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      Ultra-Structure database design methodology for managing systems biology data and analyses

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          Abstract

          Background

          Modern, high-throughput biological experiments generate copious, heterogeneous, interconnected data sets. Research is dynamic, with frequently changing protocols, techniques, instruments, and file formats. Because of these factors, systems designed to manage and integrate modern biological data sets often end up as large, unwieldy databases that become difficult to maintain or evolve. The novel rule-based approach of the Ultra-Structure design methodology presents a potential solution to this problem. By representing both data and processes as formal rules within a database, an Ultra-Structure system constitutes a flexible framework that enables users to explicitly store domain knowledge in both a machine- and human-readable form. End users themselves can change the system's capabilities without programmer intervention, simply by altering database contents; no computer code or schemas need be modified. This provides flexibility in adapting to change, and allows integration of disparate, heterogenous data sets within a small core set of database tables, facilitating joint analysis and visualization without becoming unwieldy. Here, we examine the application of Ultra-Structure to our ongoing research program for the integration of large proteomic and genomic data sets (proteogenomic mapping).

          Results

          We transitioned our proteogenomic mapping information system from a traditional entity-relationship design to one based on Ultra-Structure. Our system integrates tandem mass spectrum data, genomic annotation sets, and spectrum/peptide mappings, all within a small, general framework implemented within a standard relational database system. General software procedures driven by user-modifiable rules can perform tasks such as logical deduction and location-based computations. The system is not tied specifically to proteogenomic research, but is rather designed to accommodate virtually any kind of biological research.

          Conclusion

          We find Ultra-Structure offers substantial benefits for biological information systems, the largest being the integration of diverse information sources into a common framework. This facilitates systems biology research by integrating data from disparate high-throughput techniques. It also enables us to readily incorporate new data types, sources, and domain knowledge with no change to the database structure or associated computer code. Ultra-Structure may be a significant step towards solving the hard problem of data management and integration in the systems biology era.

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          Most cited references 25

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          Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.

          We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.
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            The Diploid Genome Sequence of an Individual Human

            Introduction Each of our genomes is typically composed of DNA packaged into two sets of 23 chromosomes; one set inherited from each parent whose own DNA is a mosaic of preceding ancestors. As such, the human genome functions as a diploid entity with phenotypes arising due to the sometimes complex interplay of alleles of genes and/or their noncoding functional regulatory elements. The diploid nature of the human genome was first observed as unbanded and banded chromosomes over 40 years ago [1–4] , and karyotyping still predominates in clinical laboratories as the standard for global genome interrogation. With the advent of molecular biology, other techniques such as chromosomal fluorescence in situ hybridization (FISH) and microarray-based genetic analysis [5,6] provided incremental increases in the resolution of genome analysis. Notwithstanding these approaches, we suspect that only a small proportion of genetic variation is captured for any sample in any one set of experiments. Over the past decade, with the development of high-throughput DNA sequencing protocols and advanced computational analysis methods, it has been possible to generate assemblies of sequences encompassing the majority of the human genome [7–9]. Two versions of the human genome currently available are products of the Human Genome Sequencing Consortium [9] and Celera Genomics [7], derived from clone-based and random whole genome shotgun sequencing strategies, respectively. The Human Genome Sequencing Consortium assembly is a composite derived from haploids of numerous donors, whereas the Celera version of the genome is a consensus sequence derived from five individuals. Both versions almost exclusively report DNA variation in the form of single nucleotide polymorphisms (SNPs). However smaller-scale ( 150 kb (Table 2) [41]. We determined that 144 of the 553 large HuRef scaffolds could be joined by two or more of the WGSA BAC mate pairs, and 98 more by a single WGSA BAC mate pair (see Materials and Methods), suggesting that use of large insert BAC libraries (>150 kb) would generate larger scaffolds. Table 2 Summary of HuRef Assembly Statistics and Comparison to the Human NCBI Genome Assembly-to-Assembly Mapping Genomic variation was observed by two approaches. First, we identified heterozygous alleles within the HuRef sequence. This variation represents differences in the maternal and paternal chromosomes. In addition, a comparison between HuRef and the National Center for Biotechnology Information (NCBI) version 36 human genome reference assembly, herein referred to as a one-to-one mapping, also served as a source for the identification of genomic variation. These comparisons identified a large number of putative SNPs as well as small, medium, and large insertion/deletion events and some major rearrangements described below. For the most part, the one-to-one mapping showed that both sequences are highly congruent with very large regions of contiguous alignment of high fidelity thus enabling the facile detection of DNA variation (Table S2). The one-to-one mapping to NCBI version 36 (hereafter NCBI) was also used to organize HuRef scaffolds into chromosomes. HuRef scaffolds were only mapped to HuRef chromosomes if they had at least 3,000 bp that mapped and the scaffold was mostly not contained within a larger scaffold. With the exception of 12 chimeric joins, all scaffolds were placed in their entirety with no rearrangement onto HuRef chromosomes. The 12 chimeric regions represent the misjoining of a small number of chimeric scaffold/contigs by the Celera Assembly [40], as detected with mate pair patterns [7,42], and are also apparent by comparison to another assembly (Materials and Methods). The 12 chimeric joins in the HuRef scaffolds were split when these scaffolds were assigned to build HuRef chromosomes. Inversions and translocations within the nonchimeric scaffolds relative to NCBI are thus maintained within the HuRef chromosomes. The final set of 24 HuRef chromosomes were thus assembled from 1,408 HuRef assembly scaffolds and contain 2,782 Mb of ordered and oriented sequence. The NCBI autosomes are on average 98.3% and 97.1% represented by runs and matches, respectively, in the one-to-one mapping to HuRef scaffolds (Table S3). A match is a maximal high-identity local alignment, usually terminated by indels or sequence gaps in one of the assemblies. Runs may include indels and are monotonically increasing or decreasing sets of matches (linear segments of a match dot plot) with no intervening matches from other runs on either axis. The Y chromosome is 59% covered by the one-to-one mapping due to difficulties when producing comparison between repeat rich chromosomes. In addition, the Y chromosome is more poorly covered because of the difficulties in assembling complex regions with sequencing depth of coverage only half that of the autosomal portion of the genome. The X chromosome coverage with HuRef scaffolds is at 95.2%, which is typical of the coverage level of autosomes (mean 98.3% using runs). However it is clear that the X chromosome has more gaps, as evidenced by the coverage with matches (89.4%) compared with the mean coverage of autosomes using matches (97.1%). The overall effects of lower sequence coverage on chromosomes X and Y are clearly evident as a sharp increase in number of gaps per unit length and shorter scaffolds compared to the autosomes (Figure 3). Similarity between the sex chromosomes is another source of assembly and mapping difficulties. For example, there is a 1.5-Mb scaffold that maps equally well to identical regions of the X and Y chromosomes and therefore cannot be uniquely mapped to either (see Materials and Methods and Figure 3). From our one-to-one mapping data, we are also able to detect the enrichment of large segmental duplications [10] on Chromosomes 9, 16, and 22, resulting in reduced coverage based on difficulties in assembly and mapping (Table S3). Figure 3 Sequencing Continuity Plot for the HuRef Autosomes Compared to HuRef X and Y Chromosomes Note that the autosomes have more contiguous sequence with fewer gaps compared to chromosomes X and Y, probably due to half the read depth compared to the autosomes and the presence of extensive sequence similarity between the sex chromosomes. Since NCBI, WGSA, and HuRef are all incomplete assemblies with sequence anomalies, assembly-to-assembly mappings also reflect issues of completeness and correctness. We compared three sets of chromosome sequences to evaluate this issue (see Materials and Methods): NCBI with the exclusion of the small amount of unplaced sequences, HuRef, and WGSA (Table S2) were thus compared in a pairwise manner. The comparison of WGSA and HuRef revealed 83 Mb more sequence in HuRef in matched segments of these genomes. This sequence is predominantly from HuRef that fills gaps in WGSA. Comparisons of HuRef and WGSA to NCBI showed the considerable improvement of HuRef over WGSA. Correspondingly, in HuRef there are approximately 120 Mb of additional aligned sequence, composed of 47 Mb of HuRef sequence that aligns to NCBI that was not aligned in WGSA and 73 Mb within aligned regions that fill gaps in WGSA. This comparison also showed an improvement factor of two in rearrangement differences (order and orientation) from WGSA to HuRef when mapped to the NCBI reference genome at small ( 50 kb) levels of resolution (Table S2). HuRef includes 9 Mb of unmatched sequence that fill gaps in NCBI or are identified as indel variants. An additional 14 Mb of HuRef chromosome sequence outside of aligned regions with NCBI represents previously unknown human genome sequence. The large regions of novel HuRef sequence are identified to be either: (a) gap filling or insertions, (b) unaligned NCBI chromosome regions, or (c) large scaffolds not mapped to NCBI chromosomes. Some of these were investigated using FISH analysis and are discussed below. Although we were able to organize HuRef scaffolds into HuRef chromosome sequence, all of the subsequent analyses in this report were accomplished using HuRef scaffold sequences. Identification of DNA Variants Variant identification internal to the one-to-one map. The HuRef assembly and the one-to-one mapping between the HuRef genome and the NCBI reference genome resulted in the identification of 5,061,599 putative SNPs, heterozygous indels, and a variety of multi-nucleotide variations events (see Figure 4 for a definition), of which 62% are in the database for DNA variants (dbSNP; http://www.ncbi.nlm.nih.gov/SNP/). A significant fraction of these putative variants resulted from sequence reads with variant base having reduced quality value (QV) scores, the presence of variants in homopolymer runs and erroneous base calls at the beginning and end of reads. The inclusion of these reads was important to the assembly process, and therefore we chose to perform post-assembly processing to filter these variants to reduce false positives while limiting false negatives (column %red/%FN in Table 3 and detailed discussion in Material and Methods). The filters deemed most productive in creating a high-confidence variant set involved the application of a minimal QV threshold and testing for the location of a variant in sequence read. In addition, we applied the filter that a variant required supporting evidence from at least two reads and that the second allele had a minimum fraction of representative reads (20% reads with minor allele for heterozygous SNP and 25% for heterozygous indels). As indicated in Table 3, a significant improvement in reducing false positives while limiting false negatives is possible when the filters are applied independently on QV and read location–filtered variants. However, the maximum benefit from this filtering approach was achieved by applying filters cumulatively, and it was the three aforementioned filters (bold rows in Table 3) that were applied ultimately. After applying the filters, 81% of heterozygous indels, 29% of heterozygous SNPs, 7% of homozygous SNPs, and 19% homozygous indels were removed from the initial set. The filtering mainly affects heterozygous variants by reducing the number of reads that can be used for support. The cumulative application of the filters generated a set of variants from which a subset of 95,733 could be combined further into clusters. The first case where variants were clustered was when two SNPs were within 2 bp of each other. We clustered these, because there was more accuracy in classifying whether the variant caused a change in protein coding and not because they necessarily represent single mutational events. The second scenario for clustering involved non-SNP variants within 10 bp of other non-SNP variants, such as indels or complex variants. We decided to cluster these variants because extensive manual inspection showed that closely spaced indels were frequently better defined as one variant after realignment. Consequently, the clustering of variant positions was coupled with a localized realignment of sequence reads to define either two distinct alleles or haplotypes. Overall, the filtering and clustering refinements that were applied to the “raw” variant set resulted in a set of 3,325,530 variants within the one-to-one HuRef-to-NCBI mapping, of which 85% were found in dbSNP (Table 4). Figure 4 The Different Variant Types Identified from the HuRef Assembly and the HuRef-NCBI Assembly-to-Assembly Mapping HuRef consensus sequence (in red) with underlying sequence reads (in blue). Homozygous variants are identified by comparing the HuRef assembly with NCBI reference assembly. Heterozygous variants are identified by base differences between sequence reads. SNP = single nucleotide polymorphism; MNP = multi-nucleotide polymorphism, which contains contiguous mismatches. Table 3 The Application of Distinct, Independent, Filtering Methods on the Detection Rate of SNPs, Heterozygous Indels, and Complex Variants Identified from the HuRef Assembly Table 4 Identification of Variants Found within the HuRef-NCBI One-to-One Assembly Map (Internal HuRef-NCBI map) and Those Variants in HuRef Sequence Not Aligned to NCBI (External HuRef-NCBI Map) Variant identification external to the one-to-one map. The one-to-one mapping of HuRef to NCBI produced approximately 150 Mb of unaligned HuRef sequence inclusive of partially mapped and nonmapped HuRef scaffolds. Within this unaligned HuRef sequence, we identified 233,796 heterozygous variants including SNPs, indels, and complex variants after application of the same filters described above (see Table 4, variants labeled External HuRef-NCBI map). Other sources of variant external to the one-to-one mapping between the HuRef and NCBI human genome assemblies are putative homozygous insertions, deletions, and inversions (see Figure 4 for definitions), of which 693,941 were detected. This number of putative insertions and deletions was reduced by 19% by the application of a series of filters designed to eliminate the bulk of spurious variation. Therefore, variants were not called at the read margins (thresholds were the same as previously used for SNP and indels internal to the HuRef-NCBI map), and any identified variants required the supporting evidence of at least two reads and one satisfied mate pair with no ambiguous bases constituting the sequence of the insertion or deletion. In addition to the aforementioned filtering approach, a small fraction (∼1%) of the 693,941 putative homozygous insertion/deletion variants were subsequently characterized as heterozygous variants. This was accomplished by finding exact matches of 100-bp sequence 5′ and 3′ of the insertion point sequence and the deletion sequence in both HuRef scaffolds and unassembled reads. This fraction of heterozygotes is likely to be a conservative estimate of the total number of true heterozygotes (see below). The alternate alleles of these heterozygous variants were primarily found (96% of the time) in scaffolds less than 5,000 bp long or in unassembled reads. This highlights the value of small scaffolds and unassembled reads in defining the variant set in an assembled genome and suggests that these elements are a rich source of genomic variation. Therefore, subsequent to the removal of the variants by read-based filtering (19% mentioned above) and the recategorization as heterozygous variants (1% above), the remaining variants included approximately equal numbers of insertion (275,512) and deletion (283,961) alleles and 90 inversions as outlined in Table 4. In summary, using the combined identification and filtering approaches, it was possible to identify an initial “raw” set of 5,775,540 variants, from which we generated a higher-confidence set of 4,118,889 variants, of which 1,288,319 variants are novel relative to current databases (dbSNP). Initial Characterization of Variants To examine sequence diversity in the genome, we estimated nucleotide diversity using the population mutation parameter θ [43]. This measure is corrected for sample size and the length of the region surveyed. In the case of a single genome with two chromosomes, θ simplifies to the number of heterozygote variants divided by the number of base pairs (see Materials and Methods). We define θSNP as the nucleotide diversity for SNPs (number of heterozygous SNPs/number of base pairs) and θindel as the diversity for indels (number of heterozygous indels/number of base pairs) [44]. For both θSNP and θindel, the 95% confidence interval would be [0, 3θ] due to the small number of chromosomes (n = 2) being sampled (see Materials and Methods). Across all autosomal chromosomes, the observed diversity values for SNPs and indels are 6.15 × 10−4 and 0.84 × 10−4 respectively. When restricted to coding regions only, θSNP = 3.59 × 10−4 and θindel = 0.07 × 10−4, indicating that 42% of SNPs and 91% of indels have been eliminated by selection in coding regions. The strong selection against coding indels is not surprising, because most will introduce a frameshift and produce a nonfunctional protein. Our observed θSNP falls within the range of 5.4 × 10−4 to 8.3 × 10−4 that has been previously reported by other groups [44–47]. Our observed θindel (0.84 × 10−4) is approximately 2-fold higher than the diversity value of 0.41 × 10−4 that was reported from SeattleSNPs (http://pga.gs.washington.edu), which was derived from directed resequencing of 330 genes in 23 individuals of European descent [44]. The values of θindel in repetitive sequence regions are 1.2 × 10−4 for regions identified by RepeatMasker (http://www.repeatmasker.org) and 4.9 × 10−4 for regions identified by TandemRepeatFinder [48], respectively. Thus, the indel diversity in repetitive regions is between 1.4 and 5.8 times higher than the genome-wide rate. This suggests that the high value of θindel over all loci is likely mediated by the abundance of indels in repetitive sequence. It is also possible that repetitive regions in genic sequence are under stronger selective pressure and therefore have lower indel diversity. These are precisely the regions that have been targeted in previous resequencing projects [44] from which indel diversity values have been determined. Additionally, repetitive regions also have more erroneous variant calls due to technical difficulties in sequencing and assembly of these types of regions. Therefore, our estimate for θindel is likely a combination of both a true higher mutation rate in repetitive regions and sequencing errors. Values of θindel are consistent among the chromosomes (Figure 5). Chromosomes with high θindel values also have a larger fraction of tandem repeats. For example, Chromosome 19 has the highest θindel (1.1 × 10−4 compared with the chromosomal average of 0.86 × 10−4), and it also has the highest proportion of tandem repeats (13% compared with the chromosomal average of 7%). The fraction of tandem repeats of a chromosome is positively correlated with the value of θindel for each chromosome (r = 0.73), so that the diversity of indels is associated with the underlying sequence composition. Figure 5 Diversity for SNPs and Indels in Autosomes This is most likely an under-estimate of the true diversity, because a fraction of real heterozygotes were missed due to insufficient read coverage. The SNP variants identified in the HuRef genome include a larger-than-expected number of homozygous variants than those commonly observed in population-based studies (compare ratios of heterozygous SNP:homozygous SNP in Table 5). Our homozygous variants are detected as differences between the HuRef genome and the NCBI genome. One common interpretation of a homozygous variant is that given a common allele A and a rare allele B, the homozygous SNP is BB. However, because not all variant frequencies are known, we cannot determine if a position may carry the minor B allele in homozygous form. We analyzed ENCODE data using this definition and found the ratio of heterozygous SNPs to homozygous SNPs is 4.9 in an individual [49]. For our dataset, the observed ratio of heterozygous to homozygous SNP, where our “homozygous” SNPs are detected as bases differing from the NCBI human genome, is 1.2. To resolve this discrepancy, we examined the homozygous positions in the HuRef assembly and found that the increased frequency of homozygous SNPs results from the presence of minor alleles (BB) in the NCBI genome assembly. We observed that 75% of the homozygous positions in HuRef also had a SNP identified by the ENCODE [49]. A comparison of the alleles at these positions revealed that in 56% of the instances the HuRef genome had the more common allele, whereas the NCBI genome contained the minor allele. The remaining homozygous SNPs tended to be common minor alleles (76% had minor allele frequency [MAF] ≥ 0.30), consistent with their observation in homozygous form in the HuRef genome. Therefore, we confirmed that a large fraction of homozygous alleles from HuRef are real, and that differences between the HuRef and NCBI assemblies are due to NCBI containing the minor allele at a given SNP position, or HuRef containing a common SNP in homozygous form. Table 5 Modeling the Occurrence of Heterozygous to Homozygous Variant in a Shotgun Assembly We also modeled the inter/intraindividual genome comparison using directed resequencing data from SeattleSNPs data (see Materials and Methods) to determine if our variant detection frequencies were commonly found for different types of variants. By sampling and comparing the genotypes of two individuals from the SeattleSNPs data, we were able to simulate the conditions for calling “heterozygous” and “homozygous” variants as we have defined them in an independently generated set (Table 5). The ratio of heterozygous variants to homozygous variants from the modeled SeattleSNPs is lower in the HuRef genome compared with the SeattleSNPs data. This suggests that there are an overabundance of homozygous variants and/or an under-representation of heterozygous variants, and this trend is more pronounced for indels compared to SNPs. A possible explanation for this is that homozygous genotypes are actually heterozygous and the second allele is missed due to low sequence coverage. Our attempts to explain this phenomenon using statistical modeling did support our hypothesis that low sequence coverage resulted in excess homozygous over heterozygous variant calls. Indeed, our modeling provided us with a bound on the missed heterozygous calls for both indels (described below) and SNPs (see section below titled: Experimental Validation of SNP Variants). In an attempt to explain the discrepancy in the heterozygous to homozygous indel ratio (Table 5), we modeled the rate of identification of true heterozygous variants given the depth of coverage of HuRef sequencing reads and the various variant filtering criteria. This enabled us to determine that between 44% and 52% of the time, heterozygous indels will be missed due to insufficient read coverage at 7.5-fold redundancy and these indels be erroneously called homozygous. Therefore, the projection for the true number of homozygous indels is between 418,731 and 459,639, a reduction of 17%–25% from the original number of 559,473 homozygous indels, and the corresponding ratio of heterozygous to homozygous indels is between 1:1 and 1.3:1. Furthermore, our modeling also allowed us to determine that approximately 20× sequence coverage would be required to detect a heterozygous variant with 99% probability in unique sequence given our current filtering criteria of random shotgun sequence reads. Another further explanation for the overabundance of homozygous indels is the error-prone nature of repeat regions. Using a subset of genes (55) completely sequenced by SeattleSNPs, we found that 28% of the potential 92 HuRef homozygous indels overlap with indels in these genes, as opposed to 75% confirmation rate for homozygous SNPs described earlier. When one categorizes the repeat status of a homozygous indel, a higher confirmation rate (46%) is seen for indels excluded from regions identified by RepeatMasker or TandemRepeatFinder. The confirmation rate for an indel in a transposon or tandem repeat region is much lower at 16%. Therefore, indels in nonrepetitive loci have a higher probability of authenticity than indels in repeat regions. The ratio of SNPs to indels is lower in the HuRef assembly than what is observed by the SeattleSNPs data (Table 5), indicating that relatively fewer SNPs or relatively more indels are called. This is likely due to relatively more indels being identified, as discussed above. We note that a large fraction of indels occur in repeat sequence (Table 6), which has higher indel frequency as well as higher incidence of sequencing error. Moreover, SeattleSNPs resequencing data is focused on variant discovery in genic regions, which may not reflect genome-wide indel rates. Table 6 Summary of Variant Types Identified in the HuRef Genome Assembly We identified in the HuRef assembly 263,923 heterozygous indels spanning 635,314 bp, with size ranges from 1 to 321 bp. The characteristics of the indels we detected, their distribution of sizes 5 kb with >90% sequence identity) annotated in the NCBI assembly are represented in the HuRef genome sequence. We analyzed the NCBI sequence (90.1 Mb) external to the one-to-one mapping with the NCBI assembly for segmental duplication content by comparison to the Human Segmental Duplication Database (http://projects.tcag.ca/humandup/) [61]. More than 70% of these nucleotides (63.6 Mb) are contained within segmental duplications, compared with 5.14% across the entire NCBI assembly. This suggests that the regions of the NCBI assembly that are not aligned to HuRef likely result from the absence of assembled segmental duplication regions in HuRef. This is further supported by the fact that only 57.2% of all regions annotated as segmental duplications in NCBI are present in HuRef. Clearly, these are some of the most difficult regions of the genome to represent accurately with a random shotgun approach and de novo assembly. However, it is also important to note that at least 25% of segmental duplication regions differ in copy number between individuals [62], and the annotation of such sequences will certainly differ between independent genomes. Copy Number Variants Copy number variants (CNVs) have been identified to be a common feature in the human genome [11,15,62–64]. However, such variants can be difficult to identify and assemble from sequence data alone, because they are often associated with the repetition of large segments of identical or nearly identical sequences. We tested for CNVs experimentally to compare against those annotated computationally, and also to discover others not represented in the HuRef assembly. We used comparative genomic hybridization (CGH) with the Agilent 244K array and Nimblegen 385K array, as well as comparative intensity data from the Affymetrix and Illumina SNP genotyping platforms (using three analysis tools for Affymetrix and one for Illumina). In total, 62 CNVs (32 losses and 30 gains) were identified from these experiments (Table S7). It is noteworthy that the Agilent and Nimblegen CGH experiments, as well as the analysis of Affymetrix data using the GEMCA algorithm, were run against a single reference sample (NA10851). Therefore, a subset of the regions reported as variant may reflect the reference sample rather than the HuRef donor, even though all previously identified variants in the reference sample [62] were removed from the final list of CNV calls in the present study. The majority of the variant regions were detected by only one platform, reflecting the difference in probe coverage and sensitivity among various approaches [12,62]. As an independent form of validation, the CNVs detected here were compared to those reported in the Database of Genomic Variants (DGV) [63], and 54 of the variants (87%) have been described previously (with the thresholds used for these analyses we expect approximately 5% of calls to be false positive). A summary of the genomic features overlapped by these CNVs is presented in Table 12. Approximately 55% of the CNVs overlap with annotated segmental duplications, which is slightly higher than reported in previous studies [63,64]. The CNVs also overlap 95 RefSeq genes, seven of which are described in the Online Mendelian Inheritance in Man database (OMIM) as linked to a specific phenotype (Table S7). These include blood group determinants such as RHD and XG, as well as a gain overlapping the coagulation factor VIII gene. Table 12 Copy Number Variants Identified on the HuRef Sample FISH of Unmapped HuRef Scaffolds Numerous HuRef sequences that span the entire or partial scaffolds did not have a matching sequence in the NCBI genome. Some had putative chromosomal location assignments (e.g., sequences extending into NCBI gaps), whereas others were unanchored scaffolds with no mapping information. We selected sequences >40 kb in length with no match to the NCBI genome and identified fosmids (derived from the Coriell DNA NA18552) mapping to these sequences based clone end-sequence data. The fosmids were then used as FISH probes with the aim of confirming annotated locations for anchored sequences and assigning chromosomal locations to unanchored scaffolds. Fosmids were hybridized to metaphase spreads from two different cells lines. At least 10 metaphases were scored for each probe, and a differentially labeled control fosmid was included for each hybridization. For 23 regions, there was no mapping information available from mate-pair data or the one-to-one mapping comparison. Of the remaining 26 regions, 24 had a specific chromosomal location assigned at the nucleotide level (Figure 10A and 10B), whereas two regions were assigned to specific chromosomes but lacked detailed mapping information. The results of the FISH experiments are outlined in Table S8. Of the 23 regions with no prior mapping information, 13 gave a single primary mapping location (Figure 10C). The majority of the remaining 10 regions located to multiple centromeric regions (Figure 10D), suggesting that there are large euchromatic-like sequences present as low-copy repeats in the current centromeric assembly gaps. For the 26 regions with mapping information, the expected signal was observed for 22 (85%). However, in six of these hybridizations, there were additional signals of equal intensity at other locations. Ten of the scaffolds chosen for FISH extend into contig or clone gaps in the current reference assembly. Of these 10 regions, the expected localization was corroborated for seven. The combined data indicate that the HuRef assembly contributes significant amounts of novel sequence important for generating more complete reference assemblies. Figure 10 Non-Mapped HuRef Sequences Mapped to Coriell DNA Samples by FISH Sequences from the HuRef donor that had no match based on the one-to-one mapping or BLAST when compared to the NCBI Human reference genome were tested by FISH. Fosmids were used as probes and the experiments were run, using Coriell DNA, to confirm the localization of the contigs or to map contigs with no prior mapping information. Shown here are four representative results. (A) An insertion at 7q22 where the FISH confirmed the HuRef mapping, (B) FISH result confirming the mapping of a sequence extending into a gap at 1p21. (C) Localization of a contig with no prior mapping information to chromosomal band 1q42. (D) An example of euchromatic-like sequence with no prior mapping information, which hybridizes to multiple centromeric locations. Haplotype Assembly Haplotypes have more power than individual variants in the context of association studies and predicting disease risk [65–67] and also permit the selection of reduced sets of “tagging” SNPs, where linkage disequilibrium is strong enough to make groups of SNPs largely redundant [68,69]. The potential for shotgun sequences from a single individual to be used to separate haplotypes has been examined previously [70,71]. For a given polymorphic site, sequencing reads spanning that variant can be separated based on the allele they contain. For data from a single individual, this amounts to separation based on chromosome of origin. When two or more variant positions are spanned by a single read, or occur on paired reads derived from the same shotgun clone, alleles can be linked to identify larger haplotypes. This is sometimes known as “haplotype assembly.” When single shotgun reads are considered, the problem is computationally tractable [70,71] but the resulting partial haplotypes would be quite short with reads produced by existing sequencing technology, given the observed density of polymorphisms in the human genome (R. Lippert, personal communication). Mate pairing has the potential to increase the degree of “haplotype assembly,” but finding the optimal solution in the presence of errors in the data has been shown to be computationally intractable [71]. Nevertheless, we show that the character and quality of the data is such that heuristic solutions, while not guaranteed to find the best possible solution, can provide long, high-quality phasing of heterozygous variants. The set of autosomal heterozygous variants described above (n = 1,856,446) was used for haplotype assembly. The average separation of these variants on the genome was ∼1500 bp (twice the average read length). Fewer than 50% of variants could be placed in “chains” of six or more variants where successive variants were within 1 kb of one another. Consequently, single reads cannot connect these variants into large haplotypes. However, the effect of mate pairing is substantially greater than would be observed simply by doubling the length of a read, as shown in Figure 11: variants are linked to an average of 8.7 other variants. Figure 11 Degree of Linkage of Heterozygous Variants The distribution of the number of other variants to which a given variant can be linked using sequencing reads only or using mated reads as well is shown. Linkage of variants based on individual sequencing reads is limited, regardless of sequence coverage beyond a modest level, but is substantially increased by the incorporation of mate pairing information. The size of the effect is considerably more than simply doubling read length, due to variation in insert size; consequently, benefits of increasing sequencing coverage drop off much more slowly. Using this dataset, haplotype assembly was performed as described in Materials and Methods. Half of the variants were assembled into haplotypes of at least 401 variants, and haplotypes spanning >200 kb cover 1.5 Gb of genome sequence. The full distributions of haplotype sizes, both in terms of bases spanned and in terms of numbers of variants per haplotype, are shown in Figure 12 . Although haplotypes inferred in this fashion are not necessarily composed of continuous variants, haplotypes do in fact contain 91% of the variants they span. More than 75% of the total autosomal chromosome length is in haplotypes spanning at least four variants, and 89% of the variants are in haplotypes that include at least four heterozygous HapMap (phase I) variants. Figure 12 Distribution of Inferred Haplotype Sizes (A) Reverse cumulative distribution of haplotype spans (bp) (N50 ∼ 350 kb). (B) Reverse cumulative distribution of variants per haplotype (N50 ∼ 400 variants). Both internal consistency checks and comparison to HapMap data indicate that the HuRef haplotypes are highly accurate. Comparing individual clones against the haplotypes to which they are assigned, 97.4% of variant calls were consistent with the assigned haplotype. Moreover, the HuRef haplotypes were strongly consistent with those inferred as part of the HapMap project [35]. Where a pair of variants is in strong LD according to the HapMap haplotypes, the correct phasing of the HuRef data would be expected to match the more frequent phasing in the HapMap set in most cases. Exceptions would require a rare recombination event, convergent mutation in the HuRef genome, or an error in the HapMap phasing in multiple individuals. We accessed the 120 phased CEU haplotypes from HapMap and identified the subset of heterozygous HuRef SNP variants that also coincided with the HapMap data. For adjacent pairs of such variants that were in strong LD (r 2 ≥ 0.9; n = 197,035), fewer than 1 in 40 of the HuRef-inferred haplotypes conflicted with the preferred HapMap phasing. Figure 13 shows more generally the consistency of HuRef haplotypes with the HapMap population data as a function of r 2 and D′. Because the inference of HuRef haplotypes is completely independent of the data and methods used to infer HapMap haplotypes, this is a remarkable confirmation of the HuRef haplotypes. Figure 13 Consistency of HuRef Haplotypes with HapMap Data Haplotypes inferred from the HuRef data are strongly consistent with HapMap haplotypes. The probability in the HapMap CEU panel of the observed genotypes being phased as per the HuRef haplotypes is high for variants in strong LD (as measured either by D′ or r 2). The restriction to variants in strong LD has no clear selection bias with respect to our inferred haplotypes. On the other hand, it provides only weaker confirmation for the HapMap phasing, since it is restricted to the easiest cases for phasing using population data—namely only those pairs of variants in strong linkage disequilibrium. The lengths and densities of the inferred HuRef haplotypes described above are possible due to the use of paired end reads from a variety of insert sizes. Given the relatively simple means that were used for separating haplotypes, the high accuracy of phasing is likewise due to the quality of the underlying sequence data, the genome assembly, and the set of identified variants. The rate of conflict with HapMap with regard to variants in high LD can be further decreased by filtering the variants more aggressively (particularly excluding indels; unpublished data), although at the expense of decreasing haplotype size and density. It is also possible to improve the consistency measures described above by using more sophisticated methods for haplotype separation. One possibility we have explored is to use the solutions described above as a starting point in a Markov chain Monte Carlo (MCMC) algorithm. This produces solutions for which the fraction of high LD conflicts with HapMap is reduced by ∼30%. This approach has other advantages as well: MCMC sampling provides a natural way to assess the confidence of a partial haplotype assignment. Assessment of this and other measures of confidence is a topic for future investigation. We used the generated haplotypes to view how well they span the current gene annotation. We were able to identify 84% (19,407 out of 23,224 protein coding genes) of Ensembl version 41 genes partially contained within a haplotype block and 58% of protein coding genes completely contained within a haplotype block. We note that in population-based haplotypes, denser sampling of SNPs in regions of low LD leads to reduction in the size of the average haplotype block [72]. In contrast to this finding, detection of additional true heterozygous variants through personal sequencing, regardless of LD, would lead to larger partial haplotypes, because additional variants increase the density of variants and thus their linkage to one another. Gene-Based Variation in HuRef The sequencing, assembly, and cataloguing of the variant set and the corresponding haplotypes of the HuRef donor provided unprecedented opportunity to study gene-based variation using the vast body of scientific literature and extensively curated databases like OMIM [73] and Human Genetic Mutation Database (HGMD, [18]). A preliminary assessment indicates that 857 OMIM genes have at least one heterozygous variant in the coding or UTR regions, and 314 OMIM genes have at least one nonsynonymous SNP (Figure 14A). Overall, we observed 11,718 heterozygous and 9,434 homozygous coding SNPs and 236 heterozygous and 627 homozygous coding indels (Figure 14B). In addition, 4,107 genes have 6,114 nonsynonymous SNPs indicating that at least 17% (4,107/23,224) of genes encode differential proteins. The nonsynonymous SNPs define a lower limit of a potentially impacted proteome, because 44% of genes (10,208/23,224) have at least one heterozygous variant in the UTR or coding region and these variants could also affect protein function or expression. Therefore, almost half of the genes could have differential states in this diploid human genome, and this estimate does not include variation in nonexonic regions involved in gene regulation such as promoters and enhancers. Figure 14 Distribution of HuRef Variants in OMIM and Ensembl Genes (A) The distribution of the OMIM genes in Ensembl version 41 protein coding genes that contain one or more SNP or indel in their coding and/or UTR regions. (B) A similar distribution for the variants found in coding and/or UTR regions for all Ensembl version 41 genes. Understanding potential genotype-to-phenotype relationships will require many more extensive population-based studies. However, the complexities of assessing genotype–phenotype relationships begin to emerge even from a very preliminary glimpse of an individual human genome (Table 13). For Mendelian conditions such as Huntington disease (HD), the predictive nature of the genomic sequence is more definitive. Our data reveal the donor to be heterozygous (CAG)18/(CAG)17 in the polymorphic trinucleotide repeat located in the HD gene (HD affected individuals have more than 29 CAG repeats) [74]. The genotype matches the phenotype in this case, since the donor does not have a family history of Huntington disease and shows no sign of disease symptoms, even though he is well past the average onset age. The HuRef donor's predisposition status for multifactorial diseases is, as expected, more complicated. For example, the donor has a family history of cardiovascular disease prompting us to consider potentially associated alleles. The HuRef donor is heterozygous for variants in the KL gene; F352V (r9536314) and C370S (rs9527025). It has previously been observed that these heterozygous alleles present a lower risk for coronary artery disease [75]. However, the donor is also homozygous for the 5A/5A in rs3025058 in the promoter of the matrix metalloproteinase-3 (MMP3) [76]. This genotype is associated with higher intra-arterial levels of stromelysin and has a higher risk of acute myocardial infarction. This observation highlights the forthcoming challenge toward assessing the effects of the complex interactions in the multitude of genes that drive the development and progression of phenotypes. On occasion, these variant alleles may provide either protective or deleterious effects, and the ascertainment of resulting phenotypes are based on probabilities and would need to account for impinging environmental effects. Table 13 Genotypes for Some Traits in the HuRef Donor In our preliminary analysis of the HuRef genome, we also identified some genetic changes related to known disease risks for the donor. For example, approximately 50% of the Caucasian population is heterozygous for the GSTM1 gene, where the null mutation can increase susceptibly to environmental toxins and carcinogens [77–79]. The HuRef assembly identifies the donor to be heterozygous for the GSTM1 gene. Currently, it is not possible without further testing (including somatic analysis) and comparison against larger datasets to determine if this variant contributes to the reported health status events experienced by the donor, such as skin cancer. We also found some novel changes in the HuRef genome for which the biological consequences are as yet unknown. For example, we found a 4-bp novel heterozygous deletion in Acyl-CoA Oxidase 2 (ACOX2) causing a protein truncation. ACOX2 encodes an enzyme activity found in peroxisomes and associates intimately with lipid metabolism and further was found to be absent from livers of patients with Zellweger syndrome [80]. The deletion identified would likely abolish peroxisome targeting, but the biological function of the mutation remains to be tested. We have also been able to detect inconsistencies between detected genotypes in the donor's DNA and the expected phenotype based on the literature given the known phenotype of the HuRef donor. For example, the donor's LCT genotype should confer adult lactose tolerance according to published literature [81], but this does not match with the self-reported phenotype of the donor's lactose intolerance. Apparent inconsistencies of this nature may be explained by considering the modifying effect of other genes and their products, as well as environmental interactions. Discussion We describe the sequencing, de novo assembly, and preliminary analysis of an individual diploid human genome. In the course of our study, we have developed an experimental framework that can serve as a model for the emerging field of en masse personalized genomics [82]. The components of our strategy involve: (i) sample consent and assessment, (ii) genome sequencing, (iii), genome assembly, (iv) comparative (one-to-one) mapping, (v) DNA variation detection and filtering, (vi) haplotype assembly, and (vii) annotation and interpretation of the data. We were able to construct a genome-wide representation of all DNA variants and haplotype blocks in the context of gene annotations and repeat structure identified in the HuRef donor. This provides a unique glimpse into the diploid genome of an individual human (Poster S1). The most significant technical challenge has been to develop an assembly process (points ii–v) that faithfully maintains the integrity of the allelic contribution from an underlying set of reads originating from a diploid DNA source. As far as we know, the approach we developed is unique and is central to the identification of the large number of indels less than 400 bp in length. We attempted de novo recruitment of sequence reads to the NCBI human reference genome, using mate pairing and clone insert size to guide the accurate placement of reads [83]. Although this approach can produce useful results, it does limit variant detection to completed regions of the reference genome and, like genome assembly, can be confounded by segmentally duplicated regions. The genome assembly approach with allelic separation allows the detection of heterozygous variants present in the individual genome with no further comparison. The one-to-one mapping of our HuRef assembly against a nearly completed reference genome permits the detection of the remaining variants. These variants arise from sequence differences found within and also outside the mapped regions, where the precision of the compared regions is being provided by the genome-to-genome comparison [59]. The ability to provide a highly confident set of DNA variants is challenging, because more than half of the variants are a single base in length but include both SNPs and indels. A filtering approach was used that accounts for the positional error profile in a Sanger sequenced electropherogram in relation to the called variant. Additional filtering considerations necessitated minimal requirements for read coverage and for the proportional representation of each allele. The filtering approaches were empirical and used the large amounts of previously described data on human variation (dbSNP). The utility of using paired-end random shotgun reads and the variant set defined on the reads via the assembly enabled the construction of long-range haplotypes. The haplotypes are remarkably well constructed given that the density of the variant map is comparable to those used in other studies [35], reflecting the utility of underlying sequence reads beyond just genome assembly. To understand how an individual genome translates into an individual transcriptome and ultimately a functional proteome, it is important to define the segregation of variants among each chromosomal copy. While several new approaches for DNA sequencing are available or being developed [84–86], we chose to use proven Sanger sequencing technology for this HuRef project. The choice was obviously motivated in part for historical reasons [7], but not solely. We attached a high importance to generating a de novo assembly including maximizing coverage and sensitivity for detecting variation. We further anticipated that long read lengths (in excess of 800 nucleotides), compatibility with paired-end shotgun clone sequencing, and well-developed parameters for assessing sequencing accuracy would be required. High sequence accuracy is essential to avoid calling large numbers of false-positive variants on a genome-wide scale. Long paired-end reads are especially useful for achieving the best possible assembly characteristics in whole-genome shotgun sequencing and for providing sufficient linkage of variants to determine large haplotypes. We have been able to categorize a significant amount of DNA variation in the genome of a single human. Of great interest is the fact that 44% of annotated genes have at least one, and often more, alterations within them. The vast majority—3,213,401 events (78%) of the 4.1 million variants detected in the HuRef donor—are SNPs. However, the remaining 22% of non-SNP variants constitute the vast majority, about 9 Mb or 74%, of variant bases in the donor. Using microarray-based methods, we also detected another 62 copy number variable regions in HuRef, estimated to add some 10 Mb of additional heterogeneity. Given these potential sources of measured DNA variation, we can, for the first time, make a conservative estimate that a minimum of 0.5% variation exists between two haploid genomes (all heterozygous bases, i.e., SNP, multi-nucleotide polymorphisms [MNP], indels, [complex variants + putative alternate alleles + CNV]/genome size; [2,894,929 + 939,799 + 10,000,000]/2,809,547,336) namely those that make up the diploid DNA of the HuRef assembly. We also note that there will be significantly more DNA variation discovered in heterochromatic regions of the genome [87], which largely escaped our analysis in this study. We had mixed success when attempting to find support for the experimentally determined CNVs in the HuRef assembly itself or the data from which it was derived. More than 50% of the CNVs overlapped segmental duplications, and these regions are underrepresented in HuRef, which complicated the analysis. We attempted to map the sequence reads onto the NCBI human genome and then identify CNVs by detecting regions with significant changes in read depth. However, we found significant local fluctuations in read depth across the genome, limiting the ability for comparison and suggesting that a higher coverage of reads may be required to use this approach effectively. As we have emphasized throughout, a major difference of the genomic assembly we have described is our approach to maintain, wherever possible, the diploid nature of the genome. This is in contrast to both the NCBI and WGSA genomes, which are each consensus sequences and, therefore, a mosaic of haplotypes that do not accurately display the relationships of variants on either of the autosomal pairs. For BAC-based genome assemblies such as the NCBI genome assembly, the mosaic fragments are generally genomic clone size (e.g., cosmid, PAC, BAC), with each clone providing contiguous sequence for only one of the two haplotypes at any given locus. Moreover, there are substantial differences in the clone composition of different chromosomes due to the historical and hierarchical mapping and sequencing strategies used to generate the NCBI reference assemblies [7,8]. In contrast, for WGSA, the reads that underlie most of the consensus sequence are derived from both haplotypes. This can result in very short-range mosaicism, where the consensus of clustered allelic differences does not actually exist in any of the underlying reads. To address this issue, the Celera assembler was modified to consider all variable bases within a given window and to group the sequence forms supporting each allele before incorporation into a consensus sequence (see Materials and Methods). In our experience, this reduces the incidence of local mosaicism, although, between windows, the consensus sequence remains a composite of haplotypes. Efforts to build haplotypes from the genome assembly (Haplotype Assembly) will likely lead to future modification of the assembler, allowing it to output longer consensus sequences for both haplotypes at many loci. Clearly, a single consensus sequence for a diploid genome, whether derived from BACs or WGS, has limitations for describing allelic variants (and specific combinations of variants) within the genome of an individual. Partial haplotypes can be inferred for an individual from laboratory genotype data (e.g., from SNP microarrays) in conjunction with population data or genotypes of family members. However, at least in the absence of sets of related individuals (e.g., family trios), it is difficult to determine haplotypes from genotype data across regions of low LD. We have shown that sequencing with a paired-end sequencing strategy can provide highly accurate haplotype reconstruction that does not share these limitations. The assembled haplotypes are substantially larger than the blocks of SNPs in strong LD within the various populations investigated by the HapMap project. In addition to being larger, haplotypes inferred in our approach can link variants even where LD in a population is weak, and they are not restricted to those variants that have been studied in large population samples (e.g., HapMap variants). We note that in addition to the implications for human genetics, this approach could be applied to separating haplotypes of any organism of interest—without the requirement for a previous reference genome, family data, or population data—so long as polymorphism rates are high enough for an acceptable fraction of reads or mate pairs to link variants. There are several avenues for extending our inference of haplotypes. As noted, although the naive heuristics used here give highly useful results, other approaches may give even more accurate results, as we have observed with an MCMC algorithm. There are various natural measures of confidence that can be applied to the phasing of two or more variants, including the minimum number of clones that would have to be ignored to unlink two variants, or a measure of the degrees of separation between two variants. The analysis presented here provides phasing only for sites deemed heterozygous, but data from apparently homozygous sites can be phased as well, so we can tell with confidence whether a given site is truly homozygous (i.e., the same allele is present in both haplotypes) or whether the allele at one or even both haplotypes cannot be determined, as occurs as much as 20% of the time with the current dataset. Lastly, it should be possible to combine our approach with typical genotype phasing approaches to infer even larger haplotypes. Our project developed over a 10-year period and the decisions regarding sample selection, techniques used, and methods of analysis were critical to the current and continued success of the project. We anticipated that beyond mere curiosity, there would be very pragmatic reasons to use a donor sample from a known consented individual. First and foremost, as we show in a preliminary analysis, genome-based correlations to phenotype can be performed. Due to the still rudimentary state of the genotype-phenotype databases it can be argued that at the present time, DNA sequence comparisons do not reveal much more information than a proper family history. Even when a disease, predisposition, or phenotypically-relevant allele is found, further familial sampling will usually be required to determine the relevance. Eventually, however, populations of genomes will be sequenced, and at some point, a critical mass will dramatically change the value of any individual initiative providing the potential for proactive rather than reactive personal health care. In a simple analogy, absent of family history, genealogical studies can now be quite accurate in reconstructing ancestral history based purely on marker-frequency comparisons to databases. Here, with a near-unlimited amount of variation data available from the HuRef assembly, we can reconstruct the chromosome Y ethno-geneographic lineage (Figure 15), which is not only consistent with, but better defines the self-reported family tree data (Figure 1A and unpublished data). Figure 15 Chromosome Y ethno-genogeographic lineage The HuRef donor Y chromosome haplotype suggests descent from several European/US groups given the Y chromosome ethno-geographic markers. The haplogroup membership is R1b6 with includes individuals from the United Kingdom, Germany, Russia, and the United States, which is consistent with the self-reported family tree provided by the HuRef donor. The thick red line denotes the markers needed to trace the haplotype from the mapping of the chromosome Y markers to the HuRef genome. Data and figure from the Y Chromosome Consortium; http://ycc.biosci.arizona.edu/nomenclature_system/frontpage.html. There are always issues regarding the generation and study of genetic data and these may amplify as we move from what are now primarily gene-centric studies to the new era where genome sequences become a standard form of personal information. For example, there are often concerns that individuals should not be informed of their predisposition (or fate) if there is nothing they can do about it. It is possible, however, that many of the concerns for predictive medical information will fall by the wayside as more prevention strategies, treatment options, and indeed cures become realistic. Indeed we believe that as more individuals put their genomic profiles into the public realm, effective research will be facilitated, and strategies to mitigate the untoward effects of certain genes will emerge. The cycle, in fact, should become self-propelling, and reasons to know will soon outweigh reasons to remain uninformed. Ultimately, as more entire genome sequences and their associated personal characteristics become available, they will facilitate a new era of research into the basis of individuality. The opportunity for a better understanding of the complex interactions among genes, and between these genes and their host's personal environment will be possible using these datasets composed of many genomes. Eventually, there may be true insight into the relationships between nature and nurture, and the individual will then benefit from the contributions of the community as a whole. Materials and Methods External data sources. We used the assembled chromosome sequence of the human genome available as NCBI version 36. The gene annotation of this genome was provided by Ensembl (http://www.ensembl.org) version 41, which incorporates dbSNP version 126. Haplotype map data was obtained from http://www.hapmap.org, Release version 21a. Celera-generated chromatograms for the HuBB individual [7] were obtained from the NCBI trace archive. These included reads from two tissues sources: blood and sperm. Sequence reads were generated from these traces using Phred version 020425.c [88] and a modified version of Paracel TraceTuner (http://sourceforge.net/projects/tracetuner/). This reprocessing significantly improved accuracy and quality in the 5′ portion of the reads, increasing their usable length by 7%, and reducing variants encoding spurious protein truncations, as well as reducing apparent heterozygous variants in the assembly. DNA extraction. 200-μl aliquots of thawed, whole blood were processed using the MagAttract DNA Blood Mini M48 Kit and the MagAttract DNA Blood >200 μl Blood protocol on the BioRobot M48 Workstation running the GenoM-48 QIAsoft software (version 2.0) (Qiagen; http://www.qiagen.com). Tris:EDTA (10:0.1) was used for the final 200 μl elution step. A260/A280 readings (SPECTRAmax Plus spectrophotometer (Molecular Devices; http://www.moleculardevices.com) or an ND-1000 spectrophotometer (NanoDrop Technologies; http://www.nanodrop.com), and gel images were used to quantify the DNA and to confirm that high-quality, high–molecular weight DNA was available for downstream processing. 1.0 μl of extracted DNA was run on a 0.8% agarose gel containing ethidium bromide, for 4 h at 60 V and imaged using Gel Doc and Quantity One Software (Bio-Rad Laboratories; http://www.bio-rad.com). Cytogenetic analysis. Phytohemagglutin-stimulated lymphocytes from peripheral blood were cultured for 72 h with thymidine synchronization. G-banding analysis was performed on metaphase spreads from peripheral blood lymphocytes using standard cytogenetic techniques. Spectral karyotyping. Spectral karyotyping was performed on metaphase spreads from cultured lymphocytes. SkyPaint probes were used according to manufacturer's instructions (Applied Spectral Imaging; http://www.spectral-imaging.com). Metaphases were viewed with a Zeiss epifluorescence microscope and spectral images were acquired with an SD300 SpectraCube system and analyzed using SkyView software 1.6.2 (Applied Spectral Imaging). High-throughput Applied Biosystems 3730xl Sanger sequencing processing. Plasmid and Fosmid Library Construction. We nebulized genomic DNA to produce random fragments with a distribution of approximately 1–25 kb, end-polished these with consecutive BAL31 nuclease and T4 DNA polymerase treatments, and size selected using gel electrophoresis on 1% low–melting-point agarose. After ligation to BstXI adapters, we purified DNA by three rounds of gel electrophoresis to remove excess adapters, inserted fragments into BstXI-linearized medium-copy pBR322 plasmid vectors, and inserted the resulting library into GC10 cells by electroporation. To ensure that plasmid libraries contained few clones without inserts and no clones with chimeric inserts, we used vectors (pHOS) that include several features: (i) the sequencing primer sites immediately flank the BstXI cloning site to avoid sequencing of vector DNA, (ii) there are no strong promoters oriented toward the cloning site, and (iii) BstXI sites for cloning facilitate a high frequency of single inserts and rare no-insert clones. Sequencing from both ends of cloned inserts produced pairs of linked sequences of ∼800 bp each. We constructed fosmid libraries with approximately 30 μg of DNA that was sheared using bead beating and repaired by filling with dNTPs. We used a pulsed-field electrophoresis system to select for 39–40 kb fragments, which we ligated to the blunt-ended pCC1FOS vector. Clone Picking and Inoculation. Libraries were propagated on large-format (16 × 16 cm) diffusion plates and colonies were picked for template preparation using a Q-bot or Q-Pix colony-picking robots (Genetix; http://www.genetix.com) and inoculated into 384-well blocks. DNA Template Preparation. We prepared plasmid DNA using a robotic workstation custom built by Thermo CRS, based on the alkaline lysis miniprep [89], modified for high-throughput processing in 384-well plates. The typical yield of plasmid DNA from this method was approximately 600–800 ng per clone, providing sufficient DNA for at least four sequencing reactions per template. Sequencing Reactions. Sequencing protocols were based on the di-deoxy sequencing method [90]. Two 384-well cycle-sequencing reaction plates were prepared from each plate of plasmid template DNA for opposite-end, paired-sequence reads. Sequencing reactions were completed using Big Dye Terminator (BDT) chemistry version 3.1 Cycle Sequencing Ready Reaction Kits (Applied Biosystems) and standard M13 forward and reverse primers. Reaction mixtures, thermal cycling profiles, and electrophoresis conditions were optimized to reduce volume and extend read lengths. Sequencing reactions were set-up by the Biomek FX (Beckman Coulter; http://www.beckmancoulter.com) pipetting workstations. Templates were combined with 5-μl reaction mixes consisting of deoxy- and fluorescently labeled dideoxynucleotides, DNA polymerase, sequencing primers, and reaction buffer. Bar coding and tracking promoted error-free transfer. Amplified reaction products were transferred to a 3730xl DNA Analyzer (Applied Biosystems). Genome assembly and initial variant identification. The Celera Assembler Software (https://sourceforge.net/projects/wgs-assembler/) [7,40,91] generated contiguous sequences (contigs) that could be linked via mate-pair information into scaffolds. It has a phase for splitting initial apparently chimeric contigs (referred to as unitigs), but this process is not repeated for the final set of contigs and scaffolds as with some other assemblers (Arachne 2 [92]). This leaves a small number of chimeric scaffolds, which can be detected and split as described below. All assemblers fail to discriminate alternate alleles in polymorphic regions from distinct regions of the genome. These polymorphic regions, containing highly repetitive sequence with short unique anchoring sequence and simple algorithmic failures, result in a number of small scaffolds that are highly redundant. Although there are valuable data in these small scaffolds, they are usually not treated as part of the assembled sequence. For this project we made specific modifications to the Celera Assembler to enable the grouping of reads into separate alleles when heterozygous variants were encountered. Instead of taking a column-by-column approach to determine the consensus sequence from a set of aligned reads, the region of variation was considered as a whole, defined as that between at least 11 bp nonvariant columns. In practice, variant regions would most frequently be single columns (SNPs), but the new algorithm only applied to longer regions. The reads spanning a variant region were split between alleles. An allele, for this purpose, was one or more spanning reads sharing an identical sequence for the variant region, and was considered confirmed if represented by two or more reads. Each allele was assigned a score equal to the sum of average quality values for the spanning portions of its reads. The highest-scoring confirmed allele was used for the consensus sequence. Alternate confirmed allele sequences were reported separately. As expected, there were usually two confirmed alleles in each region of sequence variation. Regions with more than two apparent confirmed alleles represented either collapsed repetitive sequence or a group of reads with systematic base calling error, rather than true genetic variation. BAC end mapping. The set of The Institute for Genome Research (TIGR) BAC ends [41] used in the WGSA [40] assembly were aligned to the 553 HuRef scaffolds of at least 100 kb in length. We kept BAC ends that mapped uniquely to a single scaffold and near the end of a scaffold, such that their mate was likely to reside outside of the scaffold. Mate pairs were kept if both BAC ends passed the above criterion, and these indicated a possible joining of two scaffolds in a certain orientation. There were 144 consistent scaffold joins with at least two supporting mate pairs and 98 with one supporting mate pair. Using these scaffold joins would result in 409 or 311 scaffolds, respectively, of at least 100 kb, with a concomitant increase in the scaffold N50 length. Assembly-to-assembly mapping. We used open-source software (http://sourceforge.net/projects/kmer/) [40,93,94] to generate a one-to-one comparison between HuRef and NCBI human genome reference assembly. For sequences that do not contain very large, nearly identical duplications, this mapping is accurate [93]. Nearly identical duplicated regions tend to be underrepresented in whole-genome shotgun assemblies such as HuRef [10]. Segments that are duplicated in one sequence but not the other (for instance when failing to merge overlapping contigs) cannot be fully included in any one-to-one mapping. For example the first few megabases of NCBI version 36 Chromosomes X and Y are identical; therefore, a 1.5-Mb scaffold from HuRef that maps to both of these regions is not part of the one-to-one mapping. Tandem repeats with variable unit copy number are also problematic for a one-to-one mapping. For each one-to-one mapping we determined three levels: matches, runs, and clumps. A match is a maximal high-identity local alignment, usually terminated by indels or sequence gaps in one of the assemblies. Runs may include indels, and are monotonically increasing or decreasing sets of matches (linear segments of a match dot plot) with no intervening matches from other runs on either axis. Clumps are similar to runs but allow small intervening matches/runs (such as small inversions) to be skipped over. The total number of base pairs in matches is a measurement of how much of the sequence is shared between assemblies. Within a run, the number of base pairs in each assembly is different, because indels are allowed among matches in the run. These could be gaps that are filled in one assembly but not the other, polymorphic insertions or deletions, or artifactual sequence. Runs span regions in both assemblies that have no rearrangements with respect to each other, providing a direct measure of the order and orientation differences between a pair of assemblies. Clumps provide a similar measure of rearrangement but allow for small differences that may be due to noise or polymorphic inversions. Remaining sequence may be unique to one assembly or the other, but some will also be large repetitive regions without good one-to-one mapping but present in some copy number in both assemblies. Apparently unique sequence may also represent some form of contaminant. We determined an initial set of potentially chimeric scaffolds by finding those that contained more than one clump of at least 5,000 bp relative to NCBI version 36. By mapping all HuRef and Coriell fosmid mate pairs to NCBI human reference genome and to HuRef, we assessed whether mate pair constraints were violated at the potentially chimeric junctions. Accordingly, we split 12 scaffolds. Variant refinement process. DNA variants were characterized by alignment of sequencing reads in the HuRef assembly and by comparison of regions of difference in the one-to-one HuRef to NCBI reference genome map. The contribution of each sequence read to a single position in the HuRef consensus was evaluated both during and after the assembly process to identify positions that contain more than one allele. This process identified heterozygous SNPs and indel polymorphisms, and typically two or more reads were required for the initial identification of an alternate allele. Homozygous SNPs and MNPs were identified when (respectively) single or multiple contiguous loci differed in the one-to-one mapping, and all underlying HuRef reads supported one allele. Finally, homozygous insertion or deletion loci were identified where the HuRef assembly had or lacked sequence relative to the NCBI assembly, respectively. These were commonly referred to as homozygous indels unless it was relevant for analysis purposes, computational or experimental, to refer to a homozygous insertion or deletion as a way of indicating presence or absence of the sequence, respectively, in the HuRef assembly. Filtering of variants. DNA variations were identified by examining the base changes within the HuRef assembly multialignment and between the HuRef assembly and the NCBI reference human genome. 5,061,599 SNPs and heterozygous variants were identified initially, after which filters were applied to eliminate erroneous calls. For a potential SNP, each read supporting that SNP was considered, and if the QV was 96% for all four all hybridizations; 0.1% discordant genotype calls between the technical replicates were excluded from further analysis. The NspI and StyI array scans were analyzed for copy number variation using a combination of DNA Chip Analyzer (dChip) [97], Copy Number Analysis for GeneChip (CNAG) [98], and Genotyping Microarray-based CNV Analysis (GEMCA) [99]. Analysis with dChip (http://www.dchip.org) was performed using a Hidden Markov Model (HMM) as previously described [100], and a set of 50 samples run in the same facility were used as reference. For analyses with CNAG version 2.0 (http://www.genome.umin.jp), the copy number changes were inferred using a HMM built into CNAG [98]. GEMCA analysis was performed essentially as described [99], except that we used one designated DNA samples (NA10851) as reference for pair-wise comparison. This sample has been screened for CNVs in a previous study [62] and the CNVs known to be present in the reference genome were excluded. Illumina HumanHap650Y Genotyping BeadChip. The HuRef sample was genotyped using the Sentrix HumanHap650Y Genotyping BeadChip according to the manufacturer's instructions. All chips were scanned using the Sentrix Bead-Array reader and the Sentrix Beadscan software application. The results from the BeadChip were analyzed for CNV content using QuantiSNP as previously described [101]. Human genome Agilent 244K CGH arrays. The Agilent human genome CGH array contains 244,000 60mer probes on a single slide. The experiment was run using 2.5 μg of genomic DNA for Cy3/Cy5 labeling for each hybridization, with a standard dye-swap experimental design. DNA sample NA10851 was used as a reference. The slides were scanned at 5-μm resolution using the Agilent G2565 Microarray Scanner System (Agilent Technologies; http://www.agilent.com). Feature extraction was performed using Feature Extraction v9.1 and results were analyzed using CGH Analytics v3.4.27. Nimblegen human whole-genome 385K CGH array. CGH was performed using the Nimblegen human genome CGH array. The array contains 385,000 isothermal probes yielding a median spacing of 6 kb across the human genome. The experiment was performed as previously described [102] with a standard dye-swap experimental design. Results were analyzed using the CNVfinder algorithm [103]. One of the dye-swap experiments did not meet the quality control cut-offs, and because of this, the Nimblegen CNV calls were only employed for confirmation of CNV identified by the other platforms, and not used for identification of additional CNVs FISH. FISH analysis was performed to find the location of DNA segments present in the HuRef DNA but either missing or represented by gaps in HuRef assembly. The FISH analysis was performed as previously described [104]. Initially, fosmids representing 107 different regions were chosen and end-sequenced to confirm that they mapped to the intended scaffolds. After excluding fosmids for which the original mapping was erroneous or uncertain, 88 fosmids remained. The entire sequence for each fosmid was then computationally excised from the scaffolds sequence and analyzed for repeat content using RepeatMasker. Fosmids with more than 6 kb (∼17%) satellite repeat content were excluded from further analysis. All fosmids that passed these filtering criteria were analyzed on metaphase spreads from two different cell lines (GM10851 and GM15510) to determine the chromosomal location of the fosmid probe. At least 10 metaphases were scored for each probe, all in duplicate by two experienced cytogeneticists. Supporting Information Figure S1 Detection of an 8-bp Indel in the HuRef and Three Coriell DNA Donors PAGE detection of an 8-bp indel (GATAAGTG/--------) in three Coriell DNA samples (lane 1 = NA05392 , lane 2 = NA05398, lane 3 = NA07752, and lane 4 = HuRef donor DNA). Note the detection of two bands signifying the presence of two allelic forms in individual NA05392 and HuRef and the short and long alleles in individuals NA05398 and NA07752 respectively. (118 KB PDF) Click here for additional data file. Figure S2 The Distribution of the Relative Position of SNPs and Heterozygous Indels Found in Sequence Reads Note the increased occurrence of variant at the beginning and end of reads. The relative position of increased rate of variant identification was used and reads calling variant outside these threshold were removed as positive evidence for the presence of that particular variant. This approach led to a significant improvement in the quality of the variant sets and was used as part of the variant filtering process. (132 KB PDF) Click here for additional data file. Figure S3 The Distribution of the Percentage of Reads Supporting the Alternate (i.e., Non–Consensus Sequence Reads) for All Raw SNP and Heterozygous Indels The dbSNP distribution indicates which “raw” variant have been previously reported in dbSNP. The intersection point of these two distributions at lower values determines the minimum percentage of minor allele threshold with which variant could be filtered to improve their quality using dbSNP as a guide. These threshold values were deemed to be 25% for SNP and 20% for indels. Indels are referred to separately as insertions and deletions depending on whether the shorter or longer form, respectively, was used in the HuRef consensus sequence. Ultimately these variant loci are all determined heterozygous indels as indicated in Figure 4. (121 KB PDF) Click here for additional data file. Table S1 Detailed Specification of Libraries Used for Sequencing of the HuRef Donor (82 KB DOC) Click here for additional data file. Table S2 Four-Way Comparison of the Genome Assemblies Using WGSA, HuRef all Scaffolds, HuRef Chromosomes, and Human NCBI Version 36 Matches are maximal high identity local aligned segments with no indels. Runs are sequentially adjacent matches with no intervening matches from other genomic sequences with the possibility of indels. Clumps are the same as runs but allow small intervening matches/runs to be skipped over in addition to indels (small is a settable parameter). This allows for example small inversions not to interrupt a longer alignment. All subsequent analyses (i.e., variant detection and analysis) discussed in the manuscript were performed using HuRef scaffolds. N50 is the scaffold length such that 50% of all base pairs are contained in scaffolds of this length or larger. (64 KB DOC) Click here for additional data file. Table S3 Percentage Coverage of NCBI Chromosomes with HuRef Chromosomes Matches are maximal local identical alignments with no indels. Runs are monotonically increasing sets of matches with no intervening matches with indels allowed. The values in the table denote the percentage of the NCBI chromosome found in matches or runs counting alignments containing bases (i.e., no ambiguous or gaps, Ns.) (44 KB DOC) Click here for additional data file. Table S4 AluY Insertions That Differ between the HuRef Genome and the NCBI Human Reference Genome (205 KB XLS) Click here for additional data file. Table S5 Mapping of Fosmid Clones from Multiple Individuals to the Sites of the Longest ATAC-Derived Indels Fosmid clones were mapped to the sites of large insertions that are predicted to occur uniquely on HuRef (insertions) or NCBI (deletions) as described in the Materials and Methods. For each example of inserted DNA, the table lists the assembly source (HuRef scaffold or NCBI chromosome), the start and end coordinates for the inserted DNA, the span of the inserted DNA, and the predicted insertion point on the assembly that lacks the insertion. Fosmid clones that support the presence or absence of inserted DNA are termed (+) insert fosmids and (−) insert fosmids, respectively. For each (+) insert fosmid, one end of the fosmid clone was mapped within the unique insert DNA while the other was mapped to common flanking DNA. For each (−) insert fosmid, the mapped ends span the site of insertion, but the implied insert length suggests that insert DNA is absent from the clone. Fosmid clones that support the presence (+) or absence (−) of inserted DNA were derived from the following Coriell cell lines; A, NA18517; B, NA18507; C, NA18956; D, NA19240, E, NA18555; F, NA12878; G, NA18552; H, NA15510. (168 KB DOC) Click here for additional data file. Table S6 Homozygous Insertions Tested in 93 Coriell DNA Samples and the HuRef Donor DNA I/I provides the number of Coriell DNA samples that are homozygous for the inserted sequence, heterozygous (I/N), and homozygous for no insertion (N/N). The Hardy-Weinberg p-value is based on CEU individuals. (95 KB DOC) Click here for additional data file. Table S7 CNV Identified in the Donor DNA Overlapping Genes (59 KB XLS) Click here for additional data file. Table S8 FISH Performed Using Coriell Fosmid Clones and Coriell DNA Metaphase Spreads (24 KB XLS) Click here for additional data file. Poster S1 The Diploid Genome Sequence of J. Craig Venter This genome-wide view attempts to illustrate the wide spectrum of DNA variation in the diploid chromosome set of an individual human, J. Craig Venter. The genome sequence is displayed on a nucleotide scale of approximately 1Mb/15 mm. The background of the chromosome tracks shows an approximate correspondence of features from the chromosome cytogenetic map. The different data tracks are grouped into two major sections: a representation of a current set of transcription units and a set of summary plots for different variation features at sequence level. For each DNA strand (forward and reverse), each mapped gene is shown at genomic scale and is color-coded according to the presence of transcript isoforms (see Gene Variants panel on figure key). A total of 54,253 transcript isoforms were mapped. The genes are given a minimum length of 10 kb for display purposes at this level. The largest transcript isoform for all genes that are between 2.5 kb and 250 kb and have at least five exons are shown, in an additional pair of tracks, expanded to a resolution close to 100 kb /15 mm. Due to their high gene density, the resolution is smaller for Chromosomes 17, 19, and 22 at approximately 80 kb/15 mm. In these expanded tiers, exons are depicted as black boxes and introns are color coded according to a set of Gene Ontology categories (GO, http://www.geneontology.org), as shown in the corresponding panel on the figure key. Gene symbols appear close to the corresponding expanded transcript when space permits, Ensembl transcript identifiers are shown if there is no Hugo gene symbol associated with that transcript. In order to produce more compact labels the “ENST0+” prefix has been replaced by “ET.” Between the forward and the reverse strand annotations, two color gradients show the nucleotide exon coverage detected in 5-kb-long sliding windows for each strand. Below the reverse strand annotations track there is the copy number variation (CNV) track. Here, results from four different experimental platforms (Affymetrix, Illumina, Agilent, and Nimblegen) determine regions where a CNV gain or loss was detected, shown as green or red boxes respectively. Nonoverlapping haplotype blocks are distributed into nine tracks, using distinct colors to enhance visibility. The longest blocks at each given loci are drawn as yellow boxes with a red outline to highlight them from the rest. A summary of the variation features defined for all the haplotype blocks is shown as a gray-scale gradient. Alternating color gradient tracks display count densities for homozygous SNP, heterozygous SNP, multiple nucleotide (MNPs), insertion/deletion polymorphisms, and complex forms of variation. The last two gradients contain count densities for tandem repeats and transposon repeats respectively. All these color gradients were produced using a 5-kb sliding window. The figure was generated with “gff2ps” (http://genome.imim.es/software/gfftools/GFF2PS.html), a genome annotation tool that converts General Feature Formatted records (http://www.sanger.ac.uk/Software/formats/ GFF/) to a PostScript output [J F Abril, R Guigó (2000) gff2ps: Visualizing genomic annotations. Bioinformatics 16: 743–744]. To navigate the interactive poster, go to http://journals.plos.org/plosbiology/suppinfo/pbio.0050254/sd001.php. (88 MB PDF) Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank) accession number for sequences discussed in the paper are: AADD00000000 (WGSA) and ABBA01000000 (the consensus sequences of both HuRef scaffolds and chromosomes).
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              The complete genome of an individual by massively parallel DNA sequencing.

              The association of genetic variation with disease and drug response, and improvements in nucleic acid technologies, have given great optimism for the impact of 'genomic medicine'. However, the formidable size of the diploid human genome, approximately 6 gigabases, has prevented the routine application of sequencing methods to deciphering complete individual human genomes. To realize the full potential of genomics for human health, this limitation must be overcome. Here we report the DNA sequence of a diploid genome of a single individual, James D. Watson, sequenced to 7.4-fold redundancy in two months using massively parallel sequencing in picolitre-size reaction vessels. This sequence was completed in two months at approximately one-hundredth of the cost of traditional capillary electrophoresis methods. Comparison of the sequence to the reference genome led to the identification of 3.3 million single nucleotide polymorphisms, of which 10,654 cause amino-acid substitution within the coding sequence. In addition, we accurately identified small-scale (2-40,000 base pair (bp)) insertion and deletion polymorphism as well as copy number variation resulting in the large-scale gain and loss of chromosomal segments ranging from 26,000 to 1.5 million base pairs. Overall, these results agree well with recent results of sequencing of a single individual by traditional methods. However, in addition to being faster and significantly less expensive, this sequencing technology avoids the arbitrary loss of genomic sequences inherent in random shotgun sequencing by bacterial cloning because it amplifies DNA in a cell-free system. As a result, we further demonstrate the acquisition of novel human sequence, including novel genes not previously identified by traditional genomic sequencing. This is the first genome sequenced by next-generation technologies. Therefore it is a pilot for the future challenges of 'personalized genome sequencing'.
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                Author and article information

                Affiliations
                [1 ]Department of Microbiology and Immunology, UNC Chapel Hill, NC, USA
                [2 ]Independent Researcher, San Francisco, CA, USA
                [3 ]School of Information and Library Science, UNC Chapel Hill, NC, USA
                [4 ]Department of Biomedical Engineering, UNC Chapel Hill, NC, USA
                [5 ]Department of Computer Science, UNC Chapel Hill, NC, USA
                Contributors
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2009
                19 August 2009
                : 10
                : 254
                2748085
                1471-2105-10-254
                19691849
                10.1186/1471-2105-10-254
                Copyright © 2009 Maier et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Methodology Article

                Bioinformatics & Computational biology

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