Systematic Comparison of Three Methods for Fragmentation of Long-Range PCR Products for Next Generation Sequencing

Next Generation Sequencing (NGS) technologies are gaining importance in the routine clinical diagnostic setting. It is thus desirable to simplify the workflow for high-throughput diagnostics. Fragmentation of DNA is a crucial step for preparation of template libraries and various methods are currently known. Here we evaluated the performance of nebulization, sonication and random enzymatic digestion of long-range PCR products on the results of NGS. All three methods produced high-quality sequencing libraries for the 454 platform. However, if long-range PCR products of different length were pooled equimolarly, sequence coverage drastically dropped for fragments below 3,000 bp. All three methods performed equally well with regard to overall sequence quality (PHRED) and read length. Enzymatic fragmentation showed highest consistency between three library preparations but performed slightly worse than sonication and nebulization with regard to insertions/deletions in the raw sequence reads. After filtering for homopolymer errors, enzymatic fragmentation performed best if compared to the results of classic Sanger sequencing. As the overall performance of all three methods was equal with only minor differences, a fragmentation method can be chosen solely according to lab facilities, feasibility and experimental design

A systematic, large-scale comparison of transcription factor binding site models

Background The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified “real” in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. Results While the area- under-curve was low for most of the tested models with only 47 % reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. Conclusions Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM (http:/mutationtaster.charite.de/ePOSSUM/) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites

Mutations in FKBP10 can cause a severe form of isolated Osteogenesis imperfecta

<p>Abstract</p> <p>Background</p> <p>Mutations in the <it>FKBP10 </it>gene were first described in patients with Osteogenesis imperfecta type III. Two follow up reports found <it>FKBP10 </it>mutations to be associated with Bruck syndrome type 1, a rare disorder characterized by congenital contractures and bone fragility. This raised the question if the patients in the first report indeed had isolated Osteogenesis imperfecta or if Bruck syndrome would have been the better diagnosis.</p> <p>Methods</p> <p>The patients described here are affected by severe autosomal recessive Osteogenesis imperfecta without contractures.</p> <p>Results</p> <p>Homozygosity mapping identified <it>FKBP10 </it>as a candidate gene, and sequencing revealed a base pair exchange that causes a C-terminal premature stop codon in this gene.</p> <p>Conclusions</p> <p>Our study demonstrates that <it>FKBP10 </it>mutations not only cause Bruck syndrome or Osteogenesis imperfecta type III but can result in a severe type of isolated Osteogenesis imperfecta type IV with prenatal onset. Furthermore, it adds dentinogenesis imperfecta to the spectrum of clinical symptoms associated with <it>FKBP10 </it>mutations.</p

VarFish - Collaborative and comprehensive variant analysis for diagnosis and research

VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of panel and exome variant data for rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Filtered variants are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows user to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the software’s extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g., after update of genotype or phenotype databases

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks

BACKGROUND: Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation. RESULTS: In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach. CONCLUSIONS: Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions

PINTA: a web server for network-based gene prioritization from expression data

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user

HomozygosityMapper—an interactive approach to homozygosity mapping

Homozygosity mapping is a common method for mapping recessive traits in consanguineous families. In most studies, applications for multipoint linkage analyses are applied to determine the genomic region linked to the disease. Unfortunately, these are neither suited for very large families nor for the inclusion of tens of thousands of SNPs. Even if less than 10 000 markers are employed, such an analysis may easily last hours if not days. Here we present a web-based approach to homozygosity mapping. Our application stores marker data in a database into which users can directly upload their own SNP genotype files. Within a few minutes, the database analyses the data, detects homozygous stretches and provides an intuitive graphical interface to the results. The homozygosity in affected individuals is visualized genome-wide with the ability to zoom into single chromosomes and user-defined chromosomal regions. The software also displays the underlying genotypes in all samples. It is integrated with our candidate gene search engine, GeneDistiller, so that users can interactively determine the most promising gene. They can at any point restrict access to their data or make it public, allowing HomozygosityMapper to be used as a data repository for homozygosity-mapping studies. HomozygosityMapper is available at http://www.homozygositymapper.org/

Fatal Cardiac Arrhythmia and Long-QT Syndrome in a New Form of Congenital Generalized Lipodystrophy with Muscle Rippling (CGL4) Due to PTRF-CAVIN Mutations

We investigated eight families with a novel subtype of congenital generalized lipodystrophy (CGL4) of whom five members had died from sudden cardiac death during their teenage years. ECG studies revealed features of long-QT syndrome, bradycardia, as well as supraventricular and ventricular tachycardias. Further symptoms comprised myopathy with muscle rippling, skeletal as well as smooth-muscle hypertrophy, leading to impaired gastrointestinal motility and hypertrophic pyloric stenosis in some children. Additionally, we found impaired bone formation with osteopenia, osteoporosis, and atlanto-axial instability. Homozygosity mapping located the gene within 2 Mbp on chromosome 17. Prioritization of 74 candidate genes with GeneDistiller for high expression in muscle and adipocytes suggested PTRF-CAVIN (Polymerase I and transcript release factor/Cavin) as the most probable candidate leading to the detection of homozygous mutations (c.160delG, c.362dupT). PTRF-CAVIN is essential for caveolae biogenesis. These cholesterol-rich plasmalemmal vesicles are involved in signal-transduction and vesicular trafficking and reside primarily on adipocytes, myocytes, and osteoblasts. Absence of PTRF-CAVIN did not influence abundance of its binding partner caveolin-1 and caveolin-3. In patient fibroblasts, however, caveolin-1 failed to localize toward the cell surface and electron microscopy revealed reduction of caveolae to less than 3%. Transfection of full-length PTRF-CAVIN reestablished the presence of caveolae. The loss of caveolae was confirmed by Atomic Force Microscopy (AFM) in combination with fluorescent imaging. PTRF-CAVIN deficiency thus presents the phenotypic spectrum caused by a quintessential lack of functional caveolae

SIGLEC1 (CD169): a marker of active neuroinflammation in the brain but not in the blood of MS patients

OBJECTIVE: We aimed to evaluate SIGLEC1 (CD169) as a biomarker in Multiple Sclerosis (MS) and Neuromyelitis optica spectrum disorder (NMOSD) and to evaluate the specificity of SIGLEC1+ myeloid cells for demyelinating diseases. METHODS: We performed flow cytometry-based measurements of SIGLEC1 expression on monocytes in 86 MS patients, 41 NMOSD patients and 31 healthy controls. Additionally, we histologically evaluated the presence of SIGLEC1+ myeloid cells in acute and chronic MS brain lesions as well as other neurological diseases. RESULTS: We found elevated SIGLEC1 expression in 16/86 (18.6%) MS patients and 4/41 (9.8%) NMOSD patients. Almost all MS patients with high SIGLEC1 levels received exogenous interferon beta as an immunomodulatory treatment and only a small fraction of MS patients without interferon treatment had increased SIGLEC1 expression. SIGLEC1+ myeloid cells were abundantly present in active MS lesions as well as in a range of acute infectious and malignant diseases of the central nervous system, but not chronic MS lesions. CONCLUSION: In our cohort, SIGLEC1 expression on monocytes was – apart from those patients receiving interferon treatment – not significantly increased in patients with MS and NMOSD, nor were levels associated with more severe disease. The presence of SIGLEC1+ myeloid cells in brain lesions could be used to investigate the activity in an inflammatory CNS lesion

Refinement of the GINGF3 locus for hereditary gingival fibromatosis

Hereditary gingival fibromatosis (HGF) is a rare, clinically variable disorder characterized by slowly progressive fibrous overgrowth of the gingiva. Four gene loci have been mapped for autosomal dominant non-syndromic HGF (adHGF). The molecular basis of adHGF remains largely unknown, with only a single SOS1 gene mutation identified so far at the gingival fibromatosis 1 (GINGF1) locus in one family. We identified an adHGF family with ten affected individuals in whom onset of gingival fibromatosis concurred with the eruption of the primary teeth. In order to identify the molecular basis in this family, we tested for linkage of the disease to known adHGF loci. A maximal multipoint logarithm of the odds score of 3.91 was obtained with marker D2S390 (θ = 0) at the GINGF3 locus on chromosome 2p23.3–p22.3, and linkage to other known loci was excluded. Sequencing two candidate genes, ALK and C2orf18, and a single nucleotide polymorphisms array analysis did not reveal a mutation or copy number variation in a patient from the family. We refined the GINGF3 locus to a 6.56-cM, 8.27-Mb region containing 112 known and hypothetical genes, and our data and a search of the literature suggest that GINGF3 is a major adHGF locus