses kmer counting [20]. Clearly, these applications are beyond the scope of this paper. However, these paragraphs underline that the proposed data structure suits the needs of read processing in various application contexts, and will provide a unified framework for building read analysis programs.
Results and Discussion
This section contains the main contribution: a data structure to index large read collections, the Gk arrays. To describe it, we first introduce the notation, formalize the queries, exhibit the index data structure, give its construction algorithm, and the procedures for answering all queries. This makes the content of the Algorithms section. Then, in the Comparison section we investigate its practical usability compared to two alternatives: one based on a generalized Suffix Array (SA) and another based on a hash table. This includes theoretical and practical comparisons.
Algorithms
Here, we detail the algorithms to build the Gk arrays and to answer the queries. We start by defining more formally the queries we want to answer and introduce the necessary notation.
Notation and definition of the queries
Let Σ be an alphabet of size σ. Σ* denotes the set of words, strings or sequences over Σ and, for any integer n, Σ ^{n} denotes the set of words of length n over Σ. For a word x, x denotes the length of x. Given two words x and y, we denote by xy the concatenation of x and y. For every 0 ≤ i ≤ j ≤ x  1, x[i] denotes the (i + 1)^{th} element of x, and x[i.. j] denotes the substring x[i]x[i + 1] . . . x[j]. Let ≤ _{ L } denotes the comparison operator for the lexicographic order on words. Lexicographic ranks start from zero and all arrays are indexed from zero. For any finite set A, we denote its cardinality by #A.
The input consists a list R = (r_{0}, . . ., r_{q1}) of q short sequences of length m, called reads, which are not necessarily distinct. We know that m, k, q ∈ ℕ satisfy m ≥ k > 0.
A klong substring of a word is called a kmer. For any u ∈ Σ*, we denote by F_{ k } (u) the set of kmers in u: F_{ k } (u) = {v ∈ Σ ^{k}  ∃p ∈ [0, u  k] such that v = u[p. . p + k  1]}. Let f ∈ Σ ^{k} and let us denote the set of indexes of the reads in which f occurs by Ind_{ k } (f) = {j ∈ [0, q[ f ∈ F_{ k } (r_{ j } )}, and the set of positioned occurrences of f in all reads by Pos_{ k } (f) = {(j, ℓ )  r_{ j } [ℓ. . ℓ + k  1] = f}, where a positioned occurrence is given by the pair made of the read index in R and the beginning position of f in this read. Let us denote the restriction of Ind_{ k } (f) (resp. Pos_{ k } (f)) to subset of read indexes where f occurs only once by UInd_{ k } (f) (resp. UPos_{ k } (f)). Formally, UPos_{ k } (f) = {(j, ℓ)  r_{ j } [ℓ. . ℓ + k  1] = f and ∀i ≠ ℓ, r_{ j } [i. . i + k  1] ≠ f}, and UInd_{ k } (f) = {j  (j, ℓ) ∈ UPos_{ k } (f)}. Let i ∈ [0, q[, j″ ∈ [0, m  k + 1[, and let f be the kmer starting at j″ in read r_{ i } . Note that here we require the knowledge of the pair (i, j″), which defines the kmer f. Now, the seven kmer queries can be formally defined as computing
Clearly, it appears (see Additional File 1: Proof and queries' algorithms) that the algorithms to compute UInd_{ k } (f), resp. UPos_{ k } (f), for answering Q5/Q7, simply filter Ind_{ k } (f), resp. Pos_{ k } (f), on the fly, and are thus similar to the algorithms for Q1/Q3. For place sake, we will only detail the solutions for Q1Q4 in the sequel.
The index structure
Our algorithm relies on four arrays that allow to query the kmers of all reads. Hence, we define a word made of the concatenation of all reads: C_{ R } = r_{0}r_{1} ⋯ r_{q1}. Of course, a kmer that overlaps two reads in C_{ R } is not necessarily a kmer of some read. Hence, we introduce a system to renumber the positions of interest in C_{ R } . The rationale behind is to save place in the Gk arrays by discarding the positions of overlapping kmers in C_{ R } . Let us denote by the number of distinct kmers of all reads, and for the sake of legibility we set and (the number of interesting positions in a read and in C_{ R } , respectively). We call:

Pposition, a starting position in C_{ R }of a kmer that is not overlapping two reads, i.e. an element of .

g, the function that renumbers Ppositions in order such that their index are consecutive; g is defined by:

Qposition, an image of a Pposition by g(.), i.e. an element of . Note that the set Q_{pos} is not a query.
Clearly, P_{pos} and Q_{pos} have the same cardinality , and as (j ≠ j') implies g(j) ≠ g(j'), g is bijective. Hence, g^{1} exists and maps a Qposition back to its corresponding Pposition in C_{ R } . Proposition 1 explicits the conversion between a positioned occurrence and a Pposition.
Proposition 1. Let (j, ℓ) with j ∈ [0,q[, be a positioned occurrence of a kmer in a read. The corresponding Pposition in C_{ R } is jm+ℓ. Conversely, let j' be a Pposition, the corresponding positioned occurrence in a read is (⌊j'/m⌋, j' mod m).
This numbering system is important for it allows us to go back and forth between a positioned occurrence in a read, its corresponding Pposition in C_{ R } , and its Qposition that will be stored in our arrays.
Let j be a Qposition. We denote by s_{ Q } (j), resp. f_{ Q } (j), the suffix, resp. the kmer, of C_{ R } beginning at the Pposition g^{1}(j), i.e. s_{ Q } (j) = C_{ R } [g^{1}(j) . . qm  1] and f_{ Q } (j) = C_{ R } [g^{1}(j) . . g^{1}(j) + k  1]. We call s_{ Q } (j) a Psuffix. Note that all suffixes beginning at Ppositions have different length and are pairwise distinct; thus, there are such suffixes and they all have a different lexicographic rank. However, this may, and in real data applications will, not be the case for the kmers, i.e. the f_{ Q } (j). We call the set {f_{ Q } (j)  j ∈ Q_{pos}} the set of P_{ k } factors, whose cardinality is with our notation.
Now, we define the Gk arrays:
GkSA(Generalized k Suffix Array) is a modified Suffix Array of C_{ R } that lexicographically sorts only the Psuffixes,
GkIFA(Generalized k Inverse Factor Array) is a modified Inverse Suffix Array (ISA) that stores for each Qposition, in position order in C_{ R } , the lexicographic rank of the P_{ k } factors starting at the corresponding Pposition,
GkCFA(Generalized k Counting Factor Array) is an array that associates to a kmer (actually, to its rank) its number of occurrences at Ppositions in C_{ R } ,
GkCFPS(Generalized k Counting Factor Prefix Sum) stores the prefix sums of GkCFA. Since GkCFA and GkCFPS are equivalent only one of them is necessary at a time.
Formally, the definitions are (see Figure 1 for an example and Figure 2):

For i a suffix lexicographic rank and j a Qposition (i.e. i, j ∈ Q_{pos}),
GkSA[i] = j iff s_{ Q } (j) has lexicographic rank i among the Psuffixes.

For i a kmer lexicographic rank and j a Qposition (i.e. and j ∈ Q_{pos}),
GkIFA[j] = i iff f_{ Q } (j) has lexicographic rank i among the P_{ k } factors.

For i a kmer lexicographic rank (i.e. ),
GkCFA[i] = #{j ∈ Q_{pos} f_{ Q } (j) = f_{ Q } (GkSA[i])},

For i a kmer lexicographic rank (i.e. ), the definition of the prefix sum is and
GkCFPS[1] = 0.
Remark 1. The array GkCFPS is not essential to the algorithm: it is solely there to avoid multiple, time consuming computations of prefix sums over GkCFA (see GkCFPS definition above). Moreover, any value of GkCFA can also be accessed in constant time using GkCFA[i] = GkCFPS[i] GkCFPS[i 1]. Thus, GkCFPS will be kept in memory to replace GkCFA.
We give some useful properties of Gk arrays.
Proposition 2. For, GkCFPS[i] = #{j ∈ Q_{ pos }  f_{ Q } (j) ≤_{ L } f_{ Q } (GkSA[i])} (Proof by induction).
In other words, GkCFPS[i] is the number of P_{ k } factors having lexicographic rank less than or equal to i. Since GkSA is sorted on the lexicographic order of the Psuffixes, it is also sorted on the lexicographic order of the P_{ k } factors. Hence, we get:
Proposition 3. Let f ∈ Σ ^{k} such that Ind_{ k } (f) ≠ ∅. All occurrences of f have the same rank among the P_{ k }factors, and are stored consecutively in GkSA.
Construction algorithm
First, we detail the algorithm for building GkSA, and then the one computing GkIFA and GkCFA.
Computation of GkSA
We first build the full Suffix Array (SA) of C_{ R } using a linear time and space algorithm. Since C_{ R } = mq this first step can be done in O(mq). Then GkSA is obtained from SA by selecting only the Ppositions and by renumbering them to Qpositions using function g. This second step is performed in O(mq) time and space. Moreover, GkSA is built in place of the Suffix Array: our algorithm allocates only the memory for the SA table. When answering Q1/Q2, each read where a given P_{ k } factor occurs should be counted only once (even if the P_{ k } factor occurs more than once in the read). Similarly, for Q5/Q6, we count only reads where a given P_{ k } factor occurs exactly once. To avoid using masks on the reads, we sort in increasing order the values of GkSA corresponding to P_{ k } factors sharing the same lexicographic rank (see Table in Additional File 1: Proof and queries' algorithms). The values that have to be sorted are Qpositions, i.e. integers, thus the sort can be performed in linear time on values of GkSA using e.g. radix sort [21]. The whole process takes O(mq) time and space.
Computation of GkIFA and GkCFA
Algorithm 1 shows how to compute jointly GkIFA and GkCFA. Its correctness proof is given in Additional File 1: Proof and queries' algorithms.
Algorithm 1: Computation of GkIFA and GkCFA.
Data: GkSA, C_{ R } , k,
Result: GkIFA and GkCFA
1 begin
2 GkIFA[GkSA[0]] ← 0;
3 GkCFA[0] ← 1;
4 t ← 0;
5 foreach do
6 j ← GkSA[i];
7 j' ← GkSA[i  1];
8 if f_{ Q } (j) ≠ f_{ Q } (j') then
9 t ← t + 1;
10 GkCFA[t] ← 0;
11 GkIFA[j] ← t ;
12 GkCFA[t] ← GkCFA[t] + 1;
13 return (GkIFA and GkCFA);
Theorem 1. Algorithm 1 correctly computes the arrays GkIFA and GkCFA. (Proof in Additional File 1: Proof and queries' algorithms).
The comparison between two P_{ k } factors (line 8) is naively performed in O(k) time, and is the only instruction of the inner loop that takes more than constant time. Hence, the computation of both GkIFA and GkCFA is performed in O((m  k)qk) time. Let us emphasize the simplicity of the algorithm, which explains the fast construction times obtained in practice.
Remark 2. Once the values of GkCFA have been calculated, one can compute the values of GkCFPS inplace in O((m  k)q) time (see Remark 1).
Answering the queries
Assume the Gk arrays have been built in a preprocessing step (see section Construction algorithm); we show how to answer the first four queries, starting with Q4 and Q3. Let , and let f be the kmer starting at j" in read r_{ i } . This occurrence of f in C_{ R } is found at Pposition j':= im + j" and the corresponding Qposition is j := g(j').
Q4: Computing the cardinality of Pos_{ k }(f)
First, we need to find the lexicographic rank of f among the P_{ k } factors, which we obtain directly by setting t := GkIFA[j] (by definition of GkIFA). The cardinality of Pos_{ k } (f) is simply the number of occurrences starting at Ppositions in C_{ R } , which is given by GkCFA[t] (by definition of GkCFA). By Remark 1, GkCFA[t] = GkCFPS[t]  GkCFPS[t  1].
Q3: Computing Pos_{ k }(f)
By Proposition 3, all occurrences of f starting at Ppositions are stored consecutively in GkSA. It suffices to find the lower and upper indices, denoted by ℓ_{ f } and u_{ f } respectively. By the ordering of GkSA all occurrences of factors smaller than f in the lexicographic order are stored before its occurrences in GkSA. Hence, by definition of GkCFPS and Proposition 2, we have u_{ f } = GkCFPS[t] and ℓ_{ f } = GkCFPS[t  1]. Since GkSA is indexed from 0, the starting Qpositions of occurrences of f are comprised in the range [ℓ_{ f }, u_{ f } ] in GkSA. The corresponding Ppositions are obtained using g^{1}(.) and are then transformed into positioned occurrences with Proposition 1. This proves Theorem 2.
Theorem 2. Let f be a kmer of a read occurring at Qposition j in C_{ R }. Then, its lexicographic rank among the P_{ k }factors is t := GkIFA[j]. If we set u_{ f } := GkCFPS[t] and ℓ_{ f } := GkCFPS[t  1] then
1. the starting Ppositions of f's occurrences in C_{ R } are {g^{1}(GkSA[ ℓ ])  ℓ ∈ [ℓ_{ f }, u_{ f } [},
2. Pos_{ k } (f) = {(, g^{1}(GkSA[ ℓ ]) mod m)  ℓ ∈ [ ℓ _{ f }, u_{ f } [},
3. #Pos_{ k } (f) = u_{ f }  ℓ_{ f } .
Given Theorem 2, the queries regarding Ind_{ k } (f) can be answered as follows:
Q1: Ind_{ k } (f): = { ℓ ∈ [ℓ_{ f }, u_{ f } [},
Q2: by counting the elements of Ind_{ k } (f) while computing it.
The algorithms for Q1, Q3, and Q4 are given extensively in Algorithms 2, 3, and 4. The algorithms for all other queries are included in Additional File 1: Proof and queries' algorithms.
To answer Q7, one computes Pos_{ k } (f) and scans it on the fly to remove reads (or the positioned occurrences) having strictly more than one occurrence of f. A similar approach solves Q8, and Q5. Variants of these queries where the number of allowed occurrences is constrained by a parameter can be answered similarly.
Complexity
Answering Q1Q3 or Q5Q8 requires to scan the values in GkSA inside the range corresponding to the kmer f, which can be performed in O(occ_Reads(f)) time, where occ_Reads(f) denotes the occurrence number of f in the reads. Query Q4 is computed in constant time using GkCFPS.
Algorithm 2: Q1 (Ind_{ k } (f))
Data: f ∈ ∑ ^{k} , j ∈ P_{pos} such that C_{ R } [j .. j + k  1] = f
Result: The set Ind_{ k } (f)
1 begin
2 Ind_{ k } ← empty set;
3 t ← GkIFA[j];
4 ℓ_{ f } ← GkCFPS[t  1];
5 u_{ f } ← GkCFPS[t];
6 prev ←  1;
7 foreach i ∈ [ℓ_{ f }, u_{ f } [ do
8 readIndex ← ;
9 if readIndex ≠ prev then
10 Add readIndex to Ind_{ k } ; prev ← readIndex;
11 return (Ind_{ k } );
Algorithm 3: Q3 (Pos_{ k } (f))
Data: f ∈ ∑ ^{k} , j ∈ P_{pos} such that C_{ R } [j . . j + k  1] = f
Result: The set Pos_{ k } (f)
1 begin
2 Pos_{ k } ← empty set;
3 t ← GkIFA[j];
4 ℓ_{ f } ← GkCFPS[t  1];
5 u_{ f } ← GkCFPS[t];
6 foreach i ∈ [ℓ_{ f }, u_{ f } [ do
7 readIndex ← ;
8 posInRead ← g^{1}(GkSA[i]) mod m;
9 Add the pair (readIndex, posInRead) to Pos_{ k } ;
10 return (Pos_{ k } );
Algorithm 4: Q4 (The cardinality of Pos_{ k } (f))
Data: f ∈ ∑ ^{k} , j ∈ P_{pos} such that C_{ R } [j . . j + k  1] = f
Result: The cardinality of Pos_{ k } (f)
1 begin //GkCFA[t] = GkCFPS[t] GkCFPS[t  1]
2 t ← GkIFA[j];
3 return (GkCFA[t]);
Practical considerations: implementation and variable read length
The value of k, which determines the length of kmers used for querying the collection of reads, is a parameter of our index. However, the Gk arrays remain flexible. If for the simplicity of the presentation we have assumed until now that all reads have the same length, the whole structure can be adapted to a collection of reads having variable length. Indeed, since some sequencing technologies produce variablelength reads (e.g. Roche 454^{®}), this adaptation is an important issue of versatility.
Indexing variablelength reads
We show how our method can be slightly adapted to tackle this problem. Remind that the Gk arrays consider the string C_{ R } , the concatenation of all reads, and save place by discarding positions at which a kmer overlaps two reads. This was done efficiently by converting any read position, or Pposition, into a Qposition, and conversely, using function g. Up to now, this function relies on the fact that the read length is fixed. Thus, we need to modify its definition to accommodate different read lengths. For this, we use a bit vector F, as long as C_{ R } , to record which positions in C_{ R } are Ppositions: j is a Pposition iff F[j] = 1. We implement it as a vector having rank and select capabilities [22, 23]. We define these operations as

rank_{1}(F, i) is the number of ones in F[0..i].

select_{1}(F, i) is the position of the ith one in F (or F if there is less than i ones in F).
These operations can be performed in constant time, and F can be stored in a compressed form needing only FH_{0}(F) + o(F) bits, where H_{0} is the zeroth order empirical entropy of F. Then computing g(j) and g^{1}(j) can be easily performed with a single rank or select query. Indeed, we have g(j) = rank_{1}(F, j), and g^{1}(j) = select_{1}(F, j). Finally, using little extra memory, Gk arrays can also handle variablelength reads.
Implementation
Gk arrays are available as a reusable C++ library under a Cecill C licence (GPL compliant). It accepts standard formats for the input read collection (FASTA, FASTQ). Depending on the number of kmer positions, the user should turn on the 64 bit encoding at compilation. It allows to process data sets of more than 2^{31} positions. Default is set to 32 bit encoding. Another compilation option can be activated to handle variablelength reads (typically Roche 454^{®} datasets), otherwise by default Gk arrays process fixed length reads.
The data structure construction and queries algorithms are coded in standard C and C++. To reduce memory consumption, the full SA of C_{ R } is built using libdivsufsort library https://code.google.com/p/libdivsufsort/, which was chosen for its efficiency and low memory usage (see https://code.google.com/p/libdivsufsort/wiki/SACA_Benchmarks for a benchmark of uptodate SA construction algorithms). However, its worst case time complexity is not linear in the length of the input sequence. Also the sort of values in GkSA inside each range corresponding to one P_{ k } factor is performed with the quicksort algorithm. A linear time construction of the array GkIFA is possible by using an LCP array (array storing the length of the Longest Common Prefixes between two consecutive suffixes in the lexicographic order). However, building this array would need at least 9mq bytes with Manzini's algorithm [24].
We implemented two versions of the Gk arrays: one which indexes only fixedlength reads, and another for variablelength reads. When not stated otherwise, Gk arrays refers to the implementation for fixedlength reads. For managing variablelength reads we used Sux http://sux.dsi.unimi.it/, an implementation of bit vectors with rank and select operations.
Theoretical and experimental comparisons
The sequencing capacity of new technologies continues to improve. Managing ever increasing read collections will be a major bottleneck in the bioinformatic analysis of High Throughput Sequencing data. The Gk arrays implement one solution to read indexing. If plain, as well as compressed, indexing data structures have been described in the litterature (cf. Introduction), their ability to handle large read collections have not been investigated. As we seek to optimise in practice the memory consumption, the construction time, and query running time, we will compare Gk arrays to two other uncompressed indexes: a generalized SA (gSA) and hash tables. We choose these two alternatives for they represent different approaches to read indexing. Among the uncompressed text indexes that have been generalized to handle a set of texts, the gSA is reckoned to be one of the most memory efficient and has been preferred to hash tables or the suffix tree in other contexts [9, 25]. On the other side, the optimisation of web search engines have triggered recent development of highly efficient hash tables, like Google sparse hash http://code.google.com/p/googlesparsehash or the hash tables from SGI extension of the C++ Standard Library http://www.sgi.com/tech/stl. It is thus instructive to also compare Gk arrays to state of the art hash tables. As explained in Introduction, compressed indexes save memory but induce much longer running times to answer queries compared to plain indexes, and have been excluded from this comparison. Nevertheless, designing efficient compressed read indexes is a challenging future research avenue, which could be addressed by compressing the Gk arrays.
A generalized Suffix Array (gSA) solution
We detail here the solution based on a generalized Suffix Array (gSA) to index a collection of reads, all reads having the same length. We call it the gSA solution. In fact it indexes the string made of the concatenation of all reads, C_{ R } . The preprocessing consists in building the generalized Suffix Array (gSA), the Inverse Suffix Array (ISA), and the Longest Common Prefixes (LCP) array of C_{ R } . The gSA is built using the same algorithm than for Gk arrays (libdivsufsort). The ISA is built by scanning the gSA in mq time, while the LCP array is also constructed in linear time using an efficient algorithm [26]. The tables are built in this order and add up in term of memory footprint.
In Figure 3(a) and 3(b), we compare the time and space complexities of gSA and Gk arrays solutions. Since both start by building gSA(C_{ R } ) and this is the dominant term of the time complexity, we obtain O(mq) time complexity: the space occupied during the construction of that table alone is 4.02mq, while it amounts to 4mq once built [27]. The last three columns of these tables show how the cumulated memory footprint evolves after each step during construction. We also monitored the memory footprint evolution during the construction of gSA and of Gk arrays and illustrate these graphically in Figures 4(a) and 4(b), respectively. For the gSA the three tables add up in memory and each takes 4mq space. With Gk arrays

1.
the GkSA table replaces gSA(C_{ R } ) in memory and takes only ,

2.
GkIFA takes an additional while GkCFA occupies with denoting the number of distinct P_{ k } factors, and

3.
finally the GkCFPS replaces GkCFA and takes exactly the same space.
In total, gSA takes 12mq bytes of memory, while Gk arrays occupy bytes (with 32bit integers), and is smaller than m. This explains why the memory footprint of Gk arrays remains smaller in practice than that of gSA (Figures 4(a) and 4(b)), even for varying k values (see Figures 5(a), 4(a) and 4(b)). Indeed, the gain of memory provided by Gk arrays increases with both k and q. If k is small, each kmer tends to occur more in average, and thus , meaning that GkCFPS is much smaller than the LCP array. If k is large then 4(mk+1)q ≪ 4mq and thus, GkSA plus GkIFA tables occupy much less place than the gSA and ISA tables. This constitutes, in almost all cases, a saving of at least 12(k  1)q bytes.
Locating a kmer in the reads can be done with a binary search in O(k + log qm) worst case time with gSA using SA and LCP arrays and worst case time with Gk arrays using GkSA. (We recall that the binary search is the standard procedure in this context [8, 9]).
However, Manber and Myers [8] mentioned that a simple improvement over the classical binary search (namely remembering the minimum length between the longest common prefix of the left and middle elements and the longest common prefix of the right and middle elements at each step of the binary search) permits to run in practice as fast as a worst case method (see also [9] Section 7.14.3 page 152).
Thus, starting from a kmer, rather than from a position, when answering the queries will bring an overhead similar in practice for the gSA and Gk arrays.
Algorithm 5: Q1 (Ind_{ k } (f)) with the generalised Suffix Array solution
Data: f ∈ ∑ ^{k} , j ∈ P_{pos} such that C_{ R } [j .. j + k  1] = f
Result: The set Ind_{ k } (f)
1 begin
2 Ind_{ k } ← empty set;
3 Initialize the whole bit vector, D, to zero;
4 i ← ISA[j];//starting position of f occurrences in SA
5 repeat
6 if then
//the occurrence position does not overlap two reads
7 readIndex ← ;
8 if D[readIndex] ≠ 1 then
//we have not found an occurrence in this read yet
9 Add readIndex to Ind_{ k } ;
10 D[readIndex] ← 1;
11 i ← i + 1;
12 until (i ≥ qm) or (LCP[SA[i], SA[i + 1]] < k);
13 return (Ind_{ k } );
Nevertheless although we consider the same input, a position j of occurrence of the kmer in a read, answering queries differ between the Gk arrays and gSA solutions. Indeed, since the gSA stores all positions in C_{ R } , we need to filter out positions of kmers that overlap two reads in C_{ R } to keep only Ppositions. This adds instructions to the procedure compared to that for the Gk arrays: see line 6 in Algorithm 5, which gives the algorithm for query Q1 with the gSA. For answering queries Q1 and Q2, we must perform another slight modification: we use a binary mask for dealing with duplicate kmers in a same read. This mask is stored in a binary vector B having q bits, one bit per read. The bit corresponding to a read is set to one whenever the kmer has been found to occur in that read, and subsequent occurrence positions in that read will be filtered out if the corresponding bit is set (lines 8 and 10 in Algorithm 5).
Assume we query on a kmer f from one of its occurrence position j. Let us denote by occ_C_{ R }(f) the number of occurrences of f in C_{ R } , including those overlapping two reads (i.e., starting at non Ppositions), and by occ_Reads(f) the number of its occurrences that are totally included in a read (i.e., those starting at Ppositions). For Q1/Q2, Q5Q7, we obtain with gSA a complexity of O(q + occ_C_{ R }(f)) since one initializes the bit vector B of size q and scan all occ_C_{ R }(f) occurrences. While with Gk arrays, the complexity depends linearly on occ_Reads(f) and we know that occ_Reads(f) ≤ occ_C_{ R }(f).
For Q3/Q4, there is no need of a bit vector with the gSA method, hence their complexity is O(occ_C_{ R }(f)), for one needs to scan positions in the gSA using the ISA and the LCP arrays. However, Gk arrays offer a complexity of O(occ_Reads(f)) for Q3 and O(1) for Q4. We summarize all queries time complexities in Figure 3(c).
Remark 3. To avoid scanning occ_C_{ R }(f) entries, an alternative solution consists in delimiting reads inside C_{ R } using a separator. This solution would lead to a space overhead of q bytes for lowering the time complexity to occ_Reads(f). However we did not retain this solution since our goal is to diminish the space complexity and this solution would not improve much the time complexity.
A solution based on a hash table
An alternative solution is to index all kmers in a hash table and to store for each read the list of its occurrence positions in the read collection. This list will contain pairs of integers: the read index in the collection, and the starting position of the kmer in that read. The read index can be stored on a 32bit integer, while a 16bit integer suffices for the starting position. In such a case, storing the text is not necessary. The number of entries is the number of distinct kmers in the read collection, i.e. our parameter . Generally, is small compared to 4 ^{k} for values of k in [15,60]. Hence the hash table will be sparsely populated. We tried several implementation of state of the art hash tables: the Google sparse and dense hash arrays, and that from SGI extension of the C++ Standard Library (called hash map).
Preliminary experiments have shown that Google sparse requires significantly much longer to build than SGI hash map, while having a lower memory footprint. With 20 million 75 bp reads, Google sparse hash occupies one third of the memory needed by the SGI hash map, but it takes thrice more time to build. On the contrary Google dense hash tables takes twice more memory, and offers only similar construction time. Hence, SGI extension hash map exhibited the best compromise in term of memory consumption and construction time compared to Google implementations. Thus, we choose SGI extension implementation for the comparison with Gk arrays.
Experimental settings
We tested index structures on three datasets.

1.
We used a collection of 40 million Illumina^{®} RNASeq reads of length 75 from a human K562 library taken from the RGASP data (Accession number GM12878 at http://www.gencodegenes.org/rgasp with permission from B. Wold). We call it the K562 dataset.

2.
We compiled several lanes of Roche 454^{®} genomic sequencing to obtain a collection of 2.8 million reads ranging from [40,3000] bp with an average read length of 523 bp. These were sequenced on a Roche 454^{®} GS FLX platform with Titanium chemistry for the Khoisan genome project [28]. We call it the Khoisan dataset.

3.
As much longer fixed length reads are not yet available, we constructed a collection of fixed length reads by slicing the Khoisan reads in nonoverlapping pieces of 150 bp. We obtained 25 millions of 150 bp reads, a read length that will soon be generated on High Throughput Sequencing platforms.
In the first and third collections, reads have a fixed length, while in the second their length varies. The experiments were performed on an Intel Xeon 2.27 GHz equipped with 48 GB of main memory, and running Linux 2.6.18 with C++ compiled using gcc version 3.4.6 and 02 funrollloops options.
Experimental comparison
The use of read indexing raises three questions: how much computing resources does the index demand? Is it scalable? How fast can it answer large number of queries? Clearly the resources will depend on the number of reads (parameter q), their lengths and on the length of kmers (parameter k). We compare three solutions: a hash table (HT), a generalized Suffix Array (gSA), and Gk arrays.
Scalability
We measured the construction time and amount of memory taken by all solutions for various numbers of reads and kmer lengths. Figure 5(a) plots the maximal memory footprint on K562 data. At this scale, the value of k impacts only the hash table size; its influence on the gSA and Gk arrays is not visible on that graph. Second, the solutions can be ordered as follows: Gk arrays take the less memory, followed by the gSA, and then the hash table. This order is irrespective of the read number. For k = 20 e.g., Gk arrays use 10 GB, the gSA uses 20, and the HT 44, and the curves clearly indicate that these differences increase with the number of reads. Whatever the value of q, the hash table requires twice as much memory as the gSA, which itself takes at least 70% more memory than Gk arrays. With 25 million reads the hash table saturates the memory, with 30 million the gSA also does, while the Gk arrays constitute the only solution able to index the whole collection, 40 million reads, on that computer. Note that in both cases, the 64bit implementation of gSA and Gk arrays have to be used to index that amount of reads. For the whole read collection, Gk arrays needs at most 43 GB (k = 15) and at least 36 GB (k = 30).
For all solutions, construction times increase linearly with the number of reads as expected (Figure 5(b)). It remains very similar between the gSA and Gk arrays, which both takes e.g. < 1000 s. for 25 million reads. The influence of k is clearly visible on the hash table for 20 million reads: its construction time decreases with k because the parameter also does (for a given number of reads). As long as they fit in memory, all compared solutions offer practical construction times.
We examined the behavior of Gk arrays on much longer reads, 150 bp, when variablelength read option is activated and when it is not. Figure 6(a) plots space consumption, while Figure 6(b) records the construction time for both options.
We see that adding a bit vector is not space consuming since there is little difference between the two methods (Figure 6(a)). For 13 million reads, the difference is, at most, of 300 MB between the two methods. In Figure 6(b), we plotted the construction time for both indexes. The variable length read implementation becomes slower when the number of reads grows, compared to the fixed length Gk arrays. This shows that despite a constanttime theoretical complexity for rank and select operations; there is a dependency on the length of the bit vector in practice. However, the construction time remains reasonable in the variable case.
Figures 7(a) and 7(b) plot space and time measured for the hash table and Gk arrays (with variable length reads option) on the Khoisan read collection. The gSA has not been implemented to handle variable length reads; note that the relative cost would have been similar to that observed with Gk arrays between fixed and variable read length options. Here for one million reads, variable length Gk arrays require 470 s. to build vs 428 s. for the hash table, but 8 times less memory (5.5 vs 46 GB). The difference increases strongly with the read number. Above one million reads, the memory footprint of the hash table exceeds the computer memory (which is 48 GB), while Gk arrays index the complete collection of 2.8 million reads on the same hardware with < 15.6 GB. Hash tables appear to be more space consuming on the Khoisan dataset than on the K562 dataset. This can be explained by the nature of the data. Roche 454^{®} sequencers offer a coverage depth much lower than Illumina's. Hence the number of distinct kfactors in the reads is likely to be greater with the Khoisan dataset.
Answering queries
We measured the mean time needed to answer 100,000 random queries of Q1Q4. Since Q5Q7 are slight variations of Q1Q3 we do not report on these queries.
Figure 8 shows how the mean time for each solution vary with the number of indexed reads (q) and k on the K562 collection. Clearly, the influence of q is similar for all solutions, and small compared to the differences between solutions. Generally, gSA takes always longer than the hash table irrespective of the query type, and it also takes longer than Gk arrays for Q1Q2 and Q4, and a similar time for Q3. The order between the hash table and Gk arrays depends on the query type. They are equally fast on Q1, the hash table does slightly better on Q2, clearly better on Q3, while Gk arrays is much faster on Q4. Anyway, for both the hash table and Gk arrays, the mean running time is in the order of or less than 10 microseconds for Q1Q3, and around 0.1 microsecond for Q4 with the Gk arrays, meaning reasonable practical times.
For our comparison of Gk arrays with fixed or variable length read options, we see that the latter is becoming slower than the former (up to 7 times slower) when k is small, i.e. when the number of occurrences of kmers is large. With larger k, the query time of the latter diminishes and becomes 2 to 3 times slower than with fixed Gk arrays.
With variable length reads (Figure 7(c)) the query times remain practical, but the hash table needs between 1 and 32 fold less time than Gk arrays depending on the query.
In summary, under various conditions Gk arrays are equivalent in construction time to a generalized Suffix Array or to a hash table. Compared to these solutions, they also offer reasonable query times under all circumstances; however, Gk arrays clearly outperform them in terms of memory footprint, the main bottleneck for processing High Throughput Sequencing data.
Conclusions
As High Throughput Sequencing becomes widespread, computational biology will face the challenge of managing astronomical quantities of short sequences. Mining such amount of sequences is feasible if the sequences are indexed in a preprocessing step. An index is a data structure that, like a telephone book, enables one to find easily a piece of information. For some value k, it records the positions of all kmers in the reads in an organized fashion to minimize the memory usage. Then finding the reads related to some kmer takes as long as reading the kmer and listing the corresponding reads, but not as long as scanning all the reads. In other words, read indexing factorizes the results of searches, which later speeds up the numerous queries made while the index is kept in memory. Our main contribution is to propose such an index: the Gk arrays. They are fast to build, require less space than alternative uncompressed solutions, and can thus handle larger read collections: 40 million vs 20 million reads for the hash tables with a memory limited to 48 GB. It is a key issue in practice.
While being comparable to hash tables in terms of time efficiency, only the Gk arrays can completely index a large read collection (like the K562 dataset) with a memory size available on nowadays computing servers. Moreover, our index remains fast for a wide range of values of parameter k (the length of kmers). We have also shown that Gk arrays are both faster and smaller than an alternative generalized Suffix Array approach. Similarly, on variablelength reads like a Roche 454^{®} dataset, Gk arrays can handle the whole read collection using less than 16 GB while hash tables are limited to a smaller subcollection (about 1 million reads) on a 48 GB machine.
The Gk arrays answer efficiently different types of queries, but they have been optimised for queries where the searched kmer is extracted from an indexed read. Sometimes one wishes to know for a given kmer the reads in which it occurs and its positions inside those (e.g. assembly), while in other contexts one only wants the number of reads sharing this kmer (e.g. estimation of expression level). Moreover, Gk arrays adapt well to variable length reads. Their scalability and versatility are key advantages, which allows to envisage multiple applications as mentioned in Introduction. However, scaling up to gigantic datasets (terabytes of data), as the ones obtained in large metagenomic projects, will require compressed read indexes. The simplicity of use of our index, and its implementation as a C++ library make it a software brick that can be easily exploited in future programs or further developed by the community.
For mapping reads on a reference sequence, solutions exist that index reads with hash tables [6, 29]. For the error correction problem, other works have indexed reads with classical text indexing solutions: with a generalized suffix trie [15, 30], a suffix array [31], or hash tables [32]. Gk arrays represent a first, attractive read indexing solution; it is specialised for this question and should suit different applications. Nevertheless, one can envisage several research perspectives. Indexing approximate kmers or spaced seeds will authorize more types of queries, but will certainly increase the construction time and space requirements. Designing a dynamic construction algorithm for Gk arrays would futher enlarge their range of applications. Another challenge is to compress Gk arrays by storing sampled positions and recomputing other positions at run time, as done with the Burrows Wheeler transform [5]. This would enable the user to adapt the index to its computer memory, while sacrificing some of its performance.
Abbreviations
 High Throughput Sequencing:

HTS
 RNA:

ribonucleic acid
 mRNA:

messenger RNA
 RNASeq:

RNA sequencing
 ChIPSeq:

Chromatin ImmunoPrecipitation and sequencing
 SA:

Suffix Array
 gSA:

generalized SA
 LCP:

Longest Common Prefix
 SNP:

Single Nucleotide Polymorphism
 bp:

base pairs
 iff:

if and only if.
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Acknowledgements and Funding
This work is supported by a CNRS PEPS grant "Bioinformatique, séquençage hautdébit et transcrits chimères en cancérologie", a CNRS PICS grant, a BioStic grant, the Region Languedoc Roussillon, and the ATGC bioinformatics platform. MS and NP were supported by fellowships from the French Ministry of Research, and NP benefits from a fellowship from the Ligue contre le cancer. ER thanks Dortmund University for the Dortmunder Gambrinus Fellowship. We gratefully thanks A. Mancheron for packaging the Gk arrays library.
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All authors have designed the algorithm and contributed to the writing of the manuscript. NP and MS have developed the code. NP, MS, TL, ER have performed the experiments. ER supervised the manuscript redaction and submission. All authors read and approved the final manuscript.
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Philippe, N., Salson, M., Lecroq, T. et al. Querying large read collections in main memory: a versatile data structure. BMC Bioinformatics 12, 242 (2011). https://doi.org/10.1186/1471210512242
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DOI: https://doi.org/10.1186/1471210512242