text:
MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
DOI: 10.1186/gb-2014-15-1-r19
© Mort et al.; licensee BioMed Central Ltd. 2014
Received: 11 November 2013
Accepted: 13 January 2014
Published: 13 January 2014
We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.
In case-control studies, the search for disease-causing variants is typically focused on those single base substitutions that bring about a direct change in the primary sequence of a protein (that is, missense variants), the consequence of which may be structural or functional changes to the protein product. Indeed, missense mutations are currently the most frequently encountered type of human gene mutation causing genetic disease [1]. The underlying assumption has generally been that it is the nonsynonymous changes in the genetic code that are likely to represent the cause of pathogenicity in most cases. However, there is an increasing awareness of the role of aberrant posttranscriptional gene regulation in the etiology of inherited disease.
With the widespread adoption of next generation sequencing (NGS), resulting in a veritable avalanche of DNA sequence data, it is increasingly important to be able to prioritize those variants with a potential functional effect. In order to identify deleterious or disease-causing missense variants, numerous bioinformatic tools have been developed, including SIFT [2], PolyPhen2 [3], PMUT [4], LS-SNP [5], SNAP [6], SNPs3D [7], MutPred [8] and Condel [9] among others. However, the majority of these methods only consider the direct impact of the missense variant at the protein level and automatically disregard same-sense variants as being 'neutral' with respect to functional significance. Although this may well be the case in many instances, same-sense mutations can still alter the landscape of cis-acting elements involved in posttranscriptional gene regulation, such as those involved in pre-mRNA splicing [10–12]. It is clear from the global degeneracy of the 5′ and 3′ splice site consensus motifs that auxiliary cis-acting elements must play a crucial role in exon recognition [13]. To date, a considerable number of exonic splicing regulatory (ESR) and intronic splicing regulatory (ISR) elements have been identified [14–19]. Generally these are classified as either enhancers (exonic splicing enhancers (ESEs)/intronic splicing enhancers (ISEs)) or silencers (exonic splicing silencers (ESSs)/intronic splicing silencers (ISS)), which strengthen and repress, respectively, recognition of adjacent splice sites by the splicing machinery. This distinction may be to some extent artificial in so far as an ESE can act as an ESS and vice versa depending upon the sequence context and the trans-acting factor bound to it [16, 20]. These trans-acting factors include members of the serine/arginine-rich family of proteins (SR proteins) typically known to bind to splicing enhancers and the heterogeneous nuclear ribonucleoprotein family of complexes (hnRNPs), which are thought to bind splicing silencers. However, it is clear that our knowledge of the cooperative and antagonistic elements that regulate pre-mRNA splicing in a context-dependent manner is still very limited [21].
The functional consequences of a splice-altering variant (SAV) may also vary quite dramatically; thus, splicing events that alter the reading frame can introduce premature termination codons that may then trigger transcript degradation through nonsense-mediated decay. Alternatively, an aberrant splicing event may maintain the open reading frame but lead instead to a dysfunctional protein lacking an important functional domain. Even a splice-altering variant that produces only a small proportion of aberrant transcripts could still serve to alter the gene expression level [21].
Up to approximately 14% of all reported disease-causing nucleotide substitutions (coding and non-coding) listed in the Human Gene Mutation Database [1] (11,953 mutations; HGMD Pro 2013.4) are thought to disrupt pre-mRNA splicing whereas 1 to 2% of missense mutations have been reported to disrupt pre-mRNA splicing (HGMD Pro 2013.4). Previous studies have, however, found that the actual proportion of disease-causing missense mutations that disrupt pre-mRNA splicing could be rather higher [22–25]. The difference between the observed and predicted frequencies of disease-causing splicing mutations may be due in part to the frequent failure to perform routine in vitro analysis (for example, a hybrid minigene splicing assay [26]), so the impact of a given missense mutation on the splicing phenotype is generally unknown. The likely high frequency of exonic variants that disrupt pre-mRNA splicing implies that the potential impact upon splicing should not be neglected when assessing the functional significance of newly detected coding sequence variants. Coding sequence variants that disrupt splicing may not only cause disease [22] but may in some cases also modulate disease severity [27, 28] or play a role in complex disease [29]. The identification of disease-causing mutations that disrupt pre-mRNA splicing will also become increasingly important as new therapeutic treatment options become available that have the potential to rectify the underlying splicing defect [30, 31].
Current bioinformatic tools designed to assess the impact of genetic variation on splicing employ different approaches but typically focus on specific aspects of splicing regulation (for example, the sequence-based prediction of splice sites as employed by NNSplice [32] and MaxEntScan [33]) or the sequence-based identification of splicing regulatory elements as exemplified by ESEFinder [14], RESCUE-ESE [15], Spliceman [34] and PESX [19]. Other tools have employed a combination of a sequence-based approach coupled with various genomic attributes - for example, Skippy [35] and Human Splice Finder [36]. In general, however, most tools have not been optimized to deal with single base substitutions, and require the wild-type and mutant sequences to be analyzed separately with the user having to compute any difference in predicted splicing regulatory elements. Tools that are designed specifically to handle single base substitutions include Spliceman, Skippy and Human Splice Finder (HSF). In most cases, as each tool focuses on specific aspects of the splicing code, there is often a need to recruit multiple programs [37] before any general conclusions can be drawn.
An exome screen will typically identify >20,000 exonic variants [38]. This volume of data ensures that high-throughput in silico methods are an essential part of the toolset required to prioritize candidate functional variants from the growing avalanche of sequencing data now being generated by NGS. NGS data analysis normally involves applying multiple filters to the data in order to prioritize candidate functional variants. When applying NGS filters, it is important to remember that same-sense variants may alter pre-mRNA splicing via a number of different mechanisms. Hence, a naïve NGS filter that only considers variants within the splice site consensus as candidate splicing-sensitive variants would not identify same-sense variants that caused exon skipping via a change in ESR elements.
Currently, several general areas need to be improved in relation to the identification of genetic variation responsible for aberrant pre-mRNA splicing. Firstly, although the consensus splice site sequences are well defined, the auxiliary splicing elements and their interactions with splice sites are not well understood. Secondly, there is an urgent need for larger unbiased datasets of experimentally characterized variants that alter splicing and have been quantitatively assessed with respect to the mRNA splicing phenotype. This would provide better training data for new models and provide new datasets to benchmark the performance of different tools (both new and existing). Thirdly, there is an urgent need for new bioinformatic tools suitable for use in a high-throughput NGS setting. These tools promise to be invaluable for the comprehensive evaluation of the impact of a given variant on mRNA processing (that is, not just in terms of splice site disruption). It would also be beneficial if the specific consequences for the splicing phenotype (that is, multiple exon skipping, cryptic splice site utilization, and so on) could be accurately predicted so as to reduce our reliance upon expensive and time-consuming in vitro analysis. Finally, these high-throughput in silico tools should be designed in such a way as to be able to handle different types of genetic variation (that is, coding, non-coding, single base substitutions, microdeletions, microinsertions, and so on) and allow assessment of the combined impact of multiple sequence changes in cis (for example, two substitutions within the same exon).
Materials and methods
Data sets
Summary of original data sets used in this study
Data set name
Disease-causing splice altering variants (DM-SAVs)
Splice altering variants (SAVs)
Inherited disease-causing coding region mutations that disrupt pre-mRNA splicing, derived from HGMD
Disease-causing splice neutral variants (DM-SNVs)
Splice neutral variants (SNVs)
Inherited disease-causing missense mutations not reported to disrupt splicing derived from the same set of genes as the DM-SAVs. The majority are not expected to have any effect on exon splicing but approximately 25% may nevertheless disrupt splicing
Polymorphic splice neutral variants (SNP-SNVs)
Splice neutral variants (SNVs)
Putatively 'neutral' common coding region SNPs (minor allele frequency >0.3) from the 1000 Genomes Project. The majority are not expected to have any effect on pre-mRNA splicing
The first negative set of splice neutral variants (SNVs) comprised 7,729 human inherited disease-causing missense mutations from HGMD, not reported to disrupt exon splicing (August 2012) [1, 39] and restricted so as to only include mutations from the same set of 453 genes from which the positive set of DM-SAVs were derived. This negative set is referred to as disease-causing splice neutral variants (DM-SNVs; Table 1). It should be noted that whilst the majority of disease-causing missense mutations in this set of DM-SNVs are likely to exert a pathogenic effect via direct disruption to protein structure/function, it would be reasonable to suppose that approximately 25% may disrupt or modulate splicing [23–25].
The second negative set of SNVs comprised 7,339 high frequency exonic SNPs (SNP-SNVs; Table 1), which were compiled from 1000 Genomes Project data [38]. In the SNP-SNV set, only SNPs found with ≥30% minor allele frequency (MAF) in at least one HapMap population from the 1000 Genomes Project data were included. Owing to their high MAF, it is considered unlikely that the majority of these common polymorphisms would have a significant effect on the pre-mRNA splicing phenotype (that is, they may be regarded as being putatively neutral with respect to splicing).
Training sets
Summary of training set sizes derived from the data sets outlined in Table 1
Training set name
Positive set (Iter. 1, Iter. 2, Iter. 3)
Negative set (Iter. 1, Iter. 2, Iter 3.)
Disease negative set
DM-SAVs (1,189, 1,189, 2,601)
DM-SNVs (7,729, 7,363, 31,967)
SNP negative set
DM-SAVs (1,189, 1,189, 2,090)
SNP-SNVs (7,339, 7,253, 70,847)
Mixed negative set (disease and SNP)
DM-SAVs (1,189, 1,189, 6,335)
DM-SNVs and SNP-SNVs (15,068, 14,616, 111,630)
Random SNP set (control)
SNP-SNVs (50%) (3,669, 3,669, 9,901)
SNP-SNVs (50%) (3,670, 3,613, 7,349)
For the purposes of evaluating a semi-supervised learning approach, three different iterations (Iter. 1, Iter. 2 and Iter. 3) of the original training data were constructed. In the first iteration (Iter. 1), the Random Forest (RF) classification model (see Classification method section for more details) was built using the original four training sets outlined above. Performance was then evaluated with an unseen test set (see Performance evaluation section for more details); the respective model for each training set was then used to build the next iteration (Iter. 2) of the training sets. As the DM-SNV set may contain approximately 25% SAVs, the DM-SNV model built previously in Iter. 1 was then used to identify SAVs in the Disease negative set and SAVs in the SNP negative set identified using the SNP-SNV Iter. 1 model. SAVs predicted with high confidence in both negative sets (DM-SNVs and SNP-SNVs) were then removed and the model retrained to yield Iter. 2. A method for semi-supervised classification termed self-training [40] was then employed to build the next iteration (Iter. 3). Semi-supervised learning typically involves using a small amount of labeled data (for example, DM-SAV) and a large amount of unlabeled data. So, in this instance, the labeling is with respect to impact on splicing (rather than a disease-causing label). The unlabeled data sets comprised the entire HGMD inherited disease data set of 47,228 missense mutations plus the combined data set of missense and same-sense variants identified in the 1000 Genomes Project with no MAF filter applied, that is, includes common and rare variants (192,841 variants). To build the third iteration (Iter. 3), the semi-supervised labeling of variants was based on the second iteration (Iter. 2) model of the respective training sets; this classifier was then applied to the unlabeled data (47,228 disease-causing missense mutations and 192,841 missense and same-sense variants from the 1000 Genomes Project) from which confidently labeled examples were used to supplement the existing training sets used in Iter. 2. A RF classifier was then built with the expanded training sets to complete the third iteration (Iter. 3). It should be noted that the data sets employed here for both training and subsequent analysis only include variants for which all splicing-relevant features could be derived; therefore, variants with missing values were excluded from the data set.
To summarize: iteration 1 (Iter. 1), model built using original training data; iteration 2 (Iter. 2), negative sets (DM-SNVs and SNP-SNVs) had predicted SAVs removed; iteration 3 (Iter. 3), positive and negative sets were supplemented with data labeled from the respective model produced in iteration 2 (Iter. 2).
Discriminative features investigated in this study
In order to evaluate discriminative features or attributes useful in the identification of exonic single base substitutions that modulate splicing, an array of features were derived based upon the genomic coordinate of the substitution in the human reference assembly (GRCh37/hg19). The majority of existing features employed here were chosen because of prior evidence identifying them as useful in a splicing context [35, 41].
Features investigated in this study can be broadly split into three classes: (1) features directly pertaining to the variant under consideration (SNP-based); (2) features associated with the exon (and flanking intron) in which the variant is located (exon-based); (3) features pertaining to the gene in which the variant occurs (gene-based).
SNP-based features
Summary of features investigated in this study
Distance to nearest splice site
Distance between a given variant and the nearest 5′ or 3′ splice site in the target exon.
ESR change
Change in the frequency of ESR elements subsequent to a single base substitution. This includes:
ESE to neutral (ESE loss)
ESE to ESE (no change)
Neutral to ESE (ESE gain)
ESE to ESS (ESE loss and ESS gain)
Neutral to neutral (no change)
ESS to ESS
Neutral to ESS (ESS gain)
ESS to neutral (ESS loss)
ESS to ESE (ESS loss and ESE gain)
In ESE
Frequency of ESE binding sites (in the wild-type) that overlap with the location of the variant
In ESS
Frequency of ESS binding sites (in the wild-type) that overlap with the variant
ESR hexamer score (ESR-HS)
Hexamer scoring function to express the relationship between disease and neutral variants and their differential distributions with respect to loss or gain of an ESE or ESS
Spectrum kernel
Frequency of 3-mers and 4-mers over an 11 bp window (wild type and mutant)
Change in natural splice site strength
MaxEnt splice site score of natural splice site in mutant allele minus MaxEnt splice site score of wild-type allele
Maximum cryptic splice site
Maximum cryptic splice site (5′ and 3′) score (outside of the natural splice site) found overlapping the variant on the mutant allele
Evolutionarily conserved element
PhastCons conserved element probability for substitution site, based on multiple alignments of 46 placental mammals
Base-wise evolutionary conservation
PhyloP base-wise sequence conservation score at site of single base substitution based on multiple sequence alignment of 46 placental mammals
Natural wild-type splice site strength
MaxEntScan score of the natural 5′ and 3′ splice site of the wild-type target exon
Flanking intron size
Length in base-pairs of the upstream and downstream introns flanking the target exon
Intronic ESS density
Intronic ESS density was calculated for 100 bp upstream and 100 bp downstream of the target exon
Exonic ESS density
ESS density was calculated across the first 50 bp and the last 50 bp of the target exon. If the length of the exon was less than 100 bp, then the full length of the exon was used to calculate the ESS density
Exonic ESE density
Same as above but for ESEs
Internal coding exon
{true, false}, Is the target exon an internal coding exon (that is, the target exon is not the first or last coding exon)
Exonic GC content
Percentage of nucleotides that are either guanine or cytosine in the target exon
Exon size
Size of the target exon
Constitutive exon
Is the target exon constitutively spliced
Exon number
Number of exons in the transcript
Transcript number
Number of different reported isoforms that the target gene encodes
Thus, this ESR-HS is a robust independent combination of the differential strength of the hexamer plus the differential strength of the mutated base in the hexamer.
Another SNP-based feature utilized was the change in natural splice site strength (5′ and 3′) as a consequence of the substitution, as measured by the MaxEntScan algorithm [33]. To model cryptic splice site activation, the maximum splice site score overlapping the variant (not including the natural splice site) found in the mutant RNA sequence was also measured. As it is unlikely that all types of ESR (or other splicing element) have been fully characterized to date, we attempted to overcome this by applying a string-based sequence similarity kernel (the 'spectrum kernel'), first proposed for classifying protein sequences [42]. By applying the spectrum kernel to both wild-type and mutant sequences, we could identify splicing sequence motifs and measure any changes (loss or gain) consequent to a single base substitution. The spectrum kernel was then applied over an 11 bp window (that is, 5 bp upstream and 5 bp downstream of the variant) using the wild-type genomic RNA sequence to count the frequencies of all k-mers of length = 4 (for example, AGAG, and so on) and length = 3 (for example, GAA); this process was then repeated for the mutant allele. Finally, for SNP-based features, evolutionary conservation based on PhyloP at the position of the substitution [43] and PhastCons [44] was computed, based on the multiple DNA sequence alignments of 46 placental mammal species. The PhyloP score represents a base-by-base (ignores neighboring bases) conservation score for each base of the reference genome. Therefore, PhyloP measures both conservation (slower than expected evolutionary change) and accelerated evolution (faster than would be expected under neutral drift). The PhastCons score represents the probability of the mutated base being located within an evolutionarily conserved element and therefore considers the conservation of the neighboring bases. PhastCons has been used to identify candidate functional elements (for example, splicing factors) in genomic sequences [43]. Both the PhyloP and PhastCons scores were downloaded from the UCSC Genome Browser [45].
Exon-based features
With respect to the 'target' exon within which a given substitution occurred, nine exon-based features were computed. These features included natural wild-type splice site strength (5′ and 3′) using the MaxEntScan algorithm [33], flanking intron size, exon size, exonic GC content, exonic ESE density, exonic ESS density and intronic ESS density. ESE and ESS densities were calculated using a sliding window across the first 50 bp and the last 50 bp of each target exon. Where the length of the exon was <100 bp, then the full length of the exon was used to calculate the ESE and ESS density. Intronic ESS density values between 100 bp upstream and downstream of the relevant exon were calculated in the same manner as the exonic ESE and ESS density. Finally, for the exon-based features, two Boolean features were computed; internal coding exon (the target exon is neither the first nor the last coding exon) and constitutive exon (exon is present in every transcript).
Gene-based features
Two gene-based features were calculated, the first being the number of exons in the target isoform and the second being the transcript number, which records the number of known protein isoforms that the target gene encodes.
Feature ranking
Classification method
The supervised classification method employed by MutPred Splice was RF [46], an ensemble method using hundreds of decision trees to perform classification. RF has been extensively used in bioinformatics applications, including the prediction of disease-causing mutations [8, 47–49]. The popularity of RF is due in part to its simplicity with no fine-tuning of parameters required and in part to its speed of classification, which is often faster than an equivalent SVM model [50]. In this study, as we are combining multiple classification models and evaluating different training sets, this advantage of RF (limited tuning required) over SVM (tuning required) was considerable. We did nevertheless evaluate RF versus SVM and found that classification performance was broadly similar. SVM is a machine learning model that maximizes the margin of separation between examples of two classes projected into high-dimensional space [51, 52]. In this study, we used an SVM with a linear kernel for feature ranking (Figure 1). For the machine learning algorithm implementations, we used LIBSVM and R randomForest package v4.5-36. The Weka toolkit was used for data pre-processing [53].
Generally, it is preferable to use a balanced training set (equal number of positive and negative training examples) to train a supervised classifier, because training on a highly imbalanced data set can be problematic - for example, the classifier can tend to classify most examples as the majority class [54]. In this study, the number of negative examples (DM-SNVs and SNP-SNVs) outnumbered the positive examples by a large margin. To address this inequality and to balance the training sets, we employed an ensemble of RF classification models. This technique was implemented in MutPred Splice by building (in the case of the first iteration of the Disease negative set, for example) different balanced training sets, each with the same positive training set of DM-SAVs, whereas the negative set was randomly sampled (without replacement) from all available negative examples (in that training set) until a balanced set was constructed; this process was then repeated for the next model with the remaining negative DM-SNVs. In MutPred Splice, an RF classifier was then applied to each of the balanced sets of training data, with the final predictive probability being an average of all probability scores produced by each RF classification model. This final predictive probability of a variant disrupting splicing will henceforth be referred to as the general score. This ensemble of RFs approach was then repeated on all four training sets (Table 2).
Performance evaluation
Standard performance benchmarks for MutPred Splice based on an unseen test set of 352 variants (238 positive, 114 negative) using the three different iterations (Iter. 1, Iter 2. and Iter. 3) of the four different training sets identified in this study (Table 2 )
Data set
False positive rate (%)
Sensitivity (%)
Specificity (%)
Accuracy (%)
Disease negative set
Iter. 1
Iter. 2
Iter. 3
SNP negative set
Iter. 1
Iter. 2
Iter. 3
Mixed negative set
Iter. 1
Iter. 2
Iter. 3
Random SNP set
Iter. 1
Iter. 2
Iter. 3
Experimental characterization of mRNA phenotype
The impact of the inherited disease-causing mutation NM_000051.3: ATM c.5932G > T; NP_000042.3: p.E1978X was assayed in a patient-derived cell line carrying the E1978X mutation or a control cell line (HEK293). Total RNA was extracted from cells using Trireagent LS (Sigma Aldrich, St. Louis, MO USA) and analyzed by RT-PCR using One-Step RT-PCR mix (Invitrogen, Carlsbad, CA USA). Amplicons corresponding to the exon 41 included or skipped product were resolved by agarose gel electrophoresis and visualized by SYBR Gold staining (Figure S1 in Additional file 2).
Comparison with existing tools used to identify SAVs
MutPred Splice was designed to identify exonic variants that disrupt pre-mRNA splicing via multiple mechanisms: for example, splice site disruption, cryptic splice site activation and exon skipping, and so on. In order to evaluate the performance of MutPred Splice, we opted to compare MutPred Splice with ANNOVAR [61], HSF [36] and Skippy [35]. Although not all the methods evaluated here are directly comparable (since they have different applications and limitations), this selection of tools is nevertheless a fair reflection of the various types of software currently available to identify exonic SAVs. For the purposes of this evaluation, we followed, wherever appropriate and possible, the reported guidelines for performance evaluation of mutation prediction methods [62]. We employed 264 exonic variants (181 positive, 83 negative) derived from the unseen test where predictions could be obtained from all the tools evaluated here. For ANNOVAR and Skippy, the unseen test set included positive SAVs that actually lie outside of the scope of the respective method. Therefore, adjusted performance metrics are also shown using a subset of the overall test set relevant to the specific method. For methods that output multiple scores for a given variant (HSF and Skippy), performance metrics may differ depending upon both the features and the thresholds applied. For a detailed description of guidelines, applications and performance of the tools evaluated here, the reader is referred to the relevant website or original reporting publications.
Role of pre-mRNA splicing disruption in inherited disease, cancer and polymorphism
Predicted proportion of exonic variants that disrupt pre-mRNA splicing in human genetic disease (Inherited disease, that is, germline; and Cancer, that is, somatic) and also identified in the general population (1000 Genomes Project participants)
Data set
Proportion of SAVs in data set (predicted SAVs/total variants)
Inherited disease
11.0% (5,193/47,228)
90.3% (468/518)
30.5% (4,130/13,559)
16.0% (9,791/61,305)
9.2% (32,056/347,380)
8.6% (9,010/105,094)
32.4% (9,141/28,256)
10.4% (50,207/480,730)
1000 Genomes
6.8% (7,016/103,445)
6.7% (5,968/89,396)
19.5% (273/1,400)
6.8% (13,257/194,241)
Predicting the splicing mechanism disrupted by a SAV
The prediction of the underlying splicing mechanism disrupted by a SAV (for example, cryptic splice site activation) is based on a previously described method [8], which compares the relevant splicing property with that of the respective distribution of scores obtained from predicted SNVs found in the 1000 Genomes Project. A Z score P-value < 0.05 is considered a confident hypothesis.
Exonic variants in oncogenes and tumor suppressor genes
Predicted proportion of exonic variants from two gene subsets (tumor suppressor versus oncogenes) that disrupt pre-mRNA splicing in human genetic disease (Inherited disease that is, germline and Cancer that is, somatic) and also identified in the general population (1000 Genomes project participants)
Data set
Proportion of SAVs in data set (predicted SAVs/total variants)
Tumor suppressor
Inherited disease
25.3% (1,130/4,463)
10.9% (132/1,207)
16.0% (1,612/10,082)
10.9% (525/4,831)
1000 Genomes
7.4% (84/1,133)
8.0% (49/612)
MutPred splice availability
The latest MutPred Splice model is available online at [66] or to download for local installation from [67]. The source code is available from [68]. As new examples of SAVs are reported in the literature, MutPred Splice will be retrained so as to incorporate these additional positive examples of SAVs. This will help to ensure that the model is kept up to date with developments as they are reported in the literature. To facilitate the use of MutPred Splice in an NGS setting, VCF (Variant Call Format) files can be uploaded (or processed locally) for analysis.
Identification of informative features for discriminating between SAVs and SNVs
Ranking the features individually using the AUC of the ROC (linear SVM; 10-fold cross-validation) using two different training sets (Disease negative set versus SNP negative set), allowed us to compare and contrast the discriminatory importance of the different features used depending upon the specific negative set being employed (Figure 1). Training the classifier using the Disease negative set identified 11 informative features (Figure 1) that had significantly different AUC values when compared to the AUC produced by a randomly generated attribute (random attribute AUC = 50.7%; t-test with Bonferroni correction; P < 0.05). For the Disease negative set, the highest ranking features (AUC >70%) were ESR change (AUC of 81.8%), distance to nearest splice site (AUC of 80.8%) and change in natural splice site strength (AUC of 76.8%).
Using a classifier trained with the SNP negative set, we identified 18 informative features (Figure 1) that had significantly different AUC values compared to the AUC produced by a randomly generated attribute (random attribute AUC = 49.8%; t-test with Bonferroni correction; P < 0.05). For the SNP negative set, the highest ranking features (AUC >70%) were base-wise evolutionary conservation (AUC of 89.1%), distance to nearest splice site (AUC of 84.8%), ESR change (AUC of 84.3%), evolutionarily conserved element (AUC of 82.4%), change in natural splice site strength (AUC of 77.6%) and the spectrum kernel (AUC of 71.0%). Generally, features that performed significantly better than random for the Disease negative set displayed broadly similar performance irrespective of the training set (Disease negative set or SNP negative set) employed. This feature ranking using different negative data sets highlights the importance of evaluating (and experimenting with) different negative data sets, because the choice of training data has a significant impact upon error rate estimation and the ability of the classifier to generalize to other data sets [69].
Performance evaluation
We evaluated four different training sets (Table 1) and three different iterations of each set (Table 2). These different models were evaluated using a previously compiled unseen set (not present in any training set), for which the variants had been experimentally characterized with respect to their splicing phenotype (SAV or SNV). Figure 2 shows the ROC curves for the four different MutPred Splice classification models, generated using the same unseen test set. In all three iterations (Iter. 1, Iter. 2 and Iter. 3), the Mixed negative set (which combines the Disease negative and SNP negative training data) outperformed the other models within the same iteration with AUCs of 78.8% (Iter. 1), 78.6% (Iter. 2) and 83.5% (Iter. 3). The Mixed negative set also demonstrated the biggest improvement in performance by employing a semi-supervised approach (as judged by the AUC) from Iter. 1 to Iter 3, with a 4.7% AUC increase, compared with both the Disease negative set and the SNP negative set achieving an increase of 1.9%. Standard performance metrics (in addition to the AUC) for all training sets and subsequent iterations are displayed in Table 4. Interestingly, the SNP negative set initially (Iter. 1) had the highest false positive rate (FPR; 36.8%) compared with the Disease negative set (7.0% FPR) and Mixed negative set (7.9% FPR). For all training sets, the semi-supervised approach employed in Iter 3. reduced the initial FPR (Iter. 1) and in the case of both the Disease negative and Mixed negative sets, sensitivity also increased. Therefore, by the third iteration, the Mixed negative set was achieving the highest MCC score of all the training sets (0.54) and the FPR rate had diminished from 7.9% to 7.0%, whilst sensitivity had increased from 56.3% to 64.7%. Based on the results of the evaluation, the Mixed negative classification model (Iter. 3) with a 7.0% FPR, 64.7% sensitivity, 93.0% specificity, 83.5% AUC and 0.54 MCC was selected as the final MutPred Splice classification model. Therefore, all further analysis was performed using this final predictive model.
Case studies
Two inherited disease-causing mutations (neither one of which was present in either the training data or unseen test sets) were selected as case studies. These case studies were used for further additional evaluation of both the semi-supervised approach and the final predicative model (Iter. 3). For these mutations, there was no prior evidence from in vitro analysis for or against an impact on splicing, when the mutation was originally reported but subsequent experimental characterization provided evidence of a splicing defect [25, 70] (Figure S1 in Additional file 2).
Comparison with existing tools used to identify SAVs
Comparison of three existing tools used to identify exonic SAVs with MutPred Splice
Human splicing finder
MutPred splice
Splicing focus
Splice site disruption
All exonic and intronic
ESE/ESS disruption and cryptic splice site
All exonic
Prediction output
Binary label
Multiple output scores
Multiple output scores
Probabilistic, with additional hypothesis of splicing mechanism disrupted
68 (61)
140 (0)
113 (57)
Sensitivity (%)
22.7 (100.0)
37.6 (51.7)
Specificity (%)
Accuracy (%)
58.9 (97.6)
59.7 (66.8)
0.22 (0.93)
0.19 (0.34)
All tools evaluated here demonstrated utility when investigating and identifying SAVs. This notwithstanding, overall, MutPred Splice outperformed the other tools evaluated here with sensitivity of 66.9%, specificity of 91.6% and an MCC of 0.54 (Table 7). For both HSF and Skippy, multiple output scores are produced; however, since none are diagnostic on their own, manual interpretation is often required to assess the weight of evidence that a variant is a potential SAV. The strength of HSF lies in its detailed investigation into the underlying splicing signals that may be disrupted; it is therefore complementary to MutPred Splice. For example, MutPred Splice could be used to generate a hypothesis for an exonic SAV, followed by detailed investigation using HSF. In general, it is important that the user is aware of the limitations and applications of a specific tool, when using that method to interpret their data. Depending upon the application, we recommend using multiple methods, especially tools that are complementary to each other.
Mis-splicing as a functional consequence of exonic variants
Predicting the splicing mechanism disrupted by an SAV
Exonic SAVs in oncogenes and tumor suppressor genes
There is abundant evidence to suggest that, for both coding and non-coding variants, aberrant pre-mRNA splicing is a common mechanism of pathogenesis in both inherited disease and cancer. In order to predict potential disease severity from genotype data, it is necessary to comprehensively evaluate the potential functional impact of variants. Effective computational tools targeted towards the characterization of the impact of variants on posttranscriptional gene regulatory function are urgently required. Here we have developed and evaluated a novel computational model (MutPred Splice) that uses human disease alleles for training to predict exonic nucleotide substitutions that disrupt pre-mRNA splicing. This approach is complementary to other methods that utilize known splice sites or functional variants that have not been directly associated with disease. Since MutPred Splice predictions can be made for missense, same-sense and nonsense variants, this tool significantly expands the scope of existing tools, which tend to focus almost exclusively on missense variants. When applied in an NGS setting, MutPred Splice is designed to be run in parallel with other methods such as SIFT [2] or SNAP [6], which identify missense mutations that are likely to disrupt protein structure/function; however, it adds an additional degree of resolution because MutPred Splice is also able to assess same-sense variants, which are typically excluded by the majority of current NGS filtering strategies. Since we predict that approximately 7% of same-sense variants disrupt pre-mRNA splicing, it is clear that this class of variant should not be blithely dismissed from the outset as being neutral to function.
Training data
In this study, we have highlighted the difficulty in selecting an appropriate negative training set. Since the underlying training data are fundamental to any derived model, it is clear that selecting the appropriate negative (control) set is of equal importance to selecting the appropriate positive set. In this study, we found that high frequency SNPs are a valuable source of training data but combining high frequency SNPs with an additional negative set of inherited disease-causing mutations serves to increase the diversity in the training set and reduces the FPR of the model, which results in improved performance over a model built using either negative set on its own.
Classification performance
The lack of experimental splicing data for the majority of disease-causing missense mutations means that the vast majority of this data set is untested with respect to their impact on the mRNA splicing phenotype (positive or negative). To mitigate this unknown quantity, in the second iteration of our models we removed predicted SAVs from the negative set; however, model performance remained broadly constant. This demonstrates that the RF ensemble approach used throughout all iterations to balance the positive and negative sets also reduces the impact of noise in the negative set. Based on previous studies that found that approximately 25% of disease-causing missense mutations may disrupt splicing [23–25], we adopted a semi-supervised self-training approach in an attempt to label these unlabeled mutation data. This allowed us to utilize these unlabeled data in a novel way, increase the training set size and improve the identification of decision boundaries between positive and negative classes. Indeed, utilization of semi-supervised learning in this instance saw a performance increase for all models (Disease negative set, SNP negative set and Mixed negative set). Self-training does have its limitations and mistakes in the first iteration could be subsequently reinforced; to mitigate this, only confident labels were applied to expand the training sets. For the final MutPred Splice model, we selected the third iteration of the Mixed negative set, which when coupled with a conservative probability threshold (at the expense of sensitivity) becomes a useful model for prioritizing SAVs, especially in an NGS setting, with a FPR of 7.0%, sensitivity of 64.7% and specificity of 93.0%, AUC of 83.5% and an MCC of 0.54.
Exonic SAVs in inherited disease and cancer
Based on previous work and also as demonstrated here, disruption to pre-mRNA splicing via exonic substitutions underlies a large proportion of inherited disease and cancer mutations. Here we estimate, based on the sensitivity and specificity of our model, that approximately 16% of inherited disease and approximately 10 to 14% of cancer exonic mutations impact upon pre-mRNA splicing, probably as a primary mechanism for pathogenicity. This is broadly in line with the results of previous studies. It should be noted, however, that the cancer set analyzed will contain a large proportion of passenger variants, which will almost certainly lead to a serious under-estimation of the actual number of splicing-sensitive cancer driver mutations.
In recent years, evidence for the link between cancer development and aberrant splicing has grown [71, 72]. In this study, we have found that TS genes are significantly enriched (when compared to oncogenes) in predicted exonic splicing mutations in both inherited disease and cancer. This enrichment is not found in variants identified in the general population (Figure 6; 1000 Genomes Project data with no MAF filter applied). Interestingly, the disease-causing nonsense variant in the ATM gene (p.E1978X), which is experimentally demonstrated to cause exon skipping, was originally reported as causing ataxia telangiectasia [73] but has in addition been associated with breast cancer susceptibility [74].
Aberrant pre-mRNA splicing in TS genes caused by exonic variants may represent a common mechanism of TS gene inactivation, thereby contributing to oncogenesis. Whilst a role for aberrant splicing leading to TS gene loss-of-function is not altogether novel [75], the scale and potential involvement of splice-altering exonic variants in oncogenesis is not well studied. The Cancer dataset has an increased tendency towards loss of ESE and/or gain of ESS elements (compared to the Inherited disease dataset). This finding could be explicable in terms of an increased susceptibility of TS genes to aberrant splicing.
Variants affecting pre-mRNA splicing in the general population
Here we have shown that around 7% of exonic variants found in the general population may alter splicing. Such variants may exert their effects in different ways, from a subtle change that serves to modify gene expression levels, to a lesion that results in the complete deficiency of the functional protein product. In some cases, therefore, the impact of common variants on splicing may not have an obvious phenotypic effect but could nevertheless serve to modulate disease risk, especially in the context of complex disease; alternatively, it may act as a disease modifier. Interestingly, not all nonsense variants can be considered equal with respect to their impact on splicing. A nonsense mutation identified in the context of inherited disease or cancer is predicted to be approximately twice as likely to elicit a splicing defect when compared to a nonsense variant found in the general population. Since this study was initiated and the training set compiled, six variants that were initially found in the general population (1000 Genomes Project), and which MutPred Splice predicted to disrupt pre-mRNA splicing, have been subsequently reported as disease-causing, disease-associated or of functional significance (according to HGMD). For example, a predicted SAV in the NPR3 gene (NM_000908.3: c.1429G > A; NP_000899.1: p.G477S) is associated with reduced NPR3 protein expression [76]. Another example is a predicted SAV in the MACF1 gene (NM_012090.4: c.6868A > G; NP_036222.3: p.M2290V), which has been reported in association with type 2 diabetes [77]. Interestingly, all six of these predicted SAVs were also predicted to be tolerated by SIFT, highlighting the importance of using MutPred Splice in conjunction with other tools specifically designed to identify missense mutations that disrupt protein structure and/or function (for example, SIFT and Polyphen2, and so on).
Whilst the positive training set of SAVs employed here constitutes the largest available dataset of its kind, it is likely that a larger positive training set would be of considerable benefit. The other limitation is the 'noise' from actual SAVs in the Disease negative set. The semi-supervised approach was therefore employed to counteract these limitations. The MutPred Splice model will be retrained as more training data become available in the literature. Additionally, our current knowledge about the splicing code is still incomplete; for example, approximately 9% of exon skipping SAVs displayed no obvious changes in ESE/ESS elements [35], indicating that novel cis-acting splicing regulatory elements probably remain to be discovered. As our knowledge in this field advances, more informative features can be derived and incorporated.
Another limitation of our tool is the assumption that the single exonic variant that is being assessed for aberrant splicing is the only deviation between the relevant reference sequence (RefSeq), with no other relevant sequence changes being present. To illustrate this point, only 4.8% of patients in a large cohort of Duchenne muscular dystrophy patients were found to exactly match the coding region of the DMD gene with respect to the reference sequence [78]. Therefore, when considering the impact on the splicing code, it may be in some cases too simplistic to consider just one variant in isolation, because other sequence changes (in cis), within both the coding and non-coding regions, may strengthen or weaken exon definition; the resulting combined impact is therefore difficult to predict.
We note that statistically rigorous estimation of the fraction of variants (in a particular set) that disrupt splicing is a very difficult problem, caused by potentially biased training data combined with a general inability to achieve 100% classification accuracy. As the correction of sample selection bias is generally hard, in this work we chose to report the fraction of positive predictions by MutPred Splice as our best estimate.
We have used the most comprehensive splicing mutation data sets currently available to build a computational model to predict exonic substitutions that disrupt pre-mRNA splicing. To do this, we have adopted a machine learning approach using semi-supervised learning and have evaluated a combination of sequence-based and genomic attributes to build a new tool, MutPred Splice, to identify coding region splice-altering variants responsible for either somatic or inherited disease. This model is suitable for use in an NGS high-throughput setting to identify and prioritize potentially splice-altering variants that may be involved in both inherited disease and cancer.
area under the receiver operating characteristic curve
- bp:
base pair
disease-causing splice altering variant
disease-causing splice neutral variant
exonic splicing enhancer
exonic splicing regulatory
exonic splicing regulatory hexamer score
exonic splicing silencer
false positive rate
Human Gene Mutation Database
Human Splice Finder
intronic splicing enhancer
intronic splicing silencer
minor allele frequency
Matthew's correlation coefficient
next generation sequencing
neighborhood inference
Random Forest
receiver operating characteristic
splice-altering variant
single nucleotide polymorphism
splice neutral variant
single nucleotide polymorphism splice neutral variant
support vector machine
tumor suppressor.
We thank Peter Stenson for the provision of HGMD data and financial support from BIOBASE. GmbH to DNC, EVB and MM. This work is supported by NIH R01 LM009722 (PI: Mooney), NIH U54-HG004028 (PI: Musen), NIH UL1DE019608 supporting the Interdisciplinary Research Consortium on Geroscience (PI: Lithgow) and the Buck Trust.
Authors' Affiliations
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.


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Philadelphia Flyers, American professional ice hockey team based in Philadelphia that plays in the Eastern Conference of the National Hockey League (NHL). The Flyers have won two Stanley Cup championships (1974, 1975).
The Flyers were established when the NHL expanded from the so-called "Original Six" in 1967. The team saw some minor success initially, earning three postseason berths in its first five years in the NHL. In the 1972–73 season the Flyers began a streak of 18 consecutive play-off appearances. The Flyers teams of the early '70s earned the nickname "The Broad Street Bullies," a reference to the location of the team's ... (100 of 521 words)

subset: commoncrawl


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Filmmaker Jean-Luc Godard once said, "The cinema is truth at 24 frames per second," making slow-mo sports replays and sped-up Benny Hill gags nothing but cruel, cruel lies. Get the real deal with today's Groupon: for $11, you get one movie ticket (a $10 value) and two sodas (a $3.75 value each) and popcorns (a $5 value each)—with unlimited refills for both—at The Nickelodeon in Santa Cruz.
"The Nick" showcases alternative, foreign, and art-house films in a theater steeped in the stylings of Hollywood. Beyond the elegant marquee lights, in a smoothly styled lobby, the walls are accented with stained-glass detailing comedy and tragedy masks. The retro chic movie house boasts two intimate and two large screens, which will soon play host to a bevy of upcoming shows. Current features include Academy Award nominees Black Swan, Another Year, The Illusionist, and Biutiful, the story of a crime boss who is diagnosed with a serious illness which forces him to reconcile his good intentions with his lawless lifestyle.
More than 40 Yelpers give The Nickelodeon an average of 4.5 stars:
- I'm a big fan of this lovely little independent theater. They play some of the best movies and the people working there are always nice. – Murphy O., 1/2/11
- It's really just a great place to spend an evening if you're in to pretending to be classy while watching "It's Kind of a Funny Story". Or if you really want to be cool, watch the latest controversial documentary or short film series. – Evelina E., 11/4/10
Downtown Santa Cruz
210 Lincoln St.
Santa Cruz, California 95060Get Directions


text: Diablo 3 Monk trailer restores balance through the medium of punching By Chris Thursten Shares ABOUT THE AUTHOR Chris Thursten Chris is the editor of PC Gamer Pro. After many years spent turning beautiful trees into magazines, he now oversees our online coverage of competitive gaming and esports. Topics Action RPG Blizzard Videos Diablo 3 We recommendBy Zergnet


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"Loved the color and design as presented. Was easily personalized. Those who received it LOVED the invitation. Thank you Tinyprints for taking the stress out of my Baby Shower invites! I will definitely recommend you to others."
"just as pictured and suggested paper/finishes look great. No need to spend extra unless you want them to be super jazzy. Would definitely order them again!"
"These items are for my daughter's upcoming baby shower. I have never used this website before but will certainly in the future! Fabulous design! I called customer service for advice. They were exceptionally helpful with the minor wording and color changes that I wished to make. Within two days, I was sent the proofs for approval. By the following week, I received the products that were ordered. We are so happy with this product and would recommend Tiny Prints in the future!!"
"I'm very pleased with how these invitations turned out - I was looking for a gender neutral design that deviated from the normal green/yellow themes. These were perfect, and look very high quality. I highly recommend having the envelopes printed as well - it saved so much time and was worth every penny. My only request would be to allow for some different font options for the envelopes so they're more customizable! Thanks for a great product!"
"This was my first order. It was easy to personalize and order. The invitations arrived on time, exactly as I ordered and lovely. I will use Tiny Prints again!"
Copyright © 2004-2017 Shutterfly, Inc | Tiny Prints | Terms of Use. Designated trademarks and brands are the property of their respective owners.


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Invaluable cannot guarantee the accuracy of translations through Google Translate and disclaims any responsibility for inaccurate translations.
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Lot 12: Chinese Huanglong Jade Pendant - Phoenix & Peony
Chinese Art Auction
by Quan Rong Gallery
October 22, 2016
Chatham, NJ, USALive Auction
Looking for the realized and estimated price?
Description: Chinese Huanglong Jade Pendant - Phoenix & Peony Dimension: 79 x 48 x 30(mm) 3.11 x 1.89 x 1.18(inches) Weight 0.32 lbs/147 g The total weight does not include the base. The base is not included after you have won the auction.
Dimensions: 3.11 x 1.89 x 1.18(inches)
Browse more items


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Looking for More?
- Haverford Calendar
- Exhibits
- Academic Calendar
- Admission Calendar
- Athletics Calendar
- Go! Calendar
- Residential Life Calendar
Promote Your Event
Learn how College Communications can help promote your event.
February 19, 2017
Oatmeal V
Blueberry Muffins *
Blueberry Muffins V
Apple Walnut Coffee Cake *
Selection of Scones & Danish *
Buttermilk Biscuits *
Shredded Potatoes V
French Toast Casserole *
Wild Mushroom, Gruyere & Fontina Quiche V
Turkey Sausage Links
Soup Du Jour
Cowboy Brisket
Arroz Con Pollo
Steak & Greens V
Stuffed Portobello Mushroom V
Whipped Red Skin Potatoes *
Fresh Seasoned Broccoli V
Seasoned Yellow Corn V
"Super" Sundae Bar
Dinner Rolls
February 20, 2017
Oatmeal V
Corn Muffins
Corn Muffins V
Tater Tots*
Berry Pancakes
Pork Sausage Patties
Turkey Bacon
Pork Sausage, Egg & Cheese Bagel
Egg & Cheese Bagel *
Tofu Scramble V
Lamb Stew with Couscous
Fire Roasted Tomato Soup V
Fire Cracker Chicken Wrap
Gourmet Grilled Cheese
Seasoned Wedge Cut
Fried Potatoes*
Steamed & Seasoned
Brussel Sprouts V
Spinach & Chard Salad with Grilled Chicken & Feta
Selection of Gourmet Cookies *
Berry Crisp V
Pan Seared Tilapia with
Pineapple Salsa
Grilled Teriyaki
Flank Steak & Broccoli
Brown Rice V
Sautéed Baby Bok Choy V
Haricot Verts V
Mango Vegetable Curry V
Couscous with Tofu & Peas V
Oreo Cookie &
Whipped Cream Pie
Fresh Dinner Bread
Everyday Items
- Every Day at Breakfast/Brunch: 908-Omelet Bar, 438-Donuts, 203-Hard-Cooked Eggs, 517-Scrambled Eggs, 1236-Belgian Waffles, 417-Yogurt
- Every Day at Lunch: Deli, Salad and Dessert
- Every Day at Dinner: Salad, Dessert and Pasta
Dining Hours
Monday - Friday:
Full Breakfast 7:30 a.m. - 10:30 a.m.
Light Breakfast 10:30 a.m. - 11:00 a.m.
Lunch 11:00 a.m. - 3:00 p.m.
Light Lunch 3:00 p.m. - 5:00 p.m.
Dinner 5:00 p.m. - 8:00 p.m. (7:30 p.m. on Friday)
Continental Breakfast 7:30 a.m. - 10:00 a.m.
Brunch 10:00 a.m. - 2:00 p.m.
Dinner 5:00 p.m. - 7:30 p.m.
Brunch 10:00 a.m. - 2:00 p.m.
Dinner 5:00 p.m. - 7:30 p.m.
More Info
- Sophie McGlynn '18 is helping celebrate @afsc_org's centennial by curating a collaborative exhibit in @HCLibraries… https://t.co/fhTzs19Q4M — 2 days 5 hours ago
- Sunday is Plenary! See you in GIAC at 6pm. For inspiration: a #fbf of plenaries past (1985, 2009). https://t.co/yjPvKej9s7 — 2 days 7 hours ago
- Join Erin Schoneveld (East Asian Lang. & Cultures) at @VoxPopuliGlry for a lecture & performance w/Shuzo Azuchi Gul… https://t.co/NipU2fQlh9 — 2 days 9 hours ago
- RT @davidmwessel: Excited to see @Haverfordedu public policy forum coming together; Looking forward to seeing new & old friends 3/18 https://t.co/0X5jWccoMm — 3 days 4 hours ago
- Bi-Co Chamber Singers are part of a special bill w/Syrian refugees on 2/17 at 7pm at Philly Episcopal Cathedral: https://t.co/CWmo55OWNu — 3 days 6 hours ago


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Be environment aware!
Every once in a while, I see or hear someone complaining that a basic React application ran through Webpack ends up being 700 kB or more. I think it's important to take a look at why this is happening – cause it's usually all about the environment.
When you're in development, what you want is:
- Quick builds
- Being able to trace an error back to the correct line number
- Warnings if you're doing something wrong
When you're in production, what you want is:
- Smallest bundle size
- Fastest execution


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More Videos...
Introducing the Intrepid 475 Panacea (w/quad 557 HP powerplants)
Miami Boat show 2015
Intrepid 475 Panacea Quad 7 Marine 2,228 hp
First ever quad 7 Marine engines on the Intrepid 475 Panacea. This was filmed during the initial sea trial evaluations. Engine cavitation plate heights and props have been tweaked since this footage of the initial sea trial. Since then we've been able to get about 75 MPH @ 200 GPH. Turn the sound up! The engines sound more like a Boeing than an outboard. It is truly incredible to drive.
Intrepid 390 Sport Yacht with Gyro Stabilizer
2014 Miami Boat Show Interview with Intrepids President Ken Clinton on the new 475 Panacea
Television interview at the 2014 Miami Boat Show with Intrepid Powerboats President Ken Clinton introducing the new 475 Panacea


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The two doors of this cabinet feature decorative quatrefoil overlay design for a look that is unique and unlike other pieces in your home. The antiqued mirror glass of the doors is accented by the dark Raven finish of the wood edges and frame. Storage includes two adjustable or removable shelves and one fixed shelf in the center. Vented back panels and two holes with grommets mean easy access and management of any electronics stored in the cabinet. With a built-in anti-tip kit this piece will be safe and sturdy in your home. Use this piece in different rooms of your home like a dining room or living room to provide a stylish storage piece. Perfect for the trendy, contemporary home this cabinet is a versatile piece that you can use in any spot you need to.
The Haven Two Door Cabinet with Quatrefoil Overlay Design by Bernhardt from Darvin Furniture. We proudly serve the Orland Park, Chicago, IL area. Product availability may vary. Contact us for the most current availability on this product.
Large pieces in your dining room can cause a hazard for small children or pets, so pieces fitted with an anti-tip kit allow you to create a home that is safe for everyone. Rest easy knowing that your pieces are sturdy and won't fall over if someone or something bumps into it.
Adjustable shelving in a buffet or cabinet lets you create a compartment that suits your needs. Store large items like serving trays or bowls while also having items on hand in the dining room for easy access. Smaller pieces fit snugly into a space that is the perfect size for them, offering better protection for small, fragile items. Maximize your storage capabilities by adjusting the shelves to fit your exact needs.
Buffets and side pieces have grommet holes built-in the back to control unruly cords and wires. Easily and discreetly plug in electronic serving dishes to keep items hot and cords organized and out sight. This piece's built-in grommet holes can handle electronics' annoying cords, allowing you to keep your home organized.
Storing electronics can be tricky and allowing them to get proper ventilation is important. Keep your home safe by preventing your electronics from overheating with this console's ventilation holes.
The Haven collection brings you pieces for the whole home in a contemporary style with traditional influence. Slim tapered legs, fluting, geometric shapes all influence the modern and trendy look of the pieces. Wood finishes in dark brown Raven or a medium brown Brunette are used throughout while the light Cameo finish brings a lighter touch to the collection. Essential storage is provided in many of the drawers, doors and shelves allowing you to organize your home without sacrificing any of the style you desire. Use sleigh beds or a customizable upholstered bed to update your master bedroom into a space you can truly relax in while the dining pieces allow you to create a welcoming area perfect for entertaining. A variety of smaller pieces like end tables and coffee tables allow you to update spaces easily with the modern style you love. With a unique mix of materials including metal and wood are accented by faux horn inlays of the hardware, fluting and quatrefoil motifs. Use the Haven collection to update your home with sophisticated and modern pieces.
The Haven collection is a great option if you are looking for Contemporary furniture in the Orland Park, Chicago, IL area.
These images are representative of this group as a whole and may differ from this item.
Browse other items in the Haven collection from Darvin Furniture in the Orland Park, Chicago, IL area.
This item has been tagged as: China Cabinet, Corner Cabinet, China Hutch, China Storage, Kitchen Storage, China Display, Dining Room Cabinet, Buffet, Hutch, Buffet and hutch, Buffet Server, Meal Serving Furniture, Kitchen Buffet, Dining Room Buffet, China Buffet, Buffet with Storage, Storage Furniture, Dining Room Furniture, Curio, Corner Curio, Curio Cabinet, Display Cabinet, Wall Curio, Console Curio, Lighted Curio Cabinet, Cabinet, Bakers Rack, Kitchen Storage with Shelves, Bakers Wine Rack, Dining Room WIne Rack, Kitchen Baker Rack, Sideboard, Sideboard with Hutch, Buffet and Hutch Set, Server, Serving Table, Server Table, Dining Room Server, China Closet, China Cupboard, China Compartment
Nothing on this page may be copied or reproduced without explicit permission.
Drag anItem Here
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To request a new password, enter your username or e-mail address and click submit. A new password will automatically be emailed to you.
If you are still having trouble logging on after receiving your new password, please email firstname.lastname@example.com


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Great Hands I Almost Played
Pavilion Books, Feb 13, 2003 - Games - 128 pages
Look over the shoulders of the bridge experts as they tackle 60 challenging hands. By following the thought processes of leading players, readers will develop analytical skills to help them "think like an expert," too. Yes, the secrets of the real hand are revealed--and no, the experts do not always get it right! A delightful way for good bridge players to get even better.
What people are saying - Write a review
We haven't found any reviews in the usual places.


text:
As I understand it, the buss is where the 220/240 voltage comes into the
breaker box from outside your home to be distributed to the various
-----Original Message-----
From: email@example.com [mailto:firstname.lastname@example.com] On Behalf
Of Daryl
Sent: Saturday, June 10, 2006 7:12 PM
To: firstname.lastname@example.com
Subject: Re: [CHAT] weather--electrical problems
Sorry to hear about your electrical problems. We spent a couple of summers
trying to track down an elusive short - it only stunk when the sun hit the
east side of the house and then only for a couple of hours.
What's a buss?
----- Original Message -----
From: "Bonnie & Bill Morgan" <firstname.lastname@example.com>
To: <email@example.com>
Sent: Saturday, June 10, 2006 4:36 PM
Subject: RE: [CHAT] weather--electrical problems
> Daryl, wish that I could share our bounty of rain with you and Pam!
> (Thanks, Donna, for sending that this way, BTW.) Oh, well. I need to
> clear
> a spot out of the garage attic anyway. After that storm Thursday before
> last, the lights in the kitchen/dining room area and the one outlet that
> powers the microwave quit working. DH checked all the outlets and
> replaced
> all the switches, but could not find anything causing the "open" condition
> in a neutral wire keeping everything from working. The electrician was
> here
> yesterday...pretty much all day. At first he tried to say Bill had messed
> with the wiring, but after three hours of doing what Bill had already done
> and not finding the problem, he backed off his earlier presumption.
> Apparently, the builders covered some junction boxes. We found one
> (unfortunately, not the problem junction box) under some cabinets attached
> to the ceiling. At 3:00pm he gave up and started trying to figure a way
> to
> re-wire around the offending "open" in the hiding junction box.
> Unfortunately, that means bringing line up through the wall between the
> house and garage to get me working lights in the garage! He also
> discovered
> (while on the hunt) 3 open junction boxes in the attic buried in paper
> insulation. I thought he was going to fall off his ladder! He also
> discovered our buss is nearly gone as well. You don't need me to tell you
> that this means no additional purchases for the garden or anything else
> this
> year. UGH! Still, if it will prevent a fire and allow us to have
> light....
To sign-off this list, send email to firstname.lastname@example.com with the
message text UNSUBSCRIBE GARDENCHAT
To sign-off this list, send email to email@example.com with the
message text UNSUBSCRIBE GARDENCHAT


text:
Residential Leak Detection Services in Central New Jersey
Update: We are happy to announce that we have extended our service area and now offer service in the following counties:
- Bergen
- Passaic
- Sussex
- Essex
- Hudson
- Morris
- Warren
It takes a blending of expert specialists and quality equipment to detect hidden leaks. With our extensive training programs and state-of-the-art equipment we have saved homeowners billions of gallons of water. By utilizing our methods, not only are we saving water, but saving homeowners millions of dollars in costly property damage and destruction as well.
While it is true that leaks are more common in older properties, newer properties can have problems too. Poor construction and corrosion and in many cases, ground movement can cause unexpected pipe damage at any time. If undetected, the problem threatens property and makes repairs more costly.
Our Pledge
- Arrive when scheduled
- Drug-free
- Respect you & your property
- Clean before leaving premises
Local Service Area
American Leak Detection of Central Jersey offers service in the following locations and their surrounding areas:
Freehold • Manalapan • Morganville • Manasquan • Middletown • Lakewood • Jackson • New Brunswick • Toms River • Union


text:
Comfort Inn Buckhead North
No prices found through our partners. Please contact the business directly or check some of our recommended alternatives.
How has this hotel rated in the past?
Travelers also looked at:
- More recommendations
TripAdvisor Reviews Comfort Inn Buckhead North Atlanta
Travel Blogs from Atlanta
Last Day in Atlanta
Today is the last day of the Conference and our last full day in Atlanta. Drive to Orlando tomorrow to join the cruise on the 11th. I left the hotel early and caught the shuttle bus to the Martin Luther King Jnr Historic site. It included the Ebeneza Baptist Church that his father and he were ministers. The church was the centre of his civil rights work. His wife, after his assassination, took over and continued his amazing work for peace ...
Wash Day!
... to the city. Time to go to the Coca Cola Museum! It was very enjoyable. Spent about 2 hours wandering around. All the old signs, bottles etc that I used to see when I was young.....maybe I need to be in the museum too! The history of how Coke was invented etc made for a very interesting couple of hours. There was a tasting room with all the different flavours of drinks made by the company from all over the ...
Home Again, Home Again (& Resto Reviews)
... you
choose from their display. And, despite being Bouchon, the prices
are pretty much on par for any lunch spot ($9/sandwich).
Klamath Falls:
Raliberto's Tacos - We were exhausted and starving after
a long day of driving so looked on Yelp for a place to eat. Even
though this place got 4 stars, I guess that's in comparison to
anything else nearby 'cuz I'd give it 2 stars at best. It's a true
Day two, Atlanta
... has been restored but is only a tourist attraction now. It's then back to the hotel for lunch, we go next door to a deli type store. We shared a sandwich and packet of crisps, and eat them in the Olympic park built for the Atlanta games in 96. The park was busy with people walking about and kids running in and out of the fountains. The afternoon, we have planned and paid to go on one of the optional tours, the first one is to Stone Mountain, it's ...
Two Democrats in One Day in Atlanta
... deserted on Saturday afternoon except for lots of homeless people pestering me for money. I was actually somewhat worried for the half hour or so I was parked on the street in Homelessville to check out the Capitol; my car is always full of sleeping bags and other camping gear and would be a bonanza break in to the homeless.
With visiting the Jimmy Carter Presidential Library and Museum I have now been to all the ...


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When it was over, when the final horn sounded and the lights went up on the Manchester Arena in England, no one could really be sure what they had just witnessed. A 46-year-old man as UFC world middl read news >>
Michael Bisping and Dan Henderson went five rounds; Gegard Mousasi took out Vitor Belfort in impressive fashion; Jimi Manuwa brings out the heavy hands. That and more in this UFC 204 edition of Talki read news >>
UFC 204 is fast approaching and middleweight fighter Dan "Hendo" Henderson is stepping into the Octagon again to face off against Michael Bisping. read news >>
From Yahoo Sports: Hendo's contract is expiring and he's got plenty of options on the table. In his own words: 'This could actually be the last fight.' read news >>
Vitor Belfort avenged his first loss to Dan Henderson when he knocked out "Hendo" in Brazil of 2013. The two middleweights are scheduled for a grudge match on Nov. 7. read news >>
Fighting at light heavyweight, Dan "Hendo" Henderson and Mauricio "Shogun" Rua battled it out inside the Octagon at UFC Fight Night 38 in Natal, Brazil. read news >>
MMA veterans Daniel "DC" Cormier and Dan "Hendo" Henderson battled at UFC 173 to see who is the top light heavyweight contender. read news >>
Don't tell Dan "Hendo" Henderson he's 44 years old. Don't remind him he's lost four of his last five bouts, two in brutal fashion. Don't tell him it seems increasingly obvious the sport is passing hi read news >>
People like Dan Henderson. There's an uncontroversial statement if ever you saw one. The guy is well into his 40s and fighting the best of the best in the most grueling sport on Earth, often knocking read news >>
From Yahoo Sports: Hendo is one win away from another shot at UFC gold -- something that has eluded him his whole career. read news >>
Former UFC and PRIDE Champion Dan "Hendo" Henderson (30-11 MMA) shows off different techniques of his famous right-hand punch better known as the H-Bomb. read news >>
Dan Henderson has struggled to find a home for his patented "H-Bomb" when fighting in the UFC's 205-pound weight class, but as we learn in the UFC Fight Night 38 post-fight facts, he finally secured read news >>
Mauricio "Shogun" Rua and Dan Henderson will rematch in the UFC Fight Night 38 main event on Sunday in Brazil. First, check out Shogun's brutal knockout of James TeHuna. read news >>


text:
Welcome to misterknuckles.com. This site is dedicated to my view on martial arts, self-defence, fitness, health, and life. I hope to inspire thought, positivity and a healthy, balanced lifestyle. It is about taking a pro-active approach to living. I will share with you my philosophies, experiences, goals and anything else that comes my way. My mission is to create positive growth in myself, you and the world around us, even if it just for a brief moment in time.
I believe we are all capable of greatness, but for this to happen we must be improving a little bit each and every day.
"The man who views the world at 50 the same as he did at 20 has wasted 30 years of his life." Muhammad Ali.
Visit my Mister Knuckles Youtube Channel for exclusive Self-Defence footage and more.
Visit and subscribe to my Mister Knuckles Youtube Channel to receive exclusive insights on various topics, including:
Subscribe for weekly videos
- Taekwon-Do training tips.
- Self-defence tutorials.
- Fitness pointers.
- General health and well-being advice.
- My random and sometimes funny views on the world.
Catch up on recent Mister Knuckles Posts.
Tutorial videos
Sorry for the delay in adding new videos. I have had a problem with my camera which will be rectified shortly.
I have had requests for tutorials on various exercises including […]
Taekwon-Do for Adults
A topic came up the other night that is fitting for some of my thoughts in this article. In this day and age, martial arts are no longer martial arts. […]
Taekwon-Do is for?
This article will hold true for many martial arts, but I see it on a daily basis in Taekwon-Do circles. I often find myself asking, "Who is Taekwon-Do for?" In […]


text:
Asking B
How will I fix?
See example:
Your argument will be one-side and unconvincing if all you do is make a claim and support it.
asked Nov 07 '11 at 12:10 Vanza Bik New member
0 answers
Your answer
Hero of the day
Reputation: 1
Person asked the most questions.


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Hazen Star Sports
Beulah Cyclones shut out Hazen
Hazen Post 189 and the Beulah Cyclones matched up in a non-regional 9-inning game last week.
Posted 7/06/16 (Wed) read more »
Hazen sweeps doubleheader
Hazen Post 189's summer legion season is well underway and the team is performing well.
Posted 6/29/16 (Wed) read more »
Hazen sweeps Kidder County
The last time Hazen and Kidder County faced off Connor Doll was trying to close off a high school regional playoff win last spring.
Posted 6/22/16 (Wed) read more »
The humble state champions
Kaylene Klein, senior, and Grace Zimmerman, sophomore, aren't the type of athletes to indulge in self-praise.
Posted 6/15/16 (Wed) read more »
Hazen girls finish 3rd at state
The Hazen Bison girls golf team finished third at the state tournament in Carrington last week.
Posted 6/08/16 (Wed) read more »
Hazen girls track grab 2nd consecutive title
The Hazen girls' track and field team accomplished their season goal of repeating as state champs last week.
Posted 6/01/16 (Wed) read more »
One measly little point
The Hazen boys' and girls' track and field teams competed in the South Central regional last weekend.
Posted 5/25/16 (Wed) read more »
Bison come close to grabbing upset
The Hazen Bison are still fighting and trying to push bad luck into a corner and punch it out.
Posted 5/18/16 (Wed) read more »
Hazen girls keep four of the state's top marks
The Hazen Bison girls track and field team made their goal clear early this season and last week they continued their mission to repeat as state champs strongly.
Posted 5/11/16 (Wed) read more »
Beulah sweeps Hazen on Veterans night
The Hazen Bison and Beulah Miners met last week in Beulah.
Posted 5/04/16 (Wed) read more »


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Weights & Cardio
What are the 4 key elements to successful exercising and improving your health and well being? Do you just do weights? Is running the only exercise you do? Click on picture to find out why you need a variety of elements in you training schedule.
HIIT Circuit
HIIT, or high-intensity interval training, is a training technique in which you give all-out, one hundred percent effort through quick, intense bursts of exercise, followed by short, sometimes active, recovery periods. This type of training gets and keeps your heart rate up and burns more fat in less time.
Nutrition Tips
Healthy nutrition and a balanced diet is the key 'ingredient' to any weight loss program and paramount to its success. You can train all you like but if your diet is rubbish then all that good exercise could be wasted. Click on picture for more nutritional information.
Just completed my last lower body/leg session for 2011 at the best gym on the mid-north coast. Coffs Harbour Powerhouse Gym
- Olivia J Clark
After all the gyms i've trained at in Sydney and central coast, Coffs Habour Powerhouse Gym is still unmatched. Cant wait to be living back in Coffs.
- Robbie Hay


text: If you haven't seen Sean Connery in NEVER SAY NEVER AGAIN, than you haven't seen James Bond 007! "Never Again." – James Bond "Never?"- Domino Release Date: October 7, 1983 Studio: Warner Brothers Director: Irvin Kershner Starring Sean Connery, Kim Basinger, Barbara Carerra, Klaus Maria Brandauer, Bernie Casey, Gavan O'Herlihy, Pat Roach, Max Von Sydow, Edward Fox, Pamela Salem, Alex McCowen, Rowan Atkinson Plot Summary: On a trip to a health spa, James Bond 007 discovers a plot by SPECTRE to steal nuclear warheads and ransom them to the world nations. This remake of Thunderball was an unofficial rival Bond film recognized for being Sean Connery's last return as 007 (as well as his last clean-shaven appearance). Released the same year as Octopussy (interestingly, these two films were my first Bonds), NSNA is, Connery or not, inferior to that film. The story was done well the first time around, and more seriously than this campy remake (then again, what do you expect from screenwriter Lorenzo '80's Flash Gordon/60's Batman' Semple Jr.?). Laser wristwatches seem out-of-character for Connery. On a plus we have Barbara Carrera as a worthy successor of the Luciana Paluzzi femme fatale, and its good to see Blofeld return as the omnipotent head of SPECTRE, instead of the main villain (and Max Von Sydow is more European than Gray, Pleasance, and Savalas ever were). Comments?


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This is the upcoming drama on ARY Digital Dramiyan. Darmiyan is a social tragic drama.Great expectation from this upcomming serial. Syra will play different role in this serial. Sania saeed is an experienced actress.Javeria abbasi is also comming in this serial, after a long time on ARY Didital. Adnan Siddiqui is also sharing screen with sania and syra. Darmiyan story is written by Emran Hussain and co-writer Erum Aftab. And directed by Shahid Shafa'at
Cast Include:
Adnan Siddiqui
Sania Saeed
Syra Yousuf
Javeria Abbasi
Kaif Ghaznavi and Others.