Volume 14 Supplement 1
Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Genomics
BayesHammer: Bayesian clustering for error correction in singlecell sequencing
 Sergey I Nikolenko^{1}Email author,
 Anton I Korobeynikov^{1, 2} and
 Max A Alekseyev^{1, 3}
https://doi.org/10.1186/1471216414S1S7
© Nikolenko et al.; licensee BioMed Central Ltd. 2013
Published: 21 January 2013
Abstract
Error correction of sequenced reads remains a difficult task, especially in singlecell sequencing projects with extremely nonuniform coverage. While existing error correction tools designed for standard (multicell) sequencing data usually come up short in singlecell sequencing projects, algorithms actually used for singlecell error correction have been so far very simplistic.
We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BAYES HAMMER. While BAYES HAMMER was designed for singlecell sequencing, we demonstrate that it also improves on existing error correction tools for multicell sequencing data while working much faster on reallife datasets. We benchmark BAYES HAMMER on both kmer counts and actual assembly results with the SPADES genome assembler.
Background
Singlecell sequencing [1, 2] based on the Multiple Displacement Amplification (MDA) technology [1, 3] allows one to sequence genomes of important uncultivated bacteria that until recently had been viewed as unamenable to genome sequencing. Existing metagenomic approaches (aimed at genes rather than genomes) are clearly limited for studies of such bacteria despite the fact that they represent the majority of species in such important studies as the Human Microbiome Project [4, 5] or discovery of new antibioticsproducing bacteria [6].
Error correction tools usually attempt to correct the set of kcharacter substrings of reads called kmers and then propagate corrections to whole reads which are important to have for many assemblers. Error correction tools often employ a simple idea of discarding rare kmers, which obviously does not work in the case of nonuniform coverage.
Medvedev et al. [8] recently presented a new approach to error correction for datasets with nonuniform coverage. Their algorithm HAMMER makes use of the Hamming graph (hence the name) on kmers (vertices of the graph correspond to kmers and edges connect pairs of kmers with Hamming distance not exceeding a certain threshold). HAMMER employs a simple and fast clustering technique based on selecting a central kmer in each connected component of the Hamming graph. Such central kmers are assumed to be errorfree (i.e., they are assumed to actually appear in the genome), while the other kmers from connected components are assumed to be erroneous instances of the corresponding central kmers. However, HAMMER may be overly simplistic: in connected components of large diameter or connected components with several kmers of large multiplicities, it is more reasonable to assume that there are two or more central kmers (rather than one as in HAMMER). Biologically, such connected components may correspond to either (1) repeated regions with similar but not identical genomic sequences (repeats) which would be bundled together by existing error correction tools (including HAMMER); or (2) artificially united kmers from distinct parts of the genome that just happen to be connected by a path in the Hamming graph (characteristic to HAMMER).
In this paper, we introduce the BAYES HAMMER error correction tool that does not rely on uniform coverage. BAYES HAMMER uses the clustering algorithm of HAMMER as a first step and then refines the constructed clusters by further subclustering them with a procedure that takes into account reads quality values (e.g., provided by Illumina sequencing machines) and introduces Bayesian (BIC) penalties for extra subclustering parameters. BAYES HAMMER subclustering aims to capture the complex structure of repeats (possibly of varying coverage) in the genome by separating even very similar kmers that come from different instances of a repeat. BAYES HAMMER also uses a new approach for propagating corrections in kmers to corrections in the reads. All algorithms in BAYES HAMMER are heavily parallelized whenever possible; as a result, BAYES HAMMER gains a significant speedup with more processing cores available. These features make BAYES HAMMER a perfect error correction tool for singlecell sequencing.
We remark that HAMMER produces only a set of central kmers but does not correct reads, making it incompatible with most genome assemblers. QUAKE does correct reads but has severe memory limitations for large k and assumes uniform coverage. In contrast, EULERSR [9] and CAMEL [2] correct reads and do not make strong assumptions on coverage (both tools have been used for singlecell assembly projects [2]) which makes these tools suitable for comparison to BAYES HAMMER. Our benchmarks show that BAYES HAMMER outperforms these tools in both singlecell and standard (multicell) modes. We further couple BAYES HAMMER with a recently developed genome assembler SPADES [10] and demonstrate that assembly of BAYES HAMMERcorrected reads significantly improves upon assembly with reads corrected by other tools for the same datasets, while the total running time also improves significantly.
BAYES HAMMER is freely available for download as part of the SPADES genome assembler at http://bioinf.spbau.ru/spades/.
Methods
Notation and outline
Let ∑ = {A, C, G, T} be the alphabet of nucleotides (BAYES HAMMER discards kmers with uncertain bases denoted N). A kmer is an element of ∑^{ k }, i.e., a string of k nucleotides. We denote the i^{th} letter (nucleotide) of a kmer x by x[i], indexing them from zero: 0 ≤ i ≤ k  1. A subsequence of x corresponding to a set of indices I is denoted by x[I]. We use interval notation [i, j] for intervals of integers {i, i + 1,..., j} and further abbreviate x[i, j] = x [{i, i + 1,..., j}]; thus, x = x[0, k  1]. Input reads are represented as a set of strings R ⊂ Σ* along with their quality values ${\left({q}_{r}\left[i\right]\right)}_{i=0}^{\leftr\right1}$ for each r ∈ R. We assume that q_{ r }[i] estimates the probability that there has been an error in position i of read r. Notice that in practice, the fastq file format [11] contains characters that encode probabilities on a logarithmic scale (in particular, products of probabilities used below correspond to sums of actual quality values).
Algorithms
Step (1): computing kmer statistics
To collect kmer statistics, we use a straightforward hash map approach [12] that does not require storing instances of all kmers in memory (as excessive amount of RAM might be needed otherwise). For a certain positive integer N (the number of auxiliary files), we use a hash function h: ∑^{ k } →ℤ_{ N } that maps kmers over the alphabet Σ to integers from 0 to N  1.
Algorithm 1 Count kmers
for each kmer x from the reads R: do
compute h(x) and write x to File_{h(x)}.
for i ∈ [0, N  1]: do
sort File_{ i } with respect to the lexicographic order;
reading File_{ i } sequentially, compute statistics(s) for each kmer s from File_{ i }.
Step (2): constructing connected components of Hamming graph
For a subset of indices I ⊆ [0, k  1], we define a partial lexicographic ordering ≺_{ I } as follows: x ≺_{ I } y iff x[I] ≺ y[I], where ≺ is the lexicographic ordering on Σ*. Similarly, we define a partial equality =_{ I } such that x =_{ I } y iff x[I] = y[I]. We partition the set of indices [0, k  1] into τ + 1 parts of size σ and for each part I, sort a separate copy of X with respect to ≺_{ I }. As noticed above, for every two kmers x, y ∈ X with d(x, y) ≤ τ, there exists a part I such that x =_{ I } y. It therefore suffices to separately consider blocks of equivalent kmers with respect to =_{ I } for each part I. If a block is small (i.e., of size smaller than a certain threshold), we go over the pairs of kmers in this block to find those with Hamming distance at most τ. If a block is large, we recursively apply to it the same procedure with a different partition of the indices. In practice, we use two different partitions of [0, k  1]: the first corresponds to contigious subsets of indices (recall that $\sigma =\frac{k}{\tau +1}$):
Algorithm 2 Hamming graph processing
procedure HGPROCESS(X, max_quadratic)
Init components with singletons $\mathcal{X}=\left\{\left\{x\right\}:x\in X\right\}$.
for all ϒ ∈ FindBlocks$\left(X,{\left\{{I}_{s}^{\mathsf{\text{cnt}}}\right\}}_{s=0}^{\tau}\right)$do
if ϒ > max_quadratic then
for all Z ∈ FindBlocks $\left(\Upsilon ,{\left\{{I}_{s}^{\mathsf{\text{str}}}\right\}}_{s=0}^{\tau}\right)$do
ProcessExhaustively$\left(Z,\mathcal{X}\right)$
else
ProcessExhaustively$\left(\Upsilon ,\mathcal{X}\right)$.
function FindBlocks $\left(X,{\left\{{I}_{s}\right\}}_{s=0}^{\tau}\right)$
for s = 0,...,τ do
sort a copy of X with respect to ${\prec}_{{I}_{s}}$, getting X_{ s }.
for s = 0,...,τ do
output the set of equiv. blocks $\left\{\Upsilon \right\}\phantom{\rule{2.77695pt}{0ex}}\mathsf{\text{w}}.\mathsf{\text{r}}.\mathsf{\text{t}}.{=}_{{I}_{s}}$.
procedure PROCESS EXHAUSTIVELY$\left(\Upsilon ,\mathcal{X}\right)$
for each pair x, y ∈ ϒ do
if d(x, y) ≤ τ then join their sets in $\mathcal{X}$:
for all $x\in {Z}_{x}\in \mathcal{X},y\in {Z}_{y}\in \mathcal{X}$ do
Step (3): Bayesian subclustering
Under this model, the maximum likelihood center of a cluster is simply its consensus string [8].
In BAYES HAMMER, we further elaborate upon HAMMER's model. Instead of a fixed ε, we use reads quality values that approximate probabilities q_{ x }[i] of a nucleotide at position i in the kmer x being erroneous. We combine quality values from identical kmers in the reads: for a multiset of kmers X that agree on the j^{th} nucleotide, it is erroneous with probability Π_{x∈X}q_{ x }[j].
where c_{ i } is the center (consensus string) of the subcluster C_{ i }.
In the subclustering procedure (see Algorithm 3), we sequentially subcluster each connected component of the Hamming graph into more and more clusters with the classical kmeans clustering algorithm (denoted mmeans since k has different meaning). For the objective function, we use the likelihood as above penalized for overfitting with the Bayesian information criterion (BIC) [13]. In this case, there are C observations in the dataset, and the total number of parameters is 3 km + m  1:

m  1 for probabilities of subclusters,

km for cluster centers, and

2 km for error probabilities in each letter: there are 3 possible errors for each letter, and the probabilities should sum up to one. Here error probabilities are conditioned on the fact that an error has occurred (alternatively, we could consider the entire distribution, including the correct letter, and get 3 km parameters for probabilities but then there would be no need to specify cluster centers, so the total number is the same).
Algorithm 3 Bayesian subclustering
for all connected components C of the Hamming graph do
m := 1
ℓ_{1} := 2 log L_{1}(C) (likelihood of the cluster generated by the consensus)
repeat
m := m + 1
do mmeans clustering of C = C_{1} ∪...∪ C_{ m } w.r.t. the Hamming distance; the initial approximation to the centers is given by kmers that have the least error probability
ℓ_{ m } := 2 · log L_{ m }(C_{1},...,C_{ m }) (3 km + m  1) · log C
until ℓ _{ m } ≤ ℓ _{m1}
output the best found clustering C = C_{1} ∪...∪ C_{m1}
for subclustering into m clusters; we stop as soon as ℓ_{ m } ceases to increase.
Steps (4) and (5): selecting solid kmers and expanding the set of solid kmers
In contrast to HAMMER, we do not distinguish whether the cluster is a singleton (i.e., C = 1); there may be plenty of superfluous clusters with several kmers obtained by chance (actually, it is more likely to obtain a cluster of several kmers by chance than a singleton of the same total multiplicity).
Initially we mark as solid the centers of the clusters whose total quality exceeds a predefined threshold (a global parameter for BAYES HAMMER, set to be rather strict). Then we expand the set of solid kmers iteratively: if a read is completely covered by solid kmers we conclude that it actually comes from the genome and mark all other kmers in this read as solid, too (Algorithm 4).
Step (6): reads correction
Algorithm 4 Solid kmers expansion
procedure ITERATIVE EXPANSION(R, X)
while ExpansionStep(R, X) do
function EXPANSION STEP(R, X)
for all reads r ∈ R do
if r is completely covered by solid kmers then
mark all kmers in r as solid
Return TRUE if X has increased and FALSE otherwise.
Algorithm 5 Reads correction
Input: reads R, solid kmers X, clusters $\mathcal{C}$.
for all reads r ∈ R do
init consensus array υ: [0, r  1] × {A, C, G, T} → ℕ with zeros: υ(j, x[i]):= 0 for all i = 0,...,r  1 and j = 0,...,k  1
for i = 0,...,r  k do
if r[i, i + k  1] ∈ X (it is solid) then
for j ∈ [i, i + k  1] do
υ(j, r[i]):= υ(j, r[i]) + 1
if r[i, i + k  1] ∈ C for some C ∈ $\mathcal{C}$then
let x be the center of C
if x ∈ X (r belongs to a cluster with solid center) then
for j ∈ [i, i + k  1] do
υ(j, x[i]):= υ(j, x[i]) + 1
for i ∈ [0, r  1] do
r[i]:= arg max_{a∈Σ}υ(i, a).
Results and discussion
Datasets
In our experiments, we used three datasets from [2]: a singlecell E. coli, a singlecell S. aureus, and a standard (multicell) E. coli dataset. Pairedend libraries were generated by an Illumina Genome Analyzer IIx from MDAamplified singlecell DNA and from multicell genomic DNA prepared from cultured E. coli, respectively These datasets consist of 100 bp pairedend reads with insert size 220; both E. coli datasets have average coverage ≈ 600×, although the coverage is highly nonuniform in the singlecell case.
In all experiments, BAYES HAMMER used k = 21 (we observed no improvements for higher values of k).
kmer counts
kmer statistics.
Correction tool  Running time  kmers  Reads  

Total  Genomic  Nongenomic  % of all genomic k mers found in reads  % genomic among all k mers in reads  % reads aligned to genome  
Multicell E. coli, total 4,543,849 genomic kmers  
Uncorrected  187,580,875  4,543,684  183,037,191  99.99  2.4  99.05  
Quake  4,565,237  4,543,461  21,776  99.99  99.5  99.97  
HammerNoExpansion  30 m  58,305,738  4,543,674  53,762,064  99.99  8.4  95.59 
HammerExpanded  36 m  28,290,788  4,543,673  23,747,115  99.99  19.1  99.49 
BayesHammer  37 m  27,100,305  4,543,674  22,556,631  99.99  20.1  99.62 
Singlecell E. coli, total 4,543,849 genomic kmers  
Uncorrected  165,355,467  4,450,489  160,904,978  97.9  2.7  79.05  
Camel  2 h 29 m  147,297,070  4,450,311  142,846,759  97.9  3.0  81.25 
EulerSR  2 h 15 m  138,677,818  4,450,431  134,227,387  97.9  3.2  81.95 
Coral  2 h 47 m  156,907,496  4,449,560  152,457,936  97.9  2.8  80.28 
HammerNoExpansion  37 m  53,001,778  4,443,538  48,558,240  97.8  8.3  81.36 
HammerExpanded  43 m  36,471,268  4,443,545  32,027,723  97.8  12.1  86.91 
BayesHammer  57 m  35,862,329  4,443,736  31,418,593  97.8  12.4  87.12 
Singlecell S. aureus, total 2,821,095 genomic kmers  
Uncorrected  88,331,311  2,820,394  85,510,917  99.98  3.2  75.07  
Camel  5 h 13 m  69,365,311  2,820,350  66,544,961  99.97  4.1  75.27 
EulerSR  2 h 33 m  58,886,372  2,820,349  56,066,023  99.97  4.8  75.24 
Coral  7 h 12 m  83,249,146  2,820,011  80,429,135  99.96  3.4  75.22 
HammerNoExpansion  58 m  37,465,296  2,820,341  34,644,955  99.97  7.5  71.63 
HammerExpanded  1 h 03 m  23,197,521  2,820,316  20,377,205  99.97  12.1  76.54 
BayesHammer  1 h 09 m  22,457,509  2,820,311  19,637,198  99.97  12.6  76.60 
Assembly results
Assembly results, singlecell E.coli and S. aureus datasets (contigs of length ≥ 200 are used).
Statistics  BayesHammer  BayesHammer(scaff old)  Coral  Coral (scaff old)  EulerSR  EulerSR (scaff old)  Hammer, expanded  Hammer, no expansion  Hammer, no expansion(scaff old)  Hammer(scaff old) 

Singlecell E. coli, reference length 4639675, reference GC content 50.79%  
# contigs (1000 bp)  191  158  276  224  231  150  195  282  242  173 
# contigs  521  462  675  592  578  375  529  655  592  477 
Largest contig  269177  284968  179022  179022  267676  267676  268464  210850  210850  268464 
Total length  4952297  4989404  5064570  4817757  4817757  4902434  4977294  5097148  5340871  5005022 
N50  110539  113056  45672  67849  74139  95704  97639  65415  84893  109826 
NG50  112065  118432  55073  87317  77762  108976  101871  68595  96600  112161 
NA50  110539  113056  45672  67765  74139  95704  97639  65415  84841  109826 
NGA50  112064  118432  55073  87317  77762  108976  101871  68594  96361  112161 
# misassemblies  4  6  9  12  6  8  4  4  7  7 
# misassembled contigs  4  6  9  10  6  8  4  4  7  7 
Misass. contigs length  42496  94172  62114  150232  47372  149639  43304  26872  147140  130706 
Genome covered (%)  96.320  96.315  96.623  96.646  95.337  95.231  96.287  96.247  96.228  96.281 
GC (%)  49.70  49.69  49.61  49.56  49.90  49.74  49.68  49.64  49.60  49.68 
# mismatches/100 kbp  11.22  11.70  8.36  9.10  5.55  5.82  12.77  54.11  52.48  13.08 
# indels/100 kbp  1.07  8.26  9.17  12.76  0.52  47.80  0.91  1.17  7.96  8.69 
# genes  4065 +  4079 +  3998 +  4040 +  3992 +  4020 +  4068 +  4034 +  4048 +  4078 + 
124 part  110 part  180 part  143 part  140 part  107 part  123 part  152 part  136 part  111 part  
Singlecell S. aureus, reference length 2872769, reference GC content 32.75%  
# contigs (1000 bp)  95  85  132  113  82  70  114  272  258  101 
Total length (1000 bp)  3019597  3309342  3055585  3066662  2972925  2993100  3033912  3389846  3405223  3509555 
# contigs  260  241  455  423  166  134  312  721  711  292 
Largest contig  282558  328686  208166  208166  254085  535477  282558  148002  166053  328679 
Total length  3081173  3368034  3160497  3166169  3008746  3020256  3111423  3575679  3594468  3584266 
N50  87684  145466  62429  90701  101836  145466  74715  30788  34943  131272 
NG50  112566  194902  87636  99341  108151  159555  88292  39768  45889  180022 
NA50  87684  145466  62429  89365  100509  145466  68711  30788  34552  112801 
NGA50  88246  148064  74452  90101  101836  145466  88289  35998  42642  148023 
# misassemblies  15  17  11  14  4  5  11  14  18  14 
# misassembled contigs  12  14  9  10  4  5  9  14  16  12 
Misass. contigs length  340603  779785  478009  523596  377133  918380  402997  272677  324361  940356 
Genome covered (%)  99.522  99.483  99.449  99.447  99.213  99.254  99.204  98.820  98.888  99.221 
GC (%)  32.67  32.63  32.64  32.63  32.66  32.67  32.67  32.39  32.38  32.57 
# mismatches per 100 kbp  3.18  8.01  12.44  12.65  9.72  10.28  17.38  54.92  55.50  15.36 
# indels per 100 kbp  2.17  2.30  15.50  15.67  3.80  4.08  3.57  2.64  2.72  3.04 
# genes  2540 +  2547 +  2532 +  2540 +  2547 +  2550 +  2535 +  2477 +  2485 +  2539 + 
36 part  30 part  45 part  37 part  30 part  27 part  41 part  91 part  85 part  38 part 
Assembly results, multicell E.coli dataset (contigs of length ≥ 200 are used).
Statistics  BayesHammer  BayesHammer (sca_old)  Hammer, expanded  Hammer, no expansion  Hammer, no expansion (sca_old)  Hammer (sca_old)  Quake 

Multicell E. coli, 600 coverage, reference length 4639675, reference GC content 50.79%  
# contigs (≥ 500 bp)  103  102  119  238  213  115  165 
# contigs (≥ 1000 bp)  91  90  99  192  171  96  156 
Total length (≥ 500 bp)  4641845  4641790  4626515  4730338  4817457  4627067  4543682 
Total length (≥ 1000 bp)  4633361  4633306  4611745  4696966  4787210  4612838  4537565 
# contigs  122  121  146  325  303  141  204 
Largest contig  285113  285113  218217  210240  210240  218217  165487 
Total length  4647325  4647270  4635156  4756088  4844208  4635349  4555015 
N50  132645  132645  113608  59167  73113  113608  58777 
NG50  132645  132645  113608  59669  80085  113608  57174 
NA50  132645  132645  113608  59167  73113  113608  58777 
NGA50  132645  132645  113608  59669  80085  113608  57174 
# misassemblies  3  3  4  4  7  5  0 
# misassembled contigs  3  3  4  4  7  5  0 
Misassembled contigs length  44466  44466  57908  15259  30901  60418  0 
Genome covered (%)  99.440  99.440  99.383  98.891  98.925  99.385  98.747 
GC (%)  50.78  50.77  50.77  50.73  50.71  50.77  50.75 
N's (%)  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000 
# mismatches per 100 kbp  8.55  8.55  13.76  44.46  44.33  13.76  1.21 
# indels per 100 kbp  0.99  0.99  1.14  0.76  0.97  1.14  0.20 
# genes  4254+45 part  4254+45 part  4245+56 part  4196+72 part  4204+68 part  4245+56 part  4174+62 part 
In the tables, N50 is such length that contigs of that length or longer comprise $\ge \frac{1}{2}$ of the assembly; NG50 is a metric similar to N50 but only taking into account contigs comprising (and aligning to) the reference genome; NA50 is a metric similar to N50 after breaking up misassembled contigs by their misassemblies. NGx and NAx metrics have a more direct relevance to assembly quality than regular Nx metrics; our result tables have been produced by the recently developed tool QUAST [14].
All assemblies have been done with SPADES. The results show that after BAYES HAMMER correction, assembly results improve significantly, especially in the singlecell E. coli case; it is especially interesting to note that even in the multicell case, where BAYES HAMMER loses to QUAKE by kmer statistics, assembly results actually improve over assemblies produced from QUAKEcorrected reads (including genome coverage and the number of genes).
Conclusions
Singlecell sequencing presents novel challenges to error correction tools. In contrast to multicell datasets, for singlecell datasets, there is no pretty distribution of kmer multiplicities; one therefore has to work with kmers on a onebyone basis, considering each cluster of kmers separately. In this work, we further developed the ideas of HAMMER from a Bayesian clustering perspective and presented a new tool BAYES HAMMER that makes them practical and yields significant improvements over existing error correction tools.
There is further work to be done to make our underlying models closer to real life; for instance, one could learn a nonuniform distribution of single nucleotide errors and plug it in our likelihood formulas. Another natural improvement would be to try and rid the results of contamination by either human or some other DNA material; we observed significant human DNA contamination in our singlecell dataset, so weeding it out might yield a significant improvement. Finally, a new general approach that we are going to try in our further work deals with the technique of minimizers introduced by Roberts et al. [15]. It may provide significant reduction in memory requirements and a possible approach to dealing with paired information.
Declarations
The publication costs for this article were funded by the Government of the Russian Federation, grant 11.G34.31.0018.
This article has been published as part of BMC Genomics Volume 14 Supplement 1, 2013: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/14/S1.
Declarations
Acknowledgements
We thank Pavel Pevzner for many fruitful discussions on all stages of the project. We are also grateful to Andrei Prjibelski and Alexei Gurevich for help with the experiments and to the anonymous referees whose comments have benefited the paper greatly. This work was supported the Government of the Russian Federation, grant 11.G34.31.0018. Work of the first author was also supported by the Russian Fund for Basic Research grant 120100450a and the Russian Presidential Grant MK6628.2012.1. Work of the second author was additionally supported by the Russian Fund for Basic Research grant 120100747a.
Authors’ Affiliations
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