Evaluation of variant identification methods for whole genome sequencing data in dairy cattle
BMC Genomics volume 15, Article number: 948 (2014)
Advances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays.
The total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio.
Our findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection.
Practical application of genomic technologies, such as large-scale use of single nucleotide variant (SNV) arrays in animal and plant breeding, has become routine in many areas of the life sciences. Taking both polygenic additive (pedigree) effects and genomic (SNV) effects into account, between 71 and 85% of the genetic variance observed in phenotypic traits of interest in cattle can be explained solely by SNV effects  and the number of genotyped animals in cattle populations worldwide is increasing steadily . Fueled by decreasing costs, advances in next-generation sequencing (NGS) technologies enable identification of more complex forms of genetic variation (e.g. short insertions and deletions (InDels), copy number variations (CNVs), etc.). These advances will inevitably foster our ability to partition the genetic variance underlying traits of interest. While some applications of NGS require de novo sequencing of an individual organism (sample), re-sequencing may also be possible if a reference genome for the species of interest is available.
The translation of raw NGS reads into tangible variants (SNVs, InDels, CNVs, etc.) via re-sequencing is a specific, delicate and computationally demanding task  and comprises three steps. First, short reads of DNA are aligned to an existing reference genome (referred to as alignment). Second, sequence differences between the sample being sequenced and the reference genome are identified (referred to as variant calling ). A myriad of alignment (see  for a review) and variant identification software programmes are available (e.g. the UnifiedGenotyper (UG) or the HaplotypeCaller (HC) of the genome analysis toolkit (GATK) [6, 7]; Platypus (PL) ; SAMtools (SAM) ; etc.), the majority of which can be obtained free of charge. As a final step, variants are screened and filtered to remove potential false positives common in most NGS technologies (see ).
As sequencing costs decline, reference genomes are becoming available for an increasing number of organisms, including agriculturally important species such as cattle (see  for a review). The Bos taurus reference genome UMD3.1 is similar in size to the human genome and contains ~2.8 billion base pairs, approximately 10% of which are not assigned to any chromosome . The N50 size can be used to compare the quality of genome assemblies of similar size: it represents contig size such that 50% of the genome is contained in contigs of length N or greater . Because the N50 size for the UMD3.1 reference genome (UMD3.1 accession number GCA_000003055.3: N50 = 96,955b) is much shorter than that of the current human reference (GRCh38 accession number GCA_000001405.15: N50 = 56,413,054b), the UMD3.1 reference genome will not likely allow the same accuracy in alignment, variant identification and further downstream analysis as the human reference allows. Nevertheless, algorithms and software developed for alignment and variant identification of human NGS data provide an excellent resource for translating NGS data of other non-human organisms, such as cattle, into genetic variants for application in genome wide association studies and genomic selection programmes.
Several approaches to variant identification are possible. The simplest variant detection methods identify variants on a per-sample basis, one position at a time. Once a variant locus is found, the most likely genotype for that locus is determined stochastically based on a consensus of aligned reads. If multiple samples are analyzed simultaneously, an a priori likelihood of finding a variant locus given the observed data is derived, and the most likely genotype at a given position is determined. Either single or multi sample variant identification methods can be implemented in the UG [6, 7]; and SAM . More advanced haplotype-based methods incorporate the correlation between adjacent variants within the variant detection procedure. Such methods use linkage disequilibrium between nearby variants to further enrich variant identification. Haplotype-based methods are implemented in PL  and the GATK HC [6, 7]. The haplotype-based variant detection approach can also be conducted in either single or multi sample settings.
Read chimerism, base pair tautomerisms and signal intensity issues can contribute to false positive variant detection by causing stochastic inaccuracies, general sequencing errors, and misalignments in NGS data [7, 10]. Aside from the variant identification approach itself, a number of optional auxiliary steps have been recommended to improve the quality of NGS-derived variants. These steps are conducted before (preparatory steps) or after variant identification (filtering). The first generally recommended preparatory step is the identification of falsely duplicated reads (mainly artifacts caused by PCR), which reduces bias in variant detection . Secondly, local realignment around single or multiple bases that are either missing in the reference (insertion) or missing in the DNA sequence being analyzed (deletion) is also commonly recommended . Realignment cleans up spurious SNVs that result from misalignment of reads around known alignment gaps and helps detect false negative SNVs in the near vicinity of InDels (insertions and deletions). Furthermore, the full alignment context is used to determine whether the reported divergence from the reference (i.e. the insertion or deletion) actually exists . Finally, flow cell lane, machine cycle (base position within the read), sequencing context (preceding and current nucleotide) or other technical aspects may influence base quality scores, which help characterize the quality of the bases in the individual reads. Base quality score recalibration is recommended to lower the number of falsely identified SNVs and to lower false confidence in identified bases . After variant identification, further filters can be implemented according to the individual dataset under consideration.
Variant detection methods can be evaluated using genome simulations based on reference genomes . Given that all mammalian reference genomes are incomplete and whole-genome alignment is imperfect, simulations may not provide realistic results, a difficulty that has prompted the use of real data . Another common evaluation method in human studies is to compare variants from NGS data to those of array genotyping (e.g. [16–18]), to results from the 1000 genomes project , or to other existing resources of human sequence (i.e. ). Resources for cattle are not as comprehensive, however low, medium and high density SNV arrays are available (e.g. Illumina BovineHD BeadChip® or Affymetrix Axiom® Genome-Wide BOS 1 Array), thus allowing an estimation of relative accuracy between software (Table 1).
Aside from measuring concordance between NGS data and array data, the ratio of transitions (pyrimidine-pyrimidine or purine-purine mutations) to transversions (pyrimidine-purine or purine-pyrimidine mutations; Ti/Tv ratio) can be used as a convenient diagnostic to measure the quality of NGS data (e.g. [7, 21]). The genome-wide Ti/Tv ratio is reported between 2.0 and 2.2 in human whole-genome sequence data [7, 21], whereby this ratio is higher in exomes due to the increased presence of methelated cytosine in CpG dinucleotides in exonic regions . Because this bias in favour of mutations between bases with similar biochemical properties (transitions) over those with dissimilar properties (transversions) is dependent on both CpG and GC content of the region, the Ti/Tv ratio is a useful diagnostic to measure quality across the genome .
The objective of this study was to investigate which methods and software work best for detection of high quality genetic variants using NGS data in cattle, with a specific focus on single nucleotide variants. Using whole-genome sequence information from 65 individuals, we a) explore the implications of preparatory steps commonly recommended in human analysis, b) compare results of single and multi sample variant detection achieved using four publicly available variant detection software programmes, c) provide a comparison of computational processing time, and d) compare accuracy and completeness of SNVs identified in NGS data by comparing them to genotypes from the same individuals generated with either high- or medium-density SNV arrays, as well as to analyse genome-wide Ti/Tv ratios. Through benchmarking different variant detection methods in cattle, preliminary recommendations for variant identification in other organisms can be extrapolated. Our findings can serve as a reference for choosing variant identification software and can help assess the implication of preparatory steps in NGS pipelines for species with lower-quality or unfinished reference genomes.
Results and Discussion
Alignment and coverage
Approximately 24 billion paired-end reads were obtained for the 65 sequenced animals. An average of 96.8% of these reads (range 86.6% - 98.2%) were mapped to 30 chromosomes (autosomes 1 – 29, X) of the bovine reference genome assembly UMD3.1 . Approximately 1.7 billion (PCR and optical) duplicate reads (average 7.3%, range 4.2% - 10.6%) were marked and excluded from further analysis. Average coverage was 12.1 reads per base; average coverage per-animal ranged from 10.1 - 17.5. See Additional file 1: Table S1 for individual alignment and coverage information.
Single sample variant detection
The results of single sample variant detection are in Figure 1. Depending on software and preparatory steps, an average of 5,854,886 - 6,404,094 SNVs (Figure 1a), 496,203 – 832,689 InDels (Figure 1b) and 2,098 – 40,462 multi-allelic sites (i.e. sites with more than two alleles, Figure 1c) were identified per animal (n = 65). Detailed information on the sequencing process (variant counts per individual, etc.) is given in Additional file 2: Table S2. UG identified the most SNVs, followed by PL and SAM. Because PL identifies multi-nucleotide variants, PL originally had the lowest average number of SNV per sample (4,873,149) because some variants were “hidden” in multi-SNV replacements. After splitting multi-nucleotide variants into their allelic primitives (hereafter referred to as PL_PRIM), a fair comparison was achieved and the average per-sample number of variants identified with PL_PRIM increased to 5,826,468. When single-sample results of all 65 animals were combined, 20,647,891 - 21,984,283 unique SNV were identified (Table 2).
InDel realignment had a slight effect on the number of SNVs and InDels identified, with the largest effects observed in UG. InDel realignment reduced the number of SNVs identified (PL: -4,548; SAM: -1,518; UG: -47,338), increased the number of InDels detected (PL: +37,194; SAM: +8,517; UG: +68,758), and decreased the number of multi-allelic sites identified (PL: -32; SAM: -7; UG: -53) (Figure 1a, b, c) in all samples. InDel realignment did not heavily reduce the number of SNVs identified by SAM compared to PL or UG, indicating that SAM effectively removed false positives prior to IR with GATK (only 1,518 less SNVs were identified with SAM when InDel realignment was done prior to variant detection). InDel realignment resulted in less SNVs being identified, because spurious SNVs caused by incorrect alignments in the close vicinity of real InDels were no longer detected as variants. During InDel realignment, the RealignerTargetCreator of the GATK creates a list of regions in which InDels are likely to occur depending on a set of known InDels and SNVs. Following this initial identification step, local realignment of reads spanning the InDel occurs . However, if the InDel is incorrect in the primary alignment (possibly due to chimeric read fragments, structural variations and/or misassemblies due to a poor-quality reference genome), InDel realignment may incorrectly realign the read segments surrounding the InDel. InDel realignment therefore relies on a trustworthy set of known InDels and SNVs, which may not yet be available for all species.Figure 2a shows the average Ti/Tv ratios of RAW, IR and IR + BQSR for single sample results of PL, SAM, UG and PL after multi-nucleotide variants were split into their allelic primitives (PL_PRIM). The Ti/Tv ratios are in the same range as those in human data. The benefit of InDel realignment is most apparent in UG, where an increase in Ti/Tv ratio can be observed compared to RAW. For PL and SAM the Ti/Tv ratio remained virtually unchanged after realignment. Splitting multi-nucleotide variants identified by PL into allelic primitives lowered the average Ti/Tv ratio, however a slight improvement after InDel realignment was observed. The use of InDel realignment is therefore recommended for variant identification with UG and PL_PRIM, however SAM and PL do not benefit markedly from additional realignment with GATK.
Base quality score recalibration
Base quality score recalibration reduced the average number of SNVs identified in PL (-127,292) and UG (-181,119), but not in SAM (+182,943) (Figure 1a). The number of SNVs identified after InDel realignment and base quality score recalibration is expected to decrease, as quality scores are initially overestimated and more SNVs fall below the cut-off after recalibration. By lowering base quality scores through recalibration, confidence in weak variants should decrease and, as a result, the number of false positives is expected to drop . The observed increase in SNVs identified with SAM after base quality score recalibration likely corresponds with the effective removal of false positive SNVs by SAM prior to IR and BQSR and the less stringent default quality scores in SAM, though the increase is minimal. Recalibration of base quality scores slightly increased the number of InDels for PL and SAM (no change in UG, Figure 1b).The effect of IR + BQSR on the number of SNVs identified was more obvious than the effect of IR alone for PL and SAM. Depending on variant detection software used, the total average number of SNVs identified per animal increased (SAM) or decreased (PL and UG) by around 3.0 – 4.0%. In contrast, the number of InDels identified after IR + BQSR did not change markedly compared to the number of InDels identified after IR alone. The Ti/Tv ratio decreased slightly for PL_PRIM, SAM and UG (no change in PL, Figure 2a) after BQSR, indicating that BQSR using default settings and the current available resources may actually decrease overall variant quality for these variant identification methods.
Liu et al.  analysed the effect of preparatory steps in whole exome sequencing data and found no clear effect of InDel realignment or base quality score recalibration in five whole exome sequencing samples of breast cancer patients. The authors state that the relative contribution of each preparatory step to the accuracy of variant identification is dependent on read depth; the lack of a sufficient number of reads in a low-coverage scenario limits the power of local multiple sequence alignment. In contrast, local realignment can benefit from consistent alignment among reads when coverage is high, thus effectively reducing the number of false positive SNVs. Li  focused on deep Illumina sequencing data from two human cell lines (55–100 fold coverage) and found “only” a 0.1% difference in the number of variants before and after InDel realignment and base quality score recalibration using both SAM and GATK. Although the authors regarded this difference as negligible considering the increased computational costs, a 0.1% difference in the number of variants may represent a good proportion of false positives being eliminated. Unfortunately, the resulting variants were not further analysed for quality (e.g. Ti/Tv ratio) leaving the key question regarding the effect of InDel realignment or base quality score recalibration unanswered.
Both InDel realignment and base quality score recalibration rely on a reference set of high-quality known InDels and SNVs. Existing resources for human sequence (e.g. HapMap , the Omni family of arrays from Illumina  or results from the 1000 genomes project ) provide qualitatively solid references, whereas the quality of bovine resources such as dbSNP  is notably lower. Although there are currently close to 70 million bovine SNVs included in dbSNP, only very few of them are validated (i.e. at least one clustered SNV determined using a non-computational technique or both population frequency data and genotype data included in the entry). In contrast, approximately 28 million human SNV are validated. High-density array information, such as that from the Illumina (BovineHD BeadChip®) or Affymetrix (Axiom® Genome-Wide BOS 1 Array), provides higher-quality information, but for only a limited number of SNVs (there are a total of 908,866 mapped SNVs on these two chips combined). As the number and quality of known InDels and SNVs in the bovine genome increase and reference information improves, we can expect better and more dependable effects of InDel realignment and base quality score recalibration in variant identification pipelines.
Multi sample variant detection
The differences between single and multi sample variant identification methods were generally slight in terms of number of SNVs and Ti/Tv ratios, and depended on software. Results of multi sample variant detection (conducted after InDel realignment and base quality score recalibration) are shown in Table 2. Variant detection with UG (multi sample) resulted in the highest number of variants (22,048,382), followed by HC (19,901,885) and SAM (18,767,273). As observed in single sample variant identification, PL originally had the lowest number of variants (16,894,054), because some variants were “hidden” in multi-SNV replacements as described previously. After splitting multi-nucleotide variants into their allelic primitives, the number of variants identified with PL increased with PL_PRIM to 19,759,134. Le Roex et al.  compared the number of SNVs identified with SAM and GATK in African buffalo and identified considerably more SNVs with GATK than with SAM using multi sample variant identification methods. Though not as pronounced, this agrees with both our single sample and multi sample results.
The total number of SNVs identified by combining single sample results of all 65 animals was higher for PL and SAM than when multi sample variant identification was carried out on all 65 animals simultaneously (Table 2). This was not the case for the UG, although the difference was very slight. Similarly, Liu et al.  found that the UG multi sample pipeline resulted in 16.6% more raw SNVs than single sample results, although they found no difference in the number of SNVs found in single and multi sample SAM pipelines.In terms of variant quality, Ti/Tv ratios for single sample calling with PL, PL_PRIM and UG were higher than those observed in multi sample calling (Figure 2b), whereby the Ti/Tv ratio for single sample calling with SAM was lower than that for multi sample calling (no single sample results available for HC). This difference was most prominent in UG, in which the Ti/Tv ratio dropped by approximately 6% when multi sample calling was applied.
Surprisingly, the total number of InDels identified using single sample variant detection methods was also higher for PL (+4%) and SAM (+34%) than when multi sample methods were used. Again, this was not the case for the UG (-10%). In contrast, Liu et al.  analysed human data and found that multi sample analysis increased the number of InDels identified considerably (SAM: +88.2%, UG: +92.6%) compared to single sample variant detection. It is possible that the number of InDels is inherently lower in cattle populations because their effective population size is much smaller than it is in humans, however a more likely reason for this discrepancy could also be the quality of the reference genome. The percentage of variants identified as InDels in the human genome has been estimated at up to 18%  whereas the number of InDels in cattle has been estimated at only 5.65%  of the total variants identified, although this may also be only a difference in reference quality. Depending on software and methods used, we found 7.19 – 12.25% of the variation observed was due to InDels.
Consensus SNVs and InDels for multi sample results may provide a simple method to ensure higher variant accuracy, although this approach is computationally intensive. Analysis of variant sets produced in phase one of the human 1000 genomes project  showed that a consensus approach to identifying variants led to a higher quality data set; variants identified in all software applications were more accurate than those identified in any single variant set . Li  also recommended taking the intersection of raw variants from independent variant identification methods and applying a software-oblivious filter to derive a final variant set. We used the vcf-intersect command of VCFTools  to count consensus SNVs and InDels (Figure 3). In this study, 16.4 million SNV positions (unfiltered) were found by all four multi sample variant detection methods (Figure 3a), and 1.7 million InDels were found by all four multi sample variant detection methods (Figure 3b). A consensus vcf file was created using the CombineVariants walker of the GATK [6, 7] in which only SNV found in all multi sample vcf files (HC, PL_PRIM, SAM and UG) were included. The Ti/Tv ratio in the consensus vcf was higher than the Ti/Tv ratios in the individual single and multi sample variant sets (Figure 2b), indicating higher variant quality in the consensus variant set. This approach is, however, extremally computationally intensive, as variant identification must be conducted using multiple software applications and then combined. An alternative could be to use only two variant sets (i.e. SAM + PL_PRIM), which also results in improved Ti/Tv ratios (Ti/Tv =2.30), but is computationally less intensive than including all possible variant sets.
By using default parameters, we did not fine-tune all possible options available in the individual software applications. Nevertheless, using default settings in both single and multi sample variant identification yielded good performance while maintaining output quality. Our goal was to provide an initial overview of methods using the default settings recommended; it should be noted that each dataset must be treated uniquely and alternative parameter settings may deliver more accurate results.
If possible, we recommend a consensus approach for variant identification using SNVs identified by all software, which resulted in the highest SNV quality and should be considered the “golden standard” for variant identification in organisms with lower-quality reference genomes. If computational constraints do not allow a consensus approach to variant identification, the tradeoff between quality and quantity of SNVs must be faced (computation time is discussed in the next section). The UG identified the highest number of SNV in both single and multi sample methods, however the number of false positive SNVs was also highest. SNVs identified with PL had the highest quality of single sample methods, however the number of SNV identified by PL may appear to be low if “hidden” SNVs are not split into allelic primitives. SAM identified a good number of SNVs, which were of comparable quality to those identified by PL. Both PL and SAM are likely a good choice of software for organisms with lower-quality reference genomes, as the built-in InDel realignment algorithm seems to efficiently remove false positives, making the use of lower quality resources (i.e. lower-quality bovine dbSNP information) superfluous.
Average computation time required for IR and IR + BQSR is depicted in Figure 4; InDel realignment was considerably faster than base quality score recalibration. The time required for single sample variant detection varied considerably between software applications, whereby PL was fastest, followed by SAM and UG, and HC was slowest. To compare time required for single and multi sample variant detection, the time required for several single sample runs was summed and compared to multi sample runs for the same number of samples (Figure 5). Surprisingly, the difference in runtime between single and multi sample methods for the same number of samples was negligible with the exception of PL, in which a clear speed advantage of single over multi sample variant detection was observed. As expected, increasing the length of the chromosomal region and the number of samples analysed also linearly increased computation time (Figure 6), except for MS HC. This may have been caused by limited resources, which can cause unexpected behaviour. For PL, SAM and UG pipelines including InDel Realignment and base quality score recalibration, more than half of the computing time was for preparatory steps. In pipelines involving HC, which was markedly slower than the other software, variant identification required more than double the amount of time necessary for preparatory steps.
InDel realignment and base quality score recalibration had only slight effects on the number of SNVs, InDels and multi-allelic sites identified. The effect of InDel realignment on Ti/Tv ratio was only positive for UG, and the effect of base quality score recalibration on Ti/Tv ratio was negligible (PL) or even slightly negative (SAM, UG). Given that computational costs in terms of time are very high, we recommend InDel realignment only in combination with UG. The use of BQSR for organisms with lower-quality resource information seems superfluous until better resources become available.
Concordance, measured as non-reference sensitivity (NRS), non-reference discrepancy (NRD), SNV concordance and genotype concordance, was calculated by comparing variants identified in NGS data to array information from the Illumina BovineSNP50 v1 DNA Analysis BeadChip® (n = 17 samples) and the Illumina BovineHD BeadChip® (n = 48 samples; Table 1). Detailed results of NGS concordance with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® are shown in Additional file 3: Table S3; results of NGS concordance with the Illumina BovineHD BeadChip® are given in Additional file 4: Table S4. In this section we discuss concordance results with the high-density array (medium-density array results mirrored those of the high-density analysis and are not discussed in detail).
An NRS of unity represents perfect concordance between the NGS variant set and the array. Jansen et al.  compared NRS and NRD of array-derived genotypes with sequence-derived genotypes of 43 Fleckvieh animals for BTA1 using SAM and found that low coverage (<7x) had a negative effect on both of these parameters. In our study, the NRS was generally higher in multi sample methods than in single sample methods for all software (Figure 7a), however this effect was most pronounced in PL and least pronounced in SAM. Our NRS results for SAM are similar to those of Jansen et al. . Liu et al.  compared sensitivity of single and multi sample methods using whole exome sequence data of 20 individuals and observed only a slight improvement in sensitivity when multi sample methods were applied, with the exception of SAM, in which a considerable drop (30%) in sensitivity was observed. For UG, Liu et al.  observed an increase in sensitivity of around 1%, whereas our results showed a slightly more pronounced improvement of NRS when multi sample methods were applied (4%). Surprisingly, Cheng et al.  found slightly better sensitivity in single sample results of SAM and UG compared to multi sample results in a population-based sample of 96 Southeast Asian Malays with deep whole genome sequence information.
NRD results for SAM are slightly lower than those of Jansen et al. , which can be explained by the slightly higher coverage in our study. The NRD is a measure of false positives; its importance depends on the purpose of the study (less high quality variants vs. more variants which may be of lesser quality). Generally, ratios should be as close to zero as possible. Figure 7b shows that single sample variant identification resulted in marginally lower NRD values than those observed using multi sample methods. Though slight, the trend is apparent in all software tested. Liu et al.  found a higher number of false positive SNVs in multi sample results (SAM, UG and glfTools; ), whereby this observation was most pronounced in SAM. Cheng et al.  observed that the number of false positives decreased with increasing read depth, while UG showed the lowest false positive rate of all tested software.The largest difference between single and multi sample methods was observed in SNV concordance (Figure 8). Because homozygous reference loci show no difference to the reference genome, SNV concordance alone is somewhat misleading, as such loci are inherently not identified (they are not “variant”). This has a visible effect on the overall average concordance. SNV concordance by array genotype is therefore a better measure (Figure 8b). For homozygote reference loci, single sample methods provide no information whatsoever (this may be alleviated by the “emit all” option of the GATK, however this is computationally unfeasible for larger data sets). Multi sample methods identify some of the homozygous reference loci as such, however concordance reached only 83.8-88.2%, which indicated that a sample size of n = 65 is too small to have all the loci covered (at least one animal in the sample space must have a variant locus for the locus to be included). Once again, the “emit all” option of the GATK could be used, but the computational cost in a multi sample setting is even higher than that in single sample analysis. There is also visible discrepancy between single and multi sample results with respect to heterozygous loci, though this discrepancy is considerably smaller than for homozygote reference loci. SAM showed the highest SNV concordance for heterozygotes. For homozygote alternative loci, SNV concordance approached 100% for SAM, UG and HC, with slightly lower concordance observed in PL.Genotype concordance was very high in both single and multi sample results (Figure 9); depending on software, genotype concordance for single sample results was between 99.2-99.3% and between 99.1-99.3% for multi sample methods. Once a polymorphic locus is determined as such, all software applications perform very well in deciphering the correct genotype.
The use of multi sample variant detection to identify SNVs improved NRS but worsened NRD compared to single sample variant detection. SNV and genotype concordance improved when multi sample methods were applied. This effect was most pronounced in SNV concordance of homozygous reference genotypes and less pronounced in heterozygous genotypes, whereas both single and multi sample methods identified homozygote alternative genotypes equally well.
The objective of this study was to investigate which methods and software work best for detection of high quality genetic variants using NGS data in cattle. We conclude that InDel realignment and base quality score recalibration have only slight effects on the number and quality of variants identified with the currently available resources for cattle and are costly with respect to computation time. The SNV concordance between variants identified using NGS data and array-based data was higher for multi sample methods than for single sample calling methods, although this was due mainly to the lack of homozygous reference genotypes in single sample results. The quality of SNVs identified (measured as the Ti/Tv ratio) using single sample methods was higher than that of multi sample calling for PL and UG and slightly lower for SAM, whereby a consensus approach using results of different software generally provides the highest variant quality. Computation time for single and multi sample methods was similar when calculated on a per-sample basis. These findings can serve as a reference for variant detection pipeline development in various organisms and help assess the value of preparatory steps in NGS pipelines for species with lower-quality reference genomes.
We selected 65 key ancestors of the main Swiss dairy populations with an iterative algorithm, which uses the numerator relationship matrix to rank animals according to percentage of genetic diversity they explain in a given population . Specifically, m animals were selected with, where p is a vector that contains the percentage of gene pool diversity captured by m animals, is a subset of the inverse numerator relationship matrix for m animals and c is a vector representing the average relationship of the m animals selected to the entire genotyped population. The subset of selected sires consisted of key Brown Swiss (n = 7), Braunvieh (n = 17) and Original Braunvieh (n = 8) Simmental (n = 12), Swiss Fleckvieh (n = 4) and (Red) Holstein (n = 17) ancestors that accounted for 74% of the genetic diversity of currently available genotyped populations of these breeds. All animals selected were male. Pairwise identity by descent was estimated for the merged dataset of the 65 sequenced sires by using the –genome function implemented in PLINK on array genotypes . A heat map of the genomic relationships between key ancestors is given in Figure 10.
DNA preparation, sequencing and alignment
Sequencing was done at the Helmholtz Center in Munich, Germany (German Research Center for Environmental Health Center) in collaboration with the Technical University of Munich. Genomic DNA was extracted from semen samples and sequenced using an Illumina HiSeq2000 (Illumina Inc., San Diego, CA, USA). Individual samples were sequenced on individual lanes of the flow cell. The bases of the resulting paired-end reads (101 bp) were identified with the Illumina BaseCaller; FASTQ files  were produced for downstream analysis of the sequence data.
Sequence alignment was done according to the sequence alignment guidelines for producing binary alignment mapping (BAM) files for the 1000 bull genomes project . Briefly, the Burrows-Wheeler aligner (BWA version 0.6.1-r104 ) was used for read alignment to the University of Maryland Bovine reference assembly UMD3.1 build 137 . Conversion from sequence alignment map format to sorted, indexed BAM files was done using SAMtools (version 0.1.18 ). PCR-duplicates were flagged using the MarkDuplicates option of the Picard software tools (version 1.61, ) and the MD5 message-digest algorithm values were examined to ensure correct data transfer from the sequencing lab to the computation center.
Both single and multi sample methods for variant detection were applied. Single sample variant detection was performed with three different software applications: 1) SAM (MPileup / bcftools, version 0.1.18 ), 2) PL (version 0.5.2 ) and 3) the GATK UG (version 2.7-4-g6f46d11 [6, 7]) using default settings. In some cases, PL identified multi-SNV replacements (long alleles containing multiple bases) instead of SNVs. Because multiple SNVs may be embedded within a multi-SNV replacement, the number of SNVs recognised as such for PL was slightly lower than in other software. This problem was alleviated by applying the VariantsToAllelicPrimitives walker of the GATK, which splits multi-SNVs into their allelic primitive states.
For single sample variant identification, three levels of quality recalibration were compared for each animal and software application: a) no quality recalibration (RAW), b) local realignment around insertions and deletions using the GATK IndelRealigner walker (IR ) and c) local realignment around insertions and deletions using the GATK IndelRealigner walker followed by base quality score recalibration using the GATK BaseRecalibrator walker (IR + BQSR ). For IR and IR + BQSR, a set of 256,831 known InDels mapped uniquely to UMD3.1 and that passed the Ensembl quality control process was used to decrease computation time (Ensembl release 74, 2013). Reads surrounding InDels annotated by BWA but not included in the known InDel list were also used to identify further targets for realignment. Pipeline commands are available in Additional file 5. Multi sample variant detection was performed with the same three software applications above as well as with the GATK HC (version 2.7-4-g6f46d11 [6, 7]) using default settings. The HC is recommended by the GATK , but was not included in single sample variant identification due to excessive computation time. Preparatory local InDel realignment and base quality score recalibration were conducted for all 4 multi-sample analyses as described above (IR + BQSR). A schematic overview of methods and preparatory steps investigated in this study is given in Additional file 6: Figure S1.
Computation time required for preparatory steps and variant detection of a 5Mbp portion of BTA24 (single and multi sample methods) was tested on an Intel Xeon E5-2650 machine with two eight-core processors and a total of 256Gb RAM. No parallelism options were used for time calculations. For the whole genome analysis, single sample variant identification was done on a 24-node cluster at Iowa State University. Each computational node has two six-core processors, 64Gb of RAM, and 1 TB scratch space, for a total of 288 processors and 1536Gb RAM. Chromosomes were split into regions of similar size for parallelization; one chromosome was run per core and optimal threading options in GATK were implemented. Multi sample variant identification was done on the CyEnce cluster of Iowa State University, which includes 288 nodes with 128Gb RAM each.
Concordance with high and medium density SNV arrays
The accuracy and completeness of SNVs identified in NGS data were assessed by comparing them to genotypes of the same animals generated with either the Illumina BovineHD BeadChip® (48 animals) or the Illumina BovineSNP50 v1 DNA Analysis BeadChip® (17 animals) (Illumina Inc., San Diego, CA, USA). The high-density array contained 774,660 SNVs mapped to the 29 autosomal chromosomes and the X chromosome (3,302 mitochondrial, Y-chromosomal and non-positioned SNVs were excluded). For the high-density array, 772,821 SNVs were assigned refSNP cluster ID numbers from dbSNP build 137  using SNPchiMp . After passing the Ensembl quality control process (includes plausibility checks on map positions, alleles of reference SNVs, alleles in dbSNP submissions, external failure classifications, etc., ), reference and alternative allele information for 673,396 remaining SNVs was included using the variant calling format files (VCF) available from the National Center for Biotechnology Information . The medium density 50 K array originally contained 54,001 SNVs; after exclusion of unmapped SNVs, assignment of refSNP cluster IDs  and Ensembl quality control , reference and alternative allele information for 34,419 SNVs remained for analysis. The forward-forward coding from the Illumina final reports was used to compare all available array genotypes with the sequence-derived genotypes for single and multi sample variant detection.
Four measures of concordance and discrepancy between array data and NGS data were calculated as described in Table 1. The set of 673,396 SNVs on the Illumina BovineHD BeadChip® (or the set of 34,419 SNVs on the BovineSNP50 v1 DNA Analysis BeadChip®) was considered the total sample space. No differentiation was made between homozygous reference sites and those sites not identified due to low coverage (i.e. all non-polymorphic (non-variant) sites were considered homozygous reference). SNV concordance was calculated by adding the number of homozygote reference, heterozygote and homozygous alternative genotypes identified in the NGS-based data and dividing this sum by the number of SNVs in the array-based data (the total sample space). Genotype concordance was calculated as the number of correctly identified NGS-based genotypes divided by the number of homozygote reference, heterozygote and homozygous alternative genotypes identified in the NGS-based data. NRS and NRD were calculated as proposed by  and applied in . NRS measures the proportion of variant loci identified in the NGS-based data also identified as variants in the array-based data. An NRS of one indicates perfect concordance of variant loci found in the NGS variant set and in the array. NRD represents the proportion of differing genotypes between the NGS variant set and the array. As the NRD represents discrepancy between the NGS variant set and the array, the NRD value should be as close to zero as possible. These measures were calculated chromosome-wise for both single and multi sample results.
Availability of supporting data
All DNA references used were taken from the publically available bovine assembly UMD3.1 available for download from http://www.1000bullgenomes.com/. The identified variants were submitted to the Database of Single Nucleotide Polymorphisms (dbSNP) and are available from: http://www.ncbi.nlm.nih.gov/SNP/.
No animal experiments were performed.
Jensen J, Su G, Madsen P: Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle. BMC Genet. 2012, 13: 44-
Van Raden P, O’Connell JR, Wiggans GR, Weigel KA: Genomic evaluations with many more genotypes. Gen Sel Evol. 2011, 43 (1): 10-10.1186/1297-9686-43-10.
Horner DS, Pavesi G, Castrignano T, D’Onorio De Meo P, Liuni S, Sammeth M, Picardi E, Pesole G: Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing. Brief Bioinformatics. 2009, 11: 181-197.
Stratton M: Genome resequencing and genetic variation. Nat Biotechnol. 2009, 26: 65-66.
Flicek P, Birney E: Sense from sequence reads: methods for alignment and assembly. Nat Methods. 2009, 6: S6-S12. 10.1038/nmeth.1376.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20: 1297-1303. 10.1101/gr.107524.110.
DePristo M, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Ricas MA, Hanna M, McKenna A, Fennel TJ, Kernytsky AM, Sicachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011, 43: 491-498. 10.1038/ng.806.
Rimmer A, Mathieson I, Lunter G, McVean G: Platypus: an integrated variant caller. http://www.well.ox.ac.uk/platypus,
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup: The sequence alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.
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 genome sequencing using reversible terminator chemistry. Nature. 2008, 456 (7218): 53-59. 10.1038/nature07517.
Ellegren H: Genome sequencing and population genomics in non-model organisms. Trends Ecol Evol. 2014, 29: 51-63. 10.1016/j.tree.2013.09.008.
Zimin A, Delcher A, Florea L, Kelley DR, Schatz MC, Puiu D, Hanrahan F, Pertea G, Van Tassel CP, Sonstegard TS, Marçais G, Roberts M, Subramanian P, Yorke JA, Salzberg S: A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 2009, 10: R42-10.1186/gb-2009-10-4-r42.
Kozarewa I, Ning Z, Quail MA, Sanders MJ, Berriman M, Turner D: Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G + C)-biased genomes. Nat Methods. 2009, 6: 291-295. 10.1038/nmeth.1311.
Van der Auwera GA, Carneiro M, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella K, Altshuler D, Gabriel S, DePristo M: From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform. 2013, 43: 11.10.1-11.10.33.
Li H, Ruan J, Durbin R: Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008, 18: 1851-1858. 10.1101/gr.078212.108.
Li H: Towards better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics. 2014, 30: 2843-2851. 10.1093/bioinformatics/btu356.
Liu Q, Guo Y, Li J, Long J, Zhang B, Shyr Y: Steps to ensure accuracy in genotype and SNP calling from Illumina Sequencing data. BMC Genomics. 2012, 13 (Suppl 8): S8-
Cheng AY, Teo YY, Ong RT: Assessing single nucleotide variant detection and genotype calling on whole-genome sequenced individuals. Bioinformatics. 2014, 30 (12): 1707-1713. 10.1093/bioinformatics/btu067.
The 1000 Genomes Project Consortium: An integrated map of genetic variation from 1,092 human genomes. Nature. 2012, 491: 56-65. 10.1038/nature11632.
The International HapMap Project. http://hapmap.ncbi.nlm.nih.gov/thehapmap.html.en,
Ebersberger I, Metzler D, Schwarz C, Pääbo S: Genomewide comparison of DNA sequences between humans and chimpanees. Am J Hum Gen. 2002, 70 (6): 1490-1497. 10.1086/340787.
Hodges E, Smith AD, Kendall J, Xuan Z, Ravi K, Rooks M, Zhang MQ, Ye K, Bhattacharjee A, Brizuela L, McCombie WR, Wigler M, Hannon GJ, Hicks JB: High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. GenomeRes. 2009, 19 (9): 1593-1605.
dbSNP Bovine Assembly Bos_taurus_UMD_3.1. ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3.1/
Le Roex N, Noyes H, Brass A: Novel SNP Discovery in African Buffalo, Syncerus caffer. Using High-Throughput Sequencing. PLoS One. 2012, 7 (11): e48792-10.1371/journal.pone.0048792.
Liu X, Han S, Wang Z, Gelernter J, Yang B: Variant callers for next-generation sequencing data: a comparison study. PLoS One. 2013, 8 (9): e75619-10.1371/journal.pone.0075619.
Mullaney JM, Mills RE, Pittard S, Devine S: Small insertions and deletions (INDELs) in human genomes. Hum Mol Genet. 2010, 19 (R2): R131-R136. 10.1093/hmg/ddq400.
Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brøndum RF, Liao X, Djari A, Rodriguez SC, Grohs C, Esquerré D, Bouchez O, Rossignol M, Klopp C, Rocha D, Fritz S, Eggen A, Bowman PJ, Coote D, Chamberlain AJ, Anderson C, VanTassell CP, Hulsegge I, Goddard ME, Guldbrandtsen B, Lund MS, Veerkamp RF, Boichard DA, Fries R, Hayes BJ: Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Gen. 2014, 46: 858-865. 10.1038/ng.3034.
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo M, Handsacker RE, Lunter G, Marth GT, Sherry S, McVean G, Durbin R and 1000 Genomes Project Analysis Group: The variant call format and VCFTools. Bioinformatics. 2011, 27 (15): 2156-2158. 10.1093/bioinformatics/btr330.
Jansen S, Aigner B, Pausch H, Wysocki M, Eck S, Benet-Pagès A, Graf E, Wieland T, Strom TM, Meitinger T, Fries R: Assessment of the genomic variation in a cattle population by re-sequencing of key animals at low to medium coverage. BMC Genomics. 2013, 14: 446-10.1186/1471-2164-14-446.
Abecasis Lab GLF Tools. http://www.sph.umich.edu/csg/abecasis/glfTools/,
Goddard ME, Hayes BJ: Genomic selection based on dense genotypes inferred from sparse genotypes. Proc Assoc Advmt Anim Breed Genet. 2009, 18: 26-29.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, MAller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analysis. Am J Hum Gen. 2007, 81 (3): 559-575. 10.1086/519795.
Cock PJA, Fields CJ, Goto N, Heuer ML, Rice PM: The sanger FASTQ file format for sequences with quality scores, and the solexa/illumina FASTQ variants. Nucleic Acids Res. 2010, 38 (6): 1767-1771. 10.1093/nar/gkp1137.
Hayes B, Daetwyler H, Fries R, Stothard P, Pausch H, van Binsbergen R, Veerkamp R, Capitan A, Fritz S, Lund M, Boichard D, Van Tassell C, Guldbrandtsen B, Liao X, and the 1000 bull genomes consortium: Sequence Alignment Guidelines for producing bam files for the 1000 bull genomes project Version: 15.07.2013. http://www.1000bullgenomes.com/,
Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.
Picard Version 1.61. http://picard.sourceforge.net/,
Nicolazzi EL, Picciolini M, Strozzi F, Schnabel RD, Lawley C, Pirani A, Brew F, Stella A: SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock. BMC Genomics. 2014, 15: 123-10.1186/1471-2164-15-123.
Ensembl release 74. http://www.ensembl.org/info/genome/variation/data_description.html#quality_control,
NCBI Resource Coordinators: Database resources of the National Center for Biotechnology information. Nucleic Acids Res. 2013, 41: D8-D20.
The authors thank 2 anonymous reviewers whose comments and suggestions improved this manuscript considerably. C. Baes thanks M. Berner for excellent technical support, P. von Rohr and U. Schuler for fruitful discussions and critical comments on the manuscript and A. Rimmer and D. Bickhart for very insightful thoughts on variant identification methods. We would like to thank the developers of all methods compared in this paper for making their software available. Financial support from the Swiss Commission for Technology and Innovation and the Swiss Cattle Breeders Federation is gratefully acknowledged. S. Jansen was supported by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr “Synbreed –Synergistic plant and animal breeding” (FKZ: 0315528A). This research was partially computed on the HPC equipment at Iowa State University (NSF MRI grant number CNS 1229081, NSF CRI grant number 1205413).
The authors declare that they have no competing interests.
Conceived and designed the experiments: BG, JM, DJG, CS, CF, HSH, BB, CFB. Performed the experiments: CFB, EFW. Analyzed the data: CFB, JEK, JMR, CF. Contributed reagents/materials/analysis tools: JMR, RF, SJ, MAD. Wrote the paper: CFB, BG, JEK, MAD, JMR. All authors read and approved the final manuscript.
Electronic supplementary material
Additional file 1: Alignment and coverage. Total number of lanes, libraries/pool, reads, number of duplicates, number of mapped reads, net number of mapped reads, net number of bases, and net average coverage per animal. (PDF 54 KB)
Additional file 2: Variant counts by animal. a) Number of single nucleotide polymorphisms (SNPs) identified per animal using different software (Platypus, Samtools and UnifiedGenotyper) and various pre-variant identification processing steps. b) Number of insertions and deletions (INDELs) identified per animal using different software (Platypus, Samtools and UnifiedGenotyper) and various pre-variant identification processing steps. c) Number of multiallelic sites identified per animal using different software (Platypus, Samtools and UnifiedGenotyper) and various pre-variant identification processing steps. (PDF 77 KB)
Additional file 3: Concordance with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® (n = 17). a) Non-reference sensitivity (NRS) for single nucleotide variants identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1-BTA29). b) Non-reference discrepancy (NRD) for single nucleotide variants identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1-BTA29). c) Single nucleotide variant concordance identified using Platypus Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1‒BTA29). d) Single nucleotide variant concordance by genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1‒BTA29). e) Concordance for homozygous reference genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1‒BTA29). f) Concordance for heterozygous genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1‒BTA29). g) Concordance for homozygous alternative genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineSNP50 v1 DNA Analysis BeadChip® as a gold standard (BTA1‒BTA29). (PDF 82 KB)
Additional file 4: Concordance with the Illumina Concordance with the Illumina BovineHD BeadChip® (n = 48). a) Non-reference sensitivity (NRS) for single nucleotide variants identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). b) Non-reference discrepancy (NRD) for single nucleotide variants identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). c) Single nucleotide variant concordance identified using Platypus Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). d) Single nucleotide variant concordance by genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). e) Concordance for homozygous reference genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). f) Concordance for heterozygous genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). g) Concordance for homozygous alternative genotypes identified using Platypus (Primitives), Samtools, UnifiedGenotyper and Haplotype Caller (single and multi sample variant identification) using variants identified with the Illumina BovineHD BeadChip® as a gold standard (BTA1-BTA29). (PDF 123 KB)
Additional file 6: A schematic overview of variant identification pipelines and methods examined in this study.(PDF 241 KB)
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.
About this article
Cite this article
Baes, C.F., Dolezal, M.A., Koltes, J.E. et al. Evaluation of variant identification methods for whole genome sequencing data in dairy cattle. BMC Genomics 15, 948 (2014). https://doi.org/10.1186/1471-2164-15-948


text:
Man Utd's board are backing Ole Gunnar Solskjaer for now but the stats about his reign at Old Trafford paint a pretty negative picture, says Max Liu...
"Opta's data tells us that, since Solskjær was made permanent manager in March, United have lost more Premier League games (12) than they have won (11)."
Ole Gunnar Solskjaer is [2.9] to become the next Premier League manager to leave his job after Manchester United lost at home to Burnley on Wednesday. The 2-0 defeat came three days after United had been emphatically outplayed and defeated by the same scoreline away to Liverpool.
Losing at Anfield this season is no reason for alarm, even for United, but the Red Devils have lost three of their last four Premier League matches, with the victory against Norwich a lone bright spot among defeats to Arsenal, Liverpool and, most recently, Burnley. In all three of those losses, United failed to score. In his column on these pages this week, Betfair Ambassador Dimitar Berbatov said it 'was not good enough'.
Worse than Moyes and Van Gaal?
There are those who argue that, with Solskjaer encouraging his young team to play in a style befitting United's attacking traditions, the club are moving in the right direction. On the other hand, Solskjaer's detractors say he is out of his depth and point to the fact that the team have their lowest ever points total for this stage in a Premier League season - indeed, it's their worst since the 1989-90 season when Sir Alex Ferguson narrowly avoided the sack by winning the FA Cup.
Perhaps most alarming is that United's record of 34 points after 24 games this term is six fewer than at the same stage in 2013-14 under David Moyes (pictured below) and 2015-16 under Louis van Gaal.
Fans' concern isn't just about results. Solskjaer let players go in the summer without signing replacements so United are now so short of strikers Carlos Tevez was rumoured to be on the verge of a return to Old Trafford. Then there was Marcus Rashford's injury which sees the England forward out for at least two months and might have been avoided had Solskjaer not brought him on in an FA Cup replay.
United have lost more than they've won
Opta's data tells us that, since Solskjær was made permanent manager in March, United have lost more Premier League games (12) than they have won (11). They have taken 42 points in that time - a figure eight Premier League teams have bettered and which proves that, under Solskjaer, United have achieved no more than mid-table mediocrity.
Just as damaging is the fact United have failed to score in seven of their 24 Premier League games this season - not exactly a sign that the swashbuckling good times are on their way back to Old Trafford.
And yet they are fifth in the Premier League, six points behind a highly unpredictable fourth-placed Chelsea. Bettors have little faith in the Reds to put together the kind of run that could see them taken an unlikely Champions League spot and make United [6.0] for a top four finish behind Tottenham [5.8] and Frank Lampard's Blues [1.56].
Problems run deeper than management
In the past week, Roy Keane, Gary and Phil Neville have all said Solskjaer should be given more time. At today's press conference before the FA Cup tie at Tranmere - the hosts look long at [8.4] - the United boss said he had positive talks with his employers after the Burnley defeat, so he could well see out the season.
Solskjaer pointed out that Jurgen Klopp took four seasons to build his team at Anfield while the Norwegian's supports have repeatedly reminded us of the six-and-a-half years it took Sir Alex Ferguson to make United into a title-winning side. The difference is that Klopp and Ferguson had already achieved a lot at Dortmund and Aberdeen respectively.
There can be no doubt that, after four managers in seven years, United's malaise runs deeper than Solskjaer. But that doesn't mean that, if results keep going against him, he won't pay the price. United's executive vice-chairman Ed Woodward knows that, with Mauricio Pochettino and Massimiliano Allegri both available, he might have to move sooner rather than later.

domain: news


text:
THREE OAKS — The opening of Froehlich’s Kitchen & Pantry is the newest entry to Three Oaks’s downtown shopping district.
The new restaurant, retail and event space at 19 N. Elm St. held a ribbon cutting ceremony on June 8.
In front of her newly expanded kitchen, bakery, deli and gift store, Colleen Froehlich noted that many of those gathered on the sidewalk for the grand opening had watched her lay tile 27 years ago as she prepared to open her first shop just across the street.
“Back then, there were 24 vacant storefronts. Today, they are all full,” Froehlich said. The increased foot traffic and interest in farm-to-table dining, which Froehlich’s is known for, were contributors to her decision to expand.
After it officially opened June 17, the new main floor space with a “market vibe” combines an expansive open kitchen surrounded by a bright, lofty space for seating and serving, with a deli area offering carry-out sandwiches and salads.
The colorful retail area is stocked literally from floor to ceiling with a wide selection of homemade jellies and preserves and other food items surrounded by unique kitchen and home décor items. There is a separate, glassed-in beverage boutique featuring unique teas, beers, wines and other beverages.
The original store, at 26 N. Elm St., will be renamed Froehlich’s Bakery and offer breads, pies, pastries, unique desserts and special occasion cakes.
The lunch menu, to be served from 11 a.m. to 3:30 p.m. daily except Wednesday, includes a selection of small plates and salads, including peanut noodle, Grandma Faye’s shrimp and potato salad, Froehlich’s Club Salad and a selection of fruits and nuts.
The main lunch selections include Froehlich’s Burger, shrimp po’ boy, adult grilled cheese, an After-School Ham “Sammy” and other selections.
Dinners will be served Sunday through Thursday, from 5-9 p.m., and Friday and Saturday from 5-10 p.m. The dinner menu, which has several takeout selections, includes a full selection of salads, an expanded small plates selection with a charcuterie board, lemon pepper shrimp and jalapeno poppers.
Main dinner features range from Biloxi boil, crispy pan spaghetti, righteous ribs, pork chops, flank and ribeye steaks, plus oven-baked fauxrotto, Colleen’s pickled chicken, corn meal-crusted fried catfish, and cedar plank Scottish salmon.
Desserts are made fresh daily in the bakery. One of the specialties is the fan favorite – OMG! chocolate cake – which has layers of ganache, chocolate cake and chocolate mousse.
Although Froehlich has applied for a full-service liquor license, it has been delayed by the Michigan Liquor Control Commission because her husband was a Three Oaks police officer before recently resigning.
New to the Froehlich’s umbrella is the restored upstairs event and banquet space that seats up to 399 guests. It includes a bridal suite and catering kitchen. Newly sanded and varnished original wood flooring (and lots of it) contrasts with the refurbished brick walls and original windows beneath the antique tin ceiling tiles and open-ventilation system.
The space is accessed via a new, glassed-in wide staircase and elevator addition at the rear of the building which gives an expansive view of the original Featherbone Factory complex (now home to the Acorn Theatre, Journeyman Distillery and Staymaker Restaurant).
Froehlich bought the historic building and the package liquor license at 19 N. Elm St. three years ago from its previous owner, Ray Wojdula, who operated the Three Oaks Pharmacy there.
Originally, the building was a thriving department store founded in 1920 as part of the Featherbone complex that dominated Three Oaks. The store was later taken over by the Hunerjager family, which ran it for decades.
Froehlich said she could envision what she wanted from the first moment she toured the building. She admitted it had been a long journey that has included many surprises and delays along the way.
The phone number will remain 269-756-6002.

domain: food


text:
31 min ago
Volcano in Iceland starts erupting, worries travelers: Here's what you need to know
The country's meteorological office said "a small subglacial lava-eruption has begun under the DyngjujA kull glacier," adding that "the aviation color code for the BA rA arbunga volcano has been changed from orange to red."
2 hrs ago
Iceland Raises Volcano Aviation Warning To Red
A surge in seismic activity has caused Iceland to raise the aviation alert for its Bardarbunga volcano from orange to red.
3 hrs ago
Iceland's Volcanic Eruptions Could Be a Huge Headache for Air Travel
A warning sign blocks the road to Bardarbunga volcano, some 20 kilometres away, in the north-west region of the Vatnajokull glacier, Aug. 19, 2014.
Fri Aug 22, 2014
Iceland volcano: Bardarbunga shaken by 3 earthquakes
Three big earthquakes have shaken the area of an Icelandic volcano, local media report, as some two hundred emergency workers and other professionals prepare for a massive eruption.
Thu Aug 21, 2014
Salt Lake Tribune
Movie review: Laughing at old age in low-key 'Land Ho!'
If you missed your chance of getting away for summer vacation, the low-key comedy "Land Ho!" is a great way to see the sights of Iceland with some charming traveling companions.
Aviation chiefs 'better prepared' if Icelandic volcano eruption causes ash cloud
A flutter on the high seas: 80% of passengers gamble on cruise holidays, spending A 320,000 on every voyage Historic war memorial clock in Leek silenced so new Premier Inn can live up to its promise of a great night's sleep Taking a dip at 600 feet! Shangri-La hotel opens one of the highest swimming pools in the world ... on 52nd floor of London's ... (more)
Wed Aug 20, 2014
Iceland: Area north Of Bar arbunga volcano evacuated
I've been keeping an eye on this story while dealing with Ebola, but now it's getting serious.
Take a Virtual Trip to Iceland With Google Street View
Have you been dreaming of visiting Iceland? Now you can take a trip there without ever leaving the comforts of home, thanks to Google.
Iceland: Thousands Of Quakes Amid Volcano Fears
Thousands of earthquakes have been recorded in Iceland in recent days, amid concerns one of the country's volcanoes could erupt and cause travel chaos across Europe.
Tue Aug 19, 2014
Farmers in North Iceland Round up Livestock
Farmers in Oxarfjor ur, Northeast Iceland, have decided to round up their free-roaming sheep and horses because of the threat of an eruption in sub-glacial volcano Bar arbunga in the northwestern Vatnajokull glacier and major glacial flood in Jokulsa a Fjollum, ruv.is reports.
The First Post
Will Icelandic volcano leave holidaymakers stranded again?
Four years after ash clouds from Iceland's Eyjafjallajokull volcano caused the biggest airspace shutdown since World War II, experts are warning of another volcanic eruption.
Mon Aug 18, 2014
Iceland Volcano Could Hamper Air Travel
The aviation industry has been warned about the possibility of flight disruptions caused by activity at Iceland's largest volcano system.
Fri Aug 15, 2014
Movie Meter: The Giver Disappoints, Go for Land Ho! Instead
Land Ho!: Former brothers-in-law , now well-aged, travel to Iceland together in order to call-back their wild and freewheeling youth in everything from Reykjavik nightclubs to the alien terrain of the raw Icelandic outback.
"Land Ho": Pitch-perfect performances set off in road comedy
Left to right: Earl Lynn Nelson as Mitch and Paul Eenhoorn as Colin in "Land Ho."
Thu Aug 14, 2014
Barking Moonbat Early Warning System
Once In My Lifetime
No longer is there an airworthy Avro Lancaster in the Americas . On Friday morning, the Canadian Warplane Heritage Museum's MK.
Fri Aug 08, 2014
Hamilton Lancaster lands in England
But festivities at RAF Coningsby airport were dampened by rain that kept the world's only other airworthy Lancaster from greeting the Canadian Warplane Heritage Museum Lanc in the air.
Off-Road Driving Can Result in ISK 500,000 Fine
Police officers who were patrolling the VatnajA kull National Park near LakagA gar in the southern highlands, yesterday came upon a small SUV stuck in a mossy bog.
Thu Aug 07, 2014
16 And Famous How Nash Grier Became The Most Popular Kid In The World
One time in Iceland, a single tweet about his whereabouts brought 5,000 girls to a shopping center in search of Nash and his sidekick, Jerome Jarre.
Wed Aug 06, 2014
The Institute of Contemporary Art/Boston opens Ragnar Kjartansson's The Visitors
Ragnar Kjartansson, The Visitors, 2012, Nine-channel HD video projection, 64 minutes, Edition 4 of 6, Gift of Graham Gund to the Institute of Contemporary Art, Boston and the Gund Gallery, Kenyon College.
Hiker in South Iceland Seriously Injured
An 18-year-old tourist from Switzerland fell when taking a shortcut on a wet path leading up to StjA3rnarfoss waterfall near KirkjubA jarklaustur in South Iceland yesterday.

domain: geography


text:
Located 850 metres from the centre of Crikvenica, Apartments Villa Klaudia offers a outdoor pool, hot tub and a sauna, all free of charge. This self-catering accommodation features a furnished balcony or a terrace in each unit, as well as free Wi-Fi. Lučica Beach is 800 metres away. All apartments are air-conditioned and overlook the sea. They comprise a seating area with a satellite flat-screen TV and a fully equipped kitchen that comes with a dining area. Featuring a hairdryer, private bathrooms also come with towels, bathrobes and slippers for all guests. Some apartments have a dishwasher. A grocery shop is approximately 150 metres away, while the nearest bar is 450 metres from the property. The nearest restaurant can be found in the centre, while the local green market is located at a distance of 950 metres. Guests can also enjoy the breakfast in bed option. Apartments Villa Klaudia offers a garden, free barbecue facilities and a shared terrace with sun loungers and parasols. The property also has one SUP - Stand Up Paddle that is provided free of charge. Fitness centre is available at a surcharge. A public bus stop is right in front of the property, while the Main Bus Station is located in the Crikvenica centre. The property offers free parking.
We speak your language! Native language: NL, HR, EN, DE, BS. Apartments Villa Klaudia has been welcoming guests since March 11, 2019 at TodayTourism.com hotel booking website.
GPS coordinates: 45.17624, 14.70294. You can easily find us with the GPS navigation device by entering our GPS coordinates. Simply type in the coordinates in Decimal degrees, separating the latitude and longitude with a comma or space.Plan Your Route to the Apartments with Google Maps
The most expensive accommodation in this neighborhood is: Apartments Villa Klaudia ( 309€ = 350 US$). The cheapest accommodation in this neighborhood is: Apartments Villa Klaudia (62€ = 70 US$)
Average hotel price in that region is: (62€ = 70 US$)
A credit card is NOT needed for the reservation! payment for your stay is required at check-in! Need a room for today or tomorrow? Check availability & Book Now!
You can write a special request after your booking is complete! Special requests cannot be guaranteed - but the Apartments Villa Klaudia will do its best to meet your needs.
|Internet||WiFi is available in public areas and is free of charge.|
|Parking||Free private parking is possible on site (reservation is not needed).|
|Pets||Pets are not allowed.|
|Children and Extra Bed Policy||All children are welcome. There is no capacity for extra beds in the room.|
This property only accepts cash payments. No credit card needed to book. We'll send you an email confirming your reservation.
Below is a selection of comments from our clients who have stayed in Apartments Villa Klaudia
Please inform Apartments Villa Klaudia in advance of your expected arrival time. You can use the Special Requests box when booking, or contact the property directly with the contact details provided in your confirmation. Payment before arrival via bank transfer is required. The property will contact you after you book to provide instructions.
There are a variety of shops and restaurants near our accommodation - Apartments Villa Klaudia. During your stay, you and your family or group can enjoy a short walk to these nearby destinations. Whether you like pizza, sandwiches, steaks or a little of everything. And, if you want to pick up some souvenirs or gifts in Crikvenica, you'll find everything you need in the town's little boutiques and shops.Find nearby restaurants

domain: travel


Rome, 1 February 2022 – Major transformational changes will be necessary in our economies and societies to shift the world from the brink of a climate catastrophe. These changes will need to be systemic, comprehensive and implemented in record time. They will also have and include perspectives such as climate justice, just transition, and the full range of Sustainable Development Goals. High-level representatives from the worlds of policy action, research and evaluation underscored this call for action during the second climate change-focused dialogue in the three-part series organized by Wilton Park, which took place on 1 February 2022.
Indran A. Naidoo and Fabrizio Felloni, respectively Director and Deputy Director of the Independent Office of Evaluation (IOE) of IFAD, joined over fifty participants to discuss the theme ‘making transformational change for climate action post COP26 – how?’. The participant line-up also featured Rob D. van den Berg, member of IOE’s Evaluation Advisory Panel, and Jo Puri, Associate Vice President of IFAD’s Strategy and Knowledge Department.
Convened two months after COP26, the dialogue session looked at some of the evidence and research that is building-up about how transformational climate action can take place. By discussing the existing evidence, participants identified ways to break down the many practical, political and structural barriers that may lead to improved collaboration and aligned networks among policy, research and evaluation.
Issues discussed and questions addressed included what transformational climate action needs to look like after COP26, both in terms of the outcomes of COP26 and the wider ambition to keep global warming to 1.5 degrees; what progress is really being made to generate the transformational change needed; how to create change across the many, often siloed, sectoral approaches, pilots and initiatives; and how collective action can take place for adaptive, anticipatory and robust climate action.
Wilton Park is an Executive Agency of the UK’s Foreign, Commonwealth and Development Office and is considered one of the world’s leading institutions for in-depth discussion of international policy issues and challenges. To hold this event, Wilton Park partnered with IFAD, the International Development Evaluation Association (IDEAS), Climate Investment Funds, Global Environment Facility, Green Climate Fund, University of Sussex, Institute of Development Studies, United Nations Development Programme Independent Evaluation Office, and the International Evaluation Academy.
The first discussion in the Wilton Park series took place in September 2021, under the theme ‘Pathways towards transformational climate action: connecting the worlds of research, evaluation and policy for action’.
For further information, please contact Alexander Voccia at [email protected]


Nigerians have been tasked to avail themselves of preventive measures against heart failure and to engage in regular checkup, healthy eating habit on monthly basis to reduce the mortality rate of patients with heart issues.
A team of cardiologists made this known at a heart failure awareness programme organised recently by Novartis for journalists.
The team led by Consultant Cardiologist, Obafemi Awolowo University Teaching Hospital (OAUTH), Prof. Micheal Balogun said the awareness programme was to sensitize participants on early diagnosis, symptoms and possible treatments of Heart Failure.
Prof. Micheal Balogun listed factors such as obesity, lack of physical exercise, unhealthy eating habits, heavy alcohol intake, as the major causes of heart failure.
He further said Nigerians needs to go for regular medical check-ups, as the first evidence that someone has been living with heart failure may be death.
Consultant Cardiologist, Lagos University Teaching Hospital (LUTH), Prof. Amam Mbakwem advised patients to desist from eating too much at a time, take more vegetables, watch their salt intake and take enough rest.
Dr. Ikechukwu Ogah, Consultant Cardiologist University College Hospital, Ibadan further emphasized on the need to follow preventive measures, he said,
“All heart failure patients, regardless of their symptoms are at high risk of dying. 76 per cent of heart failure patients struggle to perform their daily activities and 63 per cent report symptoms that are consistent with depression”
Other cardiologist who were present at the awareness event include, Consultant Cardiologist, Aminu Kano University Teaching Hospital (AKTH), Prof. Kamilu Karaye; Consultant Cardiologist, University of Port Harcourt Teaching Hospital (UPTH), Dr. Maclean Akpa; Consultant Cardiologist, University of Ilorin Teaching Hospital (UITH), Prof. Ibraheem Katibi; and Consultant Cardiologist, University of Abuja Teaching Hospital (UATH), Dr. Dike Ojji.


text:
Between 1970 and 2004 species populations in the high Arctic have declined by 26 percent, according to the first report by the Arctic Species Trend Index (ASTI). While this may be a natural cycle, scientists are concerned that environmental impacts such as climate change are worsening natural population fluctuations in the high Arctic. Declining species include lemmings, red knot, and caribou.
“Rapid changes to the Arctic’s ecosystems will have consequences for the Arctic that will be felt globally. The Arctic is host to abundant and diverse wildlife populations, many of which migrate annually from all regions of the globe. This region acts as a critical component in the Earth’s physical, chemical, and biological regulatory system,” lead-author Louise McRae from the Zoological Society of London (ZSL) said in a press release.
A caribou fawn. Researchers are concerned about a significant drop in the caribou population (known as reindeer when domesticated) worldwide. It may be a natural cycle or due to human actions, such as climate change. Photo by Joelle Taillon.
Some migratory species are also on the decline, such as Arctic shorebirds. However, researchers are uncertain whether this is due to changes in the Arctic or to migration stops in the south.
“Migratory Arctic species such as brent goose, dunlin and turnstone are regular visitors to the UK’s shores. We need to sit up and take notice of what’s happening in other parts of the world if we want to continue to experience a diversity of wildlife on our own doorstep,” Louise McRae adds.
Loss of sea ice is also a concern for a number of high Arctic species, such as polar bears, narwhales, and ringed seals, all of employ sea ice in one way or another to survive. Studies have shown declines in polar bear populations in some areas.
However, the news is not all gloomy. When all the Arctic biomes are included—high Arctic (most northerly), low Arctic (between high and sub), and sub Arctic (most southern)—species populations have actually risen by 16 percent in the past 34 years.
Canada goose gosling: some migrating geese are on the rise due to a decline in hunting and increased agricultural waste in their wintering grounds. Photo by Donna Dewhurst/USFWS.
Marine animals in the low Arctic, such as bowheads and sea otters, have rebounded after regulations have successfully protected them from overharvesting and overfishing. Marine fish biomass has also increased in the North Pacific, which researchers speculate is due to warming waters. However, in the sub Arctic species peaked in the 1980s and then began to decline.
“The establishment of these results comes at a crucial time for finding accurate indicators to monitor global biodiversity as governments strive to meet their targets of reducing biodiversity loss,” says co-author Christoph Zöckler from the UNEP-World Conservation Monitoring Centre.
The Arctic Species Trend Index measured populations of nearly a 1,000 Arctic vertebrates, approximately 35 percent of the regions’ vertebrates.
The beautiful spectacled eider. Photo by: USFWS.
The Arctic hare. Photo by: Carsten Egevang.
Caribou herd. Photo by: Joelle Taillon.
(02/24/2010) Two of the world’s largest land carnivores are converging on the same territory, according to data recently published in Canadian Field Naturalist. Grizzly bears ( Ursus arctos horribilis) are moving into an area that has long been considered prime polar bear habitat in Manitoba, Canada. Although polar bears (Ursus maritimus) are bigger than their grizzly relatives—they are the world’s largest land carnivores—biologists are concerned that grizzlies will kill polar cubs, further threatening the polar bear, which is already thought to be imperiled by ice loss in the Arctic.
(02/03/2010) Newly released images from NASA and the National Snow and Ice Data Center show that the Arctic’s melt season has lengthened significantly over the past few decades.
(08/31/2009) If current melting trends continue, the Arctic Ocean is likely to be free of summer sea ice by 2015, according to research presented at a conference organized by the National Space Institute at Technical University of Denmark, the Danish Meteorological Institute and the Greenland Climate Center.
(07/07/2009) Arctic sea ice thinned dramatically between the winters of 2004 and 2008, with thin seasonal ice replacing thick older ice as the dominant type of sea ice for the first time on record, report NASA researchers. Scientists from NASA and the University of Washington used observations from NASA’s Ice, Cloud and land Elevation Satellite (ICESat) to make the first Arctic Ocean basin-wide estimate of the thickness and volume of sea ice cover. The researchers found that overall Arctic sea ice thinned about 17.8 centimeters (7 inches) a year, for a total of 67 cm (2.2 feet) over the four winters from 2004 to 2008. The total area covered by thick older ice that survives one or more summers (“multi-year ice”) shrank 42 percent or 1.54 million square kilometers (595,000 square miles), leaving thinner first-year ice (“seasonal ice”) as the dominant type of ice in the region.
(06/14/2009) The first ever comprehensive survey of caribou worldwide (known as reindeer in Europe) has found that the species has suffered a staggering decline. Researchers from the University of Alberta discovered that the caribou population has fallen 60 percent in half as many years. The study published in Global Change Biology points to global warming and industrial development as the reasons behind the decline.
(05/20/2009) In May 2008 the Bush Administration listed the polar bear under the Endangered Species Act (ESA). The listing immediately received allegations of being politically biased and not based on sound science. However, a new paper addresses the allegations point by point and concludes that the decision to add the polar bear under the ESA was not only scientifically sound, but right.
(04/07/2009) Recent dramatic news points to both poles undergoing transformation due to climate change. This weekend an ice bridge disintegrated on the Wilkins Ice Shelf in Antarctica, leaving the whole shelf vulnerable to melting, and then yesterday new evidence was released of the impact of warming in the Arctic. Younger thinner ice has become the dominant type in the Arctic over the past five years, reports a new study led by Research Associate Charles Fowler of the Colorado Center for Astrodynamics Research. The thinner ice does not bode well for the Arctic ecosystem, as the ice is more prone to summer melting.
(04/05/2009) Black carbon is responsible for 50 percent of the total temperature increases in the Arctic from 1890 to 2007 according to a study published in Nature Geoscience. Since 1890 the temperature in the Arctic has risen 1.9 degrees Celsius, linking black carbon to nearly an entire degree rise in Celsius or almost two degrees Fahrenheit.
(02/16/2009) A marine census has documented more than 13,000 species in the Arctic and Antarctic Oceans, including several hundred that may be new to science. Conducted over a two-year period under often perilous conditions — including monster waves and dangerous polar bears — the series of 18 surveys turned up a wealth of information on the diversity, distribution and abundance of marine life. The research will also help establish a baseline for measure changes in polar ecosystems.
(12/26/2008) Reindeer are beloved in the holiday season for the mystical role they play in guiding Santa from the North Pole to the world’s chimneys. However, according to a new book, reindeer, more commonly referred to as caribou, face increasing pressures from a variety of sources. The new book entitled, Caribou and the North: A Shared Future, draws an intimate portrait of the only deer species where both male and females sport horns, while outlining the dangers which may lead these unique animal to become globally endangered.
(12/17/2008) Arctic sea ice fell to the lowest volume — and second lowest extent — on record, according to the annual World Meteorological Organization (WMO) Statement on the Status of the Global Climate.


The Urgent Need for Digital Transformation in Indian Healthcare!
The world is increasingly becoming an interconnected social hub where people can quickly find means to solve complex problems through data sharing and collaboration.
Technology dominates almost every aspect of our lives. Today, we are witnessing an increasing shift towards digitalisation as it makes our lives much more comfortable and convenient. Digitalisation is bringing about a new revolution in almost every significant industrial sector including business, entertainment, and science, with healthcare not far behind. In fact, the healthcare industry stands on the cusp of a digital revolution that could transform the face of healthcare entirely.
Digital technologies and innovations are rapidly disrupting conventional notions of healthcare by utilising the power of data to help doctors make better decisions, automate healthcare processes (surgery, routine checkups, etc.) for rapid care delivery, and promoting the overall growth of the industry.
The world is increasingly becoming an interconnected social hub where people can quickly find means to solve complex problems through data sharing and collaboration. India, too, needs to be a part of this global hub.
With the increasing growth of smartphones and rapid internet penetration across the country, India holds a vast potential to change its healthcare industry for the better. In fact, estimates suggest that India’s digital connectivity will tremendously increase from 15%(2014) to 80% by 2034, with a 58% increase in the number of rural internet users by 2034!
- 1 Reasons Why India Needs A Digital Intervention In Healthcare
- 2 Digital Revolution in Healthcare In India
- 3 The Future of The Indian Healthcare Ecosystem
Reasons Why India Needs A Digital Intervention In Healthcare
With the ageing population steadily expanding and the rapidly increasing incidents of people suffering from chronic diseases, India urgently needs to witness a digital revolution in healthcare due to the following reasons:
Dr Joseph Kvedar, VP of Connected Health, Partners Healthcare made a compelling point during his keynote speech at the Connected Health Conference in Boston. He stated:
“We’re not getting any younger, either individually or as a society. In fact, for the first time in history, by 2020 there’ll be more people over 65 than under five.”
His statement reminds us of a stark reality of life – ageing.
Globally, the aged population is on the rise because of lower birth rates and increasing life expectancy. The elderly require more attention and constant care than the younger generation. Augmented accessibility digital technologies may help the elderly population to maintain good health.
For instance, fitness wearables and monitors can enable the elderly to take better care of themselves. Research has established that the aged populace is likely to lead longer and healthier lives if they have a trusted social circle and feel a sense of purpose.
Lack of Adequate Healthcare Facilities in Rural Areas
Even though rural areas harbour a majority of the Indian populace, most of rural India lacks the essential amenities and infrastructural facilities that are a prerogative of a good healthcare system.
The rural-urban divide of healthcare needs to be bridged urgently for the welfare of the large percentage of the population that reside in rural areas and lose their lives due to lack of healthcare facilities. In such a scenario, telemedicine connectivity can help the rural populace to get in touch with the top-tier doctors and surgeons in the country and also make treatments more cost-effective.
Surprisingly enough, till today India severely lags behind when it comes to meeting the bare minimum requirements of the workforce in the healthcare sector as laid down by WHO.
The inclusion of advanced digital technologies such as robotics in the healthcare system would not only make up for the workforce gap by automating various tasks such as paperwork, minor surgery, etc., but it would also decrease operational costs significantly.
Furthermore, automation in the healthcare sector would mean speedy care deliverance and increased regulatory compliance as every step is tracked and documented.
Disease Burden Shift Management
At present, a large part of the Indian healthcare system lacks a centralized and integrated system for storing medical records of patients. Hospitals and healthcare providers still rely on a paper-based collection of patient data which is not only cumbersome but also time-consuming.
By investing in and adopting a centralized system of retrieving population health metrics, healthcare providers can quickly identify the patient groups that require immediate attention.
Furthermore, digital analytics tools such as EMRs can help store and access valuable patient data and help provide faster, accurate, and holistic treatment by skipping unnecessary paperwork formalities and redundant tests.
With an increasing awareness and accessibility to Internet services, today, individuals are open to trying out new technologies that will enhance their life expectancy. They are more conscious, tech-savvy, and more willing than ever to embrace disruptive technologies.
Thus, the tables have turned, and the focus of healthcare has shifted from the healthcare providers to the patients.
Patients are no longer ready to be constrained by the conventional models of healthcare such as routine tests and medications. Instead, they want to take charge of their own health. Wearables are a great example. These devices can help monitor and track the basic health parameters such as heart rate and blood pressure.
For instance, a wristwatch that can double up as a personal emergency response system as well as a source that can relay the medical data and GPS location of the patient over a remote server. This behavioural change on the part of the patients is a huge driver of digitalization in the healthcare sector.
Digital Revolution in Healthcare In India
The transformation of the Indian healthcare system has already begun. While the government is increasingly encouraging digitization with initiatives such as Digital India and Aadhaar, the private sector has launched numerous mobile applications, telemedicine tools, and innovations centres throughout the country.
The three segments of digital technologies that are helping transform the face of healthcare in India are:
Mobile Health or m-health is one of the most significant parts of India’s digital health revolution. Mobile healthcare apps continuously help educate individuals about preventive healthcare measures and chronic disease management. Moreover, m-health also aids in disease surveillance, tracking epidemic outbreaks, and treatment support.
In 2015, the estimated market size of m-health in India stood at 2,083 crore INR, and by 2020, this number will apparently rise to 5,184 crore INR! According to a study, almost 68% doctors recommend m-health services and 59% of patients around the country are using it.
Remote monitoring is another useful segment of digital healthcare. It allows healthcare providers to monitor the health of patients outside the healthcare centres. It empowers the patients to carry out specific routine tests using certain devices.
Wearables and mobile apps help patients to get constant updates about their health. These devices help increase the rural population’s access to healthcare services by offering point-of-care diagnostics, teleconsultation and e-prescription facilities.
Telemedicine uses technology for remote diagnosis, health monitoring, and consultation. It enables healthcare providers to evaluate, diagnose, and treat their patients without requiring them to come in for an in-house visit.
In addition to providing healthcare facilities in the remotest of areas, telemedicine has significantly helped bring down provider as well as patient costs.
Thus, digitalization of healthcare allows for care beyond the bounds of the hospital and into the limits of your own home. With the increasing number of patients demanding health care services, often the infrastructure falls short. And hence, healthcare providers are now increasingly shifting towards remote monitoring services like telemonitoring and IoT connected wearables.
These biosensing wearables can track and monitor real-time changes in the health of patients and store the data to the health records of the patients. This data can be used for early diagnosis and to provide real-time health support to patients.
The Future of The Indian Healthcare Ecosystem
India is increasingly encouraging innovation and collaboration in the field of healthcare. While innovation is helping to change the deliverance of healthcare services in the country, global partnerships, as well as government and private collaboration projects, are helping to expand the healthcare infrastructure.
Incorporation of data analytics into this industry can provide us with valuable insights and help manage and treat chronic outbreaks better. Also, the consumer demand for digital-health services and applications is stronger than ever. Research suggests that almost 70% of patients between the age group of 18-65 would prefer digital health monitoring over conventional ways of measuring health parameters.
The government initiative of “Digital India,” has opened before us a tremendous scope for developing a smart healthcare system for the benefit of the Indian populace.
One segment of the healthcare industry that has already started to tap the power of digital technologies is the hair transplant sector. Today, a large number of hair clinics across India rely on advanced technologies and robotics to enhance the efficacy and precision of hair transplant procedures.
Unlike earlier times, hair transplant and hair fall treatment procedures are no longer painful. The same applies to laser hair removal technique that uses smart and advanced tools to target the hair from the roots and inhibits the hair growth.
If digital technologies could help transform the entire hair transplant industry, then one can only begin to imagine the potential it holds for the healthcare sector as a whole.
About the Author: This article is contributed by Dr Sonal Shah – Co-Founder at RichFeel.


text: I am really sad that Scotty is hiding here but overall I really love this shot. I keep waiting for flowers to fall from the sky or someone to throw up a peace sign. The colors remind me of the 60s or the way all of my childhood slide photos look under the light. A little imperfect shot but I was glad to finally catch one of the groom without his dress jacket on. I really love this. It is perfect in every way. I only asked for a photo of them but then this photo came next and I can see how Emily can so easily produce a great alter kiss. She was around true love, when it really mattered. I don't even know why I snapped this photo. Looking back, I don't know what I was thinking but I really love it. It is the only photo I took of someone else taking a photo, during the entire wedding and it is perfect. The light flares are complete rainbows and Scotty has this amazing glow of the setting sun around him.

domain: artistic