Comparison of alignment software for genome-wide bisulphite sequence data.
ABSTRACT Recent advances in next generation sequencing (NGS) technology now provide the opportunity to rapidly interrogate the methylation status of the genome. However, there are challenges in handling and interpretation of the methylation sequence data because of its large volume and the consequences of bisulphite modification. We sequenced reduced representation human genomes on the Illumina platform and efficiently mapped and visualized the data with different pipelines and software packages. We examined three pipelines for aligning bisulphite converted sequencing reads and compared their performance. We also comment on pre-processing and quality control of Illumina data. This comparison highlights differences in methods for NGS data processing and provides guidance to advance sequence-based methylation data analysis for molecular biologists.
- SourceAvailable from: Paul J Hurd[show abstract] [hide abstract]
ABSTRACT: Several recent studies from the field of epigenetics have combined chromatin-immunoprecipitation (ChIP) with next-generation high-throughput sequencing technologies to describe the locations of histone post-translational modifications (PTM) and DNA methylation genome-wide. While these reports begin to quench the chromatin biologists thirst for visualizing where in the genome epigenetic marks are placed, they also illustrate several advantages of sequencing based genomics compared to microarray analysis. Accordingly, next-generation sequencing (NGS) technologies are now challenging microarrays as the tool of choice for genome analysis. The increased affordability of comprehensive sequence-based genomic analysis will enable new questions to be addressed in many areas of biology. It is inevitable that massively-parallel sequencing platforms will supercede the microarray for many applications, however, there are niches for microarrays to fill and interestingly we may very well witness a symbiotic relationship between microarrays and high-throughput sequencing in the future.Briefings in Functional Genomics and Proteomics 07/2009; 8(3):174-83.
- [show abstract] [hide abstract]
ABSTRACT: For the past 30 years, the Sanger method has been the dominant approach and gold standard for DNA sequencing. The commercial launch of the first massively parallel pyrosequencing platform in 2005 ushered in the new era of high-throughput genomic analysis now referred to as next-generation sequencing (NGS). This review describes fundamental principles of commercially available NGS platforms. Although the platforms differ in their engineering configurations and sequencing chemistries, they share a technical paradigm in that sequencing of spatially separated, clonally amplified DNA templates or single DNA molecules is performed in a flow cell in a massively parallel manner. Through iterative cycles of polymerase-mediated nucleotide extensions or, in one approach, through successive oligonucleotide ligations, sequence outputs in the range of hundreds of megabases to gigabases are now obtained routinely. Highlighted in this review are the impact of NGS on basic research, bioinformatics considerations, and translation of this technology into clinical diagnostics. Also presented is a view into future technologies, including real-time single-molecule DNA sequencing and nanopore-based sequencing. In the relatively short time frame since 2005, NGS has fundamentally altered genomics research and allowed investigators to conduct experiments that were previously not technically feasible or affordable. The various technologies that constitute this new paradigm continue to evolve, and further improvements in technology robustness and process streamlining will pave the path for translation into clinical diagnostics.Clinical Chemistry 03/2009; 55(4):641-58. · 7.15 Impact Factor
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ABSTRACT: Bisulfite sequencing is a powerful technique to study DNA cytosine methylation. Bisulfite treatment followed by PCR amplification specifically converts unmethylated cytosines to thymine. Coupled with next generation sequencing technology, it is able to detect the methylation status of every cytosine in the genome. However, mapping high-throughput bisulfite reads to the reference genome remains a great challenge due to the increased searching space, reduced complexity of bisulfite sequence, asymmetric cytosine to thymine alignments, and multiple CpG heterogeneous methylation. We developed an efficient bisulfite reads mapping algorithm BSMAP to address the above issues. BSMAP combines genome hashing and bitwise masking to achieve fast and accurate bisulfite mapping. Compared with existing bisulfite mapping approaches, BSMAP is faster, more sensitive and more flexible. BSMAP is the first general-purpose bisulfite mapping software. It is able to map high-throughput bisulfite reads at whole genome level with feasible memory and CPU usage. It is freely available under GPL v3 license at http://code.google.com/p/bsmap/.BMC Bioinformatics 08/2009; 10:232. · 3.02 Impact Factor
Comparison of alignment software for genome-wide
bisulphite sequence data
Aniruddha Chatterjee1,2, Peter A. Stockwell3,*, Euan J. Rodger1and Ian M. Morison1,2
1Department of Pathology, Dunedin School of Medicine, University of Otago, 270 Great King Street, Dunedin
9054, New Zealand,2National Research Centre for Growth and Development, 2-6 Park Ave, Grafton,
Auckland 1142, New Zealand and3Department of Biochemistry, University of Otago, 710 Cumberland
Street, Dunedin 9054, New Zealand
Received August 1, 2011; Revised January 16, 2012; Accepted January 25, 2012
Recent advances in next generation sequencing
(NGS) technology now provide the opportunity
to rapidly interrogate the methylation status of the
genome. However, there are challenges in handling
and interpretation of the methylation sequence data
because of its large volume and the consequences
of bisulphite modification. We sequenced reduced
representation human genomes on the Illumina
platform and efficiently mapped and visualized the
data with different pipelines and software packages.
We examined three pipelines for aligning bisulphite
converted sequencing reads and compared their
performance. We also comment on pre-processing
and quality control of Illumina data. This comparison
highlights differences in methods for NGS data
processing and provides guidance to advance
Next generation sequencing (NGS) coupled with sodium
bisulphite modification of DNA has become a powerful
tool to quantify DNA methylation at single nucleotide
resolution (1,2). As for other NGS applications, bisulphite
sequencing presents a challenge in terms of the large
amount of raw data generated from the sequencing,
processing, analysis and finally interpretation of the
data. In particular, aligning bisulphite converted reads
to a large reference genome brings substantial computa-
unmethylated cytosines (C) to thymines (T) after subse-
quent PCR, but methylated Cs remain unchanged by the
treatment. This method is widely used to distinguish
methylated from unmethylated Cs in the DNA strands.
Since C is converted to T, a T in the sequenced reads
could be mapped against either C or T in the reference
genome but not vice versa, and so the C to T mapping is
asymmetric (3). This gives rise to the possibility of more
false-positive matches between the reads and the reference
genome and also increases the search space significantly,
As a consequence of bisulphite modification, four
distinct DNA strands are created after PCR amplification.
In shotgun sequencing, the reads can possibly be derived
from any of the four strands. However, for our data, due
to the directionality of the Illumina platform and the
protocol used, reads were obtained exclusively from
MspI digested 50CGG strands (the recognition motif of
MspI is C0CGG). But in case of non-directional libraries,
there is the potential to introduce bias into the methyla-
tion call, as MspI cut-sites need to be filled in by a
cytosine. The sequence of the read will then depend on
whether these sites are filled in with methylated or
non-methylated cytosines. In either case, the cytosine
might not retain the true genomic methylation state.
To avoid incorrect estimation of the methylation of the
initial CpG site, the filled in bases of the read should be
omitted when extracting the methylation information. In
the case of reads that cover the entire MspI fragment, the
30end of the read can be similarly affected. Furthermore,
reads from non-directional libraries can map to any of
the four different versions of the reference genome:
(i) (bisuflite) Watson or (ii) the reverse-complement of
Watson or (iii) to (bisulphite) Crick or (iv) the reverse
complement of Crick.
Compared to classical Sanger sequencing, the NGS
reads are shorter in length. When the read length is
short, alignment against a large and complex genome
(such as human) becomes more difficult. Large genomes
*To whom correspondence should be addressed. Tel: +64 3 479 7880; Fax: +64 3 479 7866; Email: email@example.com
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Published online 16 February 2012 Nucleic Acids Research, 2012, Vol. 40, No. 10e79
? The Author(s) 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
by guest on June 20, 2012
sequence, and, as a result, the percentage of uniquely
aligned sequence decreases significantly when the reads
are short. Ensuring the quality of the data received from
sequencing and appropriate computational manipulation
can increase the mapping efficiency.
The processes of mapping bisulphite reads against
genomic sequence not only have to manage mapping
asymmetry, but also must be able to allow for a reason-
able level of sequencing errors. Various computational
strategies have been employed to handle these issues:
CokusAlign (4) uses a tree-based lookup method in
which the genomic sequence is pre-processed into a
memory resident tree structure through which the
sequence data for each read causes a progressive traversal
to establish a genomic location. While this strategy works
effectively for 36bp reads versus the Arabidopsis thaliana
genome, it does not scale well for longer reads or larger
genomes. Seed lookup methods have been employed in
which the genome is pre-processed into a series of hash
values (seeds), which enable rapid indexing of oligomers
from read sequences to genomic positions. A recently
Bowtie (5) uses the Burrows-Wheeler (BW) transform-
ation to perform rapid mapping of fragments to
genomic positions. Although a moderately slow prior
build operation is required to generate BW transformed
genomic sequences, subsequent use of this data is very
rapid. The Bowtie authors distribute pre-built BW-
transformed files for a number of model genomes,
although this facility is not available for bisulphite
genomes at present.
Since Illumina read quality deteriorates with later
cycles, all aligners treat the start of each read as more
reliable and are designed to work with limited errors in
that portion. The reduced quality further into the reads is
reflected in the aligners’ permitting increased mismatches
after mapping the initial seed. Potential erroneous
methylation calls from misalignment events further into
the reads and loss of information must be balanced
against failure of unique alignment of short reads if exces-
sive quality trimming is performed. We have analysed
high-throughput Illumina data from the human genome,
sequenced from reduced representation (RR), bisulphite-
converted libraries enriched for CpG islands. In this
article, we describe three major aligners (Bismark,
BSMAP and RMAPBS) that map DNA methylation
compare their speed and mapping efficiency and also
pre-processing of Illumina reads for best possible output
and the visualization of the methylation data.
pair resolution. We
MATERIALS AND METHODS
RR bisulphite sequencing
A RR human genome was generated according to pub-
lished protocols (6–8). The RR library is enriched for CpG
islands and is predicted to include 84% of the CpG islands
in the human genome and ?3.4 million unique CpG sites
(6,7). In brief, the genomic DNA was digested with MspI
(New England Biolabs, Ipswich, MA) followed by end
repair and addition of 30A overhangs. Methylated
adaptors (Illumina, San Diego, CA) with a 30
overhang were then ligated with the generated fragments.
Following adaptor ligation, DNA fragments ranging from
40 to 220bp (preligation size) were cut from a 3% (w/v)
NuSieve GTG agarose gel (Lonza, Basel, Switzerland) and
subsequently bisulphite modified using the EZ DNA
methylation kit (Zymo Research, Irvine, CA). The final
library was amplified by PCR. The resulting library
was sequenced on an Illumina GAIIX platform with a
single-ended, 100bp run. Data from a single lane, from
which 18 490 898 sequence reads were obtained, were used
for subsequent analysis.
Quality check of the sequenced data
The Illumina base calling program converts the captured
signal images into sequence, and a technical concern of
this process is that the base calling accuracy decreases
with increased read length, since the sequential chemistry
results in progressive decline of the signal and the
corresponding increase in background noise results in
less reliable base calls.
SolexaQA (9) was used to evaluate the quality of the
data. SolexaQA is a Perl application using the R statistics
package and the matrix2png program to generate a graph-
ical representation of data quality and is in the public
domain. It samples entries in the FASTQ file and uses
the quality score for each cycle to generate a graphical
‘heat-map’, which indicates the quality of the reads
generated from a lane. One axis represents the base, and
the other represents each individual tile in a flow cell: the
darker the colour of a box, the lower the quality of that
tile for that cycle (Supplementary Figure S1).
In addition, SolexaQA generates a plot of the quality of
every tile along the reads, in which the probability of bases
being called in error is plotted against the read position
(Supplementary Figure S2). SolexaQA further plots the
distribution of read lengths where the x-axis represents
the length of contiguous sequence (with a base-calling
error rate ?5%) and the y-axis maps the proportion of
all the reads. From these data, we can work out the pro-
portion of reads with full length sequence (Supplementary
Figure S3). Together, these graphs give a good indication
of the quality of the run.
Based on the various quality indicators, it is possible to
establish where in the read cycles the base-call reliability
has declined beyond a reasonable level and, hence, where
the reads should be trimmed before further processing.
On one hand, trimming the reads discards some informa-
tion while, on the other, less reliable sequence towards the
ends of reads may contribute to misalignment or failure of
alignment. While the trim length is, to a degree, arbitrary
our experience is that the quality indicators discussed
above allow it to be set with reasonable confidence.
Our reads were 100bp but after evaluation of their
quality we hard-trimmed to 75bp for further processing.
This not only ensures better quality data for further
analysis but also it reduces the rate of mismatches
during mapping with the reference genome. Some tests
e79 Nucleic Acids Research, 2012,Vol. 40,No. 10PAGE 2 OF 8
by guest on June 20, 2012
were performed with reads hard-trimmed to 60bp.
The SolexaQA application also identifies potentially bad
tiles (Supplementary Figure S1), the sequences from which
were eliminated to reduce the risk of artefacts from less
Scan for adaptor contamination
Contamination by adaptor sequences in the data set was
also assessed. For this purpose, a program cleanadaptors
was developed. The program scans the reads, identifying
sections, which show 75% or higher matching with any of
a series of adaptor sequences (the threshold is adjustable).
The scanning was performed against 100, 75 and 60bp
data sets to estimate the amount of adaptor sequence
contamination in the reads (Supplementary Table S1).
We used the program fastq_quality_trimmer (v 0.0.13
dynamic trimming of the original 100bp and the 75bp
data sets to a Phred quality level of 30 (=0.001 probabil-
ity of a base-call error as assessed by the Illumina base
Mapping the reads
The 75 and 60bp data sets (created by hard trimming the
original data) were mapped against the human reference
genome (build GRCh37) to assess the effect of trimming
on mapping efficiency. The mapping was performed with
three aligners described below. With a purpose-written
program mkrrgenome, we also created an in silico RR
genome based on MspI fragments in our 40–220-bp size
range and mapped the data sets against this RR genome
as well (Supplementary Figure S5).
Description of the aligners
Bismark. Bismark (10) v0.2.3 is a Perl application, which
works by calling the Bowtie fragment aligner (5). Genome
files are pre-processed in a separate step to generate CT-
and GA-converted files, which are then scanned with
parallel invocations of Bowtie. By default, Bismark will
map all reads, directional or non-directional against all
four conversions, introducing possible mismapping of
directional reads. This distortion can be suppressed with
recent versions (0.2.3 or later) of the Bismark package by
using the directional switch. The output contained
genomic and read sequences for each match from which
methylated CpGs were determined either with the script
BSMAP. BSMAP (3) is a C+ + application based on a
modified version of the SOAP aligner (11) in which the
reference genome is converted to a series of typically
octamer seeds on which hashing and fast lookup
methods can be applied to attain efficient performance.
BSMAP generates C/T and G/A converted seeds for the
reference genome in which all possible methylation
patterns exist for each seed. A bit-mapping strategy is
applied within the program to highlight mismatches
from methylation and sequencing errors. Further process-
ing permits a user-specified degree of mismatching in each
read, and the algorithm expends significant effort in
resolving multiple or conflicting mapping of reads.
BSMAP output was a list for each read of its matching
status (unmatched, uniquely or multiply matched) with
suggested methylation positions for unique matches.
Initially, we used BSMAP v1.02 and subsequently v1.2.
In some cases, we have contrasted the performance of
RMAPBS. RMAPBS v2.05 (12) is a C+ + application
based on the RMAP program for mapping single-ended
bisulphite reads. The RMAP algorithm uses an advanced
seed and hashing strategy, related to that for BSMAP, to
mismatches and methylation. RMAPBS output was a
BED format file giving the chromosome, position, the
read identifier, a quality parameter and the strand.
projects/seqmonk) was used to view methylation data.
It is a graphical Java application, which is distributed
for a wide range of computer platforms (Windows,
Linux, MacOS X and as Java source code). SeqMonk is
pre-configured with a significant set of genomic sequences
and their annotations and is configured to check those in
use for any updates at each invocation as well as checking
for any updates to the application itself. When a genome is
loaded, either de novo or as a project, the displays are
capable of showing genes, mRNAs and exons, but the
displayed information is widely configurable depending
on requirements (Supplementary Figure S4).
SeqMonk is capable of importing mapping information
in a variety of mapping formats or as a tab-delimited list.
Methylation data from Bismark (from the same authors as
SeqMonk) were imported directly from Bismark files,
but each CpG was treated as a single-mapped entity
with methylated positions indicated as 50
and unmethylated as 30. Further quantification of
methylation was possible by generating ‘probes’ on a
residue-by-residue basis. Despite the significant overheads
of working on the human genome and tens of millions of
CpG positions, SeqMonk performed acceptably fast,
although the memory requirements were large. SeqMonk
could display bisulphite mapping data from more than
one treatment or pipeline in the same window, enabling
visual comparison of the methods. Feature reports
could be created in which ‘probe’ data can be related to
The importing of RMAPBS and BSMAP methylation
data into SeqMonk was not automatic, as for Bismark,
but was possible by pre-processing the various output files
into tab-delimited lists of methylated and unmethylated
CpG positions, which were then imported as raw data.
We have written rmapbsbed2cpg (a C command line appli-
cation) to do this pre-processing for both (Supplementary
PAGE 3 OF 8Nucleic Acids Research,2012, Vol.40, No. 10e79
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A purpose written program mkrrgenome was used to
generate a RR genome in silico. The program scans for
MspI recognition sites (C0CGG) saving only those
fragments that fall in the specified size range of
40–220bp. The output was a series of FASTA files one
for each chromosome. The program could return statistics
on CpG sites for each chromosome and the fragment
The mapping runs were performed on a Mac Pro with
64bit duo quad core Intel Xeon processors and with
22Gb RAM running MacOS 10.6.
Distribution of new programs
The software written to support this work is distributed as
a shell archive (meth_progs_dist.shar) along with the
supplementary data and, while developed on a MacOS
X platform, has successfully been compiled with gcc
on various Linux distributions. We have also included
a test data set containing 5000 sequenced reads along
with chromosome 22 and a file (test_progs_readme.txt)
(meth_progs_test.shar.gz) in the supplementary data.
Despite the difference in the algorithms, all three
programs efficiently mappedthe sequence reads.
Following trimming to 75bp, 42.2, 58.9 and 65.1% of
the reads were uniquely aligned by using BISMARK,
BSMAP and RMAPBS, respectively (Table 1). We
found that RMAPBS and Bismark were able to map
the reads much faster than BSMAP v1.02. Bismark has
a speed of 1642 reads/sec and RMAPBS maps 119.6
reads/sec. Despite being a single-threaded application,
RMAPBS is relatively fast in operation. BSMAP v1.02
mapped the reads with a speed of 5.6 reads/sec taking 38
days when run on 6 CPU cores (Table 1). The perform-
ance of BSMAP v1.2 was much faster than BSMAP v1.02,
and the mapping was completed in 22.2h with a speed
of 231.2 reads/sec (Table 1).
We observed that shortening the read length improves
the percentage of uniquely aligned sequences. All three
programs showed improved mapping efficiency with
shorter read length, a consequence of the poorer quality
sequence towards the ends of Illumina reads. Bismark
showed an increase of 12.1% in unique mapping when
75bp reads were trimmed to 60bp (Table 2). A similar
trend was seen for alignments against the RR genome.
However, further trimming of the data set did not
implying that the reads are of sufficient quality up to
60bp. Shorter bisulphite reads are more challenging to
map and give an increased proportion of multiple align-
ments. In their original description of RR bisulphite
sequencing (RRBS), Meissner et al. (6) found that a
significant proportion of RRBS reads did not align to
the reference genome even after allowing up to six
mismatches in the mapping, which was attributed to
repetitive sequence and sequencing process artifacts.
Table 2. Comparison of mapping against RR genome and full length genome and different read lengths
% Uniquely mapped
sequence against RRa
% Uniquely mapped sequence
against full genomeb
No. of CpG sites in
size selected region
% of CpG sites in
size selected region
aRR genome (40–220bp).
bComplete human genome GRCh37.
cThe longer time taken for the 60bp reads must reflect more time spent resolving potential mismatches in comparison with that necessary for 75bp
Table 1. Comparison of mapping performance of the different packagesa
Programme AlignerNumber of
Perl application uses Bowtie
3h 7 min
42h 48 min
aThe reads were mapped against the complete human genome GRCh37.
bBSMAP v1.02 and RMAPBS rejected a proportion of lower quality reads.
cThe reads were hard trimmed to 75bp for better alignment.
e79 Nucleic Acids Research, 2012,Vol. 40,No. 10PAGE 4 OF 8
by guest on June 20, 2012
We showed that quality control and pre-processing of the
data set improve the alignment efficiency (Table 2).
We observed that dynamic trimming (performed by
fastq_quality_trimmer) of the data set improved mapping
efficiency (Supplementary Tables S2 and S3). However,
mapping efficiency was improved to a greater extent
when adaptor sequences were trimmed (performed by
our cleanadaptor program) especially with the longer
reads (Supplementary Tables S1 and S3). An interesting
corollary was that RMAPBS required all reads to be the
same length and rejected reads that were padded with ‘N’s
to that length after adaptor trimming. Consequently,
RMAPBS could not align any reads that contained the
adaptor sequence. As a result, we could not compare the
performance of all the aligners with dynamically trimmed
or adaptor trimmed data set. But our results strongly
suggest that the trimming of adaptor sequences is an
important step for improving mapping efficiency, support-
ing the conclusion of Gu et al. (8).
Uncertainty in the selection of fragments by size from
gels poses a problem for alignments against an in silico RR
genome in that sequence reads of fragments falling outside
the size limits are unlikely to map correctly to it. Although
this will cause the rejection of some otherwise valid
sequence data, alignment against the RR genome maxi-
mizes consistency of the outputs. For the latter reason, we
have opted to use an in silico RR genome restricted to the
expected size range for our fragments in order that
outlying fragments are rejected at the mapping stage and
that experimental variation should be suppressed as a con-
sequence. An in silico genome of 40–220bp fragments
from the GRCh37 build had a total size of 74Mb from
647626 MspI fragments and a total of 4068947 CpGs
representing 13.4% of the genomic total. This corresponds
to a 5.7-fold enrichment of CpGs.
The mapping against the RR genome was comparable
to that for the full genome for both 75 and 60bp trimmed
reads. Bismark showed 42.0 and 53.2% unique matches
against 75 and 60bp reads and RMAPBS showed 59.1
and 64.0% unique mapping against them. Both versions
of BSMAP showed similar rates of unique matches against
the RR genome. For our 75bp data set, both versions of
BSMAP (v1.02 and v1.2) produced 49.3% unique
mapping, and, for 60bp data set, BSMAP v1.02 and
BSMAP v1.2 showed 58.8 and 58.7% unique mapping
respectively (Table 2). The RR genome is 42.5 times
smaller than the full genome, and, as a consequence,
alignment was much faster than for the whole genome.
Bismark took 1.3h to map 18.5 million reads (read
length=75) against the RR genome, and BSMAP v1.2
completed the run in 1.52h, whereas RMAPBS and
BSMAP v1.02 completed the run in 8.65 and 19.37h,
respectively (Table 2).
From the uniquely mapped reads against the full
genome, we determined the number of CpG sites that
were contained within the reduced representation (RR)
genome of 40–220bp for our data set. We observed that
all the aligners mapped more than 80% of the CpG sites
into the size selected region of the genome (Table 2). These
results give us confidence that the RR library was well
constructed as majority of the sequenced CpGs fell in
the size range of 40–220bp and that the mapping
processes are producing valid alignments. Different reads
vary in the number of potential CpG sites they contain,
and it is apparent that different aligners perform
differently in their abilities to uniquely map the bisulphite
converted CpGs to a genome.
Bismark and RMAPBS produced a total methylation
percentage of 44.8 and 36.9 for 75bp data set, respectively,
when aligned against the complete human genome.
When aligned against the RR genome, Bismark gave
43.2% total methylation, and RMAPBS indicated 38.6%
total methylation. Initially, BSMAP v1.02 showed 18.6%
methylation when mapped against the whole genome
(Table 3), and, for the RR genome, it found 15.0% CpG
methylation (data not shown). Detailed investigation of
the reads from the aligned output revealed that this
anomaly is due to poorly documented trimming behaviour
by an option (?c) in the program. This had unexpectedly
caused a 50single-base truncation as well as a 10bp
30truncation, causing misalignment of reads in further
processing. As a result, the aligned files produced inaccur-
ate and lower percentage of methylation. This option has
been omitted from the latest version of BSMAP v1.2.
Reanalysis of BSMAP v1.02 without the –c switch
against the RR genome indicated 42.1% methylation for
our 75bp data set. Runs against the full genome were not
repeated. On the other hand, BSMAP v1.2 showed 42.9%
methylation when mapped against the whole genome
(Table 3), and, for the RR genome, it found 42.1% CpG
methylation. We performed similar operations on the
60bp trimmed data set and found that the results are
similar to those of 75bp data set (Table 3) although the
percent CpG methylation figures decreased slightly. The
percent reductions for BSMAP and Bismark were slightly
higher than for RMAPBS.
We have visualized and compared the methylation
tracks for three aligners in SeqMonk. Regions showing
extensively methylated and unmethylated CpGs are gen-
erally comparable between the aligners. However, closer
examination of some regions revealed large differences
Table 3. Comparison of methylation mapping between different
75bp data set
60bp data set
aRR genome constructed in the size range of 40–220bp.bSee text.
PAGE 5 OF 8Nucleic Acids Research,2012, Vol.40, No. 10 e79
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between the outputs from the aligners. Figure 1 shows
CpG methylation tracks produced by three different
aligners in a 2.55 Mbp region of chromosome 1 (a
section chosenat random),
Furthermore, to assure that the mapping was performed
only with high-quality reads and misalignment events did
not affect the results, we further trimmed the data set to
50, 40 and 36bp and compared the mapping statistics of
all the aligners against them. This step would have
removed virtually all of the adaptor contamination and
sequencing errors from the data set. The results from
these runs were summarized in Table 4. As mentioned
earlier, we did not find notable improvement in the
mapping efficiency for these trimmed data sets compared
to the 60bp data set.
Hard trimming resulted in a small proportional drop
in percentage of methylation (Tables 3 and 4), but,
otherwise, no significant change was observed.
Published descriptions of the aligners used have been
based onmuch smaller
RMAPBS used human chromosome 6 for evaluation of
the program (13). Chromosome 6 contains only between
5.5 and 6% of the total DNA of the human genome.
Similarly, BSMAP performance was compared with
several other aligners using 2.9 million, 31nt reads
against the Arabidopsis genome (119Mb) (3). The time
taken to map one lane of sequenced reads against the
whole genome is much greater than had been implied
(14). The statistics described with the example data in
the programs differs significantly from our real time
data. The number of reads, the read length of the
data sets.For example,
Figure 1. SeqMonk display of differential methylation from different aligners. About 75bp trimmed read data for 18?106 reads were aligned
against the Human genome GRCh37 build by Bismark. v0.23, RMAPBS v2.05 and BSMAP v1.2 for which the methylation is displayed, respectively,
from top to bottom below the gene, mRNA and CDS panes. Methylated CpG positions are shown in the red panes for each aligner, and
unmethylated CpGs are in the blue panes. The display is of a randomly selected 2.55 Mbp region of chromosome 1. The black boxes indicate
some regions of significant difference in methylation.
Table 4. Effect of sequence trimming on alignment efficiency and
50bp data set
BSMAP v1.02 18471799
40bp data set
BSMAP v1.02 18471799
36bp data set
BSMAP v1.02 18471799
aThe runs were performed against our RR genome (40–220bp).
e79 Nucleic Acids Research, 2012,Vol. 40,No. 10PAGE 6 OF 8
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sequences and the size of the reference genome have
a pivotal role to play in determining the overall mapping
speed. The algorithm of the program also has a significant
effect. The slower performance of earlier BSMAP versions
(e.g. v1.02, see Table 1) has been noted by others on a
synthetic, smaller data set (106simulated reads aligned
to human chromosome 21) (14) and can be explained by
the nature of the program. The seeding/hashing and the
bitwise masking (a unique feature of BSMAP) confer
considerable efficiency together with multithreading, but,
despite these, the extensive alignment optimizing steps
made the older version of the aligner slower. In contrast,
the seeding method employed by RMABPS confers
substantially greater efficiency, yet returns bisulphite
alignments of comparable quality, as noted elsewhere
(12). The top performer is the BW Transform of the
BowTie aligner called by Bismark (10) in which the sig-
nificant time expended in the pre-processing step is gener-
ously compensated by the notably superior performance
of the algorithm.
The manner in which aligners manage the C-T mapping
asymmetry can provide an additional source of mapping
differences. BSMAP and RMAPBS allow both Cs and Ts
of reads to map to genomic cytosines, whereas Bismark
converts all residual Cs in the read and all genomic Cs into
Ts for mapping. This can enable BSMAP and RMAPBS
to achieve higher mapping efficiencies (as observed in our
results) but can introduce a bias by increasing the unique
mapping for more highly methylated reads compared to
those less methylated where the higher proportion of Ts
makes multiple mapping more probable. In contrast,
Bismark will generate multiple mappings of reads irre-
spective of their methylation status and therefore will
not bias for more highly methylated reads. This might
result in reduced mapping efficiency but avoids mapping
bias based on methylation status of the reads.
For libraries generated from a RR genome and enzym-
atic fragmentation, it is recommended to construct an
in silico RR genome (7) and map the reads against it.
This approach allows for fragment driven mapping
against the predicted MspI digested sites only. The
lengthy library preparation procedure might introduce
some bias in the data set. For example, over-amplification
of some fragments might occur during library preparation.
Imprecise gel selection may also generate fragments
outside the intended size range in the final library.
Mapping against a selected RR genome compensates for
experimental error to a certain extent. It also heavily
reduces the computational load as we have shown in
that mapping against RR genome is many times faster
than full genome. We have demonstrated that the RR
genome demonstrated comparable mapability to that
of the full genome.
We showed that quality checking of the data set is
important to obtain a maximum alignment output. The
choice of trimming is arbitrary: with our quality control
techniques and mapping statistics, we showed 60bp was a
reasonable choice that achieved respectable mapping effi-
ciency. Also, we showed that short NGS reads do not
necessarily increase unique mapping as they are more
likely to match in multiple positions of the genome.
If 100bp reads are sequenced, it is inefficient to trim
them to 40bp for alignment, given that the increasing
error rate further into reads does not necessarily lead to
All the aligners analysed showed comparable overall
methylation percentage for our data set. Furthermore,
the overall methylation status produced against the RR
genome is also in concordance with that for the full
genome. The overall methylation percentage of CpG
sites in the human genome is estimated to be 68.4%
(15), but the majorityof
unmethylated. Since our library is enriched for CpG
islands, we expect to see less methylation in our data set
than the overall methylation percentage of CpG sites in
the genome. However, while visualizing the data tracks
in SeqMonk, we have observed that different aligners
showed differential overall CpG methylation patterns in
the same region, noting that such variations are wide-
spread (Figure 1). These differences result from variations
in how the aligners work. Our choices of runtime param-
eters were to use defaults or those recommended by the
authors, but this may result in different performance
for aligners when tackling the complex issues of the asym-
metric mapping of bisulphite reads onto a genome.
Ideally, all aligners would create exactly the same
mapping for the same reads, but underlying differences
in their strategies for locating, extending and making
their optimized choices will
Attempting to achieve more similar mapping by the
adjustment of runtime parameters is beyond the scope of
this study. Also, the percentage of uniquely mapped
sequence is different in each aligner, and the number of
reads mapped to a particular region can differ as a result
of that. So, our results indicate that for interpreting
accurate methylation, the choice of aligner is important
as are careful evaluation of data quality, trimming of
reads as appropriate, removal of adaptor sequences and
selection of suitable parameters for analysis. Since differ-
ent experiments may produce different methylation
patterns, it is possible that different aligners may
produce varied mappings making this choice complex.
Further, the evolving computational environment may
alter preferences by, for instance, the move to large-scale
parallel processing favouring slower algorithms.
Whereas the originally described RRBS protocol by
Meissner et al. (6) is based on 36bp reads, current
Illumina sequencing protocols can produce up to 150bp
reads and are projected to give longer read lengths in the
near future. However, when libraries generated from a
significant number of smaller DNA fragments are
sequenced to longer reads, it is inevitable that some
reads will sequence into the adaptors. The Illumina
adaptors are methylated and may account for false methy-
lation calls in further analysis if such reads align to the
genome, given that the aligners used here permit some
read errors in making their alignments. Alternatively,
many such reads will not align and will be rejected
despite having valid leading sequence. Hence, in order to
avoid bias from either of these processes, it is desirable to
scan for adaptor sequences in the read file and remove
them before further alignment (8). Interestingly, we also
PAGE 7 OF 8 Nucleic Acids Research,2012, Vol.40, No. 10 e79
by guest on June 20, 2012
tried dynamic trimming based on read quality and found
that this generated less unique mapping in comparison
with adaptor trimming, both for the original 100 and the
75bp data. This probably reflects the extent to which
shorter reads are more likely to map multiply (Table 4),
whereas the adaptor sequence tends to prevent those reads
from mapping. Lower quality trailing sequence may still
map correctly and its removal may amount to the loss
of useful data.
The failure of RMAPBS to align reads that contained
adaptor sequence, even if these were trimmed, reduces its
value as an aligner for RRBS protocols, since it loses the
information from shorter reads. For our data, the newer
version of BSMAP (v1.2) is considerably improved, since
it performs over 40 times faster than the older version.
The outputs from BSMAP and RMAPBS require
further downstream processing in order to obtain unique
matches and to generate CpG methylation data.
On the basis of speed, reasonable performance and ease
of extracting methylation
SeqMonk, we would choose Bismark as our preferred
aligner at this stage, but, in establishing a processing
pipeline for methylation profiling, it remains desirable to
monitor the performance of other aligners in order to
ensure that the most appropriate choice is made. The
continuing evolution of sequencing chemistry and proto-
cols together with further improvements in computational
resources and algorithms will enhance the interpretation
of genome-wide methylation sequencing data.
Supplementary Data are available at NAR Online:
Supplementary Tables 1–3, Supplementary Figures 1–5,
Supplementary Program and Supplementary Dataset.
We gratefully acknowledge assistance and comments
provided by Dr. Felix Krueger and Dr. Simon Andrews
of the Babraham Institute, Cambridge, UK. In addition,
we appreciate the comments and suggestions made by the
reviewers to improve the quality of the manuscript.
National Research Centre for Growth and Development
(NRCGD) andHealth ResearchCouncil(HRC),
New Zealand (Grant HRC 09/085 D). A.C. is supported
by a scholarship from NRCGD. Funding for open access
charge: National Research Centre for Growth and
Development (NRCGD) and Health Research Council
(HRC), New Zealand.
Conflict of interest statement. None declared.
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