Setting up the Map Bisulfite Reads to Reference tool is very similar to the usual read mapping, with a few differences. In particular,
- Some options such as 'Global alignment' are either not available or preset.
- Only a track version of mappings is produced in output.
- The bisulfite mappings in CLC Workbenches have a special 'invisible' property set for them, to inform the downstream tool Call Methylation levels (See section 31.3.3) about the correct type of input.
Please note that, because two versions of the reference sequence (C->T and G->A - converted) have to be indexed and used simultaneously for each read, the RAM requirements for the bisulfite mapper are double than those needed for a regular mapping against a reference sequence of the same size.
To start the read mapping:
Toolbox | Epigenomics Analysis () | Bisulfite Sequencing ()| Map Bisulfite Reads to Reference ()
- Selecting reads and directionality
In the first dialog, select the sequences or sequence lists containing the sequencing data (figure 31.13). Please note that reads longer than 100,000 bases are not supported.
When the sequences are selected, click Next to specify what type of protocol you have used (directional or not).
A directional protocol yields reads from both strands of the original sample-DNA. The first read of a pair (or every read for single-end sequencing) is known to be sequenced either from the original-top (OT) or the original-bottom (OB) strand. The second read of a pair is sequenced from a complementary strand, either ctOT or ctOB. At the time of writing, examples of directional protocols include:
- Illumina TruSeq DNA Methylation Kit (formerly EpiGnome)
- Kits from the NuGen Ovation family of products
- Swift Accel-NGS Methyl-seq DNA Library Kit
- Libraries prepared by the 'Lister' method
In a non-directional protocol, the first read of a pair may come from any of the four strands: OT, OB, ctOT or ctOB. Examples include:
- Zymo Pico Methyl-Seq Library Kit
- Bioo Scientific (Perkin Elmer) NEXTflex Bisulfite-Seq Kit
- Libraries prepared by the 'Cokus' method
If you are uncertain about the directionality of your protocol, contact the protocol vendor. Note that it is sometimes possible to infer the directionality by looking at the reads: in the absence of methylation, a directional protocol will have few or no Cs in the first read of each pair. We do not recommend however using this approach.
- Selecting the reference
When the sequences and directionality are selected, click Next, and you will see the dialog shown in figure 31.14.
Click the Browse and select element () button to select either single sequences, a list of sequences or a sequence track as reference. Note the following constraints:
- single reference sequences longer than 2gb ( bases) are not supported.
- a maximum of 120 input items (sequence lists or sequence elements) can be used as input to a single read mapping run.
- Including or excluding regions (masking)
The next part of the dialog shown in figure 31.14 lets you mask the reference sequences. Masking refers to a mechanism where parts of the reference sequence are not considered in the mapping. This can be useful for example when mapping data is captured from specific regions (e.g. for amplicon resequencing). The read mapping will still base its output on the full reference - it is only the core read mapping that ignores regions.
Masking is performed by discarding the masked out nucleotides. As a result the reference is split into separate sequences, which are positioned according to the original unmasked reference sequence.
Note that you should be careful that your data is indeed only sequenced from the target regions. If not, some of the reads that would have matched a masked-out region perfectly may be placed wrongly at another position with a less-perfect match and lead to wrong results for subsequent variant calling. For resequencing purposes, we recommend testing whether masking is appropriate by running the same data set through two rounds of read mapping and variant calling: one with masking and one without. At the end, comparing the results will reveal if any off-target sequences cause problems in the variant calling.
Masking out repeats or using other masks with many regions is not recommended. Repeats are handled well and does not cause any slowdown. On the contrary, masking repeats is likely to cause a dramatic slowdown in speed, increase memory requirements and lead to incorrect read placement.
To mask a reference sequence, first click the Include or Exclude options, and second click the Browse () button to select a track to use for masking.
- Mapping parameters
Clicking Next leads to the parameters for the read mapping (see figure 31.15).
The first parameter allows the mismatch cost to be adjusted:
- Match score The positive score for a match between the read and the reference sequence. It is set by default to 1 but can be adjusted up to 10.
- Mismatch cost The cost of a mismatch between the read and the reference sequence. Ambiguous nucleotides such as "N", "R" or "Y" in read or reference sequences are treated as a mismatches and any column with one of these symbols will therefore be penalized with the mismatch cost.
- Linear gap cost The cost of a gap is computed directly from the
length of the gap and the insertion or deletion cost. This model
often favors small, fragmented gaps over long contiguous gaps.
If you choose linear gap cost, you must set the insertion cost and the
- Insertion cost
- The cost of an insertion in the read (a gap in the reference sequence). The cost of an insertion of length will be
Insertion cost (31.2)
- Deletion cost
- The cost of a deletion in the read (gap in the read sequence). The cost of a deletion of length will be
Deletion cost (31.3)
- Affine gap cost An extra cost associated with opening a gap is
introduced such that long contiguous gaps are favored over short
If you chose affine gap cost, you must also set the cost of opening an
insertion or a deletion:
- Insertion open cost
- The cost of opening an insertion in the read (a gap in the reference sequence).
- Insertion extend cost
- The cost of extending an insertion in the read (a gap in the reference sequence) by one column.
- Deletion open cost
- The cost of a opening a deletion in the read (gap in the read sequence).
- Deletion extend cost
- The cost of extending a deletion in the read (gap in the read sequence) by one column.
Using, affine gap cost, an insertion of length is penalized by
Insertion open cost Insertion extend cost (31.4)
and a deletion of length is penalized by
Deletion open cost Deletion extend cost (31.5)
In this way long consecutive gaps get a lower cost per column than small fragmented gaps and they are therefore favored.
The score of a match between the read and the reference is usually set to 1. Adjusting the cost parameters above can improve the mapping quality, e.g. when the read error rate is high or the reference is expected to differ significantly from the sequenced organism. For example, if the reads are expected to contain many insertions and/or deletions, it can be a good idea to lower the insertion and deletion costs to allow more of such errors. However, one should also consider the possible drawbacks when adjusting these settings. For example, reducing the insertion and deletion cost increases the risk of mapping reads to the wrong positions in the reference.
Figure 31.16 shows an example using linear gap cost where the read mapper is unable to map a region in a read due to insertions in the read and mismatches between the read and the reference. The aligned region of the read has a total of 57 matching nucleotides which result in an alignment score of 57 which is optimal when using the default cost for mismatches and insertions/deletions (2 and 3 respectively). If the mapper had aligned the remaining 35bp of the read as shown in figure 31.17 using the default scoring scheme, the score would become:
In this case, the alignment shown in Figure 31.16 is optimal since it has the highest score. However, if either the cost of deletions or mismatches were reduced by one, the score of the alignment shown in figure 31.17 would become 61 and 58, respectively, and thus make it optimal.
Figure 31.17: An alignment of a read containing a region with several mismatches and deletions. By reducing the default cost of either mismatches or deletions the read mapper can make an alignment that spans the full length of the read.
Once the optimal alignment of the read is found, based on the cost parameters described above, a filtering process determines whether this match is good enough for the read to be included in the output. The filtering threshold is determined by two factors:
- Length fraction The minimum percentage of the total alignment length that must match the reference sequence at the selected similarity fraction. A fraction of 0.5 means that at least half of the alignment must match the reference sequence before the read is included in the mapping (if the similarity fraction is set to 1). Note, that the minimal seed (word) size for read mapping is 15 bp, so reads shorter than this will not be mapped.
- Similarity fraction The minimum percentage identity between the aligned region of the read and the reference sequence. For example, if the identity should be at least 80% for the read to be included in the mapping, set this value to 0.8. Note that the similarity fraction relates to the length fraction, i.e. when the length fraction is set to 50% then at least 50% of the alignment must have at least 80% identity (see figure 31.18).
Figure 31.18: A read containing 59 nucleotides where the total alignment length is 60. The part of the alignment that gave rise to the optimal score has length 58 which excludes 2 bases at the left end of the read. The length fraction of the matching region in this example is therefore 58/60 = 0.97. Given a minimum length fraction of 0.5, the similarity fraction of the alignment is computed as the maximum similarity fraction of any part of the alignment which constitute at least 50% of the total alignment. In this example the marked region in the alignment with length 30 (50% of the alignment length) has a similarity fraction of 0.83 which will satisfy the default minimum similarity fraction requirement of 0.8.
- Mapping paired reads
- Global alignment By default, mapping is done with local alignment of the reads to the reference. The advantage of performing local alignment instead of global alignment is that the ends are automatically left unaligned if there are many differences from the reference at the ends. For many sequencing platforms, the quality of the bases drop along the read, and a local alignment approach is desirable. By checking this option, the mapper is forced to look for the highest scoring alignment of the entire read, meaning that the read mapping generated will have no unaligned ends even when the end of the reads align to the wrong places.
- Auto-detect paired distances If the sequence list used as input contains paired reads, this option will automatically be enabled - if it contains single reads, this option will not be applicable.
CLC Genomics Workbench offers as the default choice to automatically calculate the distance between the pairs. If this is selected, the distance is estimated in the following way:
- A sample of 100,000 reads is extracted randomly from the full data set and mapped against the reference using a very wide distance interval.
- The distribution of distances between the paired reads is analyzed, and an appropriate distance interval is selected:
- If less than 10,000 reads map, a simple calculation is used where the minimum distance is one standard deviation below the average distance, and the maximum distance is one standard deviation above the average distance.
- If more than 10,000 reads map, a more sophisticated method is used which investigates the shape of the distribution and finds the boundaries of the peak.
- The full sample is mapped using this distance interval.
- The history () of the result records the distance interval used.
Figure 31.19: To the left: mapping with a narrower distance interval estimated by the workbench. To the right: mapping with a large paired distance interval (note the large right tail of the distribution).
Sometimes the automatic estimation of the distance between the pairs may return a warning "multiple intervals detected". This may happen if the reads derive from multiple libraries or from certain types of amplicon sequencing protocols. In this case, the estimates may still be correct, but, if in doubt, the user may want to disable the option to automatically estimate paired distances and instead manually specify minimum and maximum distances between pairs on the input sequence list.
If the automatic detection of paired distances is not checked, the mapper will use the information about minimum and maximum distance recorded on the input sequence lists.
The "automatic detection of paired distance" option when mapping should be used with caution. It is possible that the estimated distance setting is too narrow and consequently many read pairs will be flagged broken.
When a paired distance interval is set, the following approach is used for determining the placement of read pairs:
- First, all the optimal placements for the two individual reads are found.
- Then, the allowed placements according to the paired distance interval are found.
- If both reads can be placed independently but no pairs satisfies the paired criteria, the reads are treated as independent and marked as a broken pair.
- If only one pair of placements satisfy the criteria, the reads are placed accordingly and marked as uniquely placed even if either read may have multiple optimal placements.
- If several placements satisfy the paired criteria, the pair is treated as a non-specific match (see Non-specific matches for more information.)
- If one read is uniquely mapped but the other read has several placements that are valid given the distance interval, the mapper chooses the location that is closest to the first read.
- Non-specific matches
At the bottom of the dialog, you can specify how Non-specific matches should be treated. The concept of Non-specific matches refers to a situation where a read aligns at more than one position with an equally good score. In this case you have two options:
- Random. This will place the read in one of the positions randomly.
- Ignore. This will not include the read in the final mapping.
For paired data, reads are only considered non-specific matches if the entire pair could be mapped elsewhere with equal scores for both reads, or if the pair is broken in which case a read can be categorized as non-specific in the same way as single reads (see Mapping paired reads).
When looking at the mapping, the default color for non-specific matches is yellow.
- Gap placement
In the case of insertions or deletions in homopolymeric or repetitive regions, the precise placement of the insertion or deletion cannot be determined from the data. An example is shown in figure 31.20.
Figure 31.20: Three A's in the reference (top) have been replaced by two A's in the reads (shown in red). The gap is placed towards the 5' end, but could have been placed towards the 3' end with an equally good mapping score for the read.
In this example, three A's in the reference (top) have been replaced by two A's in the reads (shown in red). The gap is placed towards the 5' end (left side), but could have been placed towards the 3' end with an equally good mapping score for the read as shown in figure 31.21.
Figure 31.21: Three A's in the reference (top) have been replaced by two A's in the reads (shown in red). The gap is placed towards the 3' end, but could have been placed towards the 5' end with an equally good mapping score for the read.
Since either way of placing the gap is arbitrary, the goal of the mapper is to place the gaps consistently at the same side for all reads.
- Bisulfite read mapping result handling
Click Next lets you choose how the output of the mapping should be reported. There are two independent output options available that can be (de-)activated in both cases:
- Create report. This will generate a summary report as described in
- Collect un-mapped reads. This will collect all the reads that could not be mapped to the reference into a sequence list (there will be one list of unmapped reads per sample, and for paired reads, there will be one list for intact pairs and one for single reads where the mate could be mapped).
However, the main output is a reads track:
- Reads track
- A reads track is very "lean" (i.e. with respect to memory requirements) since it only contains the reads themselves. Additional information about the reference, consensus sequence or annotations can be added and viewed alongside in the context of a Track List later (by adding, for example, a reference and/or annotation track, respectively). This kind of output is useful when working with tracks in general and especially for resequencing purposes this is recommended.
Note that the tool will output an empty read mapping and report when nothing mapped, and empty unmapped reads if everything mapped.
- Create report. This will generate a summary report as described in http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Summary_mapping_report.html.
Figure 31.22 illustrates the view of a typical directional shotgun BS-seq mapping. As with any read mapping view, the colour of the reads follows the usual CLC convention, that is green/red for forward/reverse reads, and dark/pale blue for paired reads. Independent of this orientation property, each read or read pair has an 'invisible' property indicating if it came from the original top (OT), or original bottom (OB) strand. However, if the BS-sequencing protocol is truly 100%-directional, the orientation in the mapping and the OT/OB origin of reads/read pairs will be concordant.
In this figure, blocks of reads from the original top strand are marked with squiggly brackets on the left, while the rest are from the original bottom strand. In a mapping, they can be distinguished by a pattern of highlighted mismatches to reference. OT reads will have mismatches predominantly on 'T', due to C->T conversion; OB reads will have a pattern of mismatches on 'A' symbols, corresponding to G->A conversion as can be seen on a reverse-complementary strand. When methyl-C occurs in a sample, there will be a match in the reads, instead of an expected mismatch.
In this figure, there were two positions where such events occurred, both on the original bottom strand, and both supported by a single read only. 'G' symbols in those reads are shown in red boxes. The reverse direction of an arrowhead on a base-level methylation track also reflects the OB-position of a methylation event.
Note also that it appears that there may be a G/T heterozygous SNP (C/A in the OB strand) in the second position. While such occurrences may lead to underestimation of true methylation levels of cytosine alleles in heterozygous SNPs, our current tool does not attempt to compensate for such eventualities.
To understand and interpret BS-sequencing and mapping better, it may be helpful to examine the position (marked with a red asterisk) in between the two detected methylation events. There appears to be an additional A/C heterozygous SNP with C's in reads from OT strand fully converted to T's, i.e., showing no evidence of methylation at that heterozygous position.