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Identification of genomic indels and structural variations using split reads
Zhengdong D Zhang1,
Jiang Du2,
Hugo Lam3,
Alex Abyzov1,
Alexander E Urban4,
Michael Snyder5 &
Mark Gerstein1,2,3
Recent studies have demonstrated the genetic significance of insertions, deletions, and other more complex structural variants (SVs) in the human population. With the development of the next-generation sequencing technologies, high-throughput surveys of SVs on the whole-genome level have become possible. Here we present split-read identification, calibrated (SRiC), a sequence-based method for SV detection.
We start by mapping each read to the reference genome in standard fashion using gapped alignment. Then to identify SVs, we score each of the many initial mappings with an assessment strategy designed to take into account both sequencing and alignment errors (e.g. scoring more highly events gapped in the center of a read). All current SV calling methods have multilevel biases in their identifications due to both experimental and computational limitations (e.g. calling more deletions than insertions). A key aspect of our approach is that we calibrate all our calls against synthetic data sets generated from simulations of high-throughput sequencing (with realistic error models). This allows us to calculate sensitivity and the positive predictive value under different parameter-value scenarios and for different classes of events (e.g. long deletions vs. short insertions). We run our calculations on representative data from the 1000 Genomes Project. Coupling the observed numbers of events on chromosome 1 with the calibrations gleaned from the simulations (for different length events) allows us to construct a relatively unbiased estimate for the total number of SVs in the human genome across a wide range of length scales. We estimate in particular that an individual genome contains ~670,000 indels/SVs.
Compared with the existing read-depth and read-pair approaches for SV identification, our method can pinpoint the exact breakpoints of SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. Moreover, with the advent of the third-generation sequencing technologies that produce longer reads, we expect our method to be even more useful.
One important goal in genomics is to determine the genetic differences among individuals and to understand their relationships to the phenotypic differences within a species, such as human beings. These variations consist of single nucleotide polymorphisms (SNPs) and structural variations (SVs) including short insertions/deletions (indels) and other more complex ones such as duplications and translocations. Because of the efficiency of genotyping methods and the central role they play in the genome-wide association studies, SNPs are currently the best catalogued and studied human genetic variations. Ubiquitous 1-bp indels, expansions of simple repeats and chromosomal anomalies have long been observed and acknowledged as the genetic bases for some human diseases [1, 2]. Except for these old discoveries, however, indels and SVs have been much less studied due to their wide size range, the multitude in their types, and the lack of an efficient genotyping method. After several recent studies, however, their genetic significance starts to be appreciated: not only do they exist in large numbers in the human populations, they may also have a more significant impact on phenotypic variation than SNPs [3–7].
The microarray technology, array CGH, has been widely used to detect copy number variants (CNVs), a type of SV, with kilo-bases resolutions [5, 8–11]. The advancement in high throughput sequencing technologies has enabled a new set of comparative approaches for CNV calling, such as the read-depth analysis [12–15], which computes the read coverage of different genomic regions, the read pair analysis, which focuses on cases where the distance between the two ends of a reads deviates more than expected when they are mapped back to the reference [4, 16–18]. Accompanying the advancement of these experimental approaches, different computational methods for SV detection and their breakpoint refinement have also been developed [18–25].
Because indels/SVs come in various sizes, there is an additional aspect--the size coverage--to their detection. The aforementioned methods only partially address all the requirements of indel/SV detection to various degrees. For sequence insertions and deletions, indels/SVs are conventionally defined as micro-SVs of 1-10 bp and large ones over 1 kb, respectively. In the following text, wherever the context is clear we use SV as the encompassing term, subsuming small indels. Due to methodological limitations, SVs of middle lengths have only been minimally, if not at all, studied. Indeed, over the full spectrum of the SV size, only several small size spans are covered by current methods (Figure 1). Moreover, SV detection approaches described above (e.g. array/read-pair/read-depth based methods) cannot accurately locate the breakpoints of the SV events, nor can they reveal the actual sequence content of insertions. Such information can only be gained via the direct analysis of the read sequences, instead of based on the statistics of the mappings of such reads.
The size spectrum of SVs identifiable to different methods. No method can identify SVs of all different sizes. The black bars indicate the size ranges of discoverable SVs by different methods, which include the dbSNP database, the high-resolution array CGH (hr-aCGH), the read-pair (RP) method with fosmid, 454, and Solexa sequencing, and the split-read analysis. The range of detectable indels by RP depends on three values: the mean and the standard deviation between the distances of mapped read pairs and the multiple coefficient of s.d. for significance. These triple values are (40 kb, 2.8 kb, 3), (1 kb, 0.8 kb, 3), (250 bp, 25 bp, 6) for fosmid, 454, and Solexa sequencing, respectively.
Here we report the split-read analysis, a sequence-based method that detects SVs through direct analysis of the mapping information of how high-throughput sequencing reads are aligned to the reference genome. Using alignment of read sequences to reference genomes with gaps, the method allows the precise identification of SVs covered by such reads. Building our method directly upon BLAT, a well-established sequence alignment program, we take advantage of the speed and the sensitivity of this popular sequence-to-genome alignment tool. However, more importantly, by considering both the sequencing and mapping errors in our assessment strategy to score each initial SV call, our method also takes into account the sequencing error model (especially for next-generation sequencing technologies, which were not generally available a few years ago), and distinguishes the different confidence levels in detecting different SVs based on the characteristics of supporting reads. Compared with the read-depth and the read-pair analyses, our sequence-based method can not only pinpoint the breakpoints of SV events, but also reveal the actual sequence content of insertions. The split-read analysis has another advantage--it can cover the whole size spectrum for deletions (Figure 1). We expect our method to be more useful in the future as the sequence reads become longer.
Due to both experimental and computational limitations, there are biases on multiple levels in the call sets generated by all current SV identification methods. In addition to their significantly more restricted size range of identifiable insertions than that of deletions, all current SV identification methods are sensitive to SVs of different length (Figure 1), and as a result studies using them have reported different numbers of SVs. One study using the read-pair method reported 241 SVs over 8 kb in a sampled genome [7], while another using the same approach but with a different molecular construct reported 422 and 753 SVs over 3 kb in two tested genomes [4]. In a study of whole-genome sequencing and assembly, 835,926 indels were identified in a diploid human genome [26]. Currently it is not known how many SVs, small or large, are in an individual human genome. Using empirical error models estimated from sequencing experiments to simulate high-throughput sequencing reads, we could not only parameterize our split-read method, but also, more importantly, quantify both false positive and false negative rates. Knowing these error rates enables us to estimate the total number of SVs of a given length in a human genome.
We have developed the split-read identification, calibrated (SRiC), a sequence-based method for detecting structural variants (SVs). It maps reads to the reference genome with gapped alignment and scores these mappings with consideration for sequencing and alignment errors. SRiC pinpoints exact SV breakpoints, reveals the sequence content of insertions, and covers the whole size spectrum for deletions. Simulation is used to calibrate SRiC, allowing unbiased estimation of the sensitivity and proportion of SVs across different length-scales.
Analysis of the simulated sequence data
For sequencing simulations, instead of using the whole human genome, we use the diploid human chromosome 22 (NCBI36 assembly), which counts for 1% of the human genome but has a repeat content and a gene density both representative of the whole genome, to save computational processing time. To keep the local sequence environment of indels as found in a genome, we use indels identified in Venter's genome [26] in our sequencing simulation (Additional file 1).
Determining thresholds used in the analysis
Three thresholds are used in our split-read analysis: tr, the threshold on the ratio of the score of the best alignment to that of the second best as a measure of the uniqueness of the read, tn, the threshold on the number of supportive reads for 1-bp SVs, and tc, the threshold on the maximum centeredness (the maximum ratio of the smaller length to the bigger one of two flanking alignments of a read, Additional file 1, Figure S1) for large SVs.
To determine the score ratio threshold tr for the alignment preprocessing, we simulate ~5× sequence coverage that gives ~0.6 million 454 single-end 400-bp reads and then identify SVs using different values for the score ratio threshold tr (= 1.0, 1.25, 1.5, 1.75, and 2.0) while keeping the other two parameters fixed (tn = 5, tc = 0.1). The percentage of true positives, false negatives, and false positives of deletions and insertions identified at different tr values are plotted in Figure 2A-B. There is a small decrease in the number of identified SVs when tr is increased from 1.0 to 1.25. The further increases in tr from 1.25 to 2.0 only cause negligible changes to the SV identification results. Over all, the SR method is not very sensitive to tr when it is in the range of 1.0 and 2.0. This insensitivity is a result of unique mapping to the reference genome of most 454 reads, which are much longer than those produced by other next-generation sequencing technologies.
Effect of different thresholds on SV identification. Different sets of indels are called at combinations of different values for thresholds tr, tn, and tc. Each bar shows the percentages of the true positives, the false negatives, and the false positives of each call set, which are represented by the colored, the white, and the gray portions, respectively. The bars in different shades of green and red are used for the true positive calls of deletion and insertion of different length. (A-B) The alignment score ratio threshold, tr. SV are calls for a set of simulated reads using different tr while tn = 5 and tc = 0.1 are kept unchanged. (C-D) The number of supportive read threshold, tn. SV are calls for the same set of simulated reads using different tn while tr = 1 and tc = 0.1 are kept unchanged. (E-F) The maximum centeredness threshold, tc. SV are calls for the same set of simulated reads using different tc while tn = 5 and tr = 1 are kept unchanged.
Two thresholds, tn and tc, are used for the initial SV calls (Inequalities 1 and 2). We vary the value of one of these two thresholds while fix the other to determine how they affect the accuracy and the sensitivity of the split-read method. Using the simulated sequence set with the ~5× coverage, we make SV calls with tn = 1, 2, ..., 9 while tc = 0.1 and tc = 0.1, 0.2, ..., 0.9 while tn = 5, count the true positive and the false positive calls, and calculate the percentage of true positives, false negatives, and false positives at each threshold combination. The results of this performance analysis as depicted in Figure 2C-F make it clear the effects that theses two thresholds have on the SV identification show a dichotomous dependency on the SV length. While tn affects the identification of short SVs, tc biases that of longer ones. In practice, we use the sequencing depth for tn (with a lower bound nmin = 2) and set tc to 0.1. It is also clear that the method has different sensitivities in the size range of indels that it can detect: it is less sensitive to 1-bp indels because 454 sequencing is prone to over- or under-call bases in homopolymers and thus more a stringent threshold is needed to lower the number of 1-bp false positives.
Assessing how the read length affects the performance
We first assess how the read length affects the SV identification by simulating single-end reads of 50, 100, 200, 400, and 800 bp long. For each read length, we generate sequences with ~5× coverage and analyze five sequence sets with the same set of method parameters (tr = 1, tn = 5, tc = 0.1). We compare the true SVs and the ones that we identified using the split-read analysis. The numbers of true and false positives of deletions and insertions identified using reads of different lengths are plotted in Figure 3A-B.
Effect of sequencing on SV identification. The lengths of the colored, the white, and the gray portions of each bar signify the percentages of the true positives, the false negatives, and the false positives, respectively. The bars in different shades of green and red are used for the true positive calls of deletion and insertion of different length. (A-B) Different read length. SV are calls for sets of simulated reads of different lengths with the same coverage (5×). (C-D) Different coverage. SV are calls for sets of simulated reads of the same lengths (~400-bp) with different coverage.
The general trend, which is expected and depicted in the figure, is that the SV identification is improved with longer reads. With 50-bp reads, the SV identification is the worst with low sensitivity for both short and long deletions. Because the length of discoverable insertions is capped by the read length, it is not surprising that at this read length none of the insertions of 20-bp and longer are found. When the read length is increased to 200 bp and longer, the sensitivity and the positive predictive value almost double for longer SVs. For deletions, 200-, 400-, and 800-bp reads seems to give comparable performance, and longer reads only bring marginal improvements to the results. The choice of read length for insertions identification is, however, a rather open-end question, as longer reads will always enable better identification of longer insertions.
Assessing the effects of sequence coverage on SV calls
We first simulate ~20× sequence coverage that gives ~2.5 million 454 single-end 400-bp reads. To assess how the sequencing depth affects the SV calls by the split-read analysis, we also simulate ~1×, 5×, 10×, and 15× sequencing coverage by down-sampling the 20× sequence set with appropriate numbers of reads (Table 1). We then identify SVs using default parameters (tr = 1.0, tn = coverage, tc = 0.1). The numbers of true and false positives of deletions and insertions identified at different sequencing coverage are plotted in Figure 3C-D. The general trend is that SV identification is improved with higher coverage but with diminishing returns. Comparing to the low coverage at 1×, there is a marked improvement to SV identification at higher coverage.
Table 1 Number of sequences in simulated and down-sampled datasets
To assess how sequencing coverage affects the sensitivity of our method, we determine the maximum sensitivity achievable in each simulated sequence set. The number of 'seeable' true SVs is affected by several factors, including the sequencing depth, the read mapping quality/uniqueness, and the minimum number of supportive reads required for an SV call. After the initial alignment processing to remove the mapping ambiguity, we count the number of supportive reads for the true SVs of different lengths and plot the number of true SVs with one and two or more supportive reads at different sequencing depth (Figure 4).
Discoverable simulated SVs. Not all SVs are identifiable, as some of them are not covered by any or enough sequence reads. The lengths of the gray and the colored portions of each bar signify the log-number of indels covered by only one and more than one read, respectively. The bars in different shades of green and red are used for the true positive calls of deletion and insertion of different length. A missing bar indicates a zero count. The counts of simulated deletions (A) and insertions (B) that are covered by at least two reads and by only one read are plotted as colored and gray bars.
The sequencing depth has the most significant effect on short SVs. At 1× coverage, ~1,000 of true 1-bp deletions and insertions are supported by at least one read. When the coverage is increased to 2×, these numbers almost are doubled. As the coverage increases, the percentage of supported true SVs also increases but with a diminishing pace. 80~90% true SVs are supported by at least one read at 5× to 20× coverage. One supportive read is the absolutely minimum requirement for an SV call. To reduce the false positives, we require at least two supportive reads for every SV call. This global threshold has a much more significant effect on the low-coverage sequence set than on the high-coverage one: while the percentage of true deletions with two or more supportive reads is about the same as that of true deletions with one supportive read at 1× coverage, there are very few true SVs with only one supportive read at 10× or higher coverage.
Several different approaches have been used to extensively evaluate the performance of our SRiC method (Additional file 1). First, we compare SRiC with Pindel, the only published method that can detect SV breakpoints on the nucleotide level. The comparison between the numbers of SVs that these two methods can find in simulated datasets with the same SV placements shows SRiC has a significantly higher sensitivity than Pindel at every length simulated, whether it is of deletions or insertions (Additional file 1, Tables S1 and S2). Second, we apply our split-read analysis to 454 genomic reads generated for two individuals (CEU NA12878 sequenced to 0.5× and YRI NA19240 to 5×) and calculate the positive predictive values at different thresholds on the number of supportive reads after validating deletion calls using two experimental methods, respectively--array capture followed by sequencing and trio-array comparative genomic hybridization (CGH). The experimental result shows for the type of SVs under consideration SRiC can achieve 70-80% call accuracy (Tables 2 and 3).
Table 2 Array capture validation of SR called deletions. 1, 2
Table 3 Trio-array CGH validation of result. 1, 2
Analysis of the 1000 Genomes Project data
A major sequencing project, the 1000 Genomes Project, has been launched to resequence the genomes of at least a thousand people from around the world using the new sequencing technologies to produce the most detailed map of human genetic variation for disease studies. As a proof of concept, we apply our split-read analysis to a set of 454 sequence reads generated by the 1000 Genomes Project for one individual.
The genome of an individual (NA19240) from the Yoruba in Ibadan, Nigeria has been sequenced using the 454 single-end method to ~5× sequence coverage. The sequencing generated ~49 million sequence reads, of ~17.6×109 bp in total. Mapping of the sequence reads (with a median length of ~400 bp) took ~136 hours of the wall time with exclusive access to 50 Dell PowerEdge 1955 nodes (each containing 2 dual core 3.0 Ghz Xeon 64 bit EM64T Intel CPUs model 5160 and 16 GB RAM) of a Linux cluster. Applying our SR method, we identify 13,426 deletions ranging from 1 bp to ~700 kb and 11,539 insertions ranging from 1 bp to 200 bp on the chromosome 1. Compared with 494 validated insertions in chromosome 1 from dbSNP (v129), 301 in both sets are at exactly the same genomic locations, which indicates a sensitivity of ~60% for validated insertions in dbSNP. This defines a lower bound on sensitivity as different genomic DNA sources are involved. The simulation used to compare the numbers of insertion and deletion calls (see above) enables us to determine the positive predictive values and the sensitivities of our SR method for indels identified in a sequence set at 5× coverage and subsequently estimate using equation (4) the total numbers of deletions and insertions of lengths in continuous ranges separately on chromosome 1 (Table 4). We estimate there are 53,431 SVs in chromosome 1 and extrapolate to 665,684 SVs in the whole genome of this individual.
Table 4 Corrected counts of SVs in the chromosome 1 and the whole genome of a Yoruba individual. 1
Mapping reads to the reference genome
The size of the deletions covered by the split-reads can range up to tens of thousands of bases, and this makes BLAT well suited for mapping such reads back to the genome, since it not only allows small gaps and mismatches within the alignment like many other alignment tools, but also takes into account large gaps due to its initial purpose to handle introns in RNA/DNA alignments [27]. In short, unlike the alignment results from tools such as BLAST which will generate two distinct partial alignments for a split-read covering a large deletion event, the alignment results of BLAT can directly reveal the deletion event and its up- and down-stream alignments at the same time. Recently a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), has been designed and implemented to align with gaps long reads such as 454 reads (~200 bp or longer) to the reference genome with higher accuracy and a faster speed than BLAT [28]. However, BLAT should be used to align 454 paired-end reads, because currently the average 454 read length is less than 400 bp and thus, the majority of sequences on both ends will be shorter than 200 bp.
For the non-split reads, however, using BLAT would be unnecessarily time-consuming, because their alignment results would usually only contain (if any) a small number of mismatches. Bowtie, a recently developed alignment tool, incorporates the Burrows-Wheeler transform technique to index and search the genome in a fast and memory-efficient manner, and is an immediate candidate for processing such reads [29].
The two-tiered alignment cascade is used to expedite the step of aligning reads to the reference genome. The first assortment step effectively fractions the sequence reads into two subsets: ones that can be uniquely mapped and ones that cannot. By limiting the gapped alignment of the reads in the former subset to their associated chromosomes, the tiered mapping approach removes the unnecessary mapping attempts and thus speeds up the alignment step. The speed gain is clearly related to the size ratio of the two read subsets: the more uniquely mappable reads, the bigger the speed gain. Because it is assessed by their 35-bp end tags, the genomic uniqueness of the reads is limited to the unique mappability of the 35-mers to the human genome. It has been estimated that 79.6% of the genome is uniquely mappable using 30-bp sequence tags. Since the human genome consists of 24 chromosomes, it is natural to use them as the bins for end tag assortment. It is, however, conceivable to fraction the human genome into large (e.g., 100 Mb) fragments with small (e.g., 1 kb) overlaps and use them as the assortment bins to further restrict the search space of the subsequent BLAT genomic mapping of the reads whose end tags are uniquely mapped.
Parameterization of the split-read analysis
Five parameters are intrinsic to our split-read analysis alone: the alignment score ratio threshold tr, the threshold on the number of supportive reads for 1-bp SVs tn, the threshold on the maximum centeredness for large SVs tc, the minimum number of supportive reads for every SV identification nmin, and the exponential decay parameter λ. For sequence reads that are mapped to multiple genomic locations, we use tr to control on what level of distinctiveness such reads can be used for the SV identification. A higher value of tr lowers the overall mapping ambiguity and thus reduces the number of false positives. This will, however, disqualify more correct alignments and in turn increase the number of false negatives. Small and large false SV calls have different origins: the former result from sequencing errors that under- or over-call bases while the later are mostly generated by misalignments. To count for such distinct error origins, two different threshold functions, separately parameterized with tn and tc using the same exponential base function, are used to make SV calls. λ controls how fast the threshold changes between 1-bp and large SVs and it is set to 1 in all of our split-read analyses. We require that there should be at least two supportive reads for every SV identified regardless of its length. This global threshold (nmin = 2) dramatically reduces the false positive SV calls.
Directly building our method upon BLAT, we take advantage of the speed and the sensitivity of this popular sequence-to-genome alignment tool. However, more importantly, we designed an assessment strategy to score each initial indel/SV call that takes into account both the sequencing and mapping errors. Compared with the existing read-depth and read-pair analyses, our sequence-based method can pinpoint the exact breakpoints of indel/SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. We thoroughly benchmarked and validated our SRiC method against the best available methods for detecting structural variants at relevant resolutions by using several different approaches to extensively evaluate the performance of our method. We illustrate the characteristics of our split-read method by applying it to both synthetic and experimental data sets. With the advent of the third-generation sequencing technologies that produce longer reads, we believe the split-read approach presented here can make a significant contribution to the study of indels/SVs.
Sequence data are analyzed in a stepwise fashion, as depicted in Figure 5. Below we describe our split-read analysis in detail.
The flowchart of the split-read analysis pipeline.
The data input for the split-read analysis are genomic read sequences. For sufficient alignability, these reads should have a length of hundreds of bases and currently can be generated by the Sanger sequencing or, to a much higher throughput, the 454 sequencing. However, we expect reads from other sequencing platform (e.g., paired Solexa reads with overlap) may also be used after preprocessing. The current system implementation only supports the widely used FASTA sequence format.
Tiered sequence alignment
The sequence reads are first processed to remove any terminal ambiguous bases (Ns) and then mapped to the human reference genome (NCBI Build 36.1, UCSC hg18) using BLAT with parameters tuned for short sequences with maximum sensitivity (-stepSize = 5, -tileSize = 11, -repMatch = 106, and -fine). Certain parts of the reference genome (such as low complexity regions and simple sequence repeats) can be masked out by replacing the sequences with Ns to disallow indel identification in these regions. When the set of reads is large, the aforementioned direct approach to sequence mapping will be very time-consuming. To enhance the speed of the alignment step, we use a tiered approach instead by dividing our alignment process into two steps: a fast initial assortment of the reads followed by a complete gapped alignment.
Briefly, we first take 35-mer tags at each end of a read, map them to the whole reference genome using Bowtie, a rapid alignment tool for short reads, look for those end tags that can be mapped uniquely to the genome, and assort the corresponding reads by their associated chromosomes. Using BLAT to obtain the gapped alignments, we then align the assorted reads only to their targeted chromosomes and the remaining reads whose ends cannot be uniquely mapped to the whole genome. Thanks to the modularity of the implementation, Bowtie and BLAT used here can be replaced by other alignment tools, such as MAQ and BLAST, with minor modifications.
For all uniquely mappable reads, this tiered mapping approach can speed up the alignment to the human genome by 24 times on average. The whole process is parallelized, and for a total of ~3 million reads (~60 GB in size) it takes less than an hour to finish the assortment step with 80 CPUs of a computer cluster. On average, ~70% of the single-end reads of a sequenced individual could be assorted by the aforementioned algorithm. As a result, we anticipate an overall enhancement of the alignment speed by 3 folds.
Alignment preprocessing
If a read is mapped to the genome uniquely, we keep its alignment without additional requirements. Otherwise, its alignments are scored and the alignment ratios calculated. The alignments are then sorted on their scores, ratios, and the number of alignment blocks. We only keep the top alignment when its score is at least tr times (to be determined by simulation) as big as that of the second best on the sorted list. Moreover, DNA amplification as part of the library preparation procedure increases the likelihood that a DNA fragment is sequenced multiple times. Redundant sequence reads (the same chromosome, the same strand, and the same start position) generated from the same DNA fragments are removed to prevent the inflation of the count of reads that are supportive of SVs.
For paired-end sequence reads, they are processed to release the end sequences with the pairing information preserved for later use after the linker sequence is identified and removed. The end sequences are then mapped and processed like the single-end reads as described above. Because of restriction on how two ends are mapped relatively to each other on the genome, the pairing information increases the accuracy of their genomic placement. To avoid excessive assumptions on the distribution of the insert length, we make the minimum requirement that two ends of a read should be mapped to the same strand of the same chromosome. Only read ends that make unique concordant pairs are used in the downstream analyses.
Insertion/deletion and rearrangement identification
After sequence alignment and placing reads at their most likely locations in the reference genome, the split-read analysis searches these locations for insertions and deletions in the sample genome by identifying reads that encompass SV break points (Figure 6). To find deletions in the sample genome, we search for reads that when aligned to the reference genome split on the same strand of a chromosome. Even though a deletion of an arbitrary size can be detected as long as it is covered by one or more reads, the size of insertions that can be directly detected in full is limited by the read length. To find small insertions that are fully included in the reads, we search for reads whose terminal sequences can be aligned next to each other on the reference genome. For large insertions, we look for their boundaries, which are found in reads that, except one of their ends, can be aligned to the reference genome continuously in one block.
The conceptual diagrams of the split-read analysis. SVs can be detected by sequence reads spanning their break points. The split-read analysis can directly identify deletions, small insertions, and the boundaries of large insertions. After the identification of SVs, duplications and translocations can be isolated out based on matching of insertions and deletions. Breakages in blue genomic lines denote different chromosomes.
For each identified SV, we count the number of reads that 'support' it, nsr, and measure its centeredness in each supportive read, c i (i = 1, ..., nsr), the ratio of the smaller length of its two flanking alignments to the bigger one. It is easy to see that 0 < c i ≤ 1 and if there are multiple supportive reads for an SV it is the maximum centeredness that matters the most (because the evidence best supportive of presence is the most informative). Thus, each SV identification is associated with two scoring quantities: the number of supportive reads, nsr, and the maximum centeredness, cmax (Additional file 1, Figure S1).
Considering the lists of deletions and (small) insertions together in conjunction with each other, we resolve their final SV identities as novel deletions, novel insertions, duplications, and translocations. To do this, we first extract from reads the sequences of insertion that are at least 20 bp long and then align them to the reference genome using BLAT. An insertion is classified as 'novel,' if it cannot be aligned perfectly without gaps. Otherwise, it is a duplication and potentially a translocation. To be the latter, at least one location of the perfect alignments to the reference genome needs to be precisely covered by a read with deletion. The novel deletions are the whole set of deletions excluding those 'used' by translocations.
SV call set obtention through SV call filtering and sequencing error identification
Sequencing errors or spurious sequence alignments can both lead to SVs calls by the split-read analysis. The majority of such false positives can be removed by imposing a simple global threshold that requires every SV to be found in at least two nonredundant reads. We further refine the call list, and since the false positives of the short and the long SVs arise from distinct sequencing and alignment errors, respectively, we treat the short and the long SV calls differently.
Based on the SV length, false SV calls have different origins: small sequencing errors, large misalignments, and a mixture in between. Sequencing errors that under- or over-call bases manifest as deletions and insertions in the sequence reads when they are aligned to the reference genome. False SVs of this origin have the characteristics that they are very short, mainly 1-bp SVs, and also occur largely in a random fashion. In contrast, false large SV calls are mostly generated by misalignments in which the SVs are located very closely to one end of the reads. False SV calls with lengths in the narrow middle range are thought to be a mixture of errors from either of the origins. We use exponential functions to model such a dichotomy and the quick transition between small and large SV lengths. Given their distinct origins, we remove false small and large SV calls by requiring nsr and cmax to meet the following two conditions simultaneously:
in which tn is the threshold on the number of supportive reads for 1-bp SVs, tc the threshold on the maximum centeredness for large SVs, l the length of the SV in base pair, nmin the minimum number of supportive reads for every SV identification (effectively the threshold on the number of supportive reads for large SVs), and λ the exponential decay parameter that controls how fast the threshold changes between 1-bp and large SVs (Figure 7). nmin = 2 and λ = 1 are used in all of our split-read analyses.
The curves of the threshold functions. Each SV call is scored by the number of supportive reads and the maximum centeredness in those reads. The thresholds on these two quantities are determined by two threshold functions, plotted as the read and the blue curves, respectively. The gray dashed curve is the threshold function for the number of supportive reads before rounding. The parameter values used for the shown functional curves are λ = 1, tn = 8, and tc = 0.7.
The error characteristics of different sequencing platforms are approximated by different error models. The simplest model, which considers only 1-bp SVs, specifies the probability, pe, of 1-bp SVs due to sequencing errors. After the initial call filtering, we perform a significant test for each 1-bp SV, where the null hypothesis is that the SV probability is the same as the probability specified by the error model. Because pe stays the same for all sequence reads that contain the same 1-bp SV, we use the binomial distribution to calculate the P-value, which is the probability of seeing the same number and more of the reads having this SV out of the total number of reads covering this site, ncr, given the SV probability from the error model, pe:
After the Bonferroni correction for multiple tests, the null hypothesis is rejected if P < 0.01.
Because of the increased likelihood of both under- and over-calling bases in homopolymers by 454 sequencing technology, for each SV that is a part of a homopolymer we perform a significant test after the initial call filtering, where the null hypothesis is that the SV probability is the same as the probability specified by the error model (Additional file 1, Figure S2). The calculation of the P-value is described above.
Calibration of the number of SVs in a genomic region
Previous steps will produce a set of SV calls for the assayed genomic region. Because the performance of our SR method can be assessed and quantified with the positive predictive value and the sensitivity by extensive simulation, we can use these error rates to derive less biased estimate of the number of SVs in that genomic region.
Given the number of SVs identified in sequence reads covering a genomic region (e.g., a chromosome or indeed the whole genome) to a certain depth, the total number of SVs of a certain length can be estimated using the positive predictive value and the sensitivity determined in a simulation data set with the same sequencing coverage:
in which , PPV l, c , and S l, c are the number of SVs, the positive predictive value, and the sensitivity for SVs of length l (bp) observed in reads giving c-x sequence coverage. This method is not applicable to SVs of a certain length that are not observed (i.e., ). For large SVs, it is more sensible to use a range of length, instead of discrete lengths.
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This work was supported by an NIH grant (4R00LM009770-03) from the National Library of Medicine to ZDZ. We thank the 1000 Genomes Project for providing the sequence data and carrying out array-CGH validation.
Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
Zhengdong D Zhang
, Alex Abyzov
& Mark Gerstein
Department of Computer Science, Yale University, New Haven, CT, 06520, USA
Jiang Du
Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
Hugo Lam
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
Alexander E Urban
Department of Genetics, Stanford University, Stanford, CA, 94305, USA
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Correspondence to Zhengdong D Zhang or Mark Gerstein.
ZDZ implemented the method, performed the analyses, and drafted the manuscript. JD, HL, and AA helped with the implementation of the method. AEU and MS participated in the array-CGH validation for the 1000 Genomes Project. MG conceived of the study and helped to draft the manuscript. All authors read and approved the final manuscript.
Additional file 1:Supplementary materials. PDF file includes additional Methods and associated references, Tables S1 and S2, and Figures S1, S2, and S3. (PDF 692 KB)
Zhang, Z.D., Du, J., Lam, H. et al. Identification of genomic indels and structural variations using split reads. BMC Genomics 12, 375 (2011). https://doi.org/10.1186/1471-2164-12-375
structure variation
split read
Comparative and evolutionary genomics


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text:
Shop for Moringa
Go To All Herbs
Also known as: Moringa Oleifera, Horseradish Tree, Maroom (Thai), Drumstick Tree, The Tree of Life, The Tree of Immortality, Ben Oil Tree
Benefits: Anti-Oxidant, Anti-Inflammatory, Anti-Aging, Digestive Health, Diabetes, Healthy Skin, Brain Health, Weight Loss
Recommended Usage: Take 1 to 6 capsules, or up to 3 grams of powder per day. Many regular users consume up to 6 grams per day.
What is Moringa ?
Moringa Oleifera, The Tree of Life, commonly called the 'drumstick tree", and 'horseradish tree' is native to India but has been planted around the world. Moringa is one of the most powerful health-enhancing plants yet known to man. Moringa has more nutrition in the leaf than any other plant yet known. India's ancient tradition of ayurvedic medicine sites 300 diseases that are treated with the leaves of the Moringa tree. The Sanskrit word for Moringa is saubhajnana which means 'knowledge of the heavens'. Moringa has been used for thousands of years and is very well known in other parts of the world. Health organizations have been promoting Moringa for malnutrition in many countries for many years. It is widely used for nursing and pregnant women to give them more nutrition, as well as their babies. Children are also given Moringa to improve their chances of surviving the early fragile years of life.
Other Features of Moringa
Only recently has Moringa become known as one of the most impressive herbal supplements to hit the holistic health market. In fact, in 2008 the National Institute of Health called Moringa (moringa oleifera) the "plant of the year," acknowledging that "perhaps like no other single species, this plant has the potential to help reverse multiple major environmental problems and provide for many unmet human needs."
Moringa Leaves contain 46 types of Anti-Oxidants.
Top Benefits of Moringa:
Anti-Diabetetic
Brain Health, Fatigue, Depression
Why should you consider Moringa on a regular basis ?
Through daily intake of Moringa, many people have also found health benefits of Moringa in areas of illness and ailments. While the list continues to grow, here are a few of the Moringa benefits: more energy, better sleep, improved mood, stable blood sugar, stable blood pressure, better digestion, pain relief due to reduced inflammation, more milk production for nursing mothers, acne, weight loss. And many more.
Moringa is also reputed to nurture the following illnesses: Insomnia, Fibro myalgia, Chrone's Disease, Arthritis, Rheumatoid Arthritis, Lime's Disease, Dengue Fever, Malaria, HIV Aids, Cancer, Hashimoto's Disease, Thyroid, Diabetes, Fatigue, Anemia, Liver Function, Immune support, Intestinal problems, stress, High Blood Pressure and many more. The list grows as people use it and report further health benefits and improvements.
Anti-Oxidants and Anti-Inflammatory Compounds
Moringa contains a mix of essential amino acids (the building blocks of proteins), carotenoid phytonutrients (the same kinds found in plants like carrots and tomatoes), antioxidants such as quercetin, and natural antibacterial compounds that work in the same way as many anti-inflammatory drugs.
Moringa leaves are high in several anti-aging compounds that lower the effects of oxidative stress and inflammation, including polyphenols, vitamin C, beta-carotene, quercetin, and chlorogenic acid. These are associated with a reduced risk for chronic diseases, such as stomach, lung or colon cancer; diabetes; hypertension; and age-related eye disorders.
Moringa is a nutritional storehouse for Vitamins and Antioxidants. Pound for pound, it contains 4 times the vitamin A of carrots, 7 times the vitamin C found in oranges, and plenty of vitamin E. It has 46 Antioxidants, which makes it one of the most skin nourishing and healing plants on the planet.
Moringa also contains Minerals – calcium, copper, iron, potassium, magnesium, manganese, and zinc, which are important for the continued growth and maintenance of the human body.
Due to its anti-inflammatory properties, Moringa has been used in ancient systems of medicine such as Ayurveda to prevent or treat stomach ulcers, liver disease, kidney damage, fungal or yeast infections (such as candida), digestive complaints, and infections.
A common use of Moringa oil is helping to boost liver function and therefore detoxifying the body of harmful substances, such as heavy metal toxins. It might also be capable of helping to fight kidney stones, urinary tract infections, constipation, fluid retention/edema and diarrhea.
Balances Blood Sugar Levels, Helps Fight Diabetes
Moringa contains a type of acid called chlorogenic acid, which has been shown to help control blood sugar levels and allow cells to take up or release glucose (sugar) as needed. This gives Moringa natural anti-diabetic and hormone-balancing properties. Aside from chloregnic acid, compounds called isothiocyanates that are present in Moringa have also been tied to natural protection against diabetes.
Protects and Nourishes the Skin
Moringa contains natural anti-bacterial, anti-fungal and anti-viral compounds that protect the skin from various forms of infections. Some of the common ways Moringa is used on the skin include: reducing athlete's foot, eliminating odors, reducing inflammation associated with acne breakouts, treating pockets of infection or abscesses, getting rid of dandruff, fighting gum disease (gingivitis), and helping heal bites, burns, viral warts and wounds.
Brain Health and Mood Stability
As a high protein food and a rich source of the amino acid tryptophan, Moringa benefits neurotransmitter functions, including those that produce the "feel good" hormone serotonin. Moringa is also rich in antioxidants and compounds that improve thyroid health, which makes it beneficial for maintaining high energy levels plus fighting fatigue, depression, low libido, mood swings and insomnia.
Lower Cholestrol
Both animal and human studies have shown that Moringa oleifera may have similar cholesterol-lowering effects.
Several of these studies are referred to below.
Moringa is used as a topical and internal treatment for minor infections. The antibiotic property is identified as Pterygospermin, a bacterial and fungicidal compound. The chemical description is glucosinolate 4 alpha-L-rhamnosyloxy benzyl isothiocyanate
Protects Liver
Moringa extracts exert a hepatoprotective effect on the liver. Research studies have validated the effectiveness of Moringa leaves against the liver damage caused by anti-tubercular drugs as well as its stimulating effects in speeding up the recovery process. It helps to restore the levels of glutathione content in the body and prevents radiation-induced hepatic lipid peroxidation. This protective effect is attributed to the presence of phytochemicals such as catechin, epicatechin, ferulic acid, and vitamin C found in Moringa.
One such study is referred to below as "Hepatoprotective activity of Moringa oleifera….."
Cures Ulcers and Combats Obesity
Research studies have shown that treatment with Moringa oleifera leads to reduced ulceration and its efficacy in curing ulcerative colitis is comparable with the commercially available drug prednisolone. It contains antibiotic and antibacterial properties, and it exerts inhibitory effects on the growth of various pathogens. This includes helicobacter pylori bacteria and coliform bacteria, which can trigger conditions like diarrhea. Also, Moringa helps in combating obesity thanks to its high nutritional and low-fat properties. The impressive content of vitamin B in its leaves aids in digestion and converts the food into energy rather than storing it as fat.
One such study is referred to below as "Moringa oleifera induced potentiation….."
Instructions for use: Take 1 to 6 capsules, or up to 3 grams of powder per day. Many regular users consume up to 6 grams per day.
Allergies: Suitable for vegetarians and contains no yeast, dairy, egg, gluten, corn, soy or wheat. Contains no sugar, starch, salt, preservatives, or artificial color, flavor or fragrance.
Toxicity: Moringa oleifera leaf powder does not contain any nutrients or compounds which are harmful to the human body. However, as it is such a powerful nutritional source, it can lead to an unsettled stomach if taken in too large quantities. In Burkina Faso, mal nourished children generally consume up to 10 grams per day with no negative effects.
Warnings: Excessive consumption of Moringa seeds can lead to internal stresses, particularly in the liver and kidneys. Use of Moringa leaves and Moringa leaf powder is quite safe even in larger quantities.
Nutritional Value of Moringa Leaf Powder
Links to various abstracts/studies regarding Moringa:
Health benefits of Moringa oleifera – 2014 Food Safety Research Centre (FOSREC), Faculty of Food Science and Technology, Universiti Putra Malaysia
Multiple Medicinal Uses
Moringa oleifera: a food plant with multiple medicinal uses - 2007 Department of Chemistry, University of Agriculture, Faisalabad-38040, Pakistan
Effects on Cholestrol
The in vitro and ex vivo antioxidant properties, hypolipidaemic and antiatherosclerotic activities of water extract of Moringa oleifera Lam. Leaves – 2008 Department of Pharmacology, Siriraj Hospital, Mahidol University, Thailand
Seratonin Release
Moringa oleifera induced potentiation of serotonin release by 5-HT(3) receptors in experimental ulcer model – 2011 S. N. Pradhan Centre for Neurosciences, University of Calcutta
Hyperglycemia and Dyslipidemia
Therapeutic Potential of Moringa oleifera Leaves in Chronic Hyperglycemia and Dyslipidemia: A Review – 2012 Chronic Disease Program, Ottawa Hospital Research Institute Ottawa, ON, Canada
Effect on Cancer Cells
Antiproliferation and induction of apoptosis by Moringa oleifera leaf extract on human cancer cells – 2011 Faculty of Science, National University of Singapore, Singapore
Effects on Lipid Profile
Effect of fruits of Moringa oleifera on the lipid profile of normal and hypercholesterolaemic rabbits – 2003 Department of Pharmacy, Faculty of Technology and Engineering, The M.S. University of Baroda, Kalabhavan, Gujarat, India
Hypocholesterolemic effects in high fat diet
Hypocholesterolemic effects of crude extract of leaf of Moringa oleifera Lam in high-fat diet fed wistar rats – 2000 Department of Pharmacology and Therapeutics, College of Medicine, University of Nigeria
Effect on Liver Damage
Hepatoprotective activity of Moringa oleifera on antitubercular drug-induced liver damage in rats – 2002 Department of Biochemistry, Faculty of Science, Annamalai University, Annamalai Nagar, Tamil Nadu - 608 002, India
http://online.liebertpub.com/doi/abs/10.1089/10966200260398206
Effect on Ulcers
Moringa oleifera induced potentiation of serotonin release by 5-HT(3) receptors in experimental ulcer model – 2011 S. N. Pradhan Centre for Neurosciences, University of Calcutta, 244B A.J.C. Bose Road, Kolkata 700020, India
Effect on Postmenopausal Women
Effect of supplementation of drumstick (Moringa oleifera) and amaranth (Amaranthus tricolor) leaves powder on antioxidant profile and oxidative status among postmenopausal women – 2014 Journal of Food Science and Technology Technology Technology
Anti-Inflammatory Effect on Colitis
Anti-inflammatory effect of Moringa oleifera Lam. seeds on acetic acid-induced acute colitis in rats – 2014 Avicenna J Phytomed
Anti-Diabetic Effect
The antidiabetic effect of low doses of Moringa oleifera Lam. seeds on streptozotocin induced diabetes and diabetic nephropathy in male rats – 2015 Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
Healing the Skin
Enhancement of human skin facial revitalization by moringa leaf extract cream – 2014
Postepy Dermatol Alergol

redpajama_set_name: RedPajamaCommonCrawl


In the past New Zealand has been seen by many as a back door into Australia. This perception may have been formed as skilled immigration to New Zealand has in the past been easier to achieve than to Australia, and Kiwis were once deemed as Australian permanent residents.
The Australian Minister for Immigration today announced significant changes to the General Skilled Migration (GSM) program.
"1 Settler -1 visa" refugees: Protection Visas for South Africans?
The recent case of Brandon Huntley, a white South African who was granted asylum in Canada, made big news in the international community. It was interesting to note the fierce reaction from various interest groups – both from within South Africa and from South Africans living abroad. Some criticise him for being a 'queue jumper' and traitor; others are fascinated by him pulling off the almost unthinkable. I have over the past 10 years of practice been asked by many South African clients if there would be 'refugee visa options' available for Australia, given the high levels of crime and racial tension in South Africa.
Subclass 457 visa employees may experience job insecurity during these uncertain economic times. If they are dismissed they face the challenges of finding another Australian employer to sponsor them within a very tight time frame and run the risk of repatriation where their visa is ultimately cancelled.
I have been asked to write this article to inform holders of Subclass 457 visas currently in Australia of their legal rights in a time of financial uncertainty and retrenchment.


Peder Mannerfelt - Controlling Body Peder Mannerfelt's inquiring mind has led him into some interesting projects over the years, including work with Fever Ray and as half of Roll The Dice. But most of his solo experiments have been sheathed in a dark industrial casing, which is starting to look pretty drab. Lately he's found some more eye-catching looks. There was The Swedish Congo Record, on which he used synths to painstakingly reproduce recordings made in 1930s Central Congo, and a strange and sweaty dance EP for Ultimate Hits. His new album, Controlling Body, tries out yet more fresh colours. It doesn't have a Congo-like concept (probably a good thing given the knotty appropriation questions raised by that record), but it does have a sort of protagonist, the synth pop artist Glasser, whose voice binds the album together.
Of course, Mannerfelt doesn't settle for simply framing her voice with his asymmetric beats. Instead, he weaves it deep into the fabric of his music. On opener "Building Of The Mountain," a single wordless note repeats alongside a handful of other sounds, each spinning carefully around the others like the arms of a child's mobile. On "Limits To Growth," Mannerfelt fades between the words "create" and "growth," while dry, precise drum patterns tense and relax. Much of the album uses this structure, setting a few loops into uneven motion and then letting them splay apart and reform. Most tracks don't seem to go anywhere, but by the time they finish we're somewhere quite unexpected.
Mannerfelt gets a lot of variety from his approach. The billowing chords of "BZ Reaction" and "The Confidence Of Ignorance" recall dub techno; the former's sour guitar twangs are particularly juicy. Elsewhere, the mood is spookier, as on "Coast To Coast" and "Abysmal," whose B-movie atmosphere gives way to weird blasts of choir. Sometimes Mannerfelt riffs on the club-leaning sound of his Ultimate Hits record. "Her Move" is oddball techno with the compulsive bounce of Oni Ayhun. Album highlight "Perspective" is bizarre cubist electro. It might be potent on a dance floor, but as ever, Mannerfelt can't stop tweaking things, shuffling patterns around to send dancing bodies reeling off balance.
Glasser's voice doesn't emerge fully formed from this strange soup until the closing track, "I Love You," on which Mannerfelt's exploratory loops are arranged into something like a song. It's no less original than lots of his solo work, but quite a bit more entertaining to listen to.


text:
France June 2019: Renault Clio V just misses out on first Top 10, Twingo at highest in 8 years in market down -8.4%
The Renault Clio V ranks #11 at home in June. Picture largus.fr.
This article has been updated with the Top 40 brands, Top 270 models and Top 10 private sales.
New car sales in France skid down -8.4% year-on-year in June to 230.965 units (the third highest June result below 2018 and 1998), over a particularly high comparison in June 2018 which remains a June record, the largest month this decade and third largest overall in history. The number of selling days also didn't help at 19 vs. 21 and local body the CCFA insists that the selling rate is actually up 1.2% year-on-year. Private sales continue to freefall at just 43.3% share, company sales hold 21% of total sales and dealership demo sales hit a record 19.6% in June, revealing a very unhealthy French market indeed.
As it has been the case for the most part of the past decade, Renault (-13.2%) only needs an artificially boosted end-of-quarter at 22.4% share to remain in the brands lead at home even though Peugeot (-14.7%), down to 15.8% in June, topped the monthly charts 3 times out of 6. Renault also returns to #1 brand with private buyers at 15.5% share above Peugeot (14.2%) after falling to a paltry 4th place last month, and is ultra-dominant in the company sales channel at a whopping 34.6% share above Peugeot at 19.9%.
The Renault Twingo is pushed up to #4 by its facelift.
All recoveries come to an end: for the first time in over a year (since May 2018), Citroen (-6.3%) is down year-on-year, tilting below the symbolic 10% share mark. Volkswagen (+0.5%) nudges past Dacia (-7.4%) to #4, with Audi (+17.5%) and Mercedes (+13.1%) both posting splendid scores at the tail end of the Top 10 just as Ford (+8%) and Toyota (+5.4%) are also very solid but Fiat (-17%) sinks. Further down, Tesla (+642.2%), Alpine (+98.3%), Lexus (+19.3%), DS (+12.6%), Mitsubishi (+12.5%), Seat (+12.3%) and Porsche (+11%) shine but Nissan (-37.9%), Smart (-29.3%), BMW (-26.5%), Mini (-21.6%) and Opel (-14.5%) freefall.
The VW T-Cross breaks into the Top 30…
In the models ranking, we continue to witness the transition between two generations of Renault Clio, with the Peugeot 208 II about to make its appearance in the French charts. As a result and for the 2nd month running the Peugeot 208 I (-12.2%) is the best-selling vehicle at home, this time distancing the Renault Captur (-8.6%) up to #2 for the first time since December 2017 and the Renault Clio IV (-48.7%) now bowing out. The Renault Twingo (+32.1%) takes full advantage of its facelift and hits its first Top 5 finish in over 4 years (since ranking #5 in February 2015) and its highest ranking for the 3rd generation at #4. As for the Twingo nameplate overall, the last time it ranked as high was in December 2011. All remaining Top 10 models are in decline, with the Dacia Duster (-0.6%) and Sandero (-7.2%) resisting best but the Citroen C3 (-20.4%), Peugeot 3008 (-17.5%), 2008 (-17.3%) and Renault Megane (-12%) the hardest hit.
…and the DS 3 Crossback breaks into the Top 40.
As for new launches, the Renault Clio V amps up the pressure, up 4 spots on May to end June just 71 sales from a first Top 10 finish. After promisingly breaking into the Top 10 in April, the Citroen C5 Aircross continues to slide down the charts at #18, whereas the VW T-Cross shoots up 34 spots on last month to break into the Top 50 and Top 30 at once to #30. Two additional new launches crack the French Top 50 for the first time this month: the DS 3 Crossback is up 21 ranks on May to #38 and the Toyota Corolla up 27 to #45. As for the Tesla Model 3, it slices its May ranking in two from #111 to #56 in June, but can't quite match the #42 it hit last March. Other notable evolutions include the Mercedes A-Class (+222.3%), Peugeot 508 (+205.1%), Ford Focus (+159.3%), Audi Q3 (+136.3%), Volvo XC40 (+106.5%), Jeep Renegade (+105.4%), Fiat 500X (+50.1%), Toyota RAV4 (+41.3%) and Hyundai Kona (+41.1%). In the private sales channel, the Dacia Sandero (-5.9%) reigns supreme at 5.9% share ahead of the Peugeot 208 (-4%), Renault Clio IV/V (-2.1%), Twingo, Citroen C3 (-16.7%) and Renault Captur (-16.9%) with the Toyota Yaris (-1.2%) #1 foreigner at #8.
Previous month: France May 2019: Citroen (+23.1%) continues recovery, Renault Clio 5 arrival reshuffles models and private sales order, market up 1.2%
One year ago: France June 2018: Renault, Dacia, Fiat lift market to third largest monthly volume in history
Full June 2019 Top 40 brands, Top 270 models and Top 10 private sales below.
France August 2016: Dacia, Mercedes, tactical sales lift market up 7%
The Dacia Sandero outsells the Renault Clio 2 to 1 with private buyers in August. *…
France June 2013: Private sales dive under 50% of overall market
Fiat Panda. 66% of French Fiat sales in June are "tactical". * You can check…
France December 2014: BMW and Mercedes finish on a high
The third generation Mini hits a best-ever #26 in France this month. * UPDATED -…
previous post: France May 2019: Citroen (+23.1%) continues recovery, Renault Clio 5 arrival reshuffles models and private sales order, market up 1.2%
next post: France First Half 2019: Citroen (+11.4%), Mercedes (+7.5%) strong in market down -1.8%


When one has a physical limitation, or any limitation, these are words to live by. Unfortunately, in real life one discovers there are limitations as to what one is realistically able to do.
I may not always succeed, but I always give everything I do or everything I attempt my very best. I never give up easily. I often make several attempts at doing new things. I can't skate for instance, although, God knows, I've tried. I mentioned this story before, so I will skip the monologue, and get to the gist of the story. After many, many attempts of no improvement, I threw in my skates. I guess I could have kept trying, but I didn't see any point. I had plateaued and I never saw any improvement. If I had, then I probably would have stayed with it.
For me, this is the key. I require some sense of improvement when I strive to do something. If I don't see any improvement, I generally give up.
Unlike fantasy movies and books, life has its setbacks. The key is to keep trying until it becomes evident that it is beyond one's ability. We can't do everything, or else we would all be able to do it all. We all have our individual gifts, skills, and traits which makes each of us unique.
I learnt my limitations after I have made numerous attempts. I wanted to try for track and field once. I asked my teacher and she said if I could run a mile under 5 minutes, then she would let me. I couldn't meet the time, but I completed the mile (I remained stubborn until the end).
Unfortunately these trials and failures, and what with professionals (teachers, counsellors, etc.) telling me to be realistic about my career choices, I began to lose my confidence. I stopped trying new things. I never attempted alpine skiing, and by the last surgery, I lost the flexibility in my ankle and feared I would only hurt myself.
Doubt and fear are two obstacles blocking most people from trying new things. I should have kept trying thing when I was younger now I feel I am too old.
Unfortunately one cannot go back. I have to accept that I cannot do everything, and I have. As mentioned above, no one can do everything. We all have limitations, but one must give it their all before one tosses in the towel. I still try new things. I have done IT ( I learnt to program and repair computers). I have taken up writing and I have written a book and I have this blog. I have drawn a map for my novel. I find if there is a will to do something, then one can usually accomplish the task. The end result may not be perfect, but at least it gets done.
In my case, the method I use may not be the same as someone without a physical limitation, and it may take me longer to accomplish it, but I get it done. And this is what counts. It doesn't matter how one does a thing, or how long it takes. It only matters that one accomplishes what one set out to do.


I am a Hindu girl and I believe that our existence lies deep within our religion therefore we should always stay close to it. According to me I am a humorous and fun loving person who respects her elders and culture. I am fluent in speaking Bengali.
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Not every divorced couple can get along amicably. In fact, the bitterer a couple is in the divorce, the less likely they are to comply with a court decision that isn't necessarily in their favor. In other circumstances, even with amicably divorced people, one spouse could neglect the terms of the divorce based on irresponsibility or a change in condition. Whatever the circumstances of your particular case, there are actions you could take to ensure your ex does comply with the terms of the divorce.
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If your spouse has defied court orders, a judge can hold him or her in contempt. Contempt of court means someone has failed to obey orders and can be subject to fines, sanctions, or incarceration. In order to file for a motion for contempt, your ex must have violated a court order that clearly defines an obligation or requirement, must have an ability to comply with the order, and must have willfully refused to comply with the order.
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ISLAMABAD, May 19 (APP):The citizens on Saturday urged the authorities concerned to pay surprise visits to rein the prevailing phenomena of overcharging being observed in the markets of federal capital.
The public were of the view that government had taken productive steps by introducing the fixed and affordable rates for fruits, but it is time to keep an eye on the profiteers who always look for an opportunity of fleecing the public.
According to them such visits of concerned officials in fruit and vegetable markets would help maintaining the prices of fruits and facilitate buyers in the holy month of Ramadan.
Muhammad Shafique, a resident of Sector G-13/4 said "Yesterday I had to pay Rs 280 just to buy one dozen of banana, which means I had paid Rs 140 extra just to buy one fruit." He asked the price control committee members of Islamabad Capital Territory (ICT) to check the prices of fruits which were sky rocketing despite tall claims of departments concerned.
He said that it was not possible for residents to go G-9 market, a 10 Kilometer away from G-13 and they had to buy fruits from local market where every shop keeper has its own rate list and everyone was misleading the buyers.
Another buyer from the same market informed that shopkeepers had placed unauthorized rate lists which should be checked regularly by the officials of Islamabad Capital Territory and urged them to ensure that every shopkeeper in federal capital had actual price list placed for general public.
When contacted an official of ICT, said that special committee has been formed to control the prices of edible items in the federal capital during this month. He said that teams have been regularly carrying out inspections in their assigned areas, punishing the violators. He added the campaign would continue during whole month.
He informed that the ICT administration had imposed fines amounting Rs 1,40,800 on shopkeepers for over charging the food items during its ongoing crackdown against hoarders.
The official said that as many as 20 price checking teams headed by the Magistrates checked 88 shops so far and took action against the 26 profiteers, adding, seven profiteers were also arrested on the spot for violation of Pure Food Ordinance.
He said that in some areas, the profiteers were found selling substandard food items besides complaints of displaying fake rate lists.
All such shopkeepers had been clearly directed to display rate list of food items and sell foods at fixed rate and strict action would be taken against them, he added.


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text:
Thin Lizzy je první studiové album irské skupiny Thin Lizzy, vydané v roce 1971 (viz 1971 v hudbě).

Seznam stop
 "The Friendly Ranger at Clontarf Castle" (Bell, Lynott) – 2:57
 "Honesty Is No Excuse" (Lynott) – 3:34
 "Diddy Levine" (Lynott) – 3:52
 "Ray-Gun" (Bell) – 2:58
 "Look What the Wind Blew In" (Lynott) – 3:16
 "Éire" (Lynott) – 2:04
 "Return of the Farmer's Son" (Downey, Lynott) – 4:05
 "Clifton Grange Hotel" (Lynott) – 2:22
 "Saga of the Ageing Orphan" (Lynott) – 3:39
 "Remembering, Pt. 1" (Lynott) – 5:57
 Podle poznámek na albu, nahrávání původního alba začalo 4. ledna 1971. Phil Lynott zemřel přesně o 15 let později, 4. ledna 1986. Když bylo album přepracováno na CD, byly přidány čtyři skladby, původně obsažené na EP "New Day":
 "Dublin" (Lynott) – 2:27
 "Remembering, Pt. 2 (New Day)" (Bell, Downey, Lynott) – 5:06
 "Old Moon Madness" (Lynott) – 3:56
 "Things Ain't Workin' Out Down at the Farm" (Lynott) – 4:32

Předpokládalo se, že nová reedice alba vyjde někdy v roce 2007, ale vydání se opozdilo až do dubna 2008.

Seznam stop je následující:

 Friendly Ranger At Clontarf Castle
 Honesty Is No Excuse
 Diddy Levine
 Ray Gun
 Look What The Wind Blue In
 Éire
 Return Of The Farmer's Son
 Clifton Grange Hotel
 Saga Of The Ageing Orphan
 Remembering
 The Farmer
 Dublin
 Remembering Part 2 (New Day)
 Old Moon Madness
 Things Ain't Working Out Down At The Farm
 Look What The Wind Blew In (1979 Overdubbed & Remixed Version)
 Honesty Is No Excuse (1979 Overdubbed & Remixed Version)
 Dublin (1979 Overdubbed & Remixed Version)
 Things Ain't Working Out Down At The Farm (1979 Overdubbed & Remixed Version)

Obsazení
 Eric Bell - sólová kytara, 12strunná kytara
 Brian Downey - bicí, perkusy
 Phil Lynott - baskytara, zpěv, akustická kytara

host
 Ivor Raymonde - mellotron na "Honesty Is No Excuse"

Reference

Alba Thin Lizzy
Alba z roku 1976
Eponymní alba
Alba Deram Records
Alba v angličtině

redpajama_set_name: RedPajamaWikipedia