We first used computational strategies to extract additional SV information from the existing Sanger-based sequencing data generated as paired-end (or mate-pair) reads from clone libraries of defined size 1 . First, we adopted a paired-end mapping approach 15 17 18 and aligned 11,346,790 mate-pairs from libraries with expected clone sizes of 2, 10 or 37 kb (Additional file 2) to the NCBI build 36 assembly. We found that 97.3% of mate-pairs had the expected mapping distance and orientation. Mate-pairs discordant in orientation or mapping distance were used to identify variants, and we required each event to be supported by at least two clones. In total, this strategy was used to identify 780 insertions, 1,494 deletions and 105 inversions (Figure 1; Table 1; Additional file 3). In an independent analysis of the same underlying sequencing data, we then captured SVs by examining the alignment profiles of 31,546,016 paired and unpaired reads to search for intra-alignment gaps 23 . The presence of an intra-alignment gap in the sequence read (query sequence) or in the reference genome (target sequence) would indicate a putative insertion or deletion event, respectively. The identification of such a 'split-read' alignment signature complements the mate-pair approach, as significantly smaller insertions and deletions can be discovered. We required at least two overlapping split-reads having an alignment gap >10 bp to call a variant. A total of 8,511 insertions and 11,659 deletions ranging from 11 to 111,714 bp in size were identified (Figure 1; Table 1; Additional file 4).

We used two ultra-high density custom comparative genomic hybridization (CGH) array sets and two commonly used SNP genotyping arrays to identify relative gains and losses. A significant amount of variation was detected from the two custom CGH arrays: an Agilent oligonucleotide array set with 24 million features (Agilent 24 M) 7 , and a NimbleGen oligonucleotide array set containing 42 million features (NimbleGen 42 M) 19 . The Agilent platform identified 194 duplications and 319 deletions, while the NimbleGen array set detected 366 gains and 358 losses, ranging in size from 439 bp to 852 kb, in Venter (Figure 1; Table 1; Additional files 5 and 6). Furthermore, we scanned the Venter genome using Affymetrix SNP Array 6.0 and Illumina BeadChip 1 M, and the results are summarized in Table 1 plus Additional files 7 and 8.

Most microarrays used for CNV analyses are designed based on the NCBI assemblies. Therefore, any region where the reference exhibits the deletion allele of an indel, or sequences mapping to gaps in the assembly, will not be targeted. In previous studies 16 24 , many unknown DNA segments were identified to have no or poor alignment to the NCBI reference when compared to the Celera R27C assembly. To capture genetic variation in such potentially novel sequences, we designed a custom Agilent 244 K array to target those scaffold sequences at least 500 bp in length. We then performed CGH on seven HapMap individuals and detected 231 regions (101 gains and 130 losses) in 161 scaffolds to be variable (Additional file 9). Of these, we found 44 gains and 7 losses in 36 Celera scaffolds were specific to Venter (Figure 1, Table 1). Using paired-end mapping, as well as cross-species genome comparison with the chimpanzee, we were able to find a placement in NCBI build 36 for 25 of 36 scaffolds that were copy number variable in Venter. Two of the scaffolds were mapped to regions containing assembly gaps, 15 of 25 anchored scaffolds corresponded to insertion events also detected elsewhere 15 18 , and the remaining eight represent new insertion findings (Additional file 10).

We used several computational and experimental approaches to validate our SV findings. We performed experimental validation by PCR amplification and gel-sizing and confirmed 89 of the 96 (93%) SVs predicted by sequence analysis (Additional files 11 and 12). Using quantitative real-time PCR (qPCR), we validated 20 of 25 (80.0000%) CNVs detected by microarrays, and most of these CNVs were from the custom Agilent 244 K array covering sequences not in the NCBI assembly (Additional file 13). Inversion predictions were tested by fluorescence in situ hybridization (FISH) 25 . In one such finding, a predicted 1.1-Mb inversion at 16p12 was identified to be homozygous in Venter and in all of the seven additional HapMap samples from four populations tested, suggesting that the reference at this locus represents a rare allele, or is incorrectly assembled (Additional file 14).

We then compared the SVs identified here with the previous assembly comparison-based analysis of the same genome 1 , and found that 11,140 variants were in common. We noticed that our multi-platform method excelled in calling large variants. In fact, even after excluding all of the small variants (≤ 10 bp) from the previous Levy at al. study 1 , we still observed that the current study tended to find larger SVs (a current average of 1,909.3 bp now versus a previous average of 113.4 bp). Additional file 15 shows that the sensitivity of assembly comparison dropped as size increased to over 1 kb, and the proportion of larger SVs significantly increased as a result of the present study (Figure 2b, c).

Finally, we determined the number of calls in this study that were either verified by another platform in this study or found in the Database of Genomic Variants 12 . In total, we computationally confirmed 15,642 (65.6%) of our current calls: 6,301 were gains; 9,726 were losses; and 65 were inversions.

We performed an in-depth analysis of the characteristics of the variants detected by each of the methods. First, by contrasting against a population-based study 19 , we observed highly similar size estimates for the same underlying SVs between methods (Figure 3). With sufficient genome coverage of clones with accurate and tight insert size, the mate-pair method yields precise variation size. Similarly, the split-read approach gives nucleotide resolution breakpoints, while the high-density CGH and SNP arrays have dense probe coverage to accurately identify the start and end points of SVs. Overall, our multiple approaches are highly robust in estimating variant size.

Next, we compared the variants discovered by the two whole genome CGH array sets, NimbleGen 42 M and Agilent 24 M, and investigated the primary reason for the discordance between the two data sets. Not surprisingly, a substantial portion of the discordant calls can be explained by the difference in probe coverage. In fact, approximately 70% of the unique calls on the NimbleGen 42 M array had inadequate probe coverage on the Agilent 24 M array to be able to call variants, and approximately 30% vice versa (Additional file 16). After that, we compared the number of calls uniquely identified by the SNP-genotyping microarrays, and we identified 12 and 0 novel SVs contributed by Affymetrix 6.0 and Illumina 1 M, respectively. Of the 12 new Affymetrix calls, 9 are located in complex regions containing blocks of segmental duplications.

Subsequently, when looking for enrichment of genomic features among variants detected by different approaches, we found that there was a significant enrichment (P < 0.01) of short interspersed nuclear elements (SINEs) in deletions called by sequencing-based approaches (mate-pair and split-read), but not in deletions called by the microarrays. Microarrays have low sensitivity for detecting copy number change of SINEs (for example, Alu elements), as these regions cannot be uniquely targeted by short oligo probes, and over-saturation of probe fluorescence would prevent an accurate high copy count. Meanwhile, the sequencing methods employed here do not rely on alignments within the repeat itself, and consequently they are readily able to detect gains and losses of these high-copy repeats. The complete result for enrichment of SVs with various genomic features is shown in Additional file 17.

Finally, one of the main challenges of genome assembly is to correctly assemble both alleles in regions of SV. To identify heterozygous events among the split-read indels, we searched for evidence of an alternative allele. Indels were determined to be heterozygous if two or more sequence reads could be aligned that supported the NCBI build 36 allele. From the split-read dataset alone, we identified 4,476 of 8,511 (52.6%) insertions and 6,906 of 11,659 (59.2%) deletions as heterozygous. Additionally, we found that of the 10,834 split-read indels that overlapped with results from the Levy et al. study 1 , 4,332 events annotated as heterozygous in our results were previously classified as homozygous (Additional file 4). These differences highlight the difficulty of assembling both alternative alleles in regions of SV, leading to an underestimate of the heterozygosity in Levy et al. 1 .

In an attempt to estimate the total variation content in the Venter genome, we combined the SVs previously described in the Venter genome in the Levy et al. paper 1 with the variants discovered in this study, to generate a non-redundant set of variants. We determined that 48,777,466 bp was structurally variable, of which 19,981,062 bp belonged to gains, 19,539,369 bp to losses, and 9,257,035 bp to balanced inversions (Table 1). A vast majority of this variation was discovered in the current analyses (83.3% or 40,625,059 bp) of the Venter genome. Therefore, our significant contribution in detecting novel calls underscores the importance of using multiple analysis strategies for detecting SV in the human genome. See Additional file 18 for the location of SVs >1 kb, and Additional files 19, 20 and 21 for a complete list of variation in the Venter genome.

When we compared the complete set of Venter's SVs with those from other published genomes 2 3 4 6 7 8 (Additional file 1), we found that 209,493/808,345 (25.9%) of the Venter variants overlapped variants described in one or more of the other six studies. Upon examining the size distribution of variants from different studies, particularly the size of insertions and duplications, we realized that studies based primarily on next generation sequencing (NGS) data for variation calling were unable to identify calls in certain size ranges (Figure 4). These results further signify that, at present, multiple approaches are needed to capture SVs across the entire size spectrum. The most obvious limitation is that short next generation sequencing NGS reads/inserts fail to capture insertion events greater than the size of the reads/inserts.

Next, we analyzed the complete set of SVs in Venter for overlap with features of the genome with known functional significance, which might influence health outcomes (Table 2). We found 189 genes to be completely encompassed by gains or losses, 4,867 non-redundant genes (3,126 impacted by gains and 3,025 by losses) whose exons were impacted, and 573 of these to be in the Online Mendelian Inheritance in Man (OMIM) Disease database (Additional files 22, 23, 24, 25 and 26). However, there was an overall paucity of SV (P ≥ 0.999) overlapping exonic sequences of genes associated with autosomal dominant/recessive diseases, cancer disease, and imprinted and dosage-sensitive genes. In general, there is an absence of variation in both exonic and regulatory sequences, such as enhancers, promoters and CpG islands, in the genome of this individual.

Currently, direct-to-consumer testing companies and genome-wide association studies mainly use microarray-based SNP data 26 27 , but SVs are typically not considered. Venter indels/CNVs, however, overlap with 4,565 and 7,047 of SNPs on the Affymetrix SNP-Array 6.0 and Illumina-BeadChip 1 M products (two commonly used arrays) potentially impacting genotype calling, most notably when deletions are involved.

Moreover, our attempts to impute SV calls using tagging-SNPs captured 308 of 405 (76.0%) Venter bi-allelic SVs for which we could infer genotypes (Additional file 27) 19 . Based on population data, rare SVs with minimal allele frequency ≤ 0.05 showed the lowest correlation with surrounding SNPs, thus indicating that these SVs were least imputable (Figure 5). The fraction of imputable SVs will be even lower when multi-allelic and complex SVs are considered because the new mutation rate at these sites is higher.

The sequence data of J Craig Venter's genome (or the Venter genome) used for analysis was originally produced through experiments performed in the Venter et al. 39 and Levy et al. 1 studies. The sequence trace data and information files were downloaded from NCBI. In this study, we aligned 31,546,016 Venter sequences to the NCBI human genome assembly build 36 using BLAT 40 . For paired-end mapping, the optimal placement of clone ends was determined by a modified version of the scoring scheme used in Tuzun et al. 15 . We categorized mate-pairs that mapped less than three standard deviations from the expected clone size as putative insertions, greater than three standard deviations as putative deletions, and in the wrong orientation as putative inversions. We required each variant to be confirmed by at least two clones, and for indels, we required the clones to be from libraries of the same average insert size (2 kb, 10 kb or 37 kb). To identify small variants, the read alignment profiles were further examined for an intra-alignment gap with size greater than 10 bp. Two independent 'split-reads' were required to call a putative variant.

An Agilent 24 million features CGH array set (Agilent 24 M) was designed with 23.5 million 60-mer oligonucleotide probes tiled along the NCBI build 36 assembly. The Venter genomic DNA was co-hybridized with the female sample NA15510 from the Polymorphism Discovery Resource 22 . The statistical algorithm ADM-2 by Agilent Technologies was used to identify CNVs based on the combined log ₂ ratios. Similar experimental procedures and analyses are described in other studies 7 41 . Additionally, a custom NimbleGen 42 million features CGH microarray (NimbleGen 42 M) was used in this study - its design, experimental procedures and data analysis have been described in detail elsewhere 19 22 . Venter genomic DNA was also co-hybridized with the sample NA15510. For both the Agilent 24 M and NimbleGen 42 M arrays, CNVs with >50% reciprocal overlap and opposite orientation of variants identified in NA15510 in Conrad et al. 19 were removed, as these were specific to the reference.

The Venter sample was also run on the Affymetrix SNP Array 6.0 and Illumina BeadChip 1 M genotyping arrays. We followed the protocol recommended by the manufacturers. For Affymetrix 6.0, the default parameters in the BirdSeed v2 algorithm were used to perform SNP calling. Partek Genomics Suite (Partek Inc., St. Louis, Missouri, USA), Genotyping Console (Affymetrix, Inc., Santa Clara, California, USA), BirdSuite 42 and iPattern (J Zhang et al., manuscript submitted) were used to call CNVs. For Illumina 1 M, the SNP calling was done using the BeadStudio software. QuantiSNP 43 and iPattern were used to identify CNVs. For both platforms, only variants confirmed by at least two calling algorithms were included in the final set of calls.

The Agilent Custom Human 244 K CGH array (Agilent 244 K) was designed to target 9,018 sequences >500 bp in length that were annotated as 'unmatched' sequences in Khaja et al. 16 . CGH experiments were performed with genomic DNA from Venter and six HapMap samples, hybridized against reference NA10851. Feature extraction and normalization were performed using the Agilent feature extraction software. The programs ADM-1 in the DNA Analytics 4.0 suite (Agilent Technologies, Santa Clara, California, USA), and GADA 44 were independently used to call CNVs, and those that were confirmed by both algorithms were then used in this study.

To generate a non-redundant set of Venter variants, we combined the lists of SVs generated. For CNVs, to determine if two calls are the same, we required that they shared a minimum of 50% size reciprocal overlap; for inversions, we required that they shared at least one boundary. For those calls that were indicated to be the same variant, we recorded the one with the best size/boundary estimate (with preference given to assembly comparison, then split-read, NimbleGen-42 M, Agilent 24 M, mate-pair, Affymetrix 6.0, and Illumina 1 M, in that order). For this analysis, we excluded variants called in the custom Agilent 244 K arrays.

We used multiple computational and experimental approaches to validate SVs found in this project. PCR primers were designed to target flanking sequences of indels detected by sequencing-based methods, such that PCR products representing the different alleles can be differentiated on a 1.5% agarose gel. DNA from Venter and five HapMap individuals of European ancestry were tested in PCR experiments. Amplifications and deletions detected by CGH arrays were tested by qPCR. DNA from Venter and six additional control individuals were used to assess the variability in copy number. Each assay was run in triplicate and the FOXP2 gene was used as the reference for relative quantifications. See Additional file 12 for all primer sequences.

To validate large variants, FISH experiments were performed using fosmid clones as probes on a lymphoblastoid cell line from Venter and seven other HapMap individuals. Five metaphases were first imaged to check for correct chromosome localization and hybridization, and then interphase FISH was performed to validate predicted inversions, similar to the protocol outlined in the Feuk et al. study 25 with the addition of the aqua probe, DEAC-5-dUTP (Perkin Elmer, Waltham, Massachusetts, USA; NEL455).

Overlap with other datasets, genomic features and between subsets of data in the current paper was performed using custom PERL scripts. When comparing variants, two sites were considered overlapping if the reciprocal overlap among their estimated sizes was ≥ 50%. Data sources used for the annotations of overlaps with genomic features are listed in Additional file 17. To evaluate significance, we created 1,000 randomized sets of simulated variant calls and performed overlap analysis against the same data source. For each simulation, we recorded the number of instances where we observed a higher number of overlaps than the real variant data set. A P-value was computed as the fraction of simulations whose number of overlaps was greater than the number of real overlaps.

Using a cutoff of 50% reciprocal overlap, there were 405 sites of overlap between the Venter and genotyped, validated Genome Structural Variation (GSV) loci. The best r ² value was computed between each of those GSV CNVs and a European's HapMap SNP in the neighboring genomic region. Here, we defined a minimum threshold of r ² = 0.8, below which the Venter SVs were deemed not well imputed by SNP. Detailed description on genotyping, phasing, and tagging calls onto haplotypes defined by HapMap SNPs is presented in the Conrad et al. study 19 .

The sequence trace files generated from previous studies 1 39 can be obtained from the 'NCBI Trace Archive', using queries [CENTER_NAME = "JCVI" and SPECIES_CODE = "HOMO SAPIENS" and center_project = "GENOMIC-SEQUENCING-DIPLOID-HUMAN-REFERENCE-GENOME"], [INSERT_SIZE = 10201 and CENTER_NAME = "CRA" and SPECIES_CODE = "homo sapiens"], and [INSERT_SIZE = 1925 and CENTER_NAME = "CRA" and SPECIES_CODE = "homo sapiens"]. All of the microarray data generated in this study are available at the Gene Expression Omnibus (GEO) under the accession number [GEO:GSE20290]. The SV locations, size, and zygosity (when available), are reported in Additional files 3, 4, 5, 6, 7, 8 and 9, and a non-redundant set of variant data in the Venter genome is reported in Additional files 19, 20 and 21.