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RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript.
In this study three independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage.
Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
RNA-seq (high throughput sequencing of the transcriptome) has the potential to transform the way we study gene structure and expression [1]. The ability to identify novel exons and splice sites [2-6] is just the beginning. Although there have been claims that RNA-seq is more sensitive [7] and has a larger dynamic range [3] than a microarray, these claims are now being challenged [8]. For all the optimism surrounding RNA-seq, there is growing evidence that estimating gene expression in an RNA-seq environment is not straightforward. In addition, technical variance, sequencing bias and mapping bias have been reported [9-11].
The power and promise of RNA-seq technology demand our attention. New papers on experimental design [12] will lead the way to more thoughtful experimentation. One important component in careful study planning is an understanding of the different sources of variability so they can be accounted for in the experimental design [13]. Variability can occur at many levels, both biological and technical. Biological variability is unaffected by the technology, as genotypes, individuals and even cell types vary regardless of how they are measured. Bullard et al. (2010) [14] examined the effects of library construction, flow cell and lane on detection of differential expression. The data used there was commercial grade RNA and the samples compared expected to be very divergent. They concluded that the biological variation was larger than technical variation, underscoring the importance of including biological replicates in the study design.
The observation that biological variability is large does not imply that technical variability should be ignored in the experimental design of a study. High levels of noise in a technology must be considered during experimental design [13]. Marioni et al. (2008) [15] demonstrate clearly that technical variability is not different from what is expected due to random noise (sampling error). Yet, the magnitude of the sampling error is not explicitly discussed. One particular feature of deep sequencing is that even though there are millions of reads, the proportion of the mRNA that is actually sequenced is low. Given a Solexa/Illumina RNA-seq library of 10 nM concentration with a mean insert size of 250 bp and a volume of 400 uL, the number of molecules in a typical RNA library is estimated to be 2.408 × 1012. On the Solexa/Illumina GAIIx, approximately 30 million of the total possible molecules are sampled in a given lane. This represents approximately 0.0013% of the total number of available molecules. Even with the anticipated increase in the next generation of technology, the sampling fraction is will be less than 0.004%. The impact of such a low sampling fraction on the statistical properties of RNA-seq is the focus of this manuscript. We address the following questions: 1) Does a substantial amount of technical variability exist? 2) Is the impact of technical variability the same for all levels of coverage?
The study presented here considers technical variation in three experiments using Solexa/Illumina technology. Although the number of exons consistently detected is improved by increasing the number of mappable reads, the detection of exons with low levels of coverage is inconsistent among technical replicates. Inconsistent detection of exons is most pronounced for exons with average coverage of less than 5 reads per nucleotide. In addition, there can be substantial disagreement in the estimated level of expression among technical replicates, even when the coverage is high. Although the technical variability is not unexpected, technical variability cannot be ignored in RNA-seq experiments.
Experiment 1: Three independent samples of D. melanogaster female heads were collected with each sample representing a unique pool of biological material. Each sample was prepared according to manufacturer's instructions and then the same library was run on two lanes of a Solexa/Illumina flow cell, resulting in two technical replicates for each biological replicate, runs were 36 base-pair paired end. Experiment 2: Three independent samples of D. simulans male heads were collected with each sample representing a unique pool of biological material. Each sample was prepared according to manufacturer's instructions and then the same library was run on two lanes of a Solexa/Illumina flow cell, resulting in two technical replicates for each biological replicate, runs were 36 base-pair paired end. (Data will be deposited to the SRA) Experiment 3: One sample of D. melanogaster cell line c167 was run on 5 lanes yielding 5 technical replicates for 1 biological replicate (from modENCODE experiment GSE17107) [16]. Data from these experiments were 36 bp reads. The experimental design for experiments 1 - 3 are outlined in Figure 1. All samples were run using Solexa/Illumina paired end procedures. The relationship of lanes (same or independent flow cells) is unknown for all three experiments. The first two experiments were conducted by Joe Dunham and Michele Arbeitman at USC and sequenced at Oregon State core facility while the third experiment was conducted by Susan Celniker at Lawrence Livermore national laboratory (SRA009944).
Figure 1. Experimental Design. A figure showing the design of the three experiments evaluated here. Biological replicates are separate individuals used for library construction. Technical replicates for the D. melanogaster female heads and D. simulans male heads data are a single library run on multiple lanes. For D. melanogaster c167 cell lines the exact nature of the technical replication is uncertain.
The number of molecules present in the library is estimated to be 2.408 × 1012. This estimate was derived using the Illumina protocol. Specifically, the starting material was assumed to be 100 ng of mRNA that then resulted in 500 ng of library with an insert size of 250bp in a volume of 400 uL for a final library concentration of 10 nM. The number of pmoles is therefore 4 [(10 nmol/1L) (1L/1000 mL)(1 mL/1000 uL)(400 uL)(1000 pmol/1 nmol)]. The number of molecules is found by multiplying Avogadro's number which is molecules/moles and adjusting for units [4 pmole)/1000/1000/1000/1000*6.02E + 023] to give 2.408 × 1012 molecules. On the current Solexa/Illumina technology, the GAIIx, approximately 30 million of the total possible molecules are sampled in a given lane. This represents approximately 0.0013% (30,000,000/2.408 × 1012) of the total number of available molecules for analysis (Figure 2). To understand how random sampling may produce variation in technical replicates in an RNA-seq experiment, a small simulation of two technical replicates was conducted. We assume that the approximately 2.408 × 1012 molecules of mRNA were transcribed from 20,000 genes. Gene expression for the 20,000 genes was modeled using a gamma density function with shape parameter 2 and scale parameter 1. This ensures that many genes will have a small number of molecules assigned to them and some genes will have a large number of molecules assigned to them. The range of the gamma density function was examined over the interval from 0 to 17. The integers from 0 to 17 represent bins of genes with similar relative levels of expression. To simulate relative expression for each of the i = 1, 2, ..., 20,000 individual genes, a random integer (Si) from a discrete uniform distribution between 0 and 17 were drawn and the value of the gamma density ti was calculated at Si. The number of total molecules of mRNA assigned to gene i, gi, was, Ni= ti/Σ(ti) * 2.408 × 1012 rounded to the nearest integer. Σ(gi) = Z. Due to the rounding, Z was approximately, but not exactly equal to2.408 × 1012. For each gi with Ni molecules a unique integer between 1 and Z was assigned, resulting in a unique association between an integer and a gene. To simulate the sampling of molecules of mRNA for each technical replicate, 30 million random integers between 1 and Z were sampled.
Figure 2. Library construction and sequencing. Beginning with 100 ng of mRNA the manufacturer's protocol is used to estimate a sampling fraction.
Exon definitions
For this paper exons, rather than transcripts, were used. Estimating levels for individual transcript isoforms in RNA-seq data can be quite complex as the observed data can be a mixture of isoforms [17,18]. Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms. To focus on the issue of technical replication, isoform estimation was not undertaken. Exons as defined by Flybase (http://www.flybase.org webcite) version 5.4 [19] were mapped onto the genome. Overlapping exons in the same genome region were combined. A single result per genomic region was recorded for each exon in that region (Additional File 1, Additional File 2). For example, for exons with alternative start/end sites the longest genomic region was used to represent an exon. The mapping between exons and the genome region was done via a perl script (Additional File 3). 54,607 of the 60,277 genomic position exons represent uncomplicated annotation regions. However, to ensure that results are not dependent on the exon definition, the observed data were used to derive exons. Consensus contigs were defined as contiguous regions with a per nucleotide read depth greater than or equal to 3 for all lanes within a particular experiment (Additional File 4, 5). This definition results in the evaluation of genomic regions for which RNA-seq data are available for all replicates of a particular experiment. Consensus contigs and their positions for these data are listed in Additional Files 6, 7, 8.
Additional file 1. Overlapping exons combined into single genomic region. D. melanogaster ovo gene (Flybase ID BFgn0003028) used as an example of combining overlapping exons into a single genome region for mapping purposes. Format PDF. View with Adobe.
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Additional file 2. Chromosome postions for overlapping exons combined into single genomic regions. Format TSV. View with Wordpad.
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Additional file 3. Perl script for mapping between exons and a genome region. Format PL. View with Wordpad.
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Additonal File 4. Perl script that converts contiguous sequences in a SAMtools consensus pileup to FASTA, BED, or coordinate tables. Format PL. View with Wordpad.
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Additional file 5. Perl script to find common sequences from contig BEDs. Format PL. View with Wordpad.
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Additional file 6. St_2a_D.melanogaster_common_contigs.bed. BED file containing chromosome postion, start, end and common_contig_ID for D. melanogaster experiment (Experiment 1). Format BED. View with Wordpad.
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Additional file 7. St_2b_Dsimulans_common_contigs.bed. BED file containing chromosome postion, start, end and common_contig_ID for D. simulans (Experiment 2). Format BED. View with Wordpad.
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Additional file 8. St_2c_167_common_contigs.bed. BED file containing chromosome postion, start, end and common_contig_ID for c167 experiment (Experiment 3). Format BED. View with Wordpad.
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Mapping reads
Reads spanning junctions pose a problem. Each lane of data was first evaluated with TopHat [20] to create a set of read-supported junctions. Bowtie [21] was used as the main tool for read alignment. Alignments were run with the "best" option to ensure that reported singleton alignments have the optimal score. The "tryhard" flag was also used to induce Bowtie to spend more time backtracking for hard-to-align reads. Reads aligning more than 10 times were removed from consideration. First, a paired-end alignment was run. Unaligned reads from this step were aligned unpaired. Remaining unaligned reads were mapped to the junctions from TopHat. For D. simulans samples, reads were also aligned to D. simulans genomic reference sequence [22]. Bowtie is fast but cannot handle in/dels or reads that align partially to the reference. Reads remaining unaligned at this point were aligned with LAST [9]. Results were combined into a single alignment file in SAM format and reported versus single reference (the Flybase v5 D.mel reference [19]). Analysis of read coverage was done with SAMtools [23]. For each lane the resulting pileup file was the basis for all further calculations and the remaining unmapped reads were discarded.
Detection and Quantitation
Each exon was projected onto each pileup file. If at least one read maps to a particular exon then that exon was said to be detected. RPKM which is the (number of reads in a window/length of the window)*C/mappable reads), where C is an estimate of the maximum number of reads [3] was used to estimate the quantity of each exon.
To understand variability among replicates it is necessary to understand how well replicates agree, not how well they correlate [24-26]. Agreement between categorical variables can be calculated as the proportion of observations that share the same category divided by the total number of observations [25]. If the number of observations in each category is unequal then two measurements may agree by chance more frequently than estimated using a simple agreement. Kappa calculates the agreement between two replicates and adjusts for chance agreement [25]. The Kappa statistic gives a measure of agreement and the value 1 represents perfect agreement [25]. The most basic question is whether exons can be consistently detected between replicates. The agreement of technical and biological replicates on detection of exons was calculated using both simple agreement and Kappa statistics.
For ordinal scales, a weighted Kappa allows for distance in categories to be used in the assessment of agreement and is interpreted in the same way as the Kappa [25]. The RPKM was grouped into 3 levels undetected/low-medium/high where high levels were defined as RPKM > 20. Alternatively RPKM was grouped into 9 levels using a log scale as follows: Zero reads, RPKM less than 10, 20, 40, 80, 160, 320, 1000 and greater than 1000 were the ordinal categories used. Simple agreement (two replicates having the same level of coverage) and weighted kappa statistics were calculated. Examination of the frequency table between the technical replicates can give insight into whether the disagreements are solely a function of the level of digital gene expression.
Correlation coefficients have often been used to describe agreement. However, correlation does not reflect agreement [24-26]. Spearman's correlation coefficient quantifies how well the relationship between two variables, (Y,X), can be described using a monotonic function. If one estimate is exactly twice the size of the other estimate then the correlation will be 1. However, these two measures do not agree. In addition to the scale factor, correlations depend upon the range of values observed. A large range of values will produce a higher correlation than a narrow range of values. In comparisons of whole genome expression the range is extremely large. A further problem is the large number of data points makes the linear trend unlikely to be influenced by strong disagreement for some exons.
Bland-Altman [24,26] proposed a method for visualizing data and providing insights into agreement. These plots are known as MVA or MA (minus versus average) plots in the gene expression literature and have been utilized intensively to assess presence of abundance-dependent bias [27]. The plot is defined as follows: on the X axis is the average of the two measures (in this case the technical replicates) and on the Y axis is the difference between the two measures. These plots were constructed for exons for all combinations of technical replicates where there are non-zero measurements in both replicates. An absolute difference of 10 has a different implication for agreement if the RPKM is 20 than if the RPKM is 300. For that reason, Bland-Altman plots were also made on the natural log scale. That is, the natural log of the RPKM was calculated for each technical replicate where the scale is considered to be additive rather than multiplicative and then used for computing the average and the difference.
Coverage plots [28] were constructed to examine whether exon length was associated with missing values. To formally test whether exon abundance and/or length is related to whether an exon is present in all or only a portion of the technical replicates, an analysis of the dataset for the cell line c 167 was conducted. Only exons that were observed in at least one technical replicate were considered. The dichotomous response of "in all technical replicates" versus "not in all technical replicates" was modeled using logistic regression. Formally, Let Yi = 1 if the ith exon is not present in all technical replicates, 0 otherwise. The model
was fit, where ai is the abundance of the ith exon and li is the length of the ith exon and i = 1,..,n where n is the number of exons observed in at least one technical replicate. The average RPKM, taken across all technical replicates was used as the measure of abundance for that exon and the length of the exon in base pairs was used as the measure of exon length.
For the three experiments, the number of mappable reads per lane varies among the technical replicates and the number of exons detected increases with the number of mappable reads (Table 1). The exons that are detected vary between technical and biological replicates (Table 1). Many exons are observed/present in one technical replicate but unobserved/absent in the other technical replicate. There was no exception to this observation in any comparison made (Table 2). The agreement (Kappa) in detection between technical replicates ranges from 0.63-0.81. Lanes with higher coverage have better agreement in detection, but even with the higher coverage rates, the number of exons detected varies. The comparison with the least discrepancy had ~3,600 exons missing in one of the two technical replicates and several had more than 5,000 missing (Table 2). Among biological replicates discrepancies were larger than for technical replicates in that same experiment (Table 2). The simulation study shows that this variability in detection can be explained due to random noise from a very small sampling fraction (0.0013%). A second simulation (not shown here) based upon the multinomial model came to the same conclusion. In the simulation, and in the real data examined here, discrepancies in detection are largely due to exons with coverage per nucleotide of less than 5 in the technical replicate in which they are detected (Table 2, Additional File 9). This indicates that higher coverage increases the consistency of detection across technical replicates. Unfortunately, the vast majority of exons are observed at low coverage (Table 1).
Table 1. Mappable reads per lane in each of the three experiments.
Table 2. Agreement between technical replicates and biological replicates for RPKM measured on FB 5.4 exons (n = 60,277).
Additional file 9. Agreement between technical replicates and biological replicates for RPKM measured on common contigs. A comparison in the agreement of the estimation of the amount of expression. Agreement in whether common contigs are expressed, expressed at a low level (0 < RPKM < 20), and expressed at a high level (RPKM > 20) and agreement on a 9 level ordinal scale as follows: RPKM less than 10, 20, 40, 80, 160, 320, 1000 and greater than 1000. Agreement was measured using a kappa coefficient. The number of common contigs where disagreement is greater than 2 logs and greater than one log are also given. Format XLS. View with Excel.
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Additionally, the length of the exon may contribute to the discrepancy. To examine this, coverage plots were constructed (Additional File 4). Coverage plots have a solid line if the exon was detected in the corresponding biological and technical replicate and a blank space if the exon was not present. The plots were conducted by sorting the exons by length. Subsequent plots were made for exons surrounding a given percentile of exon length. For exons that are detected inconsistently among replicates there seems to be no trend associated with the length. Plots of the 10th percentile appear to be similar to the 20th, 50th and 90th percentile (Additional Files 10). The coverage plots do not take mRNA abundance into account. To test whether exon length and abundance, and the interaction between them contribute to differences in detection among technical replicates a logistic regression model was used. The interaction between length and abundance was significant (estimate, -0.0136; p < 0.001). This means that the relationship between length and abundance differs between highly abundant and infrequently observed exons. For infrequently observed exons, longer exons are more likely to be consistently detected than shorter exons. This pattern is not observed in highly abundant exons. It must be noted that this is a trend, and that some long exons are missing among some technical replicates and some short exons are present in all. Although main effects are often difficult to describe in the presence of interactions, in this case, it is clear that regardless of the exon length lower levels of abundance are less likely to be consistently detected (estimate -0.25; p < 0.001).
Additional file 10. Length of the Exon does not explain disagreement in technical replicates. Coverage plots of the c167 cell lines data. The Y axis is the average coverage across all technical replicates. A bar is drawn if the exon is present (at any coverage level) in that technical replicate. The 10th percentile represents that bottom 10% of the exons in length while the 90th percentile represents the top 10% of exons by length. Format PDF. View with Adobe
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Within each lane, for each exon, the average number of reads per nucleotide (APN) and the standard deviation were computed. The coefficient of variation (CV, standard deviation/mean) versus the APN (Figure 3) was plotted for each exon. Coverage below APN 5 is highly variable. The CV for an exon with a coverage of less than 5 reads per nucleotide is often greater than 2 and can be greater than 20. The same pattern holds for all samples examined (Figure 3), indicating that this is not an artifact of the number of mappable reads. In fact, when reads from the c167 experiment are combined and the same plot is constructed, the same pattern is seen (data not shown). Although the maximum CV is smaller for the data combined across 5 lanes, than for a single lane of data, the trend is still apparent. This, together with the results on consistency of detection, indicates that an average coverage of 5 reads is a useful target when planning an RNA-seq study. For a lane with almost 29 million mappable reads, not quite 60% of the exons in Flybase were observed at this level of coverage.
Figure 3. Coefficient of variation (CV) plotted on Y axis and average depth per nucleotide (APN) on X axis. Points with average depth of greater than 1000 are not displayed. Panel A is D. simulans BR2 TR2. Panel B is D. melanogaster female heads BR2 TR1. Panel C is TR1 for cell line c 167. Note that despite the difference in the number of mappable reads, the pattern of CV against the mean remains the same. CVs are very large when the average expression is low. Individual points represent exonic regions (Flybase 5.4) cubic smoothing line fit using R's smooth.spline function.
There is great interest in understanding the performance of RNA-seq and to that end many studies have used the correlation coefficient as a means of assessing concordance. We plot the RPKMs of technical replicates here against each other (Figure 4A,B) to demonstrate that these plots are similar to those previously reported [15]. As expected, Pearson and Spearman correlation coefficients are very high. Spearman correlation coefficients are in excess of 0.95. However, correlation is not a measure of agreement [24-26]. By categorizing expression into several ordinal groups and then comparing two technical replicates (Table 2,3, Additional File 9), it becomes immediately obvious that there are areas of disagreement between the technical replicates, and these are not limited to exons with low coverage (Table 2,3). For one particular example given (Table 3), the Spearman correlation is 0.95 and the weighted Kappa statistic is 0.80. Although the actual agreement will depend explicitly upon the ordinal values chosen and the scale used for expression, weighted Kappa statistics for a range of possible categorizations were computed and found to be always lower than 0.9, often close to 0.6 and occasionally close to 0.5. This means that the use of RPKM as a measure of expression can be a log (or even two) different at all levels of expression. This is not sensitive to the RPKM normalization, as normalization factors [29] among technical replicates are close to 1. Indeed, this discrepancy is apparent in the simulated data, where normalization is not necessary. Sampling variance can result in differences in estimated expression of 1 log or greater. This finding may explain some of the studies which report discordance between estimates of expression from RNA-seq and known concentrations [8].
Figure 4. Scatterplot of technical replicates. Points where RPKM is 1000 or less are displayed (A). The red line is the 45 degree line. Left panel is D. simulans male heads BR2, middle panel is D. melanogaster female heads BR2 and right panel is D. melanogaster cell line c167 Tr3 vs TR4. Spearman correlation values are (0.95, 0.99, 0.96), respectively. Scatterplot of technical replicates on the log scale (log(RPKM+1)) for RPKM values of less than 1000) (B). The red line is the 45 degree line. Left panel is D. simulans male heads BR2, middle panel is D. melanogaster female heads BR2 and right panel is D. melanogaster cell line c167 Tr3 vs TR4. Spearman correlation values are (0.95, 0.99, 0.96), respectively.
Table 3. Agreement between technical replicates for biological replicate 2 D. melanogaster female heads.
The MVA plot [27], initially proposed by Bland and Altman [24,26] to assess agreement between two methods of measuring an endpoint, is an intuitive examination of agreement that helps diagnose the magnitude and functional form of disagreements. The Bland-Altman plot of the natural log-transformed data for the technical replicates of D. simulans biological replicate 3 (Figure 5, and Additional file 11) clearly shows that at lower levels of expression there is larger disagreement between technical replicates. However, although the absolute disagreement is a function of abundance, the moving average smoother indicates that average disagreement or bias between technical replicates is consistently linear (rather than nonlinear) over the abundance range. This is in contrast to what is observed for microarray data where a nonlinear bias (disagreement) is typically observed as a function of mean abundance making nonlinear normalization necessary [30,31]. This is also consistent with the findings of Bullard et al. (2010) [14] who find that no nonlinear normalizations are needed.
Figure 5. Bland-Altman plot showing level of agreement between technical replicates for natural log transformed RPKM D. simulans biological replicate 3. On the Y axis is the difference between technical replicates and on the X axis is the average between technical replicates. Green lines are the average of all differences +/- 1.96 (standard deviation of the differences). The red line is drawn at zero. The blue line is a loess fit. The discrepancy between technical replicates is a function of the estimated expression level. The horizontal line is drawn at an average coverage per nucleotide of 5. Bland-Altman plots for all the remaining comparisons among technical replicates are in Additional file 11.
Additional file 11. Bland Altman plots for each biological replicate.
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Does the choice of the Flybase definition of an exon impact the results? If the empirically defined common contig is used instead of the Flybase exon, then by definition we are only examining regions seen in all technical replicates. Common contigs show greater levels of disagreement in the estimation of amount of expression between technical replicates than the Flybase exons. Like the Flybase exons, areas of lower coverage are more likely to disagree (Additional File 9). The disagreement across technical replicates decreases as the coverage increases for all the data examined here (Table 2, Additional file 9).
Consistent with other reports [14,30] biological variation is larger than technical variation. The number of exons detected in both replicates is lower between biological replicates (Table 2). Some of these differences may be due to the flow cell/lane assignments, which are unknown for all of these experiments. Consistent with previous reports, the level of coverage makes a large difference and some of the biggest disagreements are in the D. simulans experiments where 1 biological replicate has ~5 million reads where the other biological replicates had ~28 million reads. These samples must have been run not only on different flow cells but also on different iterations of the technology. Based on previous findings, the finding that these samples have the largest differences and lowest agreement is not surprising.
The number of exons detected, was approximately 64% (out of 60,277 exons covering genome positions total) in lanes with 5-7 million reads and 84% in lanes with approximately 27 million reads. The results of this study indicate exons are not consistently detected among technical replicates when the average coverage for that exon is less than 5 reads per nucleotide. Additionally, the coefficient of variation can be very high for exons with coverage of less than 5 reads per nucleotide. Between lanes, exons that are detected in both lanes with one of those being at low coverage, disagree in the estimates of abundance (RPKM) between technical replicates, leading us to conclude that when analyzing data for differential expression, one could consider identifying exons with coverage less than 5 for closer examination. These findings are consistent across different versions of the technology, different species, and different laboratories (E1 and E2 vs E3). The number of exons covered with an average of 5 reads per nucleotide--only 21% of all exons in lanes with 5-7 million reads and 58-60% in lanes with 27 million reads--is substantially smaller than the number of exons detected.
Although agreement improves once coverage is above 5, it is not perfect and disagreement can be large even when coverage is high. Why are there such large discrepancies in detection and in estimates of expression across technical replicates? The underlying assumption for all gene expression technology, whether microarray, SAGE, Q-PCR or RNA-seq, is that the final measurement of gene expression for a particular sample is proportional to the underlying population of mRNA extracted from that sample. The variability in exon detection, and the disagreements in estimates of expression are perplexing. A possible explanation would be the choice of the particular normalization. However, the results are virtually identical if we use an average per nucleotide or RPKM. A normalization constant [29] was also estimated and found to be close to 1 among technical replicates, indicating that these results cannot be explained by improper normalization. There are at least two obvious explanations for observing this disagreement: insufficient mixing and a very low sampling fraction.
If any of the solutions generated during preparation for cluster generation are insufficiently mixed, then the distribution of reads across samples is expected to be uneven. This would be similar to what Student saw in his experiment with yeast cells [32]. In this experiment, insufficient mixing was partially attributed to the tendency of yeast cells to "stick together in groups which was not altogether abolished even by vigorous shaking." Student developed the negative binomial in response to this observation. In the negative binomial distribution the variance is larger than in a Poisson distribution. The standard Poisson has a variance equal to the mean. Poisson distributions that allow for over dispersion are general, and the negative binomial is one particular type of over dispersion. The overdispersed Poisson has been proposed for modeling RNA-seq data [12,14,15,33,34].
For both the D. melanogaster and D. simulans experiments, technical replicates were derived from an individual library analyzed on multiple lanes. Thus, discordance between the technical replicates must have occurred after library synthesis, presumably during generation of the templates on the flow cell. We can envision other experimental plans in which technical replicates are derived from an individual sample, prior to library synthesis. In this latter case, discordance between technical replicates could arise during or after library preparation. Modifications of the protocol have been developed that omit potential bias due to amplification [35]. An inspection of the cluster generation protocol (Figure 2), carried out after library synthesis, reveals additional places where variation might be introduced. The initial step of the protocol entails at least one large dilution step. Any uneven distribution of molecules in the library at this point, whether due to incomplete mixing or DNA-DNA interactions assisted by cations [36], could lead to differences in the molecules loaded onto a lane. Single stranded libraries may have better mixing. If there is aggregation of molecules in solution, then the current protocol could be modified to a dilution series with particular care taken to ensure molecules are mixed well and neither aggregate nor degrade in the process. Non-uniformity in solution is not limited to cDNA given that RNA molecules, used as input into library synthesis, can form intra- and intermolecular interactions. RNA solutions containing high concentrations of divalent metal ions are particularly prone to forming RNA aggregates [37]. As an extreme example, more than half of the RNA in a pool of random RNA sequences was found to aggregate together through self-complementarity and precipitate out of solution under conditions favoring intramolecular interactions [38]. Although it is not possible to determine from these experiments if unequal mixing is a problem, removing or reducing the potential for this problem should be a relatively straightforward modification of the current protocols.
The other possibility is that discrepancies among technical replicates are due to the sampling fraction, 0.0013% for 30 million reads (Figure 2). This fraction is so low that the behavior of random sampling in this particular scenario should be examined. The simulation study performed here indicates that data are consistent with this hypothesis. Marioni (2008) [15] also reported data consistent with randomness. Agreement between technical replicates in the simulated data (Figure 6A,B,C) shows the same patterns as the three sets of real data presented here. In the simulated data, there are also examples of genes with substantial disagreement among technical replicates. This indicates that the observed disagreement can be explained by the low sampling fraction, and that the results are consistent with the expectations of random variation. What is initially surprising is the size of the random fluctuation. However, when the sampling fraction is considered, the results are less surprising.
Figure 6. Bland-Altman plot for simulated data. The data were log transformed and the average of the two technical replicates is on the X axis and the difference between technical replicates is on the Y axis. (A) Simulated replicates 1 versus 2. (B) Simulated replicates 1 versus 3. (C) Simulated replicates 2 versus 3. Green lines are the average of all differences +/- 1.96 (standard deviation of the differences). The red line is drawn at zero. The blue line is a lowess fit.
This study demonstrates that the current nature of the technology is to expect variation between single lanes both in exon detection and estimation of gene expression using RPKM. Technical variation, while smaller than the biological variation, cannot be ignored and should be accounted for in the study design.
The fundamental principles of statistical experimental design are thus just as important as ever, even with modern technology. R.A. Fisher defined these fundamentals to be randomization, replication and blocking [13,39]. The optimal experimental design strategy will depend on the objectives of the study at hand. Randomization and blocking are important to incorporate in order to avoid confounding of biological with systematic experimental effects. Likely experimental effects in mRNA Seq data include lane, flow cell or library preparation batch. Randomized block designs have been shown to be an efficient use of resources while ensuring legitimate comparisons to be made. Multiplex designs [12] represent another good solution which would ensure more complete coverage of exons across biological replicates and eliminate lane as a potential confounding variable in small experiments. Consider the following simple example of three biological replicates for treatments and three biological replicates for controls for a Drosophila experiment. If each sample is run on a single lane and more coverage is needed, then another full set of 6 lanes is necessary. In contrast, a single multiplexed run where all 6 samples are run on all 6 lanes has been proposed [12]. Exons seen in some technical replicates but not others are clearly a result of technical variation. In this design the total coverage remains almost the same as in the simple design. The added benefit is that small numbers of extra lanes can be run if more coverage is desired.
A replication strategy must be chosen as well with an appropriate balance between biological replicates and technical replicates [40-43]. While, increasing the number of biological replicates increases the precision and generalizability of a study more than increasing the number of technical replicates; for studies where low abundant mRNAs are the focus, increasing technical replication may also be important.
RNA-seq experiments need to replicate the results both technologically and biologically as the technical variation in exon presence and absence as well as amount of coverage is not negligible. Consistent with random variation due to low sampling fractions, technical variation is most pronounced in the variability of detection of exons when coverage is low. However, disagreements between estimates of expression can occur at all levels of coverage.
Authors' contributions
LMM designed the study, did analyses and wrote the paper, KKL did analyses, designed and executed simulation study, contributed to writing of the paper, AMM designed experiments and writing of the paper, VA carried out all bioinformatics pipeline design and execution, ALO designed experiments and contributed to the paper, LJY designed experiments and contributed the paper, and SVN designed experiments and contributed to writing of the paper. All authors read and approved the final manuscript.
This work was supported by National Institutes of Health [R01-GM077618, R01-GM077618-S1, R01GM076643, N01-AI40065]; National Science Foundation [PGRP 0821954, MRI CNS-0821622]. Michelle Arbeitman and Joe Dunham are kindly acknowledged for the sharing of the D. melanogaster female head and D. simulans male head data.
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Applications: Turbomachinery Test Stands, Turbine Engine Research
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Datasheet: 9116
Description: The 9116 Pneumatic Intelligent Pressure Scanner is a completely self-contained high performance pressure acquisition module for multiple measurements of dry, non-corrosive gases. The scanner integrates 16 silicon piezoresistive pressure transducer, of any pressure range, with the latest microprocessor technology in a compact, rugged package. Each field-replaceable pressure sensor incorporates a temperature sensor and EEPROM for storage of calibration data as well as sensor identification information such as pressure range, factory calibration date, and user-managed last or next calibration date.
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A Short History of Nearly Everything
One of the world's most beloved and bestselling writers takes his ultimate journey -- into the most intriguing and intractable questions that science seeks to answer.
In A Walk in the Woods, Bill Bryson trekked the Appalachian Trail -- well, most of it. In In A Sunburned Country, he confronted some of the most lethal wildlife Australia has to offer. Now, in his biggest book, he confronts his greatest challenge: to understand -- and, if possible, answer -- the oldest, biggest questions we have posed about the universe and ourselves. Taking as territory everything from the Big Bang to the rise of civilization, Bryson seeks to understand how we got from there being nothing at all to there being us. To that end, he has attached himself to a host of the world's most advanced (and often obsessed) archaeologists, anthropologists, and mathematicians, travelling to their offices, laboratories, and field camps. He has read (or tried to read) their books, pestered them with questions, apprenticed himself to their powerful minds. A Short History of Nearly Everything is the record of this quest, and it is a sometimes profound, sometimes funny, and always supremely clear and entertaining adventure in the realms of human knowledge, as only Bill Bryson can render it. Science has never been more involving or entertaining.
From the Hardcover edition.
"Stylish [and] stunningly accurate prose. We learn what the material world is like from the smallest quark to the largest galaxy and at all the levels in between . . . brims with strange and amazing facts . . . destined to become a modern classic of science writing."
—The New York Times
"Bryson has made a career writing hilarious travelogues, and in many ways his latest is more of the same, except that this time Bryson hikes through the world of science."
"Bryson is surprisingly precise, brilliantly eccentric and nicely eloquent . . . a gifted storyteller has dared to retell the world's biggest story."
—Seattle Times
"Hefty, highly researched and eminently readable."
—Simon Winchester, The Globe and Mail
"All non-scientists (and probably many specialized scientists, too) can learn a great deal from his lucid and amiable explanations."
—National Post
"Bryson is a terrific stylist. You can' t help but enjoy his writing, for its cheer and buoyancy, and for the frequent demonstration of his peculiar, engaging turn of mind."
—Ottawa Citizen
"Wonderfully readable. It is, in the best sense, learned."
—Winnipeg Free Press
"[A Short History of Nearly Everything] is a crash course in the basics of climatology, chemistry, biology, botany, geology and physics. Bryson's enthusiasm is infectious, his explanations simple. You don't need to be a rocket scientist to get them."
—The Citizen's Weekly
From the Hardcover edition.
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Six months ago, the automotive world was tipped on its head. In a market already tight in supply, the industry had to process the aftereffects of the Japan earthquake and tsunami. Used cars were selling for near-new car prices as the Manheim Used Vehicle Value Index hit historical peaks month after month.
With such extraordinary events, it wasn't easy to see the forest through the trees. And now there's flooding in Thailand — but we'll have to wait to fully process that one.
In a market reset, where are we since we last sat down on the subject? New car sales should hit 12.6 to 12.7 million units by year end. That'll be about a million units more than 2010, a 10 percent improvement. That seems remarkable, especially in light of the spring supply disruptions, until you realize that the 2010 numbers were abysmal as we were still crawling out of the muck of the recession.
This year's increase is on the back of retail sales, as cumulative fleet sales (commercial, rental, government) through September are up only 1.7 percent. Why? A few reasons, in talking to car dealers and fleet buyers and sellers: municipalities don't have the budgets; companies aren't adding to their sales forces; the construction industry is limping along; and rental fleets are comfortable with longer hold times. And — this is a good thing — cars are being built better than ever.
"There's still a lot of value in a 100,000-mile car," says Rick Nicoletti of Napleton Fleet Group.
It should also be noted that in 2010 fleets went on a buying spree coming out of the recession (total fleet sales up 22 percent over 2009) to replace ragged fleet, and comps couldn't stay at that accelerated pace this year. While last year was more of a fleet market, this year's emphasis is on retail.
The used car market had no choice but to come down off its late spring/early summer historic highs, and it did. Ricky Beggs, vice president and managing editor of Black Book, says values started dropping early this year, starting in the end of June.
"It was just the fact that prices have gotten so high, there had to be an adjustment," he says. But there is no bubble bursting. We are still in the midst of the usual fall value deflation, but don't worry, the bottom isn't going to drop out the cars you'll be selling.
Tom Kontos of Adesa reports some added downward pressure on used vehicle values during this fall's defleeting period mostly as a result of the additional 283,644 units sold into rental fleets in 2010 versus 2009 coming into the wholesale market.
But this is mitigated, again, by lack of supply. The cars that weren't bought in 2009 and 2010 aren't turning into used cars, nor are the lease originations that didn't happen during the OEM bankruptcies.
Executive Editor
Chris is the executive editor of Business Fleet Magazine and Auto Rental News. He covers all aspects of the fleet world.
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Double standard?
If I wrote a letter to The Times Argus comparing a racial or ethnic group to animals it would rightly be rejected out of hand.
Just because the Constitution gives someone a right to sink into hate speech doesnt mean that your newspaper is obligated to publish their drivel.
Yet your editors apparently have no problem publishing a letter (March 26) that explicitly compares same-sex marriage to bestiality.
The sentiments expressed in Mr. Bairs letter are hateful in exactly the same way that any letter calling a particular group of people subhuman would be.
The only merits of the letter are that the author knows a few multisyllabic words and how to write complete sentences.
Apparently thats good enough for The Times Argus.
Phil Keller
MontpelierMORE IN LettersPutting together a budget is never an easy task, according to the governor in a recent letter, ... Full StoryWe are welcoming this warm weather and so are the ticks. Full StoryStrange days indeed Full Story -
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Statistical analysis of stimulated Raman crosstalk in distributed Raman amplifier |
Applied Optics, Vol. 50, Issue 25, pp. E162-E171 (2011)
Enhanced HTML Acrobat PDF (687 KB)
Closed-form formulae have been analytically derived to estimate crosstalk due to stimulated Raman scattering (SRS) in a multipumped, broad, and flattened gain distributed Raman amplifier (DRA). The derived formulae are applied to study the crosstalk due to SRS in a multipumped DRA having different pumping configurations. System bounds for a typical wavelength division multiplexing sys tem employing DRA have been evaluated theoretically. Performance of DRA for other parameters such as wavelength separation, input signal power, and bit rate of the system has also been investigated.
© 2011 Optical Society of America
OCIS Codes
(060.0060) Fiber optics and optical communications : Fiber optics and optical communications
(060.2320) Fiber optics and optical communications : Fiber optics amplifiers and oscillators
(060.2330) Fiber optics and optical communications : Fiber optics communications
(060.4510) Fiber optics and optical communications : Optical communications
Original Manuscript: March 3, 2011
Revised Manuscript: July 12, 2011
Manuscript Accepted: July 12, 2011
Published: August 16, 2011
Anamika and Vishnu Priye, "Statistical analysis of stimulated Raman crosstalk in distributed Raman
amplifier," Appl. Opt. 50, E162-E171 (2011)
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- J. Wang, X. Sun, and M. Zhang, "Effect of group velocity dispersion on stimulated Raman crosstalk in multichannel transmission systems," IEEE Photon. Technol. Lett. 10, 540–542 (1998). [CrossRef]
- D. Cotter and A. M. Hill, "Stimulated Raman crosstalk in optical transmission system: Effects of group velocity dispersion," Electron. Lett. 20, 185–187 (1984). [CrossRef]
- G. P. Aggarwal, Nonlinear Fiber Optics (Academic, 1995), Chap. 8.
- S. Norimatsu and T. Yamamoto, "Waveform distortion due to stimulated Raman scattering in wideband WDM transmission systems," J. Lightwave Technol. 19, 159–171 (2001). [CrossRef]
- M. N. Islam, "Raman amplifiers for telecommunications," IEEE J. Sel. Top. Quantum Electron. 8, 548–558 (2002). [CrossRef]
- S. Namiki and Y. Emori, "Ultra broadband Raman amplifiers pumped and gain equalized by wavelength-division multiplexed high-power laser diodes," IEEE J. Sel. Top. Quantum Electron. 7, 3–16 (2001). [CrossRef]
- F. Forghieri, R. W. Tkach, and A. R. Chrapylvy, "Fiber nonlinearities and their impact on transmission systems," in Optical Fiber Telecommunications III A, I.P.Kaminov and T.L.Koch, eds. (Academic, 1997), pp. 196–264. [CrossRef]
- D. N. Christodoulides and R. I. Joseph, "Theory of stimulated Raman scattering in optical fibers in pulse walk off regime," IEEE J. Quantum Electron. 25, 273–279 (1989). [CrossRef]
- D. N. Christodoulides and R. B. Jander, "Evolution of stimulated Raman crosstalk in wavelength division multiplexed systems," IEEE Photon. Technol. Lett. 8, 1722–1724 (1996). [CrossRef]
- F. Forgheiri, R. W. Tkach, and A. R. Chrapylvy, "Effect of modulation statistics on Raman crosstalk in WDM systems," IEEE Photon. Technol. Lett. 7, 998–1000 (2000).
- K.-P. Ho, "Statistical properties of stimulated Raman crosstalk in WDM systems," J. Lightwave Technol. 18, 915–921(2000). [CrossRef]
- T. Yamamoto and S. Norimatsu, "Statistical analysis on stimulated Raman crosstalk in dispersion managed fiber links," J. Lightwave Technol. 21, 2229–2239 (2003). [CrossRef]
- Anamika and V. Priye, "Power penalty in WDM system due to stimulated Raman crosstalk," presented at Photonics 2010, Guwahati, India, 11–15 December 2010.
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- I. S. Gradshetyn and I. M. Ryzhik, Tables of Integrals, Series and Products, 6th ed. (Academic, 2000).
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The incorrect address was given in the Evergreen Footwear story that appeared in the Dec. 2 edition of the Mountain Democrat on page B-2. The correct address is 1224 B Broadway in the Hangtown Village Square shopping center.
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Photo ID # | a09.28.04_292_MIC_FLM_0073_1 |
Car #: | #292 |
Driver (s) : | Al Michalchuk |
Location: | Flemington, NJ |
Date: | 1973 |
Photographer: | Bill Young |
Photo provided by: | Kevin Budden |
Comments: | No secret here as this is my all time favorite driver in my
all time favorite car. Truth is, it was Al's hard driving style combined with a mean
looking battle scarred Coupe that hooked me into a lifetime of watching dirt track races.
The ability of an individual to dream, design, fabricate and drive was never more
evident than with the mild mannered Al Michalchuk who was anything other than that once
the head sock went on... He knew only one way and that was all out and on the
outside. |
Visitor's Comments | To add your comments about THIS PHOTO - Click Here | |
Date: | Visitor's Name: | Comment: |
09/06/04 | ||


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Recipe by: Gloria
Original recipe yields 8 servings
Amount per serving ({{servings}} total)
Based on a 2,000 calorie diet
Sort stores by
Ready In
great! absolutely delicious!everyone loved it!
We really enjoyed this recipe. We could hardly wait for it to cool completely, but definitely allow it to completely cool. We didn't have light cream, so I added 1 Tbsp of melted butter to the 1/3 cup milk. We used a 9.5 inch pie pan. Nothing bubbled out. We covered the edge of our crust with foil & removed the foil the last 10 minutes. We are making this again today for a teacher appreciation luncheon. Thank you !
I love that this recipe is not too sweet, yet the cream helps cut the tartness of the rhubarb so it doesn't make you pucker to eat it. I intend to make this again, and fix a few things that happened for me while making this. First, I couldn't decide between deep dish and regular crusts. I've learned this recipe needs deep dish! In the shallow crust, the rhubarb was pretty mounded up (I was hoping it would bake down a little, which it did). There was too much custard- as I poured slowly, it would spill out the sides and at a certain point I gave up and poured the remaining custard into a cupcake tin for a second dessert; this is also an issue that can be fixed with a deep dish pie crust. As the pie baked, even more custard bubbled up and spilled out, so definitely put a pan underneath to catch the drippings, otherwise have fun cleaning your oven. Small issues aside, this was a huge delicious hit at the party I took it to and I would love to make it again.


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Found a source for magic circles
There's a visual illusion involving two circles posted at: www.techinterview.org/Puzzles/fog0000000160.html Well, I found a URL that is quite probably the source of that illusion: www.sandlotscience.com/Distortions/Distortions_frm.htm By the way, lots of topics here make me wonder where is their root message. Where can I find the root message for the topic with title "crazy guy on the airplane"? Thanks stefaan dot r dot m dot meeuws at pandora dot be
Stefaan R. M. Meeuws Monday, December 22, 2003
copy and paste the link http://www.techinterview.org/Puzzles/crazyguyontheairplane.html
JHY Monday, December 22, 2003
Recent Topics
Fog Creek Home


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So nervous now. Open morning at school earlier and barely could answer any parents' questions, was just chugging water and pacing around looking terrified.
Need to be strong and not wimp out.... But the thoughts of last year's injury are now weighing heavily on the mind
good luck everyone, the end is in sight!!!
running this for the second time and now getting v nervous. Was in chester today for school rowing event and walked the last mile with butterflies... must remember that last stretch leading to the race course is longer than I think!!!!!
12 yesterday and 5 tomorow. I just hope my stress fracture (from last year's marathon ironicallh enough) does not raise it's ugly head.
Hope everyone else is just as terrrified../excited for the feeling of achievement once it is done.
bring on the rain and clouds for next weekend the 22 degrees today was far too reminiscent of last year.
glad to hear some people are up to 12 too...
ran this last year, think I peaked too early with training and then got a combination of stress fracture/ compartment syndrome 3 weeks later during a 10 mile run (after next entry was in before I realised severity ). Hence did not run properly for 6 months and now each lsr is longer than since last October.... Will I get there I wonder?
cheered me up to hear everyone's training and hope to be there as I loved it last year (blocked out the pain, stomach cramps etc )
After running an autumn marathon, I have been pretty much out of action due to low ankle and pain either side of my shin in my right leg. I have been for MRIs (diagnosed as ligament damage in the anterior tibiofibular joint) and also been told I have chronic compartment syndrome in my lower right leg.
I have been strengthening the calf, rollering, icing etc....doing as told but no better . I am now at my wits end. I have been a runner for going on 12/13 years and this is the longest I have been out and have no idea where to turn. I am not looking for any further diagnosis but was looking for advice or if anyone else could help get me through these dark days........winter running is my forte and I am totally lost.
I was wondering how you are getting on with this? I ran a marathon in early oct, first long run after was crippled with shin and ankle pain about 5 miles in which then lasted long into the night.
was diagnosed with possible stress fracture/CS. Been waiting 3 weeks to hear back from MRI and considering going private.
To be honest, am in pain when still so am not convinced CS is the main thing,,,, what are your symptoms?


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Carnival Liberty Cruise Review by peachcreamsicle: Horrid Vacation, Buyer Beware!
Member Rating
Compare Prices on Carnival Liberty Western Caribbean Cruises
Horrid Vacation, Buyer Beware!
I read some other reviews before posting here and I have great difficulty believing that we are talking about the same cruise. I suppose the Carnival Liberty is an alright vacation if you have exceptionally low standards and even lesser taste. The ship definitely has a booze cruise / white trash feel, particularly the lido deck. I spent more time on the Serenity deck but had to constantly wear headphones to defend against the onslaught of rowdy, drunk cruisers and the DJ blasting such classics as "Party Rock Anthem." Definitely not the relaxing feel I wanted. I really wished there had been a covered deck area on board for those of us hoping to avoid future melanoma.
The gym was okay but I was disappointed to learn that there is an additional fee for classes. I went super early so I had ample choice of machines. The running track was nice for speed walking as well. I had little else to do throughout the day since most on board activities include buying either ripoff art or More
jewelry. Oh and posing for those horrendous cruise photos. I never stopped to pose for those because the last thing I wanted was a photographic evidence that I'd been on this ship.
The food was not any better. The buffet was crowded and left much to be desired in terms of choice and quality. The utensils are wrapped in some sort of dirty, blue linen closely resembling an oil change rag. By the third day I'd resorted to almost exclusively ordering my meals from room service. I think I ate more PB & J sandwiches that I did in third grade. The room service was slow and food was often cold on arrival. One time there was a lipstick stain on a glass of apple juice I'd ordered. Pretty much thought I'd puke. I dined in the MDR once and my supposedly "medium rare" steak was practically charred. Did not return as food quality did not warrant the long waits and depressing interaction with the wait staff.
The Western Caribbean itinerary is absolutely horrid. Belize City is a humid, sweltering dump. Roatan consists of watching obese Americans ride a cable car over to an unimpressive beach. In Cozumel, I did the excursion to the Tulum ruins. That was a pretty "meh" experience as well. Cayman has gotten tons of mileage out of Seven Mile Beach but that was crowded and overpriced. I personally much prefer Eastern and Southern itineraries. Live and learn I guess! Less
Published 10/24/13
Read more Carnival Liberty cruise reviews >>
Read Cruise Critic's Carnival Liberty Review >>
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Terrible Service and Boring, T...
A Happy Return to Silversea
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Great Winter Getaway on the Fr...
Go with another cruise line or...
OK Time; Ship Needs a Good Cle...
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Second Hand Smoke; Bad Food
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text:
I just read the article and was left at, "OH GOD, WHY?!?"
YaST is written in C++. See https://github.com/yast/yast-core/tree/master/liby2/src
In addition to C++ code YaST also has a scripting engine. The home-grown scripting engine is being replaced with Ruby. That's all.
I just read the article and was left at, "OH GOD, WHY?!?"
Interesting. That should make things easier to manage, I suppose.
The theming is due to special YaST theming packages. If you remove them, or just move the /usr/share/YaST2/theme/current symlink somewhere else, it will use the system styles (the root ones, which you can configure by doing a kdesu systemsettings if on KDE).
Half of the people on openSUSE use KDE. The other half uses GNOME. Roughly speaking, that is (always bound to be XFCE, E17, etc. users as well).
It is Qt-based. Or GTK-based. Or ncurses-based. You can install either of the three packages, or all of them, and then choose one through the /etc/sysconfig editor.
I really hope it stops running as root in the future: https://features.opensuse.org/314778
If they want a scripting language they should just do JavaScript... WAY faster than Ruby these days but a little less sane from the prototypical inheritance etc, a bit easier to read since its C-Style syntax, and I'd say millions more devs can use JS good or bad.
But even if they didn't want to use V8 to run YaST most desktop environments use JS scripting features built-in already for extensions & applet functionality so integration to YaST could be tighter to the desktop for notifications etc.
At least they went with Ruby and not Python :P
Strange, I'd think they'd want to go with Python...


text:
Bonds: "Sure, y'know, I knew there was a possibility of side-effects, but this is ridiculous."
"...You just have a wider lens than one game."
--Former Reds GM Wayne Krivsky, on why he didn't fly Josh Hamilton to Colorado for one game.
"...its money well-spent. Don't screw around with your freedom."
--Roy Tucker, on why you need to lawyer up when you find yourself swimming with sharks.
On "The Old Red Guard" Red Leader came up the my favorite caption for these photos: "Whitney Houston still denies taking steroids...."
"Hey...Dad. Wanna Have A Catch?" Kevin Costner in "Field Of Dreams."
Board Moderators may, at their discretion and judgment, delete and/or edit any messages that violate any of the following guidelines: 1. Explicit references to alleged illegal or unlawful acts. 2. Graphic sexual descriptions. 3. Racial or ethnic slurs. 4. Use of edgy language (including masked profanity). 5. Direct personal attacks, flames, fights, trolling, baiting, name-calling, general nuisance, excessive player criticism or anything along those lines. 6. Posting spam. 7. Each person may have only one user account. It is fine to be critical here - that's what this board is for. But let's not beat a subject or a player to death, please. |


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- Georgia does not recognize the referendum conducted in Crimea
- Georgia was left with no choice in 2008 - Matthew Bryza
- The Russian Economy Is A Disaster Waiting To Happen
- The closed security zone of Sochi 2014 Winter Olympics
- Russia deployed the tactical-missile system on the Baltic Sea between Poland and Lithuania
The truth about the problems in Abkhazia and how to solve them
Abkhazia (Original non-Russian name: Apkhazeti) and South Ossetia (Original, non-Soviet name: Shida Kartli) remain territories occupied by Russia. Both regions are artificially created, soviet-style dictatorships.
They are completely isolated from the rest of the world. No international monitoring or organisations. Instead the regions are full of Russian military and secret services, that keep everything closed and under control. People are being kidnapped and tortured by these structures on a daily basis, often based on ethnic background.
In 1992-1993 and in 2008, Russia organized an ethnic cleansing of Georgians in Abkhazia and South Ossetia. More than 400,000 Georgians were driven out of Abkhazia & South Ossetia and are not allowed to return to their homes until today.
The soviet-style puppet regime of Abkhazia exists only because Russia backs it with military might and financial support. Calls for international recognition conveniently overlook how it was established: through the killing of around 10,000 civilians in the 1990s and the expulsion of more than 300,000 people from Abkhazia over the past two decades.
It is for the international courts to define the legal nature of the atrocities committed by the Abkhaz militia and their Russian allies. But no one should ignore these acts while considering the future of a region that has been forcefully emptied of the overwhelming majority of its population.
The 1992-1993 conflict and the 2008 Russian invasion -- together with the constant harassment and intimidation of the non-Abkhaz civilian population -- have radically altered Abkhazia's demographics. According to Soviet census data, ethnic Abkhaz comprised 17.8 percent of the 525,000 residents of Abkhazia in 1989, while ethnic Georgians accounted for 45.7 percent, numbering roughly 240,000. By 2003, the ethnic Georgian population had decreased by 81 percent to just 46,000 (mostly in the Gali and Tkvarcheli districts); (Ukrainians, Estonians, Jews by 81 percent).
In the same period, the Abkhaz were the only ethnic group whose ranks increased - from the prewar tally of just 17 percent to about half the population. The outrageous process by which this occurred has been denounced as "ethnic cleansing" by the United Nations, the Organization for Security and Co-operation in Europe, and many others.
Abkhazia's declaration of independence
The illegitimacy of Abkhazia's independence is not solely due to the failure of the international community to accept its sovereignty. It stems from deeper problems: the past and current actions of Abkhazia's leaders, their ideology of ethnic supremacy, and the Russian military occupation of its territory.
After its actions in 2008(the August 2008 war), Russia effectively lost its status as a peace-keeper in the region. It has become a party in the conflict, the enemy of the republic of Georgia. The Russian Federation troups lost any right to be in Abkhazia and the South Caucasus.
So to solve this problem, Moscow instructed the Abkhazian government to declare independence shortly after the war with Georgia. And to hold a referendum about this independence, in which only 14% of the population - the ones in favour of independence - could take part.
Only certain residents of Abkhazia are allowed to take part in the vote. The ethnic Georgians who bravely remain in situ on their own land in their own country are forbidden to take part(More than 300,000 Georgians).
The EU rightly argued that "elections in this region of Georgia can only be valid after all refugees and internally displaced persons are given the right to a safe, secure and dignified return to their homes in Abkhazia".
The Abkhaz people do need cooperation with Europe, and they deserve to be part of the world community. But the manner in which this happens is crucial. It cannot be done by validating ethnic cleansing, by ignoring the annexation of Georgia's sovereign territory, or by recognizing elections held in a society that is built on apartheid -- where a vast majority of the population has been expelled and most ethnic Georgians still remaining are not allowed to vote.
Meanwhile, proponents of the Abkhaz cause ask a powerful question: Why not apply the precedent of Kosovo, which achieved international recognition after a violent separation from Serbia, to Abkhazia?
But replicating Kosovo (a process of recognition that can hardly be described as flawless) is not applicable. The differences between the two cases are stark.
1. First, the most heinous crimes in Kosovo were committed by Milosevic regime, the adversaries of secession; in Abkhazia, they were committed by the secessionists and their Russian allies.
2. Second, the right of return of refugees to Kosovo was a precondition for self-determination; in Abkhazia, the so-called self-determination is linked with the refusal to allow the return of internally displaced people.
Put simply, Kosovo's independence was a way of punishing ethnic cleansing. In Abkhazia, such recognition would represent a chilling validation of ethnic cleansing, and a reward to its authors.
And b.t.w. can Kosovo serve as a model for Chechnya or Dagestan to solve the ongoing problems there?
And there's more that makes the Kosovo parallel problematic. The processes leading to independence and recognition also could not have been more different.
Abkhaz leaders have refused several peace plans proposed by the Georgian government, the United Nations, and Germany. In Kosovo's case, however, it was the Serbian government of Slobodan Milosevic that rejected peace efforts.
After the war, Kosovo came under U.N. administration for nine years before its independence was recognized by a vast coalition of countries, including the United States and most European nations. In Abkhazia, international organizations have been denied entry, and its so-called independence has been recognized only by Russia and three other non-European countries, which all receive Russian financial support.
Why Abkhazia's independence is fake?
Abkhazia's independence is a fake by the simplest norms of human rights and democracy:
1. The majority of the population was excluded from the referendum. Many are not even allowed to return to their own homes.
2. De facto there is no independent Abkhazia: the region is under Russian miltary rule.
3. The present government is not chosen by the people, it is a puppet regime that receives instructions from Moscow.
4. There is no democracy and no basic rights for the people remaining in Abkhazia. They are in poverty and under threat.
How to help the Abkhaz and Georgian peoples to gain back their freedom and human rights:
1. Acknowledge and explain the manipulation of people that has happened over the last two centuries. Do this actively through school curricula and the media;
2. Stop talking about it as an ethnic conflict or a conflict between Georgia and Abkhazia;
3. Send Russian troops, Secret Services and military bases out of Abkhazia. Place an international mission of truly neutral parties in charge of this transition;
4. Organize the return of the refugees to their homes;
5. Let all the people of Abkhazia (including the Georgian and other refugees) decide on their future. With all options objectively open.
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I am a professional photographer working in various areas of the craft. I am here to help you with your documentation needs for your home or rental space. Please contact me so I can help you reach your goals in home rental and any other area in which my talents and skills can be of assistance!
I travel outside of the country every year and come back with thousands of photos and memories. Meeting people around the globe is one of my treasured opportunities in this lifetime. Horses are my other favorite thing in the world. I ride them, train them, learn from them, and love them more than anything. A professional dressage and show jumping trainer, I spend every day out at the barn with my gentle giants.


text:
Sleeps | 8 |
Bedrooms | 4 |
Bathrooms | 4 |
Property type | house |
Minimum Stay | 7 nights |
Welcome to paradise, this 6,486 Square foot home is your perfect vacation retreat. Located on the 14th hole of the Private Palms Golf Club which offers breathtaking views of the golf course, if you are interested in playing golf, PGA West is adjacent to The Palms and offers a public course. This home is highly upgraded and offers four bedrooms as well as a detached casita, it sleeps 8.The owner has spared no expense from the resort style Pebble Tec Pool, to the indoor pool table and bar this home offers everything you will need to entertain your guests. From the moment you enter the foyer you will be captivated by the sheer elegance and craftsmanship of this home. Custom travertine tiling, high vaulted ceilings and pillars throughout the great room create an open and inviting space. Put your bartending talents to the test and make custom cocktails for your friends, the bar area seats five people and overlooks the billiards table with a vast picture window behind the bar the beauty of the desert is welcomed within. This area separates the living room and family room. Enjoy late night talks in front of a roaring fire, or your favorite show in the family room. The gourmet kitchen comes equipped with everything you will need to craft a masterpiece meal in the utmost elegant manner. Granite countertops, double ovens and two refrigerators' paired with plenty of counter space and an island this is sure to give you enough space for the ultimate feast. The dining room offers a table for ten and a wine cabinet. This home offers a custom built office with a meeting table. Separate seating areas and a 2-line phone line, so don't let business stop you from vacationing. Take full advantage of the resort style, salt water pool and spa that sits amongst the beautifully landscaped greenery with west facing exposure brings scenic views and maximum afternoon sunshine. The master suite offers a King bed, fireplace and flat-screen television. The master restroom offers a spa tub, vanity and massage room. The second and third suite offer queen beds, flat screen televisions and private restrooms. The fourth room is located in the detached casita; the casita offers its own kitchen, living area, private bedroom and restroom. This home is sure to suit all your needs, hurry and book before it's too late.
Rate Period | Nightly | Weekend Night | Weekly | Monthly | Event | ||||
My Standard Rate
7 night minimum stay
Notes |
Novemeber 1 - March 30 $4500 weekly, $13.500 Monthly
April 1 - April 30 $16, 319 Weekly May 1 - October 31 $3200 weekly, $10,200 Monthly |
Payment is usually accepted in the quoted currency (USD) unless the currency and the amount is specifically agreed in advance with the owner / advertiser.


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Feb 28, 2012 10:45 AM
17 yr. old experienced 3rd baseman looking for serious travel team
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I have been playing softball since the peewee league and have played on a local travel team and area leagues. I am looking for a travel team who is in need of a dedicated 3rd basemen. I can be reached through email at email@example.com or by calling 574-896-5837. I live in North Judson and I am willing to travel in order to be placed on a team.
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