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Enzyme activity is affected by amino acid mutations, particularly mutations near the active site. Increasing evidence has shown that distal mutations more than 10 Å away from the active site may significantly affect enzyme activity. However, it is difficult to study the enzyme regulation mechanism of distal mutations due to the lack of a systematic collection of three-dimensional (3D) structures, highlighting distal mutation site and the corresponding enzyme activity change. Therefore, we constructed a distal mutation database, namely, D3DistalMutation, which relates the distal mutation to enzyme activity. As a result, we observed that approximately 80% of distal mutations could affect enzyme activity and 72.7% of distal mutations would decrease or abolish enzyme activity in D3DistalMutation. Only 6.6% of distal mutations in D3DistalMutation could increase enzyme activity, which have great potential to the industrial field. Among these mutations, the Y to F, S to D, and T to D mutations are most likely to increase enzyme activity, which sheds some light on industrial catalysis. Distal mutations decreasing enzyme activity in the allosteric pocket play an indispensable role in allosteric drug design. In addition, the pockets in the enzyme structures are provided to explore the enzyme regulation mechanism of distal mutations. D3DistalMutation is accessible free of charge at https://www.d3pharma.com/D3DistalMutation/index.php.

Marfan syndrome (MFS) is an autosomal dominant genetic condition that mainly affects connective tissue in many parts of the body. Cardinal manifestations involve the ocular, skeletal, and cardiovascular systems. The diagnosis of MFS relies on the revised Ghent criteria, outlined by international expert opinion to facilitate accurate recognition of this syndrome as well as to improve patient management and counseling. However, it may not always be possible to make a definitive diagnosis according to these criteria in each patient and thus molecular confirmation is necessary in subjects with suspected MFS. This debilitating, if not fatal, disorder is caused by mutations in FBN1, which encodes a major constitutive element of extracellular microfibrils. Here, we present a detailed clinical and molecular analysis of 76 Turkish patients with definitive or suspected MFS diagnosed at our center between 2014 and 2019. We were able to identify a total of 51 different FBN1 variants in our cohort, 31 of which have previously been reported in the relevant scientific literature. The remaining 20 variants have not been documented to date. In one patient, we detected a large deletion including the entire FBN1 gene using the array CGH approach. Currently, there are very few studies on the genotype-phenotype correlation of patients with MFS, and no clear genotype-phenotype maps for MFS have been constructed so far, except for some cases. We believe that our findings will make a rich and peculiar contribution to the elusive genotype-phenotype relationship in MFS, especially in this large and populous ethnic group. WOS:000610028100001 2-s2.0-85099760154 PubMed: 33483584

Ras is frequently mutated in cancer, however, there is a lack of consensus in the literature regarding the cancer mutation frequency of Ras, with quoted values varying from 10%–30%. This variability is at least in part due to the selective aggregation of data from different databases and the dominant influence of particular cancer types and particular Ras isoforms within these datasets. To provide a more definitive figure for Ras mutation frequency in cancer, we cross-referenced the data in all major publicly accessible cancer mutation databases to determine reliable mutation frequency values for each Ras isoform in all major cancer types. These percentages were then applied to current U.S. cancer incidence statistics to estimate the number of new patients each year that have Ras-mutant cancers. We find that approximately 19% of patients with cancer harbor Ras mutations, equivalent to approximately 3.4 million new cases per year worldwide. We discuss the Ras isoform and mutation-specific trends evident within the datasets that are relevant to current Ras-targeted therapies.

Motivation Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next-generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources. Results We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for non-coding regions. Availability and implementation https://prix.hanyang.ac.kr/download/mutcombinator.jsp. Supplementary information Supplementary data are available at Bioinformatics online.

Mutations in the X-linked phosphatidylinositol glycan, class A gene (Pig-a) lead to loss of glycosylphosphatidylinositol (GPI) anchors and GPI-anchored proteins from the surface of erythrocytes and other mammalian cells. The Pig-a gene mutation assay quantifies in vivo gene mutation by immunofluorescent labeling and flow cytometry to detect the loss of GPI-anchored proteins on peripheral blood erythrocytes. As part of the regulatory acceptance of the assay, a public database has been created that provides detailed information on Pig-a gene mutation assays conducted in rats and mice. A searchable version of the database is available through a website designed and hosted by the University of Maryland School of Pharmacy. Currently, the database contains only mouse and rat data, but it is anticipated that it will expand to include data from other species, including humans. A major purpose in developing the database was to aid in the preparation of a Retrospective Performance Analysis and Detailed Review Paper required for Organisation for Economic Co-operation and Development Test Guideline acceptance. We anticipate, however, that it also will be useful for accessing and comparing Pig-a data to data from other assays and for conducting quantitative assessments of Pig-a gene mutation responses. Environ. Mol. Mutagen., 60:759-762, 2019. © 2019 Wiley Periodicals, Inc.

Background Recently, guidelines for variant interpretation in cancer have been established. However, these guidelines do not mention which databases are most suited to performing this task. Materials and methods We give an overview of existing databases and evaluate their benefit in practical application. We compared three meta-databases and 12 databases for a dataset of patients with myelodysplastic syndrome or acute myeloid leukemia. Results Clinical implications were found for 13% of all variants. One-third of variants with therapeutic implications were uniquely contained in one database. The VICC meta-database was the most extensive source of information, featuring 92% of variants with a drug association. However, a comparison of meta-databases and original sources indicated that some variants are missing in those meta-databases. Conclusion Public databases provide decision support for interpreting variants but there is still need for manual curation. Meta-databases facilitate the use of multiple resources but should be interpreted with caution.

Background/Introduction Familial hypercholesterolemia (FH) is genetically very heterogeneous and genomic and locus-specific public databases describing putative FH mutations are assumed to be of limited clinical utility because of classification errors. Purpose A description of all currently known publicly available putative FH mutations in order to determine the reliability of the classification of FH mutations described. Methods The LOVD and ClinVar databases were interrogated for the phenotype and genes of interest. Additional information on each variant was obtained using the Bioconductor toolset, gnomAD, and GETex. Results Currently know putative FH variants included 4,529 variants (97.2%) in the classical FH-genes LDLR (61%), PCSK9 (10%), and APOB (29%). Single nucleotide variants constituted 83% and 17% were copy number variants. Exonic variants contributed 78%, 14% of the variants were intronic, 7% large CNV, and 1% in upstream or downstream regions. Of the 4,529 variants, 45% were classified as pathogenic or likely-pathogenic (Fig. 1a). The ratio of exonic/intronic variants was 10.1 for pathogenic variants, 6.9 for likely pathogenic variants, 2.8 for likely benign and 1.4 for benign variants (p-value for class difference For 502 frameshift mutations in exons that are particularly damaging, only 93.2% were classified as pathogenic or likely-pathogenic and 1.7% as benign or likely benign (Fig. 1b). Across 222 exon-covering deletions of >100 nucleotides, also particularly damaging, only 90.5% were classified as pathogenic or likely-pathogenic. Of the 4,529 variants, 1,561 (34.5%) were polymorphic in gnomAD. For these variants, the gnomAD sample size was on average 227420 (26102–282902). Of all 1,561 polymorphic variants in gnomAD 182 (11.6%) were classified as pathogenic or likely-pathogenic (Fig. 1c) and the variant frequency ranged from 10–4 to 10–6 (average 1.8x10–5), 43 with a frequency >1.8x10–5. Among variants found in gnomAD and classified as non-pathogenic, we observed ∼2000x higher frequencies (average 0.04). Of the 4,529 variants, 100 matched eQTLs or sQTLs in GTEx, none was annotated as pathogenic (Fig. 1d). Conclusion We review all currently known putative FH mutations with pathogenic or likely-pathogenic variants being mostly exonic. Highly damaging mutations are largely classified as pathogenic or likely-pathogenic, but up to 9% are not classified as pathogenic in the two public FH mutation databases. Assuming an FH population prevalence of 1/250 and 2000 pathogenic variants with the most frequent variant 10x more frequent than the average, we expect most pathogenic FH mutations at frequencies 2x10–5 in the general population, evidence for misclassification. No pathogenic FH variant was found among GETEx eQTLs or sQTLs. Our data showcase the utility and weaknesses of the current public FH mutation databases. FH variants overview Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): Hôpitaux Universitaire de Genève - Fonds privés

Background: The occurrence of mosaicism in hemophilia A (HA) has been investigated in several studies using different detection methods. Objectives: To characterize and compare the ability of AmpliSeq/Ion Torrent sequencing and droplet digital polymerase chain reaction (ddPCR) for mosaic detection in HA. Methods: Ion Torrent sequencing and ddPCR were used to analyze 20 healthy males and 16 mothers of sporadic HA patients. Results: An error-rate map over all coding positions and all positions reported as mutated in the F8-specific mutation database was produced. The sequencing produced a mean read depth of >1500X where >97% of positions were covered by >100 reads. Higher error frequencies were observed in positions with A or T as reference allele and in positions surrounded on both sides with C or G. Seventeen of 9319 positions had a mean substitution error frequency >1%. The ability to identify low-level mosaicism was determined primarily by read depth and error rate of each specific position. Limit of detection (LOD) was 1% require repeated testing and mononucleotide repeats with more than four repeat units need an alternative analysis strategy. Mosaicism was detected in 1 of 16 mothers and confirmed using ddPCR. Conclusions: Deep sequencing using an AmpliSeq/Ion Torrent strategy allows for simultaneous identification of disease-causing mutations in patients and mosaicism in mothers. ddPCR has high sensitivity but is hampered by the need for mutationspecific design.

The thyrotropin receptor (TSHR) mutation database, consisting of all known TSHR mutations and their clinical characterizations, was established in 1999. The database contents are updated here with the same website (tsh-receptor-mutation-database.org). The new database contains 638 cases of TSHR mutations: 448 cases of gain of function mutations (7 novel mutations and 41 new cases for previously described mutations since its last update in 2012) and 190 cases of loss of function mutations (28 novel mutations and 31 new cases for previously described mutations since its last update in 2012). This database is continuously updated and allows for rapid validation of patient TSHR mutations causing hyper- or hypothyroidism or insensitivity to TSH.

The massive amount of data generated from genome sequencing have given rise to several mutation predictor tools although no mutation database or predictor tool have been developed specifically for the transmembrane region of membrane proteins.We present TMSNP, a database that currently contains information from 2624 pathogenic and 195964 non-pathogenic reported mutations located on the TM region of membrane proteins. The computed conservation parameters and annotations on these mutations were used to train a machine-learning model that classifies TM mutations as pathogenic or non-pathogenic. The presented tool improves considerably the prediction power of commonly used mutation predictors and additionally represents the first mutation prediction tool specific for TM mutations.TMSNP is available at http://lmc.uab.es/tmsnp/Contactmireia.olivella@esci.upf.edu

Background Next-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important to facilitate a better understanding of disease entities and prognoses. Furthermore, variant calling allows to adapt and optimize specific treatments of individual patients, and thus is an integral part of personalized medicine.However, the analysis of NGS data typically requires a number of complex bioinformatics processing steps. A flexible and reliable software that combines the variant analysis process with a simple, user-friendly interface is therefore highly desirable, but still lacking. Results With AMLVaran (AML Variant Analyzer), we present a web-based software, that covers the complete variant analysis workflow of targeted NGS samples. The software provides a generic pipeline that allows free choice of variant calling tools and a flexible language (SSDL) for filtering variant lists. AMLVaran’s interactive website presents comprehensive annotation data and includes curated information on relevant hotspot regions and driver mutations. A concise clinical report with rule-based diagnostic recommendations is generated.An AMLVaran configuration with eight variant calling tools and a complex scoring scheme, based on the somatic variant calling pipeline appreci8, was used to analyze three datasets from AML and MDS studies with 402 samples in total. Maximum sensitivity and positive predictive values were 1.0 and 0.96, respectively. The tool’s usability was found to be satisfactory by medical professionals. Conclusion Coverage analysis, reproducible variant filtering and software usability are important for clinical assessment of variants. AMLVaran performs reliable NGS variant analyses and generates reports fulfilling the requirements of a clinical setting. Due to its generic design, the software can easily be adapted for use with different targeted panels for other tumor entities, or even for whole-exome data. AMLVaran has been deployed to a public web server and is distributed with Docker scripts for local use.

Rett syndrome (RTT) is a monogenic rare disorder that causes severe neurological problems. In most cases, it results from a loss‐of‐function mutation in the gene encoding methyl‐CPG‐binding protein 2 (MECP2). Currently, about 900 unique MECP2 variations (benign and pathogenic) have been identified and it is suspected that the different mutations contribute to different levels of disease severity. For researchers and clinicians, it is important that genotype–phenotype information is available to identify disease‐causing mutations for diagnosis, to aid in clinical management of the disorder, and to provide counseling for parents. In this study, 13 genotype–phenotype databases were surveyed for their general functionality and availability of RTT‐specific MECP2 variation data. For each database, we investigated findability and interoperability alongside practical user functionality, and type and amount of genetic and phenotype data. The main conclusions are that, as well as being challenging to find these databases and specific MECP2 variants held within, interoperability is as yet poorly developed and requires effort to search across databases. Nevertheless, we found several thousand online database entries for MECP2 variations and their associated phenotypes, diagnosis, or predicted variant effects, which is a good starting point for researchers and clinicians who want to provide, annotate, and use the data.

Background In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics. Results Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases. Conclusions We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided. Electronic supplementary material The online version of this article (doi: 10.1186/s12859-017-1951-y) contains supplementary material, which is available to authorized users.

In current practice, Next Generation Sequencing (NGS) applications start with mapping/aligning short reads to the reference genome, with the aim of identifying genetic variants. Although existing alignment tools have shown great accuracy in mapping short reads to the reference genome, a significant number of short reads still remain unmapped and are often excluded from downstream analyses thereby causing nonnegligible information loss in the subsequent variant calling procedure. This paper describes Genesis-indel, a computational pipeline that explores the unmapped reads to identify novel indels that are initially missed in the original procedure. Genesis-indel is applied to the unmapped reads of 30 breast cancer patients from TCGA. Results show that the unmapped reads are conserved between the two subtypes of breast cancer investigated in this study and might contribute to the divergence between the subtypes. Genesis-indel identifies 72,997 novel high-quality indels previously not found, among which 16,141 have not been annotated in the widely used mutation database. Statistical analysis of these indels shows significant enrichment of indels residing in oncogenes and tumour suppressor genes. Functional annotation further reveals that these indels are strongly correlated with pathways of cancer and can have high to moderate impact on protein functions. Additionally, some of the indels overlap with the genes that do not have any indel mutations called from the originally mapped reads but have been shown to contribute to the tumorigenesis in multiple carcinomas, further emphasizing the importance of rescuing indels hidden in the unmapped reads in cancer and disease studies.

BackgroundMore than 11,000 laboratories and companies developed their own next-generation sequencing (NGS) for screening and diagnosis of various diseases including cancer. Although inconsistencies of mutation calls as high as 43% in databases such as GDSC (Genomics of Drug Sensitivity in Cancer) and CCLE (Cancer Cell Line Encyclopedia) have been reported, not many studies on the reasons for the inconsistencies have been published. Methods: Targeted-NGS analysis of 151 genes in 35 cell lines common to GDSC and CCLE was performed, and the results were compared with those from GDSC and CCLE wherein whole-exome- or highly-multiplex NGS were employed.ResultsIn the comparison, GDSC and CCLE had a high rate (40-45%) of false-negative (FN) errors which would lead to high rate of inconsistent mutation calls, suggesting that highly-multiplex NGS may have high rate of FN errors. We also posited the possibility that targeted NGS, especially for the detection of low-level cancer cells in cancer tissues might suffer significant FN errors.ConclusionFN errors may be the most important errors in NGS testing for cancer; their evaluation in laboratory-developed NGS tests is needed.

As outlined in their recent paper (A Tubulin Mutation Database: A Resource for the Cytoskeletal Community), Catherine Pham and Naomi Morrissette from the University of California, Irvine, scoured the literature and catalogued data for 489 point mutations for 𝛂-tubulin, 729 for β-tubulin, and 343 for 𝛄, ẟ, 𝛆, and 𝛇 tubulins to create the tubulin mutation database (http://tubulinmutations.bio.uci.edu). The database is a searchable catalog of missense mutations and phenotypes that is expected to grow with biannual updates. Data entries regarding the species and isoform, as well as links to available sequences and the original study which characterized the mutant are intuitively displayed and color coded (Pham & Morrissette, 2019). This database represents a unique opportunity for clinicians and cell biologists to rapidly connect sequence data to mutant phenotypes and gather primary literature which promises to facilitate discoveries on topics including microtubule dynamics, antimitotic drug use and resistance, and evolution. We expect that many researchers will find this tool of great use to their research. This article is protected by copyright. All rights reserved.

There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.

With the development of technology, an enormous amount of sequencing data is being generated rapidly. However, transforming this data into patient care is a critical challenge. There are two difficulties: how to integrate functional information into mutation interpretation and how to make the integration easy to apply. One solution is to visualize amino acid changes with protein structure and function in web app platform. There are multiple existing tools for plotting mutations, but the majority of them requires programming skills that are not common background for clinicians or researchers. Furthermore, the recurrent mutations are the focus and the recurrence cutoff varies. Yet, none of the current software offers customer-defined cutoff. Thus, we developed this user-friendly web-based tool, Mutplot (https://bioinformaticstools.shinyapps.io/lollipop/). Mutplot retrieves up-to-date domain information from the protein resource UniProt (https://www.uniprot.org/), integrates the submitted mutation information and produces lollipop diagrams with annotations and highlighted candidates. It offers flexible output options. For data that follows security standards, the app can also be hosted in web servers inside a firewall or computers without internet with Uniprot database stored on them. Altogether, Mutplot is an excellent tool for visualizing protein mutations, especially for clinicians or researchers without any bioinformatics background.

Virologists. You might know a couple of them, but unless you are a virologist yourself, the probability that you have collaborated with one in the past is low. The community is relatively small, but they pack a heavy punch and are expected to play a leading role in the research into pathogens that lies ahead. You may ask why we think virologists are our future. Suffice it to say that it is not just because they have invented technologies that belong to the space age, including use of viruses as vehicles to shuttle genes into cells[1], organic nanoparticles with specific tools attached to their surfaces to get inside target cells[2], and using genetically modified viruses as therapies to fight against cancer[3]. Did you know that virologists currently only know of about 3,200 viral species but that more than 320,000 mammal-associated viruses[4] are thought to await discovery? Just think about the viruses hidden in the Arctic ice[5] or in the insects and other animals from once cut-off regions in the world, which now face ever-increasing human exposure[6]. But a heroic (as well as an apocalyptic) role for virologists may also be on the horizon, as the adoption of phage therapy may, in the future, be used to control harmful bacteria when antibiotics fail[7].

BackgroundBasal cell nevus syndrome (BCNS) is an autosomal dominant disorder characterized by multiple basal cell carcinomas (BCCs), maxillary keratocysts, and cerebral calcifications. BCNS most commonly is caused by a germline mutation in the patched-1 (PTCH1) gene. PTCH1 mutations are also described in patients with holoprosencephaly.MethodsWe have established a locus-specific database for the PTCH1 gene using the Leiden Open Variation Database (LOVD). We included 117 new PTCH1 variations, in addition to 331 previously published unique PTCH1 mutations. These new mutations were found in 141 patients who had a positive PTCH1 mutation analysis in either the VU University Medical Centre (VUMC) or Maastricht University Medical Centre (MUMC) between 1995 and 2015.ResultsThe database contains 331 previously published unique PTCH1 mutations and 117 new PTCH1 variations.ConclusionWe have established a locus-specific database for the PTCH1 gene using the Leiden Open Variation Database (LOVD). The database provides an open collection for both clinicians and researchers and is accessible online at http://www.lovd.nl/PTCH1..

Protein thermostability engineering is a powerful tool to improve resistance of proteins against high temperatures and thereafter broaden their applications. For efficient protein thermostability engineering, different thermostability-classified data sources including sequences and 3D structures are needed for different protein families. However, no data source is available providing such data easily. It is the first release of ProtDataTherm database for analysis and engineering of protein thermostability which contains more than 14 million protein sequences categorized based on their thermal stability and protein family. This database contains data needed for better understanding protein thermostability and stability engineering. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. This database is available at http://profiles.bs.ipm.ir/softwares/protdatatherm. As a proof of concept, the thermostability that improves mutations were suggested for one sample protein belonging to one of protein families with more than 20 mesophilic and thermophilic sequences and with known experimentally measured ΔT of mutations available within ProTherm database.

Genetic analysis of Neurofibromatosis type 1 (NF1) may facilitate the identification of patients in early phases of the disease. Here, we present an overview of our diagnostic research spanning the last 11 years, with a focus on the description of 225 NF1 mutations, 126 of which are novel, found in a series of 607 patients (513 unrelated) in Italy. Between 2003 and 2013, 443 unrelated patients were profiled by denaturing high pressure liquid chromatography (DHPLC) analysis of 60 amplicons derived from genomic NF1 DNA and subsequent sequencing of heterozygotic PCR products. In addition, a subset of patients was studied by multiplex ligation‐dependent probe amplification (MLPA) to identify any duplications, large deletions or microdeletions present at the locus. Over the last year, 70 unrelated patients were investigated by MLPA and sequencing of 22 amplicons spanning the entire NF1 cDNA. Mutations were found in 70% of the 293 patients studied by DHPLC, thereby fulfilling the NIH criterion for the clinical diagnosis of NF1 (detection rate: 70%); furthermore, 87% of the patients studied by RNA sequencing were genetically characterized. Mutations were also found in 36 of the 159 patients not fulfilling the NIH clinical criteria. We confirmed a higher incidence of intellectual disability in patients harboring microdeletion type 1 and observed a correlation between a mild phenotype and the small deletion c.2970_2972delAAT or the missense alteration in amino acid residue 1809 (p.Arg1809Cys). These data support the use of RNA‐based methods for genetic analysis and provide novel information for improving the management of symptoms in oligosymptomatic patients.

Modern high-throughput screening methods allow researchers to generate large datasets that potentially contain important biological information. However, oftentimes, picking relevant hits from such screens and generating testable hypotheses requires training in bioinformatics and the skills to efficiently perform database mining. There are currently no tools available to general public that allow users to cross-reference their screen datasets with published screen datasets. To this end, we developed CrossCheck, an online platform for high-throughput screen data analysis. CrossCheck is a centralized database that allows effortless comparison of the user-entered list of gene symbols with 16,231 published datasets. These datasets include published data from genome-wide RNAi and CRISPR screens, interactome proteomics and phosphoproteomics screens, cancer mutation databases, low-throughput studies of major cell signaling mediators, such as kinases, E3 ubiquitin ligases and phosphatases, and gene ontological information. Moreover, CrossCheck includes a novel database of predicted protein kinase substrates, which was developed using proteome-wide consensus motif searches. CrossCheck dramatically simplifies high-throughput screen data analysis and enables researchers to dig deep into the published literature and streamline data-driven hypothesis generation. CrossCheck is freely accessible as a web-based application at http://proteinguru.com/crosscheck.

Motivation As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Availability and implementation Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. Supplementary information Supplementary data are available at Bioinformatics online.

DNA polymerase gamma (POLG) is the replicative polymerase responsible for maintaining mitochondrial DNA (mtDNA). Disorders related to its functionality are a major cause of mitochondrial disease. The clinical spectrum of POLG syndromes includes Alpers-Huttenlocher syndrome (AHS), childhood myocerebrohepatopathy spectrum (MCHS), myoclonic epilepsy myopathy sensory ataxia (MEMSA), the ataxia neuropathy spectrum (ANS) and progressive external ophthalmoplegia (PEO). We have collected all publicly available POLG-related patient data and analyzed it using our pathogenic clustering model to provide a new research and clinical tool in the form of an online server. The server evaluates the pathogenicity of both previously reported and novel mutations. There are currently 176 unique point mutations reported and found in mitochondrial patients in the gene encoding the catalytic subunit of POLG, POLG. The mutations are distributed nearly uniformly along the length of the primary amino acid sequence of the gene. Our analysis shows that most of the mutations are recessive, and that the reported dominant mutations cluster within the polymerase active site in the tertiary structure of the POLG enzyme. The POLG Pathogenicity Prediction Server (http://polg.bmb.msu.edu) is targeted at clinicians and scientists studying POLG disorders, and aims to provide the most current available information regarding the pathogenicity of POLG mutations., Highlights • Multi-level access to crucial data supporting diagnosis/prognosis of POLG syndromes • Clustering protocol enables identification of novel neutral polymorphisms • Identical alleles displaying variable symptoms evidence unidentified components • POLG enzymes with premature stop codons, insertions/deletions group biochemically • Dominant POLG mutations all lie within a critical location in the structure

Services such as Facebook, Amazon, and eBay were once solely accessed from stationary computers. These web services are now being used increasingly on mobile devices. We acknowledge this new reality by providing users a way to access publications and a curated cancer mutation database on their mobile device with daily automated updates. Availability: http://hive.biochemistry.gwu.edu/tools/HivePubcast .