Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease.

PURPOSE: Current diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test. METHODS: We performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases. RESULTS: We found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs. CONCLUSION: Robust identification of CNVs by GS is possible within a clinical testing environment.

tHapMix: simulating tumour samples through haplotype mixtures.

MOTIVATION: Large-scale rearrangements and copy number changes combined with different modes of clonal evolution create extensive somatic genome diversity, making it difficult to develop versatile and scalable variant calling tools and create well-calibrated benchmarks. RESULTS: We developed a new simulation framework tHapMix that enables the creation of tumour samples with different ploidy, purity and polyclonality features. It easily scales to simulation of hundreds of somatic genomes, while re-use of real read data preserves noise and biases present in sequencing platforms. We further demonstrate tHapMix utility by creating a simulated set of 140 somatic genomes and showing how it can be used in training and testing of somatic copy number variant calling tools. AVAILABILITY AND IMPLEMENTATION: tHapMix is distributed under an open source license and can be downloaded from https://github.com/Illumina/tHapMix CONTACT: sivakhno@illumina.comSupplementary information: Supplementary data are available at Bioinformatics online.

Canvas: versatile and scalable detection of copy number variants.

MOTIVATION: Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples. RESULTS: We have developed a new tool, Canvas, for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and easy-to-run workflows that can scale to thousands of samples and can be easily incorporated into variant calling pipelines. AVAILABILITY AND IMPLEMENTATION: Canvas is distributed under an open source license and can be downloaded from https://github.com/Illumina/canvas CONTACT: eroller@illumina.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms.

SUMMARY: An ultrafast DNA sequence aligner (Isaac Genome Alignment Software) that takes advantage of high-memory hardware (>48 GB) and variant caller (Isaac Variant Caller) have been developed. We demonstrate that our combined pipeline (Isaac) is four to five times faster than BWA + GATK on equivalent hardware, with comparable accuracy as measured by trio conflict rates and sensitivity. We further show that Isaac is effective in the detection of disease-causing variants and can easily/economically be run on commodity hardware. AVAILABILITY: Isaac has an open source license and can be obtained at https://github.com/sequencing.

Mercury in natural health products as a cause of membranous nephropathy.

Membranous nephropathy is a glomerular disease characterized by diffuse subepithelial immune complex deposition along the glomerular basement membrane. It can be both primary and secondary to malignancy and various drugs. An emerging area of concern is heavy metal exposure from use of largely unregulated natural health products. Here we present a case of biopsy-proven membranous nephropathy due to natural health products contaminated with mercury.

The "nursing cheat sheet," a guide to aid nursing in management of patient care.

The aim of this study was to promote nursing and practitioner satisfaction by improving communication and reducing rapid response team (RRT) activations and code blues hospitalwide by implementing the nursing cheat sheet, a list of key steps to be done before calling the primary provider. This prospective observational study took place over a year at a 636-bed teaching hospital in Central Texas. Education regarding the nursing cheat sheet was provided to nurses at monthly meetings, unit huddles, and interdisciplinary rounds. Units received monthly follow-up education and reminders, and the nursing cheat sheet was posted at nursing work areas. RRT activations and code blues were counted daily. After implementing the nursing cheat sheet, RRT activations were lower than the pre-intervention average and were outside the control band for July, August, and November. The number of code blues did not deviate from the control bands. The authors did not see a reduction in code blues after implementation of the nursing cheat sheet, but the average number of RRT activations decreased, indicating a significant difference in the number of RRT activations. It is not clear that this decrease was due to the intervention.

Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

BACKGROUND: Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map. RESULTS: We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository. CONCLUSION: Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

Identification of post-translational modifications by blind search of mass spectra.

Most tandem mass spectrometry (MS/MS) database search algorithms perform a restrictive search that takes into account only a few types of post-translational modifications (PTMs) and ignores all others. We describe an unrestrictive PTM search algorithm, MS-Alignment, that searches for all types of PTMs at once in a blind mode, that is, without knowing which PTMs exist in nature. Blind PTM identification makes it possible to study the extent and frequency of different types of PTMs, still an open problem in proteomics. Application of this approach to lens proteins resulted in the largest set of PTMs reported in human crystallins so far. Our analysis of various MS/MS data sets implies that the biological phenomenon of modification is much more widespread than previously thought. We also argue that MS-Alignment reveals some uncharacterized modifications that warrant further experimental validation.

A case of adult granulosa cell tumor of the testis.

BACKGROUND: Adult granulosa cell tumors of the testis (AGCTT) are classified as sex cord-stromal tumors. Only 31 cases have been reported. Typical presentation includes a slowly enlarging, painless testicular mass. Associated findings are gynecomastia, decreased libido, and erectile dysfunction. Immunohistochemistry can be used to confirm the diagnosis. CASE REPORT: A 22-year-old male presented with complaint of mild pain in both testicles. A testicular ultrasound revealed a 4.0×3.8×4.6 mm hypoechoic lesion within the left testicle. Serum tumor markers (STM) included lactate dehydrogenase (LDH) measuring 146 IU/L (98-192), serum alpha-1-fetoprotein (AFP), 2.89 ng/mL (0-9), and plasma beta human chorionic gonadotropin (Beta HCG) measuring less than 0.50 mIU/mL (<0.50-2.67). Computed tomography (CT) of the abdomen and pelvis with oral and intravenous contrast was normal. A radical orchiectomy was recommended but the patient refused. He agreed to surveillance with imaging and serum tumor markers (STM). The patient's testicular ultrasound showed the mass to be stable in size and STMs remained negative. The patient agreed to an orchiectomy 9 months after his diagnosis. This case is the first reported with c-kit-positive immunohistochemistry. His post-operative course has been unremarkable. CONCLUSIONS: AGCTT is a rare tumor and information regarding its presentation, gross and microscopic morphology, and immunohistochemical characteristics is lacking. This report provides an update of the immunohistochemical findings and adds to the available data concerning this tumor. Based on the results of this case, future reports that include c-kit immunohistochemistry would be beneficial to evaluate its utility in diagnosing AGCTT.

Intraoperative factors and the risk of respiratory complications after pneumonectomy.

BACKGROUND: The potential effect of intraoperative factors on respiratory complications after pneumonectomy is still unclear. METHODS: We conducted a retrospective cohort study; charts of 129 patients who underwent elective pneumonectomy at the University of Virginia were reviewed. Logistic regression was used to estimate the effect of anesthetic factors on the odds of at least one respiratory complication. Linear regression models were fit to assess the impact of these outcomes on length of stay (LOS). RESULTS: The incidence of respiratory complications in this cohort was 21%. In univariate analysis total nonblood fluids (p = 0.001), and the blood products packed red blood cells (p < 0.001), plasma (p < 0.001), and platelets (p = 0.044) were significantly associated with respiratory complications. In a multivariable logistic regression analysis, single unit transfusion of any blood product (packed red blood cells, plasma, or platelets) was identified as a major risk factor for respiratory complications after controlling for covariates (odds ratio = 1.47, 95% confidence interval 1.06 to 2.05). Respiratory failure and complications were closely related to LOS, increasing the LOS by a factor of 4.7 (95% confidence interval 3.51 to 6.18) and 3.5 (95% confidence interval 2.69 to 4.41), respectively. CONCLUSIONS: Blood product transfusion affects respiratory function and is an independent risk factor for respiratory complications after pneumonectomy.

Clustering millions of tandem mass spectra.

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.

Phosphorylation-specific MS/MS scoring for rapid and accurate phosphoproteome analysis.

The promise of mass spectrometry as a tool for probing signal-transduction is predicated on reliable identification of post-translational modifications. Phosphorylations are key mediators of cellular signaling, yet are hard to detect, partly because of unusual fragmentation patterns of phosphopeptides. In addition to being accurate, MS/MS identification software must be robust and efficient to deal with increasingly large spectral data sets. Here, we present a new scoring function for the Inspect software for phosphorylated peptide tandem mass spectra for ion-trap instruments, without the need for manual validation. The scoring function was modeled by learning fragmentation patterns from 7677 validated phosphopeptide spectra. We compare our algorithm against SEQUEST and X!Tandem on testing and training data sets. At a 1% false positive rate, Inspect identified the greatest total number of phosphorylated spectra, 13% more than SEQUEST and 39% more than X!Tandem. Spectra identified by Inspect tended to score better in several spectral quality measures. Furthermore, Inspect runs much faster than either SEQUEST or X!Tandem, making desktop phosphoproteomics feasible. Finally, we used our new models to reanalyze a corpus of 423,000 LTQ spectra acquired for a phosphoproteome analysis of Saccharomyces cerevisiae DNA damage and repair pathways and discovered 43% more phosphopeptides than the previous study.

Accurate annotation of peptide modifications through unrestrictive database search.

Proteins are extensively modified after translation due to cellular regulation, signal transduction, or chemical damage. Peptide tandem mass spectrometry can discover post-translational modifications, as well as sequence polymorphisms. Recent efforts have studied modifications at the proteomic scale. In this context, it becomes crucial to assess the accuracy of modification discovery. We discuss methods to quantify the false discovery rate from a search and demonstrate how several features can be used to distinguish valid modifications from search artifacts. We present a tool, PTMFinder, which implements these methods. We summarize the corpus of post-translational modifications identified on large data sets. Thousands of known and novel modification sites are identified, including site-specific modifications conserved over vast evolutionary distances.

Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms.

Molecular dynamics simulation methods produce trajectories of atomic positions (and optionally velocities and energies) as a function of time and provide a representation of the sampling of a given molecule's energetically accessible conformational ensemble. As simulations on the 10-100 ns time scale become routine, with sampled configurations stored on the picosecond time scale, such trajectories contain large amounts of data. Data-mining techniques, like clustering, provide one means to group and make sense of the information in the trajectory. In this work, several clustering algorithms were implemented, compared, and utilized to understand MD trajectory data. The development of the algorithms into a freely available C code library, and their application to a simple test example of random (or systematically placed) points in a 2D plane (where the pairwise metric is the distance between points) provide a means to understand the relative performance. Eleven different clustering algorithms were developed, ranging from top-down splitting (hierarchical) and bottom-up aggregating (including single-linkage edge joining, centroid-linkage, average-linkage, complete-linkage, centripetal, and centripetal-complete) to various refinement (means, Bayesian, and self-organizing maps) and tree (COBWEB) algorithms. Systematic testing in the context of MD simulation of various DNA systems (including DNA single strands and the interaction of a minor groove binding drug DB226 with a DNA hairpin) allows a more direct assessment of the relative merits of the distinct clustering algorithms. Additionally, means to assess the relative performance and differences between the algorithms, to dynamically select the initial cluster count, and to achieve faster data mining by "sieved clustering" were evaluated. Overall, it was found that there is no one perfect "one size fits all" algorithm for clustering MD trajectories and that the results strongly depend on the choice of atoms for the pairwise comparison. Some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. Issues related to the choice of a pairwise metric, clustering metrics, which atom selection is used for the comparison, and about the relative performance are discussed. Overall, the best performance was observed with the average-linkage, means, and SOM algorithms. If the cluster count is not known in advance, the hierarchical or average-linkage clustering algorithms are recommended. Although these algorithms perform well, it is important to be aware of the limitations or weaknesses of each algorithm, specifically the high sensitivity to outliers with hierarchical, the tendency to generate homogenously sized clusters with means, and the tendency to produce small or singleton clusters with average-linkage.

Improving gene annotation using peptide mass spectrometry.

Annotation of protein-coding genes is a key goal of genome sequencing projects. In spite of tremendous recent advances in computational gene finding, comprehensive annotation remains a challenge. Peptide mass spectrometry is a powerful tool for researching the dynamic proteome and suggests an attractive approach to discover and validate protein-coding genes. We present algorithms to construct and efficiently search spectra against a genomic database, with no prior knowledge of encoded proteins. By searching a corpus of 18.5 million tandem mass spectra (MS/MS) from human proteomic samples, we validate 39,000 exons and 11,000 introns at the level of translation. We present translation-level evidence for novel or extended exons in 16 genes, confirm translation of 224 hypothetical proteins, and discover or confirm over 40 alternative splicing events. Polymorphisms are efficiently encoded in our database, allowing us to observe variant alleles for 308 coding SNPs. Finally, we demonstrate the use of mass spectrometry to improve automated gene prediction, adding 800 correct exons to our predictions using a simple rescoring strategy. Our results demonstrate that proteomic profiling should play a role in any genome sequencing project.

Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation.

While bacterial genome annotations have significantly improved in recent years, techniques for bacterial proteome annotation (including post-translational chemical modifications, signal peptides, proteolytic events, etc.) are still in their infancy. At the same time, the number of sequenced bacterial genomes is rising sharply, far outpacing our ability to validate the predicted genes, let alone annotate bacterial proteomes. In this study, we use tandem mass spectrometry (MS/MS) to annotate the proteome of Shewanella oneidensis MR-1, an important microbe for bioremediation. In particular, we provide the first comprehensive map of post-translational modifications in a bacterial genome, including a large number of chemical modifications, signal peptide cleavages, and cleavages of N-terminal methionine residues. We also detect multiple genes that were missed or assigned incorrect start positions by gene prediction programs, and suggest corrections to improve the gene annotation. This study demonstrates that complementing every genome sequencing project by an MS/MS project would significantly improve both genome and proteome annotations for a reasonable cost.

Functional interrogation of the kinome using nucleotide acyl phosphates.

The central role of protein kinases in signal transduction pathways has generated intense interest in targeting these enzymes for a wide range of therapeutic indications. Here we report a method for identifying and quantifying protein kinases in any biological sample or tissue from any species. The procedure relies on acyl phosphate-containing nucleotides, prepared from a biotin derivative and ATP or ADP. The acyl phosphate probes react selectively and covalently at the ATP binding sites of at least 75% of the known human protein kinases. Biotinylated peptide fragments from labeled proteomes are captured and then sequenced and identified using a mass spectrometry-based analysis platform to determine the kinases present and their relative levels. Further, direct competition between the probes and inhibitors can be assessed to determine inhibitor potency and selectivity against native protein kinases, as well as hundreds of other ATPases. The ability to broadly profile kinase activities in native proteomes offers an exciting prospect for both target discovery and inhibitor selectivity profiling.

Unrestrictive identification of post-translational modifications through peptide mass spectrometry.

Proteins are post-translationally modified in vivo as part of cellular regulation and signaling, and undergo further chemical modifications during laboratory processing. Even relatively simple protein samples may carry a wide range of modifications. Peptide tandem mass spectrometry provides a way to study these events. We present a protocol for computational identification of post-translational modifications (PTMs) and the sites where they occur. The protocol performs an unrestrictive search, and requires no prior knowledge of what modifications are present in the sample. We present a largely automated procedure for PTM discovery, and provide a guide for analysis of PTM annotations. This protocol requires you to type out several commands, so you may wish to enlist the help of a colleague familiar with the computer's command-line interface. A typical MS run of up to 25,000 scans can be searched and analyzed in 3 h.

Peptide sequence tags for fast database search in mass-spectrometry.

Filtration techniques in the form of rapid elimination of candidate sequences while retaining the true one are key ingredients of database searches in genomics. Although SEQUEST and Mascot perform a conceptually similar task to the tool BLAST, the key algorithmic idea of BLAST (filtration) was never implemented in these tools. As a result MS/MS protein identification tools are becoming too time-consuming for many applications including search for post-translationally modified peptides. Moreover, matching millions of spectra against all known proteins will soon make these tools too slow in the same way that "genome vs genome" comparisons instantly made BLAST too slow. We describe the development of filters for MS/MS database searches that dramatically reduce the running time and effectively remove the bottlenecks in searching the huge space of protein modifications. Our approach, based on a probability model for determining the accuracy of sequence tags, achieves superior results compared to GutenTag, a popular tag generation algorithm. Our tag generating algorithm along with our de novo sequencing algorithm PepNovo can be accessed via the URL http://peptide.ucsd.edu/.

Identification of post-translational modifications via blind search of mass-spectra.

Post-translational modifications (PTMs) are of great biological importance. Most existing approaches perform a restrictive search that can only take into account a few types of PTMs and ignore all others. We describe an unrestrictive PTM search algorithm that searches for all types of PTMs at once in a blind mode, i.e., without knowing which PTMs exist in a sample. The blind PTM identification opens a possibility to study the extent and frequencies of different types of PTMs, still an open problem in proteomics. Using our new algorithm, we were able to construct a two-dimensional PTM frequency matrix that reflects the number of MS/MS spectra in a sample for each putative PTM type and each amino acid. Application of this approach to a large IKKb dataset resulted in the largest set of PTMs reported for a single MS/MS sample so far. We demonstrate an excellent correlation between high values in the PTM frequency matrix and known PTMs thus validating our approach. We further argue that the PTM frequency matrix may reveal some still unknown modifications that warrant further experimental validation.