text:
- Research
- Open Access
- Published:
Comprehensive comparison of three commercial human whole-exome capture platforms
Genome Biology volume 12, Article number: R95 (2011)
21k Accesses
122 Citations
29 Altmetric
Exome sequencing, which allows the global analysis of protein coding sequences in the human genome, has become an effective and affordable approach to detecting causative genetic mutations in diseases. Currently, there are several commercial human exome capture platforms; however, the relative performances of these have not been characterized sufficiently to know which is best for a particular study.
We comprehensively compared three platforms: NimbleGen's Sequence Capture Array and SeqCap EZ, and Agilent's SureSelect. We assessed their performance in a variety of ways, including number of genes covered and capture efficacy. Differences that may impact on the choice of platform were that Agilent SureSelect covered approximately 1,100 more genes, while NimbleGen provided better flanking sequence capture. Although all three platforms achieved similar capture specificity of targeted regions, the NimbleGen platforms showed better uniformity of coverage and greater genotype sensitivity at 30- to 100-fold sequencing depth. All three platforms showed similar power in exome SNP calling, including medically relevant SNPs. Compared with genotyping and whole-genome sequencing data, the three platforms achieved a similar accuracy of genotype assignment and SNP detection. Importantly, all three platforms showed similar levels of reproducibility, GC bias and reference allele bias.
We demonstrate key differences between the three platforms, particularly advantages of solutions over array capture and the importance of a large gene target set.
Identifying genetic alterations underlying both rare and common diseases, and also other phenotypic variation, is of particular biological and medical relevance. Even after a decade's effort by the genetics research community since the completion of the first human genome sequences [1, 2], most genetic mutations underlying human diseases remain undiscovered. For example, the causative mutations for more than half of human rare diseases [3], the genetic architecture of most common diseases [4, 5] and the roles of somatic mutations in most cancers [6] have yet to be characterized. Whole genome re-sequencing can potentially identify these uncharacterized mutations, and in the past few years great strides have been made in this regard with massively parallel DNA sequencing technologies that can be applied to the whole genome [7–10]. However, the cost of these technologies remains too high for them to be used as a standard method. Recent integration of targeted exome capture with massively parallel sequencing to selectively re-sequence the best-understood functional parts of the human genome - comprising less than 2% of protein-coding sequences - provides an effective and affordable alternative to identify some of these causative genetic changes.
Several platforms for human exome capture for massively parallel sequencing have been developed and marketed to date [11–14]. In principle, these platforms fall into three classes: DNA-chip-based capture [11, 12], DNA-probe-based solution hybridization [14], and RNA-probe-based solution hybridization [13]. These platforms have resulted in great success in pioneering studies hunting for variants causing rare human diseases [11, 15–21], and have also been adopted in efforts towards deciphering human common disease and cancer genomes. Yet questions remain about which of these platforms is best for a given application. For example, how many human genes are targeted by each approach and how even is their coverage? How do capture efficacy, technological reproducibility and biases among the different platforms compare? How much input DNA is required and how convenient is each experimentally? How does the cost-effectiveness compare? And what is the power and accuracy of SNP calling, especially for medically important rare SNPs? Up till now, publicly accessible explorations of methodology have been limited to proof-of-concept studies [11, 13, 14, 22], reviews [23, 24], or comparisons carried out on only a subset of genes rather than at the whole-genome level [25].
To provide the community with a more solid means to determine the best platform for their experimental needs, we have performed a comprehensive comparison of three commercial human exome capture platforms: NimbleGen's Sequence Capture Array (Human Exome 2.1 M Array, Roche-NimbleGen), NimbleGen's SeqCap EZ (v1.0, Roche-NimbleGen), and Agilent's SureSelect (Human All Exon Kits, Agilent). Each of the three platforms represents one of the classes of exome capture technology currently available. To assess performance with regard to key parameters, including reproducibility, we conducted deep exome capture sequencing for each platform with two technical duplicates (>30× and >60× coverage) using DNA derived from a cell line from a previously sequenced Asian individual [26]. Other key performance parameters characterized here include the genes targeted, the efficacy of exome capture (including specificity, uniformity and sensitivity), technological biases, and the power and accuracy of exome capture data for subsequent SNP calling. Our findings provide comprehensive insights into the performance of these platforms that will be informative for scientists who use them in searching for human disease genes.
Human exome capture with the three platforms
We chose platforms that allowed a comparison of the three different methods currently in use for exome capture. The platforms are based on a chip-hybrid method (NimbleGen Sequence Capture Array) or a solution-hybridization method (NimbleGen SeqCap EZ) with a common set of DNA probes, and a solution hybridization method with RNA probes (Agilent SureSelect). The test DNA sample was from a cell line derived from the individual used in the YanHuang whole-genome sequencing analysis [26], allowing comparison with the existing high-coverage genome sequence.
We sought to comprehensively compare the performance of the three exome capture platforms using the best protocols and experimental design for each. We therefore optimized the standard library construction protocols for all three platforms (see Materials and methods): we minimized the input DNA to 10 μg, 3 μg, and 3 μg for Sequence Capture Array, SeqCap EZ and SureSelect, respectively, and set pre-capture PCR to four cycles and post-capture PCR to ten cycles for all three platforms. We included duplicates for each technique to ensure the reliability and assess the reproducibility of data production. We thus constructed a total of six libraries for the three platforms and used the HiSeq2000 to initially produce >30-fold coverage of unique mapped paired-end 90-bp reads (PE90) for each library. We further sequenced one of the two replicates for each platform to >60-fold coverage to obtain a combined coverage of approximately 100-fold for the purpose of discovering the impact of sequence depth on genotype calling for each of the platforms.
Targeted genes and coverage
One intrinsic feature of exome capture is its capacity for simultaneous interrogation of multiple targets depending directly on the genes targeted by the capture probes. We first compared the targeted genes and their coverage among the three platforms. As the two platforms (array and EZ) developed by NimbleGen shared a common set of targets, we only needed to compare the Agilent and one NimbleGen platform. We annotated protein-coding genes using a merged dataset of 21,326 genes from the CCDS database (release of 27 March 2009), refGen (release of 21 April 2009) and EnsemblGen databases (release 54), and microRNA genes using 719 genes from the human microRNA database (version 13.0). We also included the 200-bp most-flanking regions from both ends of the targeted sequences: typically, 200-bp flanking regions are co-captured with capture libraries constructed from 200- to 250-bp fragments.
The two target sets were 34.1 Mb (NimbleGen) and 37.6 Mb (Agilent) in size, and shared 30 Mb of targets in common, leaving 4.1 Mb specific to NimbleGen and 7.6 Mb specific to Agilent (Table S1 in Additional file 1). Correspondingly, although both target sets contain similar percentages of functional elements (exomic, >71%; intronic, >24%; and others, <5%), Agilent covered approximately 1,000 more protein-coding genes and approximately 100 more microRNA genes (17,199 protein coding genes, 80.6% of the database total; 658 microRNA genes, 91.4%) than NimbleGen (16,188 protein-coding genes, 75.9%; 550 microRNA genes, 76.5%) (Table S2 in Additional file 1). Of those protein-coding genes, 15,883 overlapped between NimbleGen and Agilent, while 305 were unique to NimbleGen and 1,316 were unique to Agilent. Further analyses showed no over-representation of any class of annotated disease genes in the NimbleGen- or Agilent-specific genes (Table S3 in Additional file 1). In addition, both included roughly 1.6 transcripts per gene, a value consistent with the average number of transcripts per gene in the RefSeq database. The results indicated that the majority of known human genes and their splice alternatives were well accounted for in both capture probe designs.
We assessed the coverage of the protein-coding sequences (CDs) by the two platforms, and again, Agilent-targeted regions showed much better coverage (72.0% of targeted genes with >95% CDs, and 78.5% with >90% CDs) than NimbleGen's (46.1% of targeted genes with >95% CDs, and 61.5% with >90% CDs) (Figure S1 in Additional file 2). However, when including the flanking regions, the coverage was much more improved for NimbleGen (74.2% targeted genes with >95% CDs and 76.0% with >90% CDs) than for Agilent (82.0% targeted genes with >95% CDs and 83.0% with >90% CDs) (Figure S1 in Additional file 2). This reduced the gap in CD coverage rate (from >17% to <8%) between the two analysis sets and indicated a more important role of flanking region capture for NimbleGen.
To obtain more detailed information about the target coverage of these two systems, we looked specifically at their ability to interrogate human disease genes using four known data sets (see below). Of the 5,231 unique genes collected from the Online Mendelian Inheritance in Man database (OMIM; release of 10 March 2011), Human Gene Mutation Database (HGMD; Professional 2009.2), and Genome-Wide Association Study (GWAS; release of 3 March 2011) and Cancer Genome Project (CGP; release of 1 December 2010) databases, Agilent targeted 4,871 with 86% of genes having >95% of CDs covered, in comparison with NimbleGen's 4,642 genes with 83% of genes and >95% of CDs covered (Figure S2 in Additional file 2). Thus, for the current pool of disease genes, both could interrogate most known genes, especially those linked to rare diseases, for which 85% of known causative mutations occur in CDs. This makes both capture methods especially attractive for rare disease gene identification and analysis.
Exome capture specificity
To assess the extent of exome enrichment, we compared the capture specificity of the three platforms, which was defined as the proportion of reads mapping to target regions. For the two replicates of each platform, we obtained a total of 26 to 80 million filtered reads (2.2 to 7.2 Gb; Table 1), roughly corresponding to >30- and >60-fold coverage of the targeted regions. We mapped these reads to the human genome (hg18) using the strategy described in the Materials and methods. Although the overall proportion of filtered reads that could be mapped (78.8 to 86.4%) or uniquely mapped (69.2 to 82.8%) to the human genome differed between the six replicates, the proportions of reads mapped uniquely to targeted regions were more comparable (54.2 to 58.1%) among the three platforms (Table 1). We also found the percentages of uniquely mapping reads were further improved (by up to 12%) for the two NimbleGen platforms by the inclusion of 200-bp flanking regions in the analyses (for the Agilent platform, this was only 2%). Thus, the final percentage of usable reads was 66.6% for the two NimbleGen platforms but was <60% for the Agilent platform. These results indicated that there is a general comparability of capture specificity for targeted regions among the three platforms if the mapping method does not include the flanking region sequences. However, under mapping procedures where researchers do include this information, the NimbleGen platforms perform better.
Uniformity of coverage
The uniformity of sequence depth over targeted regions determines the genotype sensitivity at any given sequence depth in exome capture. The more uniform the sequencing depth on the targeted region is for a platform, the lower the depth of sequencing that is required to obtain a desired genotype sensitivity. To assess this important quality metric, we selected and analyzed a similar number of reads (approximately 25 million filtered reads, on average approximately 30-fold coverage) from each of the six replicates (Table 2). We found that although all three platforms showed high coverage of their own targeted regions at low sequencing depth (98 to 99% with >1×), the Agilent platform showed more bias towards very low and very high coverage (21% with <10×, 20% with >50×) than the two NimbleGen platforms (<15% with <10×, 7% with >50×). As a result, the two NimbleGen platforms had 10 to 15% more targeted regions (70 to 74%) within 10× to 50× coverage than the Agilent platform (59%). This observation was further supported when we looked at the normalized single base sequencing depth distribution (Figure 1). The curve of the two NimbleGen platforms showed less skew to low and high coverage depths, and more evenness around the mean coverage (approximately 30×), than that of the Agilent platform; that is, the NimbleGen Array showed the best evenness. In addition, the two NimbleGen platforms also showed better uniformity of coverage in flanking regions (Table 2), which is consistent with their better efficiency of capture seen when including the flanking region sequences (Figure S3 in Additional file 2). Thus, the two NimbleGen platforms had a better overall uniformity of sequencing depth than Agilent, which would be expected to impact the relative genotype sensitivity when considering all targets.
Genotype sensitivity
Although the coverage of >99% of each targeted region of more than one-fold using all data sets an upper boundary for exome capture sensitivity for each replicate, only a proportion of these sites gained high-quality genotype assignments. To characterize this issue, we compared the genotype sensitivity in the 30× data sets (Figure 2a) using the criterion of >10-fold coverage and Phred-like quality >30. In these analyses, all three platforms showed very high genotype sensitivity (>77%); but, in comparison, the two NimbleGen platforms showed 6 to 8% higher (>83%) genotype sensitivity than the Agilent platform (approximately 77%), which is consistent with their better uniformity in coverage depth.
To obtain a more comprehensive insight, we further analyzed genotype sensitivity at other sequencing depths (Figure 2b) by randomly sampling from the combined sequencing data of the two replicates for each platform. Overall, the genotype sensitivity improved for all three platforms in a similar way as sequencing depth increased, and reached as high as >92% at approximately 100-fold coverage. The genotype sensitivity of the two NimbleGen platforms was often higher than the Agilent platform at a given sequencing depth. For example, genotype sensitivity was between 72% and 91% for the NimbleGen platforms at the usual sequencing depth of 20- to 50-fold, while it was 64 to 85% for the Agilent platform. Of interest, the curves of the two NimbleGen platforms nearly overlapped when sequence coverage depth was >30-fold. This indicates that these two platforms, which share a common set of DNA capture probes, have good inter-comparability.
We also analyzed genotype sensitivity at flanking regions; better NimbleGen results further emphasized the importance of the flanking regions for NimbleGen. From the above, we conclude that all three platforms had high genotype calling sensitivity at >30-fold coverage (>77%), with NimbleGen platforms showing slightly better performance.
Technical reproducibility reflects the consistency of performance of each exome capture platform. Using the replicates for each of the three exome capture platforms, we determined the level of reproducibility within each platform. In considering inter-platform comparability as well, our evaluation focused on the set of targets that were shared between all three platforms (totaling 182,259 consensus coding sequences (CCDSs) covering 25,392,537 bp). This accounted for 70.1% and 66.1% of sensitivity in the NimbleGen and Agilent targeted regions, respectively. Using the approximately 30× data set, we analyzed the correlation of both coverage rate and mean depth on the CCDSs between any two of the six replicates (Figure 3). Each platform showed high intra-platform reproducibility (correlation coefficient at >0.65 for coverage rate and >0.90 for depth). The lower correlation coefficient for coverage rate (0.65 to 0.78) than for mean depth (0.90 to 0.96) was not surprising since the two correlations reflect different aspects of the data - that is, the quantitative sequencing depth and qualitative sequence coverage. For the inter-platform comparison, the two NimbleGen platforms showed higher correlation for both coverage rate and mean depth than the Agilent platform. This is consistent with the fact that the two platforms share a common set of DNA capture probes. These results together indicate generally high and comparable technical reproducibility of the three methods.
GC bias and reference allele bias
Base composition has been shown to have a systematic effect on capture performance [13]. To explore this effect, we plotted mean sequencing depth against GC content. All three platforms showed biases against extremely low GC content (<20%) and high GC content (>75%), and the best coverage for GC content of 40 to 60% (Figure S4 in Additional file 2). However, we also observed a better coverage for the NimbleGen array platform, which had a better coverage of low GC content sequences without reduced coverage of the best-covered GC content. Thus, extreme GC content still poses a challenge for exome capture, but the chip-hybridization method (NimbleGen array platform) would likely be a better choice for targeted capture of genomic regions with lower GC content.
The allelic status of the probe sequences could also influence allelic capture efficiency at heterozygous sites, especially in situations where there are a large number of novel alleles being interrogated by exome capture. This occurs because the probes match the reference sequence and might capture perfectly matching library fragments better. To explore the impact of allelic status on the different platforms, we compared the ratio of reference allele depth to total depth for heterozygous sites in each exome capture with that in YanHuang whole-genome shotgun sequencing (WGSS). All three platforms showed consistent and significant biases towards the reference allele in capture (Figure S5 in Additional file 2), whereas WGSS did not have this bias. These results emphasize the need to account for the effect of reference allele bias in exome sequencing of tumors, in which acquired somatic mutations at any frequency may occur.
Non-covered sequences
Even at 100-fold sequencing depth, a small proportion of the target region was still not covered by each platform. To gain insight into this issue, we analyzed the base composition of these missed sequences. In total, 97,654 to 190,318 sequences (0.29 to 0.56% of two targeted regions) were not covered at all by the combined full sets of data for each platform. Of these sequences, 19,803 (10 to 20% of the non-covered sequences) overlapped in all three platforms, and 71,257 (33% and 70% of the non-covered sequences) overlapped between the two NimbleGen platforms. The GC content was >72% for Agilent, >80% for NimbleGen Array, >79% for NimbleGen EZ, and 76% for all shared sequences. Thus, at very high sequencing depth (approximately 100×), the non-covered sequences for all three platforms were biased towards extremely high GC content.
SNP detection
Given that exome capture is used primarily to identify genetic variants, we compared the SNP detection power among the three platforms. To do so, we called SNPs in the targeted regions together with 200-bp flanking sequence at high quality genotype-assigned sites in each of the approximately 30× data sets, and annotated them using the combined gene set used in the target annotation. Each platform detected roughly 25,000 to 40,000 SNPs, of which the largest group was from intronic regions, followed by synonymous SNPs and then non-synonymous SNPs, and finally by other categories (Table S4 in Additional file 1). The over-representation of intronic SNPs was more marked for the two NimbleGen platforms, where it provided over 10,000 more SNPs (35,000 to 40,000 in all) than the Agilent platform (25,000). Given the use of the same DNA and the similar proportion of intronic regions between the NimbleGen and Agilent platforms, this seems to be largely associated with the increased efficiency of capture by the NimbleGen platforms, especially in the flanking sequences. However, for synonymous and non-synonymous SNPs, which together represent the most functionally important groups, the Agilent and NimbleGen data showed substantial overlap and nearly similar levels of SNPs per gene to whole genome re-sequencing of the same individual. Thus, the three platforms could interrogate a similar high level of SNPs within protein-coding sequences in their targeted genes, which harbor changes that are most likely to have a functional impact.
Accuracy of genotype and SNP calling
To assess their accuracy, we compared the genotypes and SNPs from each replicate (30× data) of the three platforms with those from Illumina 1 M beadchip genotyping and WGSS (about 36×) from the YanHuang project [26]. For better data comparability, we also derived genotypes for the WGSS using the same software and criteria as for the exome capture (see Materials and methods).
In comparison with the Illumina 1 M beadchip genotyping, which includes 1,040,000 successfully typed sites, each replicate showed approximately 39,000 to approximately 51,000 overlapping sites depending on the platform, and showed an overall genotype concordance of >99.81% for these sites (Table 3). In addition, each platform also achieved a similar high concordance rate with those variant sites found by chip genotyping, with >99.51% for all the SNP sites, and >99.56% for non-reference homozygous sites, and of particular note, even >99.48% for heterozygous sites, the genotypes of which are more difficult to assign than homozygous sites (Table 3). Relatively, the concordance of chip genotyping to the variant sites in each exome capture was also high, with >99.81% for all the SNP sites, and >99.88% for non-reference homozygous sites, and >99.71% for heterozygous sites (Table 3). These comparisons give a maximum estimate of both the false negative rate and false positive rate of <0.52% for the three exome captures.
In contrast, the two NimbleGen and Agilent datasets overlapped at 48,000,000 sites (with 83.8% sensitivity in targets) and 34,500,000 sites (with 76.2% sensitivity in targets) with WGSS genotypes, respectively. The substantially higher overlap of NimbleGen was attributed to its greater intronic content. This time, each exome capture platform showed a concordance of >99.999% for all overlapping sites, but >99.20% for all SNP sites, >99.92% for the homozygous non-reference sites and >97.90% for the heterozygous sites found in WGSS (Table 3). In comparison, the relative concordance of WGSS to the variant sites called in each exome capture was >97.97% for all SNP sites, >99.75% for the homozygous non-reference sites, and in particular was reduced to >96.65% for the heterozygous sites (Table 3), which is still acceptable. Note that for the heterozygous sites, compared to NimbleGen, Agilent showed approximately 1% reduction in concordance. In these analyses, cell-line DNA (approximately 40 generations) derived from lymphoblasts was sequenced using a read length of 90 bp, while for WGSS reads of 36 bp in length were generated from whole blood DNA. Thus, cell-line mutations, and errors due to increased sequencing length (errors accumulate with sequencing length) in the study may account for part of the decrease in concordance. Based on these results, the general false positive and false negative rate of each exome capture platform for SNP detection was <3.4% and <1.0%, respectively.
Taken together, these results indicate that although slight differences could be observed, accuracy was both high and comparable among the three platforms.
Detection of medically interesting rare mutations
To further explore the power of the three exome capture platforms at identifying disease-causing rare mutations, we modeled the performance of each with the SNP set present in HGMD (Professional 2009.2) but absent from the 1000 Genomes Project database (BGI in-house data) (Table 4). Of the 39,906 mutations representing 1,931 diseases genes, both Agilent and NimbleGen targeted >95.8% sites, and showed >93.4% sites with at least 1× coverage and genotype sensitivity of >79% sites (>10× coverage and >Q30) at 30× sequencing depth. But in comparison, Agilent targeted more sites (98.5% compared to 95.8%), and correspondingly showed approximately 1.5% more covered sites (>1× coverage; 95.1% compared to 93.4%) than NimbleGen. In contrast, NimbleGen (the best performance was with NimbleGen Array Capture) showed 1.4% more genotype sensitivity (80.4% compared to 79%), and 3.6% less low quality coverage sites or uncovered sites (15.2% compared to 18.8%) than Agilent. The number of known potentially disease-causing SNPs detected ranged from 14 to 19 (Table 3). These observations are consistent with the larger targeted gene set of Agilent, and the higher capture efficiency of NimbleGen. Thus, the analyses demonstrated the very high power of the three exome capture platforms for identifying medically interesting rare mutations.
Performance on common targeted regions
Hitherto, most of the comparisons have been based directly on the current versions of the three platforms, which may not reflect only the intrinsic differences in performance among the three methods, but also the differences in content. To address this issue, we compared key performance parameters on the approximately 30 Mb of targeted regions in common (83.3 Mb with flanking sequences; Table S1 in Additional file 1). For specificity, we found that each replicate of the three platforms showed a somewhat reduced unique mapping rate of >44% filtered reads to the common targeted regions, and that the two NimbleGen platforms achieved, on average, a 12% higher unique mapping rate than the Agilent platform when including the 200-bp flanking sequences in the analyses (Table S5 Additional file 1). This result is consistent with the initial analyses above.
For uniformity and sensitivity, we also found that each platform showed very similar performance to that above, and that the two NimbleGen platforms performed better than the Agilent one (Table S5 in Additional file 1). For example, at a sequencing depth of 30×, NimbleGen had, on average, approximately 6% higher genotype sensitivity than Agilent (85% compared to 79%). For SNP detection, the detection level of each SNP category in each platform, including the greater detection of intronic SNPs (and thus the total SNP number) by the NimbleGen platforms (>13,000 more SNPs than Agilent, >35,000 compared to approximately 22,000), was also similar to the analyses above (Table S4 in Additional file 1); but in comparison, despite general inter-comparability, the two NimbleGen platforms detected approximately 400 more coding SNPs (12,400 compared to 12,000) in the common targeted regions while the Agilent platform detected approximately 900 more coding SNPs elsewhere (13,500 compared to 12,600) (Table S4 in Additional file 1). This difference could be explained by the fact that NimbleGen had a better capture efficiency while Agilent targeted an approximately 4-Mb larger region and correspondingly 1,000 more genes.
Finally, for the accuracy of SNP detection and genotypes, we also observed similar false positive and false negative rates for each platform at 30× coverage (Table S6 in Additional file 1) to that in whole dataset in comparison with the data from array genotyping and WGSS. Thus, we conclude that each platform was highly consistent in performance in the common targeted region analyses here compared with the analyses of the entire content above, which is not surprising given the high overlap (Agilent, 30 Mb/34.1 Mb ≈ 80%; NimbleGen, 30 Mb/40 Mb ≈ 88%).
In this study, we present a comprehensive comparison of three widely adopted human whole-exome capture platforms from two manufacturers. Since the three platforms, in principle, represent the three classes of exome capture technologies currently available, data on their performances likely also reflect the intrinsic power and limitations of exome capture as a technology.
For the current versions of the three platforms, the number of targeted genes and their CD coverage rate are important considerations for human genetic studies. Although most well-annotated human genes (>76%) were targeted by all three platforms, Agilent sought to target a larger set of genes (approximately 1,000 more protein-coding genes and approximately 100 more microRNA genes) and thus provided a better coverage of protein-coding sequences. In contrast, NimbleGen emphasized a more important role for flanking regions in capture probe design, and, in practice, had a greater number of genes with a high rate of CD coverage (Figure S6 in Additional file 2) due to better capture efficiency.
Exome capture efficiency is another important factor for comparison of capture platforms. In our hands, we observed that the two NimbleGen platforms showed better capture efficiency than the Agilent platform. Specifically, the two NimbleGen platforms showed approximately 10% higher capture specificity with the expanded targeted regions (66.6% compared to 58.3%), better uniformity of coverage, and 3 to 7% more sensitivity in genotype assignment (83 to 95% compared to 76 to 92% over the range 30× to 100× coverage of targeted regions). Thus, a lower sequencing depth was required for NimbleGen platforms for a given genotype sensitivity on targeted regions, which can impact experimental cost.
The ability to identify SNPs in protein-coding sequences, especially those medically interesting rare mutations, which ultimately measures the power of exome sequencing, was another important consideration. Despite general inter-comparability (12,500 to 13,500 SNPs), we found that, at the same sequencing depth (30×), NimbleGen detected a more complete set of SNPs (approximately 400 more SNPs) than Agilent for the common targeted coding sequences due to better exome capture efficiency, but the Agilent platform could detect more SNPs (approximately 900 SNPs) in total number due to its larger number of targeted genes. Similarly, for identifying medically interesting rare mutations, we found in model analyses that all three platforms not only showed similar high power at 30× sequencing depth in interrogating known HGMD mutations filtered to remove 1000 Genomes Project variants present in the general population, but the small differences reflected the general features of each platform (Agilent could target 1.8% more, and cover 1.5% more mutation sites, but NimbleGen showed 1.4% more mutations with high quality genotype assignment).
Input DNA amount, the convenience of conducting experiments and the cost of reagents will also be important considerations. Especially, the amount of DNA required for each method itself will impact cost as well as the ease of carrying out experiments, and is a major consideration for precious biological samples with limited availability. In these senses, the two solution hybrid platforms, Agilent and NimbleGen EZ, showed great advantages over the chip hybridization platform. These two solution-based platforms require smaller amounts of input DNA (approximately 3 μg) and no specialized equipment. In addition, reagent costs for these two platforms are lower when more than ten samples are being studied, and could possibly be further reduced with the introduction of sample pooling prior to the capture possess.
For performance aspects, such as the accuracy of SNP detection, GC bias and reference allele bias, and reproducibility, we did not observe great differences among the three platforms.
Taken together, our results here demonstrate that although the three platforms showed general comparability of performance, the two solution hybrid platforms would be the leading choice for most studies, especially those using large numbers of samples. In comparing these two, the Agilent platform showed a larger set of targets, targeting a more comprehensive set of human protein-coding genes and providing more complete coverage of their CDs, while the NimbleGen platform had better capture efficiency and could provide a higher proportion of CDs with high quality genotype assignments (thus higher completeness of SNP detection), and required lower sequence coverage because of its greater evenness. Thus, a choice between the two platforms is surprisingly difficult: both are highly effective and the number of targeted genes, their CD coverage, genotype sensitivity and sequencing amount/cost required must be balanced. The larger number of genes targeted by Agilent provides an overall advantage in the versions used here, but it is important to point out that both NimbleGen and Agilent are making great progress in target design. For example, in the latest (July 2011) versions, both target sets have been expanded (NimbleGen EZv.20 to 44 Mb, Agilent to 50 Mb), and currently cover more than 90% of annotated human genes (Table S7 in Additional file 1).
We demonstrate here a systematic evaluation of the performance of the current versions of three human whole-exome capture platforms. The data reported here will make it easier for researchers to more carefully assess the type of exome capture technology that will work best for their experimental goals and costs, and allow them to improve their own experimental design to take advantage or reduce the limitations of the available platform types.
Materials and methods
Genomic DNA and kit preparation
Genomic DNA was extracted from a lymphoblastoid cell line of YanHuang [26] using proteinase K and phenol/chloroform [27] and further subjected to RNase treatment. DNA sample quality and quantity were initially characterized by gel electrophoresis and nano-drop measurement, and further quantified using the Quant-iT dsDNA HS Assay kit (0.2 to 100 ng; Invitrogen, Q32854, Carlsbad, CA, USA). NimbleGen Sequence Capture Array (Human Exome 2.1 M Array) and SeqCap EZ (v1.0) kits, and Agilent SureSelect kits (Human All Exon Kits) were purchased from their respective manufacturer.
Exome capture library preparation
Prior to library construction, we optimized the manufacturers' standard protocols for our sequencing pipeline. Major parameters for optimization included input DNA quantity, fragmentation size, number of PCR cycles and indexing system. As a result, we adopted the following protocol.
Input DNA quantities of 10 μg, 3 μg and 3 μg were used for NimbleGen Sequence Capture Array, NimbleGen SeqCap EZ and Agilent SureSelect library preparation, respectively. The DNAs were fragmented to 200 to 250 bp in size, followed by end-repair, A-tailing and BGI paired-end index adapter ligation, following the Illumina DNA library preparation protocol described elsewhere [28]. Four cycles of pre-capture amplification were then conducted with Platinum Pfx DNA polymerase (Invitrogen) under the PCR conditions: 2 minutes at 94°C; four cycles of 10 s at 94°C, 30 s at 62°C, and 30 s at 72°C; then 300 s at 72°C. PCR products were further analyzed using a Bioanalyzer (Agilent, Santa Clara, CA, USA), and quantified by Qubit BR measurement (Invitrogen) before performing exome capture.
Exome capture was performed with the PCR products following each of the three manufacturers' standard protocols. Then, ten cycles of post-capture amplification were conducted using Platinum Pfx DNA polymerase (Invitrogen) under slightly modified PCR conditions compared to pre-capture amplification (2 minutes at 94°C; 10 cycles of 10 s at 94°C, 30 s at 58°C, and 30 s at 72°C; then 300 s at 72°C). Final libraries were validated by Bioanalyzer analysis (Agilent) and quantitative PCR, in preparation for massively parallel sequencing.
Sequencing and pre-mapping data process
Sequencing of each library was done on an Illumina HiSeq2000 as paired-end 90-bp reads (PE90) after indexing individual libraries and pooling them in pairs. Each library was initially sequenced to a depth providing an approximately 30-fold mapped coverage on targeted regions, and then one of the two replicates was further chosen from each of the three platforms to sequence to >60-fold coverage on targeted regions. Raw image files were processed by the Illumina pipeline (version 1.3.4) for base calling and to generate a raw read set. Adapter contamination and reads of low quality (more than four 'N' nucleotides) were identified and removed before mapping.
Mapping, genotype and SNP calling
SOAP (v2.21) [29] was used to align the reads to the NCBI human genome reference assembly (build 36.3) with parameters set to '-a -b -D -o -u -t -l 35 -n 4 -r 1 -2 -v 2 -s 40'. These settings provided the best mapping rate by giving highest priority to paired-end and then lower priority to unpaired single-end matches. SOAPsnp [30] (v1.03) was used to call consensus genotypes with the parameters '-i -d -o -r 0.00005 -e 0.0001 -t -s -2 -u -M -L -T', where '-T' used the targeted and flanking regions. As our sample is from a male, we added the parameter '-m' for the sex chromosomes. Then SNPs were extracted from the consensus genotype file, and those with sequencing depth <10, quality <30, copy number of a nearby region >2, or other SNPs within 5 bp were filtered out to get high-confidence calls.
Data accessibility
All data described here are being deposited to the NCBI Sequence Read Archive [SRA:035389].
- bp:
base pair
consensus coding sequences
coding sequence
- Gb:
billion base pairs
Human Gene Mutation Database
- Mb:
million base pairs
paired-end 90-bp read
single nucleotide polymorphism
whole-genome shotgun sequencing.
Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, et al: Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921. 10.1038/35057062.
Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, et al: The sequence of the human genome. Science. 2001, 291: 1304-1351. 10.1126/science.1058040.
Antonarakis SE, Beckmann JS: Mendelian disorders deserve more attention. Nat Rev Genet. 2006, 7: 277-282. 10.1038/nrg1826.
Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature. 2009, 461: 747-753. 10.1038/nature08494.
Altshuler D, Daly MJ, Lander ES: Genetic mapping in human disease. Science. 2008, 322: 881-888. 10.1126/science.1156409.
Stratton MR, Campbell PJ, Futreal PA: The cancer genome. Nature. 2009, 458: 719-724. 10.1038/nature07943.
Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437: 376-380.
Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR, Boutell JM, Bryant J, Carter RJ, Keira Cheetham R, Cox AJ, Ellis DJ, Flatbush MR, Gormley NA, Humphray SJ, Irving LJ, Karbelashvili MS, Kirk SM, Li H, Liu X, Maisinger KS, Murray LJ, Obradovic B, Ost T, Parkinson ML, Pratt MR, et al: Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008, 456: 53-59. 10.1038/nature07517.
McKernan KJ, Peckham HE, Costa GL, McLaughlin SF, Fu Y, Tsung EF, Clouser CR, Duncan C, Ichikawa JK, Lee CC, Zhang Z, Ranade SS, Dimalanta ET, Hyland FC, Sokolsky TD, Zhang L, Sheridan A, Fu H, Hendrickson CL, Li B, Kotler L, Stuart JR, Malek JA, Manning JM, Antipova AA, Perez DS, Moore MP, Hayashibara KC, Lyons MR, Beaudoin RE, et al: Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res. 2009, 19: 1527-1541. 10.1101/gr.091868.109.
Drmanac R, Sparks AB, Callow MJ, Halpern AL, Burns NL, Kermani BG, Carnevali P, Nazarenko I, Nilsen GB, Yeung G, Dahl F, Fernandez A, Staker B, Pant KP, Baccash J, Borcherding AP, Brownley A, Cedeno R, Chen L, Chernikoff D, Cheung A, Chirita R, Curson B, Ebert JC, Hacker CR, Hartlage R, Hauser B, Huang S, Jiang Y, Karpinchyk V, et al: Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science. 2010, 327: 78-81. 10.1126/science.1181498.
Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, Bamshad M, Nickerson DA, Shendure J: Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009, 461: 272-276. 10.1038/nature08250.
Choi M, Scholl UI, Ji W, Liu T, Tikhonova IR, Zumbo P, Nayir A, Bakkaloglu A, Ozen S, Sanjad S, Nelson-Williams C, Farhi A, Mane S, Lifton RP: Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci USA. 2009, 106: 19096-19101. 10.1073/pnas.0910672106.
Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM, Brockman W, Fennell T, Giannoukos G, Fisher S, Russ C, Gabriel S, Jaffe DB, Lander ES, Nusbaum C: Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol. 2009, 27: 182-189. 10.1038/nbt.1523.
Bainbridge MN, Wang M, Burgess DL, Kovar C, Rodesch MJ, D'Ascenzo M, Kitzman J, Wu YQ, Newsham I, Richmond TA, Jeddeloh JA, Muzny D, Albert TJ, Gibbs RA: Whole exome capture in solution with 3 Gbp of data. Genome Biol. 2010, 11: R62-10.1186/gb-2010-11-6-r62.
Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA, Shendure J, Bamshad MJ: Exome sequencing identifies the cause of a mendelian disorder. Nat Genet. 2010, 42: 30-35. 10.1038/ng.499.
Bilguvar K, Ozturk AK, Louvi A, Kwan KY, Choi M, Tatli B, Yalnizoglu D, Tuysuz B, Caglayan AO, Gokben S, Kaymakcalan H, Barak T, Bakircioglu M, Yasuno K, Ho W, Sanders S, Zhu Y, Yilmaz S, Dincer A, Johnson MH, Bronen RA, Kocer N, Per H, Mane S, Pamir MN, Yalcinkaya C, Kumandas S, Topcu M, Ozmen M, Sestan N, et al: Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature. 2010, 467: 207-210. 10.1038/nature09327.
Walsh T, Shahin H, Elkan-Miller T, Lee MK, Thornton AM, Roeb W, Abu Rayyan A, Loulus S, Avraham KB, King MC, Kanaan M: Whole exome sequencing and homozygosity mapping identify mutation in the cell polarity protein GPSM2 as the cause of nonsyndromic hearing loss DFNB82. Am J Hum Genet. 2010, 87: 90-94. 10.1016/j.ajhg.2010.05.010.
Haack TB, Danhauser K, Haberberger B, Hoser J, Strecker V, Boehm D, Uziel G, Lamantea E, Invernizzi F, Poulton J, Rolinski B, Iuso A, Biskup S, Schmidt T, Mewes HW, Wittig I, Meitinger T, Zeviani M, Prokisch H: Exome sequencing identifies ACAD9 mutations as a cause of complex I deficiency. Nat Genet. 2010, 42: 1131-1134. 10.1038/ng.706.
Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, Gildersleeve HI, Beck AE, Tabor HK, Cooper GM, Mefford HC, Lee C, Turner EH, Smith JD, Rieder MJ, Yoshiura K, Matsumoto N, Ohta T, Niikawa N, Nickerson DA, Bamshad MJ, Shendure J: Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet. 2010, 42: 790-793. 10.1038/ng.646.
Musunuru K, Pirruccello JP, Do R, Peloso GM, Guiducci C, Sougnez C, Garimella KV, Fisher S, Abreu J, Barry AJ, Fennell T, Banks E, Ambrogio L, Cibulskis K, Kernytsky A, Gonzalez E, Rudzicz N, Engert JC, DePristo MA, Daly MJ, Cohen JC, Hobbs HH, Altshuler D, Schonfeld G, Gabriel SB, Yue P, Kathiresan S: Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N Engl J Med. 2010, 363: 2220-2227. 10.1056/NEJMoa1002926.
Wang JL, Yang X, Xia K, Hu ZM, Weng L, Jin X, Jiang H, Zhang P, Shen L, Guo JF, Li N, Li YR, Lei LF, Zhou J, Du J, Zhou YF, Pan Q, Wang J, Li RQ, Tang BS: TGM6 identified as a novel causative gene of spinocerebellar ataxias using exome sequencing. Brain. 2010, 133: 3510-3518. 10.1093/brain/awq323.
Hodges E, Xuan Z, Balija V, Kramer M, Molla MN, Smith SW, Middle CM, Rodesch MJ, Albert TJ, Hannon GJ, McCombie WR: Genome-wide in situ exon capture for selective resequencing. Nat Genet. 2007, 39: 1522-1527. 10.1038/ng.2007.42.
Mamanova L, Coffey AJ, Scott CE, Kozarewa I, Turner EH, Kumar A, Howard E, Shendure J, Turner DJ: Target-enrichment strategies for next-generation sequencing. Nat Methods. 2010, 7: 111-118. 10.1038/nmeth.1419.
Turner EH, Ng SB, Nickerson DA, Shendure J: Methods for genomic partitioning. Annu Rev Genomics Hum Genet. 2009, 10: 263-284. 10.1146/annurev-genom-082908-150112.
Teer JK, Bonnycastle LL, Chines PS, Hansen NF, Aoyama N, Swift AJ, Abaan HO, Albert TJ, Margulies EH, Green ED, Collins FS, Mullikin JC, Biesecker LG: Systematic comparison of three genomic enrichment methods for massively parallel DNA sequencing. Genome Res. 2010, 20: 1420-1431. 10.1101/gr.106716.110.
Wang J, Wang W, Li R, Li Y, Tian G, Goodman L, Fan W, Zhang J, Li J, Guo Y, Feng B, Li H, Lu Y, Fang X, Liang H, Du Z, Li D, Zhao Y, Hu Y, Yang Z, Zheng H, Hellmann I, Inouye M, Pool J, Yi X, Zhao J, Duan J, Zhou Y, Qin J, Ma L, et al: The diploid genome sequence of an Asian individual. Nature. 2008, 456: 60-65. 10.1038/nature07484.
Blin N, Stafford DW: A general method for isolation of high molecular weight DNA from eukaryotes. Nucleic Acids Res. 1976, 3: 2303-2308.
Quail MA, Kozarewa I, Smith F, Scally A, Stephens PJ, Durbin R, Swerdlow H, Turner DJ: A large genome center's improvements to the Illumina sequencing system. Nat Methods. 2008, 5: 1005-1010. 10.1038/nmeth.1270.
Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J: SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009, 25: 1966-1967. 10.1093/bioinformatics/btp336.
Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K: SNP detection for massively parallel whole-genome resequencing. Genome Res. 2009, 19: 1124-1132. 10.1101/gr.088013.108.
We would like to acknowledge Scott Edmunds and Laurie Goodman for help with manuscript preparation; BGI-Shenzhen targeted enrichment team and BGI-Shenzhen genome sequencing platforms for assistance with exome capture, sequencing and data processing. This work was supported in part by grants from the National High Technology Research and Development Program of China (863 Program, 2006AA02A302 to HM-Y, 2009AA022707 to XQ-Z), and National Natural Science Foundation of China (30811130531 to HM-Y). Chris Tyler-Smith and Yali Xue were supported by The Wellcome Trust.
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
XZ and HY designed the study. YuX, JiaW and TJ analyzed all data. JiW, JuW, MW, XL, GT and HJ developed the optimized exome capture protocols, and conducted library preparation and sequencing. A designed the study, coordinated all work and wrote the manuscript. CTS designed the study and revised the manuscript. YaX revised the manuscript. All authors read and approved the final manuscript.
Asan, Yu Xu, Hui Jiang, Chris Tyler-Smith contributed equally to this work.
Electronic supplementary material
Authors' original submitted files for images
Below are the links to the authors' original submitted files for images.
Rights and permissions
About this article
Cite this article
Asan, Xu, Y., Jiang, H. et al. Comprehensive comparison of three commercial human whole-exome capture platforms. Genome Biol 12, R95 (2011). https://doi.org/10.1186/gb-2011-12-9-r95
DOI: https://doi.org/10.1186/gb-2011-12-9-r95
- Sequencing Depth
- Genotype Sensitivity
- Exome Capture
- Heterozygous Site
- Capture Specificity


text:
Applying for a personalised disabled parking space
Do you have a European Union (EU) parking card for people with disabilities? Do you want to park close to your home or workplace? If so, you can apply to the municipality for a personalised parking space. Only the vehicle with the registered vehicle registration number may park in that space.
The municipality stipulates a number of conditions for issuing a personalised disabled parking space:
- you have an EU parking card for people with disabilities that is valid for at least 6 months;
- you do not have a permanent parking space on the premises of your home;
- you are the driver of a car, a motorised tricycle/quadricycle or a vehicle for persons with disabilities;
- you cannot walk more than 100 metres on your own, even with aids, such as crutches or a walker;
- you must be examined by a medical examiner. Your municipality will refer you to a medical examiner. Is your disability chronic? Is your municipality already aware of it? If so, a medical examination is not required;
- you are registered in the Personal Records Database (Basisregistratie Personen – BRP) at the address for which you are requesting a personalised disabled parking space.
There are 2 types of personalised disabled parking spaces:
Personalised disabled parking space without time restriction
A personalised disabled parking space is a reserved parking space, for example at your home. A white road sign with a registration number is placed below the blue road sign with the letter P and a wheelchair symbol. Only the vehicle that has that registration number is allowed to park there.
Personalised disabled parking space with time restriction
A time-restricted personalised disabled parking space is a reserved parking space where you come at set times, for example your workplace. There is a second white sign under the sign displaying the registration number on the blue road sign with the letter P and a wheelchair symbol. This second white sign displays days and times. Only you may park in that space during those days and times. Anyone may park there outside of those days and times.
This is how it works for you
Questions about this topic?
Contact the Public Information Service

subset: commoncrawl


text:
Ubuntu's role in bug management for the whole free software stackFriday, June 27th, 2008
A distribution occupies a very specific niche in the free software ecosystem. Among other things, we need to accept some responsibility for ALL the software defects ("bugs") that users actually experience across the entire stack. Most users don't install their apps from upstream source tarballs, they install them from the packages provided by their distribution. So when they experience a bug, they don't know if it's a bug introduced by that distribution, or a bug in the underlying upstream code. They don't know, they don't care, and they shouldn't have to. More often than not they will report the issue to their distribution, and the way we respond to it is important, because it represents an opportunity to make the whole ecosystem more robust.
I had a lecturer who was very opposed to the use of the term "bugs". He said that the term "bug" was a cute-sification for "nasty biting insect", and similarly, software defects have potentially serious consequences, so we shouldn't treat them lightly. Bug work is serious work, and it's one of the most important forms of contribution to the digital commons that Ubuntu can make, so I'd like to salute the extraordinary efforts of the Ubuntu Quality Assurance Team and Bug Squad. Initiatives like five-a-day are already making a huge difference to our users. As Henrik Omma says, effective bug reporting requires a diligent and professional approach, and I've noticed a real improvement in our community. Hopefully, we can bring the benefits of that competence to the broader free software ecosystem.
Ubuntu gets as many bugs reported against it as OpenOffice, Mozilla, Gnome, and KDE combined.The vast majority of those bugs are issues that exist in upstream tarball releases, or in Debian. Our primary goals should be to ensure that fixes we produce, and information we generate in the QA process, make their way upstream where they will benefit the broadest cross-section of the community. Separately, we want to ensure that each Ubuntu release ships without major issues, regardless of where those issues originated. We are responsible for the user experience of every line of code, even though we don't produce every line of code.
In the month of April 2008, I found the following bug counts for large FLOSS projects:
With hindsight, April was possibly a bad choice, because it was an Ubuntu release month so there's usually a small spike in the number of bugs filed. It would be interesting to see the stats for other distributions, and projects, over a full year. But the general picture is clear – within our family of distributions, Ubuntu carries the brunt of the load w.r.t. bug tracking, triage and patch management – not only for our users, but for a broad cross-section of the open source stack.
When I delved into the data to see how we do with pushing bugs upstream, I found a somewhat mixed picture. In many cases, we do very well indeed. We have a very good relationship with GNOME, for example, with a very high percentage of bugs appropriately forwarded to the relevant upstream bug tracker. In other projects, it's harder to make a definitive statement. The percentage varies based on whether the Ubuntu team members have good relationships upstream, or whether there's a person acting as an ambassador from Ubuntu to upstream (this is a great way to make a difference if you care about a specific application in Ubuntu!) or whether upstream themselves have taken an interest.
We need to improve the tools that support these kinds of cross-project conversations. Launchpad does currently allow us to track the status of a bug in many different bug trackers, and there are quite a few distributions and upstreams that are now either using Launchpad directly or exchanging data efficiently. We'll keep working to improve the quality of exchange across the whole ecosystem, including those projects that don't use Launchpad themselves


text:
ATLANTA — Clemson got one last impressive drive from Tajh Boyd and company to record its first 11-win season since its 1981 national championship team.
And Tigers coach Dabo Swinney thinks this is only the beginning.
Chandler Catanzaro kicked a 37-yard field goal as time expired to give No. 14 Clemson a wild 25-24 win over No. 9 Louisiana State in the Chick-fil-A Bowl on Monday night.
Trailing 24-22, Clemson (11-2) took possession on its 20 with 1:39 remaining. Boyd passed to DeAndre Hopkins for 26 yards on a fourth-and-16 play during the decisive 10-play drive.
Catanzaro's kick set off a wild celebration on the field and in the stands. Some players collapsed on the field in apparent disbelief while most of Clemson's orange jerseys met in a midfield circle.
Clemson reached 11 wins for only the fourth time in school history and the first time since the 12-0 1981 team.
"You can't win 12 until you win 11," Swinney said. "You can't win a national championship until you learn how to win games like this.
"This was a landmark win."
Catanzaro had an extra point blocked in the second quarter, but he said that didn't cause him to doubt his chances to make the last-second field goal.
"I was ready when the opportunity came to me," Catanzaro said. "It was next-kick mentality. I was so thankful for the opportunity."
Catanzaro, a former walk-on from Greenville, S.C., made 16 of 17 field goals in the regular season.
"I didn't have any doubt," Swinney said of the junior kicker. "He's just a clutch player all the way."
Boyd completed 36 of 50 passes for 346 yards with two touchdowns and no interceptions. He set career highs for attempts and completions while winning the game MVP award.
"Tajh Boyd was phenomenal," LSU coach Les Miles said. "I did not expect the heroic, if you will, efforts that he had."
Hopkins, who had 13 catches for 191 yards and two touchdowns, also had receptions for 7 and 13 yards in the final drive. LSU safety Greg Reid was flagged for pass interference while defending Hopkins.
Jeremy Hill ran for 124 yards and two touchdowns for LSU (10-3), which carried a 24-13 lead into the final quarter.
After Hopkins' second TD catch, LSU got the ball with 2:43 remaining and threw three straight passes. Only one was complete in the three-and-out series that took only about 1 minute off the clock, leaving Clemson with enough time for its winning drive against LSU's exhausted defense.
Hill did not have a carry in the fourth quarter.
"We had to throw the football," Miles said.
"We were not running the football. They were in a position where they outnumbered us in the run."
LSU's three passes allowed Swinney to save his timeouts.
"I had three timeouts," Swinney said. "If he runs it three times, I can call timeout, timeout, timeout.
"I think (Miles) was trying to win the game. I think he felt confident in the plays. I know he knew he needed to get a first down, because if he gets a first down I have to start burning timeouts."
Boyd said having 1:39 to stage the final drive "is like having 10, 15 minutes for us."
"When I saw that clock and that we had three timeouts I said 'Let's get it.'"
LSU quarterback Zach Mettenberger was sacked six times and completed 14 of 23 passes for 120 yards. He also threw for a touchdown and an interception.
"It's a tough thing to figure our pass protection was as poor as it was," Miles said. "That's the piece that needs fixing and frankly we've got some young players here we'll address and improve and make some strides going forward."
LSU scored 10 points off Clemson's two lost fumbles, including one by Sammy Watkins on the second play of the game that set up Hill's first touchdown.
Hopkins scored on an 11-yard reception in the second quarter and a 12-yard grab in the fourth. LSU's Bennie Logan blocked Catanzaro's extra point attempt following Hopkins' first touchdown.
Clemson had a chance to tie it after Hopkins' second TD, but Boyd's pass for the 2-point conversion was incomplete.
Michael Ford had a 43-yard kickoff return for LSU to open the second half. On first down, Hill broke through the line for a 57-yard touchdown run. His 12th rushing touchdown broke Dalton Hilliard's LSU record for a freshman set in 1982.
Clemson lost Watkins to a right ankle injury on his early fumble. X-rays were negative.
Clemson already was without backup receiver Martavis Bryant, who was suspended for the game for failing to meet academic requirements.
Clemson's second costly fumble came midway through the third quarter. Andre Ellington ran for 8 yards but lost the ball when hit by defensive end Sam Montgomery. Reid recovered the fumble at the Clemson 29, setting up Drew Alleman's 20-yard field goal.
The injury to Watkins left the spotlight on Hopkins, Clemson's leading receiver. He had catches of 17 and 12 yards as Clemson pulled even with an 11-play drive capped by Boyd's 11-yard touchdown run.
After LSU regained the lead on Mettenberger's 6-yard touchdown pass to Jarvis Landry, Hopkins had a 31-yard catch to set up his 11-yard score late in the second quarter.
Hopkins' sliding grab gave him TD receptions in 10 straight games to set an Atlantic Coast Conference record. Virginia's Herman Moore had touchdown catches in nine straight games in 1990.
Talk to us
- You can tell us about news and ask us about our journalism by emailing email@example.com or by calling 425-339-3428.
- If you have an opinion you wish to share for publication, send a letter to the editor to email@example.com or by regular mail to The Daily Herald, Letters, P.O. Box 930, Everett, WA 98206.
- More contact information is here.


text:
When a late arrival thought he'd catch up on the buzz at a recent conference of CIOs, he logged into Twitter. What he found — or rather didn't find — amazed him.
"I couldn't find a single tweet about what was happening at the conference that morning — 300 CIOs in a room and not a single one using Twitter," recalls Paul Gillin, founder of Paul Gillin Communications, a social media consultancy in Framingham, Mass. Gillin, a former Computerworld editor in chief, was referring to a 2010 conference held by a vendor he didn't want to name.
Though he acknowledges that marketing is the dominant use of social media tools, the lack of Twitter activity by a group of CIOs "didn't make sense to me," Gillin says. "When a new technology comes into use, it is IT's responsibility to understand it."
"Were they healthcare CIOs? We're always five to 10 years behind," says Ed Marx, CIO at Texas Health Resources in Arlington, in response to Gillin's anecdote. Marx, who calls himself a "big-time" Twitter user, says, "it's unfortunate that CIOs who really should be out there leading and experimenting and innovating are not."
Marx says he started using Twitter two and a half years ago, and now regularly uses Facebook, LinkedIn and an internal social media tool.
Marx and others aside, IT in general appears ambivalent about social media. In a 2009 survey of over 1,400 CIOs by Robert Half Technology, more than half of the respondents said that their companies banned social media use by employees. Another 19% said their companies limited social media activity to business use only.
When the staffing firm updated its social media survey in May, only 31% of the CIOs polled said their companies ban social media outright.
A 2009 Manpower study (download PDF) based on a survey of 34,000 employers — the respondents weren't necessarily CIOs — found that only 24% percent of U.S. companies had formal policies about social media.
Outside looking in
"I don't see IT taking the function over," says Joseph Yanoska, vice president of technology at American Greetings Interactive in Cleveland. He thinks marketing should control the technology, since "it's ultimately a tool to help the relationship with customers."
But Gillin doesn't think that means IT should be less involved than it was when, for instance, companies began adopting ERP systems 15 years ago. "IT was very involved in that despite the fact that ERP was an accounting technology," he says. Social media, he adds, "is the future of how companies will operate, will engage with customers. IT should have an important role in it."
Yanoska does want a somewhat bigger part in his company's social media operation. He runs IT for the online unit of American Greetings, which operates AmericanGreetings.com, BlueMountain.com, eGreetings.com and greeting card sites for MSN and Yahoo. He says IT would like to be able to use the company's social networking tools more freely, so it could, for example, have an easier way to inform customers about new features or site maintenance.
Right now, designated non-IT gatekeepers post on the sites, and IT has to make its case when it wants to post something. Even though IT only makes such requests once every couple of weeks, "sometimes we get it, sometimes we don't," Yanoska says. So he is pushing to get IT more of a voice, and he would like more options for posting on Facebook, especially when maintenance upgrades are about to happen. "We're finding that's a really good place [to tell] customers who are really invested in the brand, 'We plan to do maintenance.' [That way] you don't get those posts, 'Hey what's going on?' because they already know," Yanoska says.
At Texas Health, Marx went the other direction and has ratcheted down his personal involvement. That's because he drove the company's adoption of the technology, using Twitter for collaboration and communications, starting Facebook fan pages, LinkedIn groups, a Texas Health Twitter feed, and microsites to connect with employees and patients. He says a microsite is crucial for getting employees in particular to adopt electronic health records, using it to encourage them to try social media tools and become more comfortable with the technology.
As more parts of the business got involved in social media, Texas Health created a 10-person social media steering committee. Members included the president of the hospital and the heads of marketing and communications, human resources, legal and compliance. Marx is also on the committee. Other groups within the company, primarily marketing, handle day-to-day social media operations now, which Marx says makes sense. He plays a strategic role, devising new ideas and figuring out new ways to leverage social media.
IT becoming more involved
IT may be stepping up its involvement as use of social media spreads. "Our reliance on IT support is heavier now" than it was two years ago, says Jesse Redniss, vice president of digital at NBC Universal/USA Network. Redniss's group not only helps market content and shows, but also runs a thriving casual gaming business, with its own profit-and-loss statement.
NBC Universal adopted a setup where IT operates infrastructure and tools used across the company, including a video content management system, data-mining tools and a single sign-on system for visitors to all of its Web properties. To handle department-specific IT projects, the company has a unit called Digital Products and Services (DPS).
Redniss says he used to think IT was too cumbersome to respond quickly enough for social media needs, but he now says "we didn't fully understand how much we needed them."
Over the past couple of years, as NBC Universal has developed broad corporate efforts involving Facebook, Twitter, Tumblr and other social media sites, data management and data mining have grown more important components. "We really do need to figure out the right kind of funnels to build to bring everything to them so they can store it, and we can sift through it," he says.
But his unit will still make its own technology decisions, too. Toward that end, Redniss recruited a DPS employee named Robin Fordham to join NBC Universal/USA Network 18 months ago. "I just jumped the fence," says Fordham, who is now director of technology for the USA television network. "Instead of USA Net being my client, I work for them directly."
Fordham has a small team of developers working for him. He says they figure out how to execute Redniss's ideas. For instance, when Redniss wanted to improve customer loyalty, Fordham's group took the lead in adopting Bunchball, which uses gaming to build loyalty. His unit also has done most of the work in adopting Echo's Streamserver commenting engine. Bunchball is gaining traction across NBC Universal's websites, and may soon become the province of the centralized IT group.
Leaping aboard
Social networking has roared to the top of many corporate to-do lists. In an eMarketer survey of 227 companies with more than 100 employees, 80% of the respondents said that their companies intended to use social media for marketing purposes in 2011, up from 58% in 2009.
Meanwhile, use of social networking is growing in other departments, thanks to new tools and new strategies.
Social media tools are spreading "across all departments," says Michael Fauscette, an analyst at research firm IDC. IDC does a twice-yearly survey measuring which departments manage social media strategies and tools. When the firm first launched the survey three and a half years ago, marketing dominated the results.
"[Marketing] was by far the most prevalent department," Fauscette noted. "Now it's pretty even distribution across all departments, including sales and customer service." Fauscette says social media is used internally for collaboration and for idea generation, and externally for marketing and crisis response.
When it comes to managing the strategy behind social media, however, IDC's survey results show that marketing still rules — it handles that responsibility at 48% of companies — and IT isn't involved at all. Corporate communications is second, cited by 29% of the respondents as one of the departments that handles social media strategy (multiple answers are permitted). Meanwhile, 26% of the respondents named product development, 23% said customer service, and 16% named sales.
Fauscette calls this shift a sign of the maturing of the social media market. He says that this means social tools will begin to become part of IT's purview. "We're starting to see IT more involved in what they should be: helping implement and find the right tools, produce the tools, and optimize the work."
He also notes that when social media moves out of marketing's control, "it puts you in a much better position from a business perspective to look at it in broader terms." That's what IT can come in and start to do, in other words.
IT's role: Tool-master
For now, though, IT shows up in the integration and implementation of the tools, a role that Fauscette says makes sense. When the technology moves beyond experimenting, or department-level expense charge-offs, "the CIO is in the room a lot of the time. Firms need a broader perspective," Fauscette says.
One CIO who's "in the room" is Craig Neeb, who is both CIO and vice president of multichannel marketing at International Speedway Corp., which operates racetracks across the country, from Daytona to Watkins Glen in New York to the Auto Club Speedway in California.
Neeb has been CIO at International Speedway in Daytona Beach, Fla., since 2000. He added his marketing role in 2008, about a year before social media became important to International Speedway's marketing strategy. The company now uses websites for each of its dozen tracks and runs mobile marketing and social media campaigns, primarily on Facebook and Twitter. It also has a customer contact center that makes and takes phone calls, and runs Web chats.
International Speedway is publicly traded, so having control over the marketing technology helps Neeb make sure the company is in compliance with regulations, such as those spelled out in the Sarbanes-Oxley Act, and guidelines promulgated by private industry groups, such as the Payment Card Industry Data Security Standards.
Unlike American Greetings, International Speedway will let pretty much any employee post on its websites and social media sites. But first they must go through what Neeb calls "a small little training program" that takes about an hour. Some of that open attitude is reflected in the company's management structure.
"We don't do anything in functional silos," Neeb says. "Our digital strategy isn't owned by a department. It's owned by consumer marketing and technology," with support from human resources, legal and compliance.
Social media's problems
Companies have found that social media does present challenges, many of which fall into IT's strategic purview. A report issued earlier this year by ISACA, an association for IT auditors and related professionals, listed the following risks that companies face when they get involved in social networking:
Increased likelihood of spreading viruses and malware.
Potential for brand hijacking.
Loss of control over corporate content.
Unrealistic customer expectations for 'Internet-speed' service.
Breaches of rules and regulations.
Neeb thinks that companies will have to adopt a broader approach to management of social media activities. "I don't see how you can effectively manage any media outlet in a vacuum," he says. IT can play a role, because it has expertise in technology for dealing with compliance, security and system management.
IT could also increase its own use of social networking tools. While companies' customer service organizations are rapidly embracing social media, Gartner analyst Jarod Greene says less than 1% of IT support groups use social media tools to communicate with their internal clients.
Don't be a noob (newbie)
CIOs who want a seat at the social media table should be using social media tools in their own work and personal lives, Marx says. "If I came up and said 'Hey, I want to be part of the social media strategy around here' and I didn't tweet, I didn't blog, I wasn't on Facebook, I didn't use LinkedIn, I'd have no cred," he says.
Marx says relies on his staff to help him keep up with trends. He also has two doctors who are involved in IT, and they help him stay in touch with the needs of medical professionals. He has also embraced reverse-mentoring, having enlisted a twentysomething staffer to counsel him on new technology.
"Young people roll to a different tune," Marx says. "CIOs can't be noobs."
Especially if they want to avoid being anti-social.


text:
Susan Warner Studio
Welcome to Susan Warner Studio. Here you will find galleries of digital artwork called photo mandalas. These images were created by the artist using Photoshop and Painter. Original photographs were used from nature, industrial debris, musical instruments, and other themes. The collection is growing! Click the Galleries button above, or an image button on the left to go to one of the gallery theme pages.
I have also added a new Nature Photo section.
Why Mandalas?
Mandala is a Sanskrit word meaning magic circle. Circular art is found in many cultures and through the ages, associated with spirituality, healing, and meditation. Mandalas can be used as an instrument for centering and contemplation. Their perfect symmetry is used as a focus to restore inner balance, or to bring meaning and order. Carl Jung saw the mandala as a symbol for the wholeness of the self, and the center as the core surrounded by a periphery containing everything that belongs to the self- the paired opposites that make up the total personality...
I find that working with circular forms is very satisfying. The intricacies of mandalas are like the life around us radiating into a beautiful kaleidoscope-like whole. I like to find little pieces of my environment to transform. I especially like to find objects that by themselves would be passed over or discarded, and bring them into a new context. (See my industrial theme mandalas.) With my mandalas, you can focus on texture, shape, and color, and still be able to detect the original subject on closer observation.
How Are Mandalas Made?
There are many different techniques for making mandalas. The images on this site are made by taking a pie slice shape from a photo, and flipping and repeating the slice for a seamless effect. The photos and mandalas are manipulated in Photoshop and Painter to enhance the beautiful forms.
Many of these images are now available as greeting cards and prints. Other art gallery gift items are for sale through cafepress.com. T-shirts, mugs, clocks, decorative tiles, calendars, and other gift items display vibrant Susan Warner Studio mandala images. See the Order Page for the great selection and pricing information. Or go right to the Gift Shop Page for gift items. I would love to receive emails with your comments: Email Susan Warner Studio.
Custom Orders
Mandalas can be very personal or reflect interests and themes. They make meaningful gifts. If you have an interesting photo or idea you would like Susan Warner Studio to make into a mandala, see the Custom Order page for more information. Check out the currency mandala examples!
Custom mandalas as well as the mandalas on this site are available for such uses as CD covers, promotion, art books, calendars, advertising, magazines, etc. See the Custom Order page for more information.
I hope you enjoy
exploring these pages and come back often!
[Home][Galleries][Order][Custom Orders]
Click here to go to my Links Page.
Copyright © Susan Warner. All rights reserved.


text:
Technical characteristics
|Main Technical data
|Length overall (incl. pulpit)
|16,08 m (52' 9'')
|Beam max
|4,65 m (15' 3'')
|Draft (incl. props at full load)
|1,52 m (4' 11'')
|Displacement (at full load)
|26,8 t (59.083 lb)
|Building material
|Exterior styling & concept
|Stefano Righini
|Interior designer
|Azimut Yachts
|Hull designer
|Pierluigi Ausonio Naval Architecture & Azimut Yachts
|Azimut Yachts
|3 + 1
|6 + 1
|Head compartments
|2 + 1
|2 x 725 mHP (533 kW) VOLVO D11
|Maximum speed (performance test mass)
|up to 32 kn
|Cruising speed (performance test mass)
|26 kn
|Tank capacity
|Fuel capacity
|2.200 l (581 US Gal)
|Water capacity
|590 l (155 US Gal)
Data refers to UNI ISO 8666 conditions
Azimut Yachts materials have been designed and developed to offer
users the most accurate representation of the yacht possible.
The finish, standard equipment and range of optional on offer may vary from hull to hull.
The information provided by materials (Virtual Tours, Images, videos, etc…)
is for information purposes only.
While Azimut Yachts endeavours to provide accurate information on this
site, some may still contain technical inaccuracies or typographical
Azimut Yachts makes no representation or warranty as to the accuracy, completeness or authenticity of the image or representation of the product displayed with the image or representation of the product in reality.
Azimut Yachts reserves the right to modify at any time, without notice, any content and characteristics of any yacht and megayacht, including, but not limited to, colours, materials, optionals and equipment.


text:
By Robert J. Sternberg
When I tell people that the University of Wyoming can be the No. 1 land-grant institution in the country, some of them look at me as though I am out of my mind. How, after all, can we compete with institutions like the University of California at Berkeley, Michigan State and Texas A&M? Well, let's consider what it might mean to be the No. 1 land-grant university.
The mission of a land-grant, I believe, is to educate the next generation of active citizens and ethical leaders who will make a positive, meaningful and enduring difference to the world. In other words, land-grant graduates succeed when they make the world a better place in which to live. The University of Wyoming likely will never be No. 1 in national media ratings, such as by U.S. News and World Report. But such ratings are largely based on an elitist mission for a university. Being No. 1 in such ratings would not constitute being No. 1 in serving the land-grant mission. Land-grant institutions differ fundamentally from elitist institutions in five key respects:
1. Access versus exclusion. Elitist universities take pride in rejecting as many applicants as possible. Land-grant institutions, in contrast, emphasize access. They want to admit every student who is capable of doing the work. Their mission is to reach out to the state and support its economic development by contributing to the creation of a highly educated workforce.
2. Abilities as modifiable versus fixed. College-admissions tests, such as the ACT and the SAT, are fundamentally based on a notion of abilities as fixed. Their publishers take pride when studies find that interventions change scores minimally or not at all. Institutions that focus on students with scores at the top of the distribution are buying into the notion that abilities are fixed. Land-grant institutions, in contrast, take pride in their view of abilities as modifiable. They are betting that students with lower test scores and even grade-point averages can become smarter through education. Indeed, the purpose of education, they believe, is to make students smarter.
3. Abilities as broad versus narrow. Standardized tests, as they now exist, measure primarily memory and analytical abilities. When admissions officers focus on test scores, they are implicitly buying into a notion of a person's potential as susceptible to being captured by a number. But successful leaders show the creative skills to formulate a vision, the analytical skills to ascertain whether their vision is a good one, the practical skills to implement their vision and persuade others of its value, and the wisdom-based and ethical skills to ensure that their vision helps attain a common good. Great leaders further have a strong work ethic, a sense of responsibility, the skills needed to work smoothly on a team, the willingness to learn from their mistakes, humility, high motivation to achieve, integrity, and resilience in the face of failure. Standardized tests do not measure these essential leadership skills.
4. Practical education versus the "life of the mind." In some elitist institutions, professors harp on college as developing the "life of the mind." From their point of view, the more removed an institution is from the complexities and harshness of the everyday world, the better. Land-grant institutions, in contrast, emphasize a practical education -- one that will prepare students not only for their first job, but also for their second, third and fourth jobs. We want our students to be job-ready on Day 1 as they enter the workforce. We do not believe in an education divorced from everyday reality. Both our general education and specialized education teach students to think, to reflect and to appreciate how others before them have approached and solved life's problems. In our teaching, research or outreach, we at the University of Wyoming want to embrace the real world, not be divorced from it.
5. Public versus private accountability. Many, but certainly not all, elitist universities are private. Their responsibility is primarily focused on private stakeholders of the university (e.g., students, professors, administrators, alumni, donors) as envisioned by a president and a board of trustees. Public institutions, and especially land-grant institutions, have a responsibility to everyone in their states, because they are taxpayer-supported. They cannot afford to be anything less than fully and publicly transparent, because the public typically is their largest donor. They cannot risk turning inward or battening down the forts. Without public accountability, they are in jeopardy for their existence.
So, how can the University of Wyoming become the No. 1 land-grant institution in the country?
First, UW can strive to educate the future ethical leaders who will make the world a better place. It can recognize that when leaders fail, it is rarely because of a lack of IQ or ACT points, but rather because of foolish or unethical behavior. (When scandal tarnishes a leader, it almost always is foolishness or lack of ethics or both, not lack of IQ points, that topple the leader.) This means admitting students to the university for ethical leadership qualities, not just for ACT scores and grades; educating students for ethical leadership through academic and student-affairs programs that emphasize practical case studies illustrating ethical and unethical behavior; and assessing students broadly for their life learning, not just their memory for facts, many of which will soon become irrelevant or forgotten.
Second, UW can strive to be No. 1 in its service to its state as well as its communities and its nation. Such service was the reason UW was created in the first place. This requires the university proactively to reach out to businesses, nonprofits, K-12 education and state government to learn how it better can partner with these institutions better to serve the interests of the Great State of Wyoming.
Third, the university has to strive to be the very best it can be in the three components of a land-grant institution -- teaching, research and outreach/extension. You cannot reach the top unless you provide world-class academic services at an affordable price. We constantly need to strive to be better in the products and services we deliver to our stakeholders.
Finally, UW can remember that becoming No. 1 is a process, not merely a destination. Ironically, when institutions reach the top or even near the top of whatever rating scale they care about, they often become complacent and self-satisfied. Instead of trying to improve, they focus on maintaining the status quo. Once you stop striving to become better, you inevitably fall victim to hungry competitors ready to knock you off your smug pedestal.
The University of Wyoming can become No. 1 -- not at everything, but at what it cares about most: its education of the next-generation workforce with workers who feel not a sense of entitlement, but rather a keen desire to serve the employers for whom they work and the states in which they live -- graduates who will create a better world for all.
Robert J. Sternberg is president of the University of Wyoming.
View all President's Communications


text:
Who knew there were so many ways to tie a scarf? If ever there was an epic scarf-wearing video, this one by Wendy's Lookbook is it.
She shows options for both square scarves and typical long ones (both silk and cashmere), so you can wear a scarf every season and mix up your look just about every day.
25 Ways to Wear a Scarf in 4.5 Minutes! | YouTube


text:
Hangover II Leaves Viewers with a Headache
- Christian Hamaker Contributing Film and Culture Writer
- 2011 26 May
DVD Release Date: December 6, 2011
Theatrical Release Date: May 26, 2011
Rating: R (for pervasive language, strong sexual content including graphic nudity, drug use and brief violent images)
Genre: Comedy
Run Time: 102 min.
Director: Todd Phillips
Actors: Bradley Cooper, Zach Galifianakis, Ed Helms, Jeffrey Tambor, Mike Tyson, Justin Bartha, Ken Jeong, Paul Giamatti, Nick Cassavetes
"It happened again," a flustered, apologetic Phil (Bradley Cooper) tells his girlfriend over the phone in the first scene in The Hangover Part II. And by "it," the character refers to pretty much everything from The Hangover. The sequel regurgitates the original movie in nearly every way, from Phil's phone call explaining his predicament early in the story to the end-credits photo montage picturing the hedonistic excesses of the main characters—Phil, Stu (Ed Helms) and Alan (Zach Galifianakis)—can't recall.
Creative sequels are the exception to the rule in Hollywood. For every The Empire Strikes Back or The Lord of the Rings: The Two Towers, there are dozens of Jaws 2s, Psycho IV: The Beginnings and Sex and the City 2s—movies that ride on the coattails of the original stories without expanding those plots in ways that make the effort and expense put into subsequent chapters seem worthwhile.
The Hangover Part II epitomizes the pointless Hollywood sequel. Following director Todd Phillips' lucrative 2009 comedy hit, this second swing aims for the upper decks but is, instead, a major whiff—unnecessary, witless and inferior in every way to the highly overrated first film.
Stu (Ed Helms) is preparing to marry his sweetheart, Lauren (Jamie Chung), at a resort in Thailand, and he insists that Phil (Bradley Cooper) and Doug (Justin Bartha) travel to Thailand to celebrate his nuptials. Stu wants nothing to do with Alan (Zach Galifianakis), the cause of the friends' escapades in Las Vegas in the first Hangover, but Phil's cajoling persuades Stu to allow Alan to come along.
Lauren's family, including young brother Teddy and her stern father, are still warming to Stu, but those relationships are the least of his problems. After a safe, sober "bachelor brunch" leads to just "one beer" with the guys, the friends wake up in Bangkok, and to a repeat of the first film's best gag: the strange sights and sounds the morning after a night the men can't remember, with evidence of excess all around them. Not only are there empty bottles of alcohol and filled ashtrays throughout the room, but Stu has a new tattoo—on his face. There's a monkey. But then Mr. Chow (Ken Jeong) unexpectedly appears and the film takes a turn for the dark, as Chow keels over and is disposed of in an ice chest.
This is funny?
Writer/director Phillips seems to thinks so. In addition to the sight gags, he and co-writers Craig Mazin and Scot Armstrong have substituted the "f" word (this reviewer lost count of such uses well into the 30s) for any attempt at creativity in their humor. The team never catches the bizarre comic momentum of the first film, even as they produce carbon copies of many of that film's jokes. The jokes feel forced, highlighted by an is-she-a-woman-or-a-man sight gag that will have some in the audience gagging.
Helms, a likeable actor, strains to pull off Stu's radical mood changes as the character rides the improbable emotional rollercoaster the writers have built for him. Cooper's Phil is a cipher who helps set the story in motion but doesn't have much else to do. Galifianakis' unbalanced man-child Alan provides the movie's few unexpected laughs, as when he dubs himself a "stay at home son" or cites the Jonas Brothers' touring schedule.
Given that Alan is the only character whose behavior and dialogue has moments that aren't predictable, maybe Phillips' planned third Hangover should focus on that character. Based on the overall merits of The Hangover Part II, let's hope the series never makes it that far.
- Language/Profanity: Lord's name taken in vain; many uses of the "f" word; "bull-hit"; reference to a lap dance; "whore"; "b-tches"; racial epithets; "penis"; crude discussion about orgasms; crude play on the word "Bangkok"; "boobies"; explicit reference to anal sex; reference to "happy endings."
- Alcohol/Smoking/Drugs: Phil wants a hit of nitrous oxide while in Stu's dentist office; Phil tries to steal Stu's prescription pad; Stu says he doesn't want to be "Rufi-ed"; Alan immunizes himself through injections; beer drinking; empty bottles and dried cigarette butts shown the morning after a wild night; cocaine use; images of teens drinking, doing drugs; a monkey smokes.
- Sex/Nudity: Kissing; strippers and pole dancers; frontal and rear nudity; a man learns he had sex with someone who isn't who she appears to be.
- Violence/Crime:A monk hits men with a large stick; Alan accidentally fires a weapon; high-speed car and motorcycle chase; a severed finger.
- Religion/Morals: Alan mentions that Stu's upcoming marriage won't be his first; Alan crosses himself; Stu says, "I have a demon in me."
Questions? Comments? Contact the writer at email@example.com.


text:
Milk chocolate pastry cream
The key to delicious cream puffs, luscious cupcakes or a heavenly éclair? Their filling of course. This recipe yields an incredibly rich and creamy milk chocolate pastry cream (or crème pâtissière) that turns all your pastries, cakes and tarts into best-sellers. Expect a pronounced milk chocolate taste with plenty of cocoa flavours, and creamy and caramelly hints. Wondering about which chocolate you should use? Experiment with Finest Belgian Milk Chocolate or Single Origin milk chocolate to find out which pastry cream flavour you like best. With recipe n° 823 for instance, you'll create a cream with rich cocoa and caramelly notes. We recommend using a standard three-drop fluidity (💧💧💧) to achieve a perfect end result.
Recipe components
Callebaut® ingredients
Milk chocolate pastry cream
Mix in.
|Bring to a boil and temper the egg yolk mixture with it. Bring the whole to a boil again.
|Pour the boiling mixture onto the chocolate, emulsify and leave to cool in refrigerator immediately.


text:
Donating in honor of: Kari Stuart's fundraising efforts for the United Airlines NYC Half
Will you help create a better tomorrow for vulnerable wildlife and their habitats around the world?
Be a hero for nature and protect our beautiful planet today. Together, we will save precious wildlife, conserve amazing wild places, and protect nature for future generations.
Will you protect nature today? Make your tax-deductible donation and make WWF's critical conservation work possible.
Click only once to submit your donation. Thank you for your generous support.
World Wildlife Fund is a 501(c)(3) charitable organization.
Elephants © naturepl.com / Anup Shah / WWF
© 2023 World Wildlife Fund |
1250 Twenty-Fourth Street, N.W. P.O. Box 97180 Washington, DC 20090-7180 | Privacy Policy / Your Privacy Rights | Site Terms | State Disclosures