Identification of genomic indels and structural variations using split reads
© Zhang et al; licensee BioMed Central Ltd. 2011
Received: 26 January 2011
Accepted: 25 July 2011
Published: 25 July 2011
Recent studies have demonstrated the genetic significance of insertions, deletions, and other more complex structural variants (SVs) in the human population. With the development of the next-generation sequencing technologies, high-throughput surveys of SVs on the whole-genome level have become possible. Here we present split-read identification, calibrated (SRiC), a sequence-based method for SV detection.
We start by mapping each read to the reference genome in standard fashion using gapped alignment. Then to identify SVs, we score each of the many initial mappings with an assessment strategy designed to take into account both sequencing and alignment errors (e.g. scoring more highly events gapped in the center of a read). All current SV calling methods have multilevel biases in their identifications due to both experimental and computational limitations (e.g. calling more deletions than insertions). A key aspect of our approach is that we calibrate all our calls against synthetic data sets generated from simulations of high-throughput sequencing (with realistic error models). This allows us to calculate sensitivity and the positive predictive value under different parameter-value scenarios and for different classes of events (e.g. long deletions vs. short insertions). We run our calculations on representative data from the 1000 Genomes Project. Coupling the observed numbers of events on chromosome 1 with the calibrations gleaned from the simulations (for different length events) allows us to construct a relatively unbiased estimate for the total number of SVs in the human genome across a wide range of length scales. We estimate in particular that an individual genome contains ~670,000 indels/SVs.
Compared with the existing read-depth and read-pair approaches for SV identification, our method can pinpoint the exact breakpoints of SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. Moreover, with the advent of the third-generation sequencing technologies that produce longer reads, we expect our method to be even more useful.
One important goal in genomics is to determine the genetic differences among individuals and to understand their relationships to the phenotypic differences within a species, such as human beings. These variations consist of single nucleotide polymorphisms (SNPs) and structural variations (SVs) including short insertions/deletions (indels) and other more complex ones such as duplications and translocations. Because of the efficiency of genotyping methods and the central role they play in the genome-wide association studies, SNPs are currently the best catalogued and studied human genetic variations. Ubiquitous 1-bp indels, expansions of simple repeats and chromosomal anomalies have long been observed and acknowledged as the genetic bases for some human diseases [1, 2]. Except for these old discoveries, however, indels and SVs have been much less studied due to their wide size range, the multitude in their types, and the lack of an efficient genotyping method. After several recent studies, however, their genetic significance starts to be appreciated: not only do they exist in large numbers in the human populations, they may also have a more significant impact on phenotypic variation than SNPs [3–7].
The microarray technology, array CGH, has been widely used to detect copy number variants (CNVs), a type of SV, with kilo-bases resolutions [5, 8–11]. The advancement in high throughput sequencing technologies has enabled a new set of comparative approaches for CNV calling, such as the read-depth analysis [12–15], which computes the read coverage of different genomic regions, the read pair analysis, which focuses on cases where the distance between the two ends of a reads deviates more than expected when they are mapped back to the reference [4, 16–18]. Accompanying the advancement of these experimental approaches, different computational methods for SV detection and their breakpoint refinement have also been developed [18–25].
Here we report the split-read analysis, a sequence-based method that detects SVs through direct analysis of the mapping information of how high-throughput sequencing reads are aligned to the reference genome. Using alignment of read sequences to reference genomes with gaps, the method allows the precise identification of SVs covered by such reads. Building our method directly upon BLAT, a well-established sequence alignment program, we take advantage of the speed and the sensitivity of this popular sequence-to-genome alignment tool. However, more importantly, by considering both the sequencing and mapping errors in our assessment strategy to score each initial SV call, our method also takes into account the sequencing error model (especially for next-generation sequencing technologies, which were not generally available a few years ago), and distinguishes the different confidence levels in detecting different SVs based on the characteristics of supporting reads. Compared with the read-depth and the read-pair analyses, our sequence-based method can not only pinpoint the breakpoints of SV events, but also reveal the actual sequence content of insertions. The split-read analysis has another advantage--it can cover the whole size spectrum for deletions (Figure 1). We expect our method to be more useful in the future as the sequence reads become longer.
Due to both experimental and computational limitations, there are biases on multiple levels in the call sets generated by all current SV identification methods. In addition to their significantly more restricted size range of identifiable insertions than that of deletions, all current SV identification methods are sensitive to SVs of different length (Figure 1), and as a result studies using them have reported different numbers of SVs. One study using the read-pair method reported 241 SVs over 8 kb in a sampled genome , while another using the same approach but with a different molecular construct reported 422 and 753 SVs over 3 kb in two tested genomes . In a study of whole-genome sequencing and assembly, 835,926 indels were identified in a diploid human genome . Currently it is not known how many SVs, small or large, are in an individual human genome. Using empirical error models estimated from sequencing experiments to simulate high-throughput sequencing reads, we could not only parameterize our split-read method, but also, more importantly, quantify both false positive and false negative rates. Knowing these error rates enables us to estimate the total number of SVs of a given length in a human genome.
We have developed the split-read identification, calibrated (SRiC), a sequence-based method for detecting structural variants (SVs). It maps reads to the reference genome with gapped alignment and scores these mappings with consideration for sequencing and alignment errors. SRiC pinpoints exact SV breakpoints, reveals the sequence content of insertions, and covers the whole size spectrum for deletions. Simulation is used to calibrate SRiC, allowing unbiased estimation of the sensitivity and proportion of SVs across different length-scales.
Analysis of the simulated sequence data
For sequencing simulations, instead of using the whole human genome, we use the diploid human chromosome 22 (NCBI36 assembly), which counts for 1% of the human genome but has a repeat content and a gene density both representative of the whole genome, to save computational processing time. To keep the local sequence environment of indels as found in a genome, we use indels identified in Venter's genome  in our sequencing simulation (Additional file 1).
Determining thresholds used in the analysis
Three thresholds are used in our split-read analysis: tr, the threshold on the ratio of the score of the best alignment to that of the second best as a measure of the uniqueness of the read, tn, the threshold on the number of supportive reads for 1-bp SVs, and tc, the threshold on the maximum centeredness (the maximum ratio of the smaller length to the bigger one of two flanking alignments of a read, Additional file 1, Figure S1) for large SVs.
Two thresholds, tn and tc, are used for the initial SV calls (Inequalities 1 and 2). We vary the value of one of these two thresholds while fix the other to determine how they affect the accuracy and the sensitivity of the split-read method. Using the simulated sequence set with the ~5× coverage, we make SV calls with tn = 1, 2, ..., 9 while tc = 0.1 and tc = 0.1, 0.2, ..., 0.9 while tn = 5, count the true positive and the false positive calls, and calculate the percentage of true positives, false negatives, and false positives at each threshold combination. The results of this performance analysis as depicted in Figure 2C-F make it clear the effects that theses two thresholds have on the SV identification show a dichotomous dependency on the SV length. While tn affects the identification of short SVs, tc biases that of longer ones. In practice, we use the sequencing depth for tn (with a lower bound nmin = 2) and set tc to 0.1. It is also clear that the method has different sensitivities in the size range of indels that it can detect: it is less sensitive to 1-bp indels because 454 sequencing is prone to over- or under-call bases in homopolymers and thus more a stringent threshold is needed to lower the number of 1-bp false positives.
Assessing how the read length affects the performance
The general trend, which is expected and depicted in the figure, is that the SV identification is improved with longer reads. With 50-bp reads, the SV identification is the worst with low sensitivity for both short and long deletions. Because the length of discoverable insertions is capped by the read length, it is not surprising that at this read length none of the insertions of 20-bp and longer are found. When the read length is increased to 200 bp and longer, the sensitivity and the positive predictive value almost double for longer SVs. For deletions, 200-, 400-, and 800-bp reads seems to give comparable performance, and longer reads only bring marginal improvements to the results. The choice of read length for insertions identification is, however, a rather open-end question, as longer reads will always enable better identification of longer insertions.
Assessing the effects of sequence coverage on SV calls
Number of sequences in simulated and down-sampled datasets
Number of sequences
Number of base pairs
The sequencing depth has the most significant effect on short SVs. At 1× coverage, ~1,000 of true 1-bp deletions and insertions are supported by at least one read. When the coverage is increased to 2×, these numbers almost are doubled. As the coverage increases, the percentage of supported true SVs also increases but with a diminishing pace. 80~90% true SVs are supported by at least one read at 5× to 20× coverage. One supportive read is the absolutely minimum requirement for an SV call. To reduce the false positives, we require at least two supportive reads for every SV call. This global threshold has a much more significant effect on the low-coverage sequence set than on the high-coverage one: while the percentage of true deletions with two or more supportive reads is about the same as that of true deletions with one supportive read at 1× coverage, there are very few true SVs with only one supportive read at 10× or higher coverage.
Array capture validation of SR called deletions. 1, 2
t n 3
Positive predictive value 4
Trio-array CGH validation of result. 1, 2
t n 3
Positive predictive value 4
Analysis of the 1000 Genomes Project data
A major sequencing project, the 1000 Genomes Project, has been launched to resequence the genomes of at least a thousand people from around the world using the new sequencing technologies to produce the most detailed map of human genetic variation for disease studies. As a proof of concept, we apply our split-read analysis to a set of 454 sequence reads generated by the 1000 Genomes Project for one individual.
Corrected counts of SVs in the chromosome 1 and the whole genome of a Yoruba individual. 1
SV size range (bp)
Whole genome 2
Mapping reads to the reference genome
The size of the deletions covered by the split-reads can range up to tens of thousands of bases, and this makes BLAT well suited for mapping such reads back to the genome, since it not only allows small gaps and mismatches within the alignment like many other alignment tools, but also takes into account large gaps due to its initial purpose to handle introns in RNA/DNA alignments . In short, unlike the alignment results from tools such as BLAST which will generate two distinct partial alignments for a split-read covering a large deletion event, the alignment results of BLAT can directly reveal the deletion event and its up- and down-stream alignments at the same time. Recently a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), has been designed and implemented to align with gaps long reads such as 454 reads (~200 bp or longer) to the reference genome with higher accuracy and a faster speed than BLAT . However, BLAT should be used to align 454 paired-end reads, because currently the average 454 read length is less than 400 bp and thus, the majority of sequences on both ends will be shorter than 200 bp.
For the non-split reads, however, using BLAT would be unnecessarily time-consuming, because their alignment results would usually only contain (if any) a small number of mismatches. Bowtie, a recently developed alignment tool, incorporates the Burrows-Wheeler transform technique to index and search the genome in a fast and memory-efficient manner, and is an immediate candidate for processing such reads .
The two-tiered alignment cascade is used to expedite the step of aligning reads to the reference genome. The first assortment step effectively fractions the sequence reads into two subsets: ones that can be uniquely mapped and ones that cannot. By limiting the gapped alignment of the reads in the former subset to their associated chromosomes, the tiered mapping approach removes the unnecessary mapping attempts and thus speeds up the alignment step. The speed gain is clearly related to the size ratio of the two read subsets: the more uniquely mappable reads, the bigger the speed gain. Because it is assessed by their 35-bp end tags, the genomic uniqueness of the reads is limited to the unique mappability of the 35-mers to the human genome. It has been estimated that 79.6% of the genome is uniquely mappable using 30-bp sequence tags. Since the human genome consists of 24 chromosomes, it is natural to use them as the bins for end tag assortment. It is, however, conceivable to fraction the human genome into large (e.g., 100 Mb) fragments with small (e.g., 1 kb) overlaps and use them as the assortment bins to further restrict the search space of the subsequent BLAT genomic mapping of the reads whose end tags are uniquely mapped.
Parameterization of the split-read analysis
Five parameters are intrinsic to our split-read analysis alone: the alignment score ratio threshold tr, the threshold on the number of supportive reads for 1-bp SVs tn, the threshold on the maximum centeredness for large SVs tc, the minimum number of supportive reads for every SV identification nmin, and the exponential decay parameter λ. For sequence reads that are mapped to multiple genomic locations, we use tr to control on what level of distinctiveness such reads can be used for the SV identification. A higher value of tr lowers the overall mapping ambiguity and thus reduces the number of false positives. This will, however, disqualify more correct alignments and in turn increase the number of false negatives. Small and large false SV calls have different origins: the former result from sequencing errors that under- or over-call bases while the later are mostly generated by misalignments. To count for such distinct error origins, two different threshold functions, separately parameterized with tn and tc using the same exponential base function, are used to make SV calls. λ controls how fast the threshold changes between 1-bp and large SVs and it is set to 1 in all of our split-read analyses. We require that there should be at least two supportive reads for every SV identified regardless of its length. This global threshold (nmin = 2) dramatically reduces the false positive SV calls.
Directly building our method upon BLAT, we take advantage of the speed and the sensitivity of this popular sequence-to-genome alignment tool. However, more importantly, we designed an assessment strategy to score each initial indel/SV call that takes into account both the sequencing and mapping errors. Compared with the existing read-depth and read-pair analyses, our sequence-based method can pinpoint the exact breakpoints of indel/SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. We thoroughly benchmarked and validated our SRiC method against the best available methods for detecting structural variants at relevant resolutions by using several different approaches to extensively evaluate the performance of our method. We illustrate the characteristics of our split-read method by applying it to both synthetic and experimental data sets. With the advent of the third-generation sequencing technologies that produce longer reads, we believe the split-read approach presented here can make a significant contribution to the study of indels/SVs.
The data input for the split-read analysis are genomic read sequences. For sufficient alignability, these reads should have a length of hundreds of bases and currently can be generated by the Sanger sequencing or, to a much higher throughput, the 454 sequencing. However, we expect reads from other sequencing platform (e.g., paired Solexa reads with overlap) may also be used after preprocessing. The current system implementation only supports the widely used FASTA sequence format.
Tiered sequence alignment
The sequence reads are first processed to remove any terminal ambiguous bases (Ns) and then mapped to the human reference genome (NCBI Build 36.1, UCSC hg18) using BLAT with parameters tuned for short sequences with maximum sensitivity (-stepSize = 5, -tileSize = 11, -repMatch = 106, and -fine). Certain parts of the reference genome (such as low complexity regions and simple sequence repeats) can be masked out by replacing the sequences with Ns to disallow indel identification in these regions. When the set of reads is large, the aforementioned direct approach to sequence mapping will be very time-consuming. To enhance the speed of the alignment step, we use a tiered approach instead by dividing our alignment process into two steps: a fast initial assortment of the reads followed by a complete gapped alignment.
Briefly, we first take 35-mer tags at each end of a read, map them to the whole reference genome using Bowtie, a rapid alignment tool for short reads, look for those end tags that can be mapped uniquely to the genome, and assort the corresponding reads by their associated chromosomes. Using BLAT to obtain the gapped alignments, we then align the assorted reads only to their targeted chromosomes and the remaining reads whose ends cannot be uniquely mapped to the whole genome. Thanks to the modularity of the implementation, Bowtie and BLAT used here can be replaced by other alignment tools, such as MAQ and BLAST, with minor modifications.
For all uniquely mappable reads, this tiered mapping approach can speed up the alignment to the human genome by 24 times on average. The whole process is parallelized, and for a total of ~3 million reads (~60 GB in size) it takes less than an hour to finish the assortment step with 80 CPUs of a computer cluster. On average, ~70% of the single-end reads of a sequenced individual could be assorted by the aforementioned algorithm. As a result, we anticipate an overall enhancement of the alignment speed by 3 folds.
If a read is mapped to the genome uniquely, we keep its alignment without additional requirements. Otherwise, its alignments are scored and the alignment ratios calculated. The alignments are then sorted on their scores, ratios, and the number of alignment blocks. We only keep the top alignment when its score is at least tr times (to be determined by simulation) as big as that of the second best on the sorted list. Moreover, DNA amplification as part of the library preparation procedure increases the likelihood that a DNA fragment is sequenced multiple times. Redundant sequence reads (the same chromosome, the same strand, and the same start position) generated from the same DNA fragments are removed to prevent the inflation of the count of reads that are supportive of SVs.
For paired-end sequence reads, they are processed to release the end sequences with the pairing information preserved for later use after the linker sequence is identified and removed. The end sequences are then mapped and processed like the single-end reads as described above. Because of restriction on how two ends are mapped relatively to each other on the genome, the pairing information increases the accuracy of their genomic placement. To avoid excessive assumptions on the distribution of the insert length, we make the minimum requirement that two ends of a read should be mapped to the same strand of the same chromosome. Only read ends that make unique concordant pairs are used in the downstream analyses.
Insertion/deletion and rearrangement identification
For each identified SV, we count the number of reads that 'support' it, nsr, and measure its centeredness in each supportive read, c i (i = 1, ..., nsr), the ratio of the smaller length of its two flanking alignments to the bigger one. It is easy to see that 0 < c i ≤ 1 and if there are multiple supportive reads for an SV it is the maximum centeredness that matters the most (because the evidence best supportive of presence is the most informative). Thus, each SV identification is associated with two scoring quantities: the number of supportive reads, nsr, and the maximum centeredness, cmax (Additional file 1, Figure S1).
Considering the lists of deletions and (small) insertions together in conjunction with each other, we resolve their final SV identities as novel deletions, novel insertions, duplications, and translocations. To do this, we first extract from reads the sequences of insertion that are at least 20 bp long and then align them to the reference genome using BLAT. An insertion is classified as 'novel,' if it cannot be aligned perfectly without gaps. Otherwise, it is a duplication and potentially a translocation. To be the latter, at least one location of the perfect alignments to the reference genome needs to be precisely covered by a read with deletion. The novel deletions are the whole set of deletions excluding those 'used' by translocations.
SV call set obtention through SV call filtering and sequencing error identification
Sequencing errors or spurious sequence alignments can both lead to SVs calls by the split-read analysis. The majority of such false positives can be removed by imposing a simple global threshold that requires every SV to be found in at least two nonredundant reads. We further refine the call list, and since the false positives of the short and the long SVs arise from distinct sequencing and alignment errors, respectively, we treat the short and the long SV calls differently.
After the Bonferroni correction for multiple tests, the null hypothesis is rejected if P < 0.01.
Because of the increased likelihood of both under- and over-calling bases in homopolymers by 454 sequencing technology, for each SV that is a part of a homopolymer we perform a significant test after the initial call filtering, where the null hypothesis is that the SV probability is the same as the probability specified by the error model (Additional file 1, Figure S2). The calculation of the P-value is described above.
Calibration of the number of SVs in a genomic region
Previous steps will produce a set of SV calls for the assayed genomic region. Because the performance of our SR method can be assessed and quantified with the positive predictive value and the sensitivity by extensive simulation, we can use these error rates to derive less biased estimate of the number of SVs in that genomic region.
in which , PPV l, c , and S l, c are the number of SVs, the positive predictive value, and the sensitivity for SVs of length l (bp) observed in reads giving c-x sequence coverage. This method is not applicable to SVs of a certain length that are not observed (i.e., ). For large SVs, it is more sensible to use a range of length, instead of discrete lengths.
This work was supported by an NIH grant (4R00LM009770-03) from the National Library of Medicine to ZDZ. We thank the 1000 Genomes Project for providing the sequence data and carrying out array-CGH validation.
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