A novel cause of DKC1-related bone marrow failure: Partial deletion of the 3' untranslated region.

Telomere biology disorders (TBDs), including dyskeratosis congenita (DC), are a group of rare inherited diseases characterized by very short telomeres. Mutations in the components of the enzyme telomerase can lead to insufficient telomere maintenance in hematopoietic stem cells, resulting in the bone marrow failure that is characteristic of these disorders. While an increasing number of genes are being linked to TBDs, the causative mutation remains unidentified in 30-40% of patients with DC. There is therefore a need for whole genome sequencing (WGS) in these families to identify novel genes, or mutations in regulatory regions of known disease-causing genes. Here we describe a family in which a partial deletion of the 3' untranslated region (3' UTR) of DKC1, encoding the protein dyskerin, was identified by WGS, despite being missed by whole exome sequencing. The deletion segregated with disease across the family and resulted in reduced levels of DKC1 mRNA in the proband. We demonstrate that the DKC1 3' UTR contains two polyadenylation signals, both of which were removed by this deletion, likely causing mRNA instability. Consistent with the major function of dyskerin in stabilization of the RNA subunit of telomerase, hTR, the level of hTR was also reduced in the proband, providing a molecular basis for his very short telomeres. This study demonstrates that the terminal region of the 3' UTR of the DKC1 gene is essential for gene function and illustrates the importance of analyzing regulatory regions of the genome for molecular diagnosis of inherited disease.

Telomerase promotes formation of a telomere protective complex in cancer cells.

Telomerase is a ribonucleoprotein complex that catalyzes addition of telomeric DNA repeats to maintain telomeres in replicating cells. Here, we demonstrate that the telomerase protein hTERT performs an additional role at telomeres that is independent of telomerase catalytic activity yet essential for telomere integrity and cell proliferation. Short-term depletion of endogenous hTERT reduced the levels of heat shock protein 70 (Hsp70-1) and the telomere protective protein Apollo at telomeres, and induced telomere deprotection and cell cycle arrest, in the absence of telomere shortening. Short-term expression of hTERT promoted colocalization of Hsp70-1 with telomeres and Apollo and reduced numbers of deprotected telomeres, in a manner independent of telomerase catalytic activity. These data reveal a previously unidentified noncanonical function of hTERT that promotes formation of a telomere protective complex containing Hsp70-1 and Apollo and is essential for sustained proliferation of telomerase-positive cancer cells, likely contributing to the known cancer-promoting effects of both hTERT and Hsp70-1.

Delayed recruiting of TPD52 to lipid droplets - evidence for a "second wave" of lipid droplet-associated proteins that respond to altered lipid storage induced by Brefeldin A treatment.

Tumor protein D52 (TPD52) is amplified and overexpressed in breast and prostate cancers which are frequently characterised by dysregulated lipid storage and metabolism. TPD52 expression increases lipid storage in mouse 3T3 fibroblasts, and co-distributes with the Golgi marker GM130 and lipid droplets (LDs). We examined the effects of Brefeldin A (BFA), a fungal metabolite known to disrupt the Golgi structure, in TPD52-expressing 3T3 cells, and in human AU565 and HMC-1-8 breast cancer cells that endogenously express TPD52. Five-hour BFA treatment reduced median LD numbers, but increased LD sizes. TPD52 knockdown decreased both LD sizes and numbers, and blunted BFA's effects on LD numbers. Following BFA treatment for 1-3 hours, TPD52 co-localised with the trans-Golgi network protein syntaxin 6, but after 5 hours BFA treatment, TPD52 showed increased co-localisation with LDs, which was disrupted by microtubule depolymerising agent nocodazole. BFA treatment also increased perilipin (PLIN) family protein PLIN3 but reduced PLIN2 detection at LDs in TPD52-expressing 3T3 cells, with PLIN3 recruitment to LDs preceding that of TPD52. An N-terminally deleted HA-TPD52 mutant (residues 40-184) almost exclusively targeted to LDs in both vehicle and BFA treated cells. In summary, delayed recruitment of TPD52 to LDs suggests that TPD52 participates in a temporal hierarchy of LD-associated proteins that responds to altered LD packaging requirements induced by BFA treatment.

The temporal profile of activity-dependent presynaptic phospho-signalling reveals long-lasting patterns of poststimulus regulation.

Depolarization of presynaptic terminals stimulates calcium influx, which evokes neurotransmitter release and activates phosphorylation-based signalling. Here, we present the first global temporal profile of presynaptic activity-dependent phospho-signalling, which includes two KCl stimulation levels and analysis of the poststimulus period. We profiled 1,917 regulated phosphopeptides and bioinformatically identified six temporal patterns of co-regulated proteins. The presynaptic proteins with large changes in phospho-status were again prominently regulated in the analysis of 7,070 activity-dependent phosphopeptides from KCl-stimulated cultured hippocampal neurons. Active zone scaffold proteins showed a high level of activity-dependent phospho-regulation that far exceeded the response from postsynaptic density scaffold proteins. Accordingly, bassoon was identified as the major target of neuronal phospho-signalling. We developed a probabilistic computational method, KinSwing, which matched protein kinase substrate motifs to regulated phosphorylation sites to reveal underlying protein kinase activity. This approach allowed us to link protein kinases to profiles of co-regulated presynaptic protein networks. Ca2+- and calmodulin-dependent protein kinase IIα (CaMKIIα) responded rapidly, scaled with stimulus strength, and had long-lasting activity. Mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) was the main protein kinase predicted to control a distinct and significant pattern of poststimulus up-regulation of phosphorylation. This work provides a unique resource of activity-dependent phosphorylation sites of synaptosomes and neurons, the vast majority of which have not been investigated with regard to their functional impact. This resource will enable detailed characterization of the phospho-regulated mechanisms impacting the plasticity of neurotransmitter release.

Telomere sequence content can be used to determine ALT activity in tumours.

The replicative immortality of human cancer cells is achieved by activation of a telomere maintenance mechanism (TMM). To achieve this, cancer cells utilise either the enzyme telomerase, or the Alternative Lengthening of Telomeres (ALT) pathway. These distinct molecular pathways are incompletely understood with respect to activation and propagation, as well as their associations with clinical outcomes. We have identified significant differences in the telomere repeat composition of tumours that use ALT compared to tumours that do not. We then employed a machine learning approach to stratify tumours according to telomere repeat content with an accuracy of 91.6%. Importantly, this classification approach is applicable across all tumour types. Analysis of pathway mutations that were under-represented in ALT tumours, across 1,075 tumour samples, revealed that the autophagy, cell cycle control of chromosomal replication, and transcriptional regulatory network in embryonic stem cells pathways are involved in the survival of ALT tumours. Overall, our approach demonstrates that telomere sequence content can be used to stratify ALT activity in cancers, and begin to define the molecular pathways involved in ALT activation.

MatCol: a tool to measure fluorescence signal colocalisation in biological systems.

Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student's t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R2 = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.

Extensive Proliferation of Human Cancer Cells with Ever-Shorter Telomeres.

Acquisition of replicative immortality is currently regarded as essential for malignant transformation. This is achieved by activating a telomere lengthening mechanism (TLM), either telomerase or alternative lengthening of telomeres, to counter normal telomere attrition. However, a substantial proportion of some cancer types, including glioblastomas, liposarcomas, retinoblastomas, and osteosarcomas, are reportedly TLM-negative. As serial samples of human tumors cannot usually be obtained to monitor telomere length changes, it has previously been impossible to determine whether tumors are truly TLM-deficient, there is a previously unrecognized TLM, or the assay results are false-negative. Here, we show that a subset of high-risk neuroblastomas (with ∼50% 5-year mortality) lacked significant TLM activity. Cancer cells derived from these highly aggressive tumors initially had long telomeres and proliferated for >200 population doublings with ever-shorter telomeres. This indicates that prevention of telomere shortening is not always required for oncogenesis, which has implications for inhibiting TLMs for cancer therapy.

Limiting Thymic Precursor Supply Increases the Risk of Lymphoid Malignancy in Murine X-Linked Severe Combined Immunodeficiency.

In early gene therapy trials for SCID-X1, using γ-retroviral vectors, T cell leukemias developed in a subset of patients secondary to insertional proto-oncogene activation. In contrast, we have reported development of T cell leukemias in SCID-X1 mice following lentivirus-mediated gene therapy independent of insertional mutagenesis. A distinguishing feature in our study was that only a proportion of transplanted γc-deficient progenitors were transduced and therefore competent for reconstitution. We hypothesized that reconstitution of SCID-X1 mice with limiting numbers of hematopoietic progenitors might be a risk factor for lymphoid malignancy. To test this hypothesis, in the absence of transduction, SCID-X1 mice were reconstituted with serially fewer wild-type hematopoietic progenitors. A robust inverse correlation between hematopoietic progenitor cell dose and T-lymphoid malignancy was observed, with earlier disease onset at lower cell doses. Malignancies were of donor origin and carried activating Notch1 mutations. These findings align with emerging evidence that thymocyte self-renewal induced by progenitor deprivation carries an oncogenic risk that is modulated by intra-thymic competition from differentiation-committed cells. Although insertional proto-oncogene activation is required for the development of malignancy in humans, failure of γc-deficient thymocytes to effectively compete with this at-risk cell population may have also contributed to oncogenesis observed in early SCID-X1 trials.

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein.

BACKGROUND: Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity. RESULTS: We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells. CONCLUSION: MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis.

Comparative analysis of whole genome sequencing-based telomere length measurement techniques.

Telomeres are regions of repetitive DNA at the ends of human chromosomes that function to maintain the integrity of the genome. Telomere attrition is associated with cellular ageing, whilst telomere maintenance is a prerequisite for malignant transformation. Whole genome sequencing (WGS) captures sequence information from the entire genome, including the telomeres, and is increasingly being applied in research and in the clinic. Several bioinformatics tools have been designed to determine telomere content and length from WGS data, and include Motif_counter, TelSeq, Computel, qMotif, and Telomerecat. These tools utilise different approaches to identify, quantify and normalise telomeric reads; however, it is not known how they compare to one another. Here we describe the details and utility of each tool, and directly compare WGS telomere length output with laboratory-based telomere length measurements. In addition, we evaluate the accessibility, practicality, speed, and additional features of each tool. Each tool was tested using a range of telomere read extraction criteria, to determine the optimal parameters for the specific WGS read length. The aim of this article is to improve the accessibility of WGS telomere length measurement tools, which have the potential to be applied to WGS cohorts for clinical as well as research benefit.

Is cell culture a risky business? Risk analysis based on scientist survey data.

Cell culture is a technique that requires vigilance from the researcher. Common cell culture problems, including contamination with microorganisms or cells from other cultures, can place the reliability and reproducibility of cell culture work at risk. Here we use survey data, contributed by research scientists based in Australia and New Zealand, to assess common cell culture risks and how these risks are managed in practice. Respondents show that sharing of cell lines between laboratories continues to be widespread. Arrangements for mycoplasma and authentication testing are increasingly in place, although scientists are often uncertain how to perform authentication testing. Additional risks are identified for preparation of frozen stocks, storage and shipping.

Reactive Oxygen Species (ROS)-Activated ATM-Dependent Phosphorylation of Cytoplasmic Substrates Identified by Large-Scale Phosphoproteomics Screen.

Ataxia-telangiectasia, mutated (ATM) protein plays a central role in phosphorylating a network of proteins in response to DNA damage. These proteins function in signaling pathways designed to maintain the stability of the genome and minimize the risk of disease by controlling cell cycle checkpoints, initiating DNA repair, and regulating gene expression. ATM kinase can be activated by a variety of stimuli, including oxidative stress. Here, we confirmed activation of cytoplasmic ATM by autophosphorylation at multiple sites. Then we employed a global quantitative phosphoproteomics approach to identify cytoplasmic proteins altered in their phosphorylation state in control and ataxia-telangiectasia (A-T) cells in response to oxidative damage. We demonstrated that ATM was activated by oxidative damage in the cytoplasm as well as in the nucleus and identified a total of 9,833 phosphorylation sites, including 6,686 high-confidence sites mapping to 2,536 unique proteins. A total of 62 differentially phosphorylated peptides were identified; of these, 43 were phosphorylated in control but not in A-T cells, and 19 varied in their level of phosphorylation. Motif enrichment analysis of phosphopeptides revealed that consensus ATM serine glutamine sites were overrepresented. When considering phosphorylation events, only observed in control cells (not observed in A-T cells), with predicted ATM sites phosphoSerine/phosphoThreonine glutamine, we narrowed this list to 11 candidate ATM-dependent cytoplasmic proteins. Two of these 11 were previously described as ATM substrates (HMGA1 and UIMCI/RAP80), another five were identified in a whole cell extract phosphoproteomic screens, and the remaining four proteins had not been identified previously in DNA damage response screens. We validated the phosphorylation of three of these proteins (oxidative stress responsive 1 (OSR1), HDGF, and ccdc82) as ATM dependent after H2O2 exposure, and another protein (S100A11) demonstrated ATM-dependence for translocation from the cytoplasm to the nucleus. These data provide new insights into the activation of ATM by oxidative stress through identification of novel substrates for ATM in the cytoplasm.

Single nucleotide differences (SNDs) continue to contaminate the dbSNP database with consequences for human genomics and health.

It has been established that up to 8.3% of the biallelic coding SNPs present in dbSNP are actually artefactual polymorphism-like errors, previously termed single nucleotide differences, or SNDs. In this study, a previous analysis of SNPs in dbSNP was extended and updated to examine how the incidence of SNDs has changed over an intervening five year period. The incidence of SNDs was found to be lower than in the previous analysis at 2.2% of all biallelic SNPs. There was only a modest reduction in the percentage of SNDs in the original set of biallelic coding SNPs tested. This suggests that the overall reduction in the incidence of SNDs over the intervening 5-year period is related to an improvement in SNP detection methods and more rigorous curation, rather than efforts to ameliorate the presence of SNDs. We note that SNDs contaminating the dbSNP may lead to erroneous conclusions on human conditions.

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function.

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.

Predicting peptide binding to Major Histocompatibility Complex molecules.

The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.

Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration.

BACKGROUND: Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying "metrics" that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset. RESULTS: We present Harvest: an open source software tool for analysing fragmentation patterns and assessing the power of a new piece of information about the MS/MS fragmentation process to more clearly differentiate between correct and random peptide assignments. We demonstrate this functionality using data metrics derived from the properties of individual datasets in a peptide identification context. Using Harvest, we demonstrate how the development of such metrics may improve correct peptide assignment confidence in the context of a high-throughput proteomics experiment and characterise properties of peptide fragmentation. CONCLUSIONS: Harvest provides a simple framework in C++ for analysing and prototyping metrics for peptide matching, the core of the protein identification problem. It is not a protein identification package and answers a different research question to packages such as Sequest, Mascot, X!Tandem, and other protein identification packages. It does not aim to maximise the number of assigned peptides from a set of unknown spectra, but instead provides a method by which researchers can explore fragmentation properties and assess the power of novel metrics for peptide matching in the context of a given experiment. Metrics developed using Harvest may then become candidates for later integration into protein identification packages.

Novel approaches to detect serum biomarkers for clinical response to interferon-beta treatment in multiple sclerosis.

Interferon beta (IFNbeta) is the most common immunomodulatory treatment for relapsing-remitting multiple sclerosis (RRMS). However, some patients fail to respond to treatment. In this study, we identified putative clinical response markers in the serum and plasma of people with multiple sclerosis (MS) treated with IFNbeta. In a discovery-driven approach, we use 2D-difference gel electrophoresis (DIGE) to identify putative clinical response markers and apply power calculations to identify the sample size required to further validate those markers. In the process we have optimized a DIGE protocol for plasma to obtain cost effective and high resolution gels for effective spot comparison. APOA1, A2M, and FIBB were identified as putative clinical response markers. Power calculations showed that the current DIGE experiment requires a minimum of 10 samples from each group to be confident of 1.5 fold difference at the p<0.05 significance level. In a complementary targeted approach, Cytometric Beadarray (CBA) analysis showed no significant difference in the serum concentration of IL-6, IL-8, MIG, Eotaxin, IP-10, MCP-1, and MIP-1alpha, between clinical responders and non-responders, despite the association of these proteins with IFNbeta treatment in MS.

Modeling single nucleotide polymorphisms in the human AKR1C1 and AKR1C2 genes: implications for functional and genotyping analyses.

Enzymes encoded by the AKR1C1 and AKR1C2 genes are responsible for the metabolism of progesterone and 5α-dihydrotestosterone (DHT), respectively. The effect of amino acid substitutions, resulting from single nucleotide polymorphisms (SNPs) in the AKR1C2 gene, on the enzyme kinetics of the AKR1C2 gene product were determined experimentally by Takashi et al. In this paper, we used homology modeling to predict and analyze the structure of AKR1C1 and AKR1C2 genetic variants. The experimental reduction in enzyme activity in the AKR1C2 variants F46Y and L172Q, as determined by Takahashi et al., is predicted to be due to increased instability in cofactor binding, caused by disruptions to the hydrogen bonds between NADP and AKR1C2, resulting from the insertion of polar residues into largely non-polar environments near the site of cofactor binding. Other AKR1C2 variants were shown to involve either conservative substitutions or changes taking place on the surface of the molecule and distant from the active site, confirming the experimental finding of Takahashi et al. that these variants do not result in any statistically significant reduction in enzyme activity. The AKR1C1 R258C variant is predicted to have no effect on enzyme activity for similar reasons. Thus, we provide further insight into the molecular mechanism of the enzyme kinetics of these proteins. Our data also highlight previously reported difficulties with online databases.

Single nucleotide differences (SNDs) in the dbSNP database may lead to errors in genotyping and haplotyping studies.

The creation of single nucleotide polymorphism (SNP) databases (such as NCBI dbSNP) has facilitated scientific research in many fields. SNP discovery and detection has improved to the extent that there are over 17 million human reference (rs) SNPs reported to date (Build 129 of dbSNP). SNP databases are unfortunately not always complete and/or accurate. In fact, half of the reported SNPs are still only candidate SNPs and are not validated in a population. We describe the identification of SNDs (single nucleotide differences) in humans, that may contaminate the dbSNP database. These SNDs, reported as real SNPs in the database, do not exist as such, but are merely artifacts due to the presence of a paralogue (highly similar duplicated) sequence in the genome. Using sequencing we showed how SNDs could originate in two paralogous genes and evaluated samples from a population of 100 individuals for the presence/absence of SNPs. Moreover, using bioinformatics, we predicted as many as 8.32% of the biallelic, coding SNPs in the dbSNP database to be SNDs. Our identification of SNDs in the database will allow researchers to not only select truly informative SNPs for association studies, but also aid in determining accurate SNP genotypes and haplotypes.

Information management for proteomics: a perspective.

Proteomics is a data-rich discipline that makes extensive use of separation tools, mass spectrometry and bioinformatics to analyze and interpret the features and dynamics of the proteome. A major challenge for the field is how proteomics data can be stored and managed, such that data become permanent and can be mined with current and future tools. This article details our experience in the development of a commercial proteomic information management system. We identify the challenges faced in data acquisition, workflow management, data permanence, security, data interpretation and analysis, as well as the solutions implemented to address these issues. We finally provide a perspective on data management in proteomics and the implications for academic and industry-based researchers working in this field.