varAmpliCNV: analyzing variance of amplicons to detect CNVs in targeted NGS data.

MOTIVATION: Computational identification of copy number variants (CNVs) in sequencing data is a challenging task. Existing CNV-detection methods account for various sources of variation and perform different normalization strategies. However, their applicability and predictions are restricted to specific enrichment protocols. Here, we introduce a novel tool named varAmpliCNV, specifically designed for CNV-detection in amplicon-based targeted resequencing data (Haloplex™ enrichment protocol) in the absence of matched controls. VarAmpliCNV utilizes principal component analysis (PCA) and/or metric dimensional scaling (MDS) to control variances of amplicon associated read counts enabling effective detection of CNV signals. RESULTS: Performance of VarAmpliCNV was compared against three existing methods (ConVaDING, ONCOCNV and DECoN) on data of 167 samples run with an aortic aneurysm gene panel (n = 30), including 9 positive control samples. Additionally, we validated the performance on a large deafness gene panel (n = 145) run on 138 samples, containing 4 positive controls. VarAmpliCNV achieved higher sensitivity (100%) and specificity (99.78%) in comparison to competing methods. In addition, unsupervised clustering of CNV segments and visualization plots of amplicons spanning these regions are included as a downstream strategy to filter out false positives. AVAILABILITY AND IMPLEMENTATION: The tool is freely available through galaxy toolshed and at: https://hub.docker.com/r/cmgantwerpen/varamplicnv. Supplementary Data File S1: https://tinyurl.com/2yzswyhh; Supplementary Data File S2: https://tinyurl.com/ycyf2fb4. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Public Perception of COVID-19 Vaccines on Twitter in the United States

BackgroundCOVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. ObjectiveIn this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. MethodsThrough Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5th, 2020 to January 25th, 2021 using relevant keywords (such as "corona", "covid19", and "covid"). Based on geolocation information provided in tweets and vaccine-related keywords (such as "vaccine" and "vaccination"), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. ResultsOur longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. ConclusionsPublic discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users.

Mental Health Concerns Related to the COVID-19 Pandemic on Twitter in the UK

BackgroundAmid the COVID-19 pandemic, mental health-related symptoms (such as depression and anxiety) have been actively mentioned on social media. ObjectiveIn this study, we aimed to monitor mental health concerns on Twitter during the COVID-19 pandemic in the United Kingdom (UK), and assess the potential impact of the COVID-19 pandemic on mental health concerns of Twitter users. MethodsWe collected COVID-19 and mental health-related tweets from the UK between March 5, 2020 and January 31, 2021 through the Twitter Streaming API. We conducted topic modeling using Latent Dirichlet Allocation model to examine discussions about mental health concerns. Deep learning algorithms including Face++ were used to infer the demographic characteristics (age and gender) of Twitter users who expressed mental health concerns related to the COVID-19 pandemic. ResultsWe showed a positive correlation between COVID-19-related mental health concerns on Twitter and the severity of the COVID-19 pandemic in the UK. Geographic analysis showed that populated urban areas have a higher proportion of Twitter users with mental health concerns compared to England as a whole. Topic modeling showed that general concerns, COVID-19 skeptics, and Death toll were the top topics discussed in mental health-related tweets. Demographic analysis showed that middle-aged and older adults might be more likely to suffer from mental health issues or express their mental health concerns on Twitter during the COVID-19 pandemic. ConclusionsThe COVID-19 pandemic has noticeable effects on mental health concerns on Twitter in the UK, which varied among demographic and geographic groups.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

BackgroundMental health illness is a growing problem in recent years. During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. ObjectiveIn this study, we aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the United States and infer the demographic composition of Twitter users who had mental health concerns. MethodsCOVID-19 related tweets from March 5th, 2020 to January 31st, 2021 were collected through Twitter streaming API using COVID-19 related keywords (e.g., "corona", "covid19", "covid"). By further filtering using mental health keywords (e.g., "depress", "failure", "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users discussions surrounding mental health concerns. Demographic inference using deep learning algorithms (including Face++ and Ethnicolr) was performed to infer the demographic composition of Twitter users who had mental health concerns during the COVID-19 pandemic. ResultsWe observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home", "death poll" and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns. ConclusionsThe COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males, White) were more likely to have mental health concerns during the COVID-19 pandemic.

Delineation of a new fibrillin-2-opathy with evidence for a role of FBN2 in the pathogenesis of carpal tunnel syndrome.

BACKGROUND: Although carpal tunnel syndrome (CTS) is the most common form of peripheral entrapment neuropathy, its pathogenesis remains largely unknown. An estimated heritability index of 0.46 and an increased familial occurrence indicate that genetic factors must play a role in the pathogenesis. METHODS AND RESULTS: We report on a family in which CTS occurred in subsequent generations at an unusually young age. Additional clinical features included brachydactyly and short Achilles tendons resulting in toe walking in childhood. Using exome sequencing, we identified a heterozygous variant (c.5009T>G; p.Phe1670Cys) in the fibrillin-2 (FBN2) gene that co-segregated with the phenotype in the family. Functional assays showed that the missense variant impaired integrin-mediated cell adhesion and migration. Moreover, we observed an increased transforming growth factor-ß signalling and fibrosis in the carpal tissues of affected individuals. A variant burden test in a large cohort of patients with CTS revealed a significantly increased frequency of rare (6.7% vs 2.5%-3.4%, p<0.001) and high-impact (6.9% vs 2.7%, p<0.001) FBN2 variants in patient alleles compared with controls. CONCLUSION: The identification of a novel FBN2 variant (p.Phe1670Cys) in a unique family with early onset CTS, together with the observed increased frequency of rare and high-impact FBN2 variants in patients with sporadic CTS, strongly suggest a role of FBN2 in the pathogenesis of CTS.

Social Distancing Has Merely Stabilized COVID-19 in the US

Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify the reduction in doubling rate, by state, that is associated with social distancing. We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states. At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases. Instead, social distancing has merely stabilized the spread of the disease. We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing. We also discuss the policy implications of our findings.

Multiple solvent elution, a method to counter the effects of coelution and ion suppression in LC-MS analysis in bottom up proteomics.

On average a human cell type expresses around 10,000 different protein coding genes synthesizing all the different molecular forms of the protein product (proteoforms) found in a cell. In a typical shotgun bottom up proteomic approach, the proteins are enzymatically cleaved, producing several 100,000 s of different peptides that are analyzed with liquid chromatography-tandem mass spectrometry (LC-MSMS). One of the major consequences of this high sample complexity is that coelution of peptides cannot be avoided. Moreover, low abundant peptides are difficult to identify as they have a lower chance of being selected for fragmentation due to ion-suppression effects and the semi-stochastic nature of the precursor selection in data-dependent shotgun proteomic analysis where peptides are selected for fragmentation analysis one-by-one as they elute from the column. In the current study we explore a simple novel approach that has the potential to counter some of the effect of coelution of peptides and improves the number of peptide identifications in a bottom-up proteomic analysis. In this method, peptides from a HeLa cell digest were eluted from the reverse phase column using three different elution solvents (acetonitrile, methanol and acetone) in three replicate reversed phase LC-MS/MS shotgun proteomic analysis. Results were compared with three technical replicates using the same solvent, which is common practice in proteomic analysis. In total, we see an increase of up to 10% in unique protein and up to 30% in unique peptide identifications from the combined analysis using different elution solvents when compared to the combined identifications from the three replicates of the same solvent. In addition, the overlap of unique peptide identifications common in all three LC-MS analyses in our approach is only 23% compared to 50% in the replicates using the same solvent. The method presented here thus provides an easy to implement method to significantly reduce the effects of coelution and ion suppression of peptides and improve protein coverage in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD011908.

ROBO4 variants predispose individuals to bicuspid aortic valve and thoracic aortic aneurysm.

Bicuspid aortic valve (BAV) is a common congenital heart defect (population incidence, 1-2%)1-3 that frequently presents with ascending aortic aneurysm (AscAA)4. BAV/AscAA shows autosomal dominant inheritance with incomplete penetrance and male predominance. Causative gene mutations (for example, NOTCH1, SMAD6) are known for ≤1% of nonsyndromic BAV cases with and without AscAA5-8, impeding mechanistic insight and development of therapeutic strategies. Here, we report the identification of variants in ROBO4 (which encodes a factor known to contribute to endothelial performance) that segregate with disease in two families. Targeted sequencing of ROBO4 showed enrichment for rare variants in BAV/AscAA probands compared with controls. Targeted silencing of ROBO4 or mutant ROBO4 expression in endothelial cell lines results in impaired barrier function and a synthetic repertoire suggestive of endothelial-to-mesenchymal transition. This is consistent with BAV/AscAA-associated findings in patients and in animal models deficient for ROBO4. These data identify a novel endothelial etiology for this common human disease phenotype.

pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.

Motivation: Computational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter-feature dependencies within annotation sources, and the impact of bias towards specific annotations. Results: pBRIT models feature dependencies and sparsity by an Information-Theoretic (data driven) approach and applies intermediate integration based data fusion. Following the hypothesis that genes underlying similar diseases will share functional and phenotype characteristics, it incorporates Bayesian Ridge regression to learn a linear mapping between functional and phenotype annotations. Genes are prioritized on phenotypic concordance to the training genes. We evaluated pBRIT against nine existing methods, and on over 2000 HPO-gene associations retrieved after construction of pBRIT data sources. We achieve maximum AUC scores ranging from 0.92 to 0.96 against benchmark datasets and of 0.80 against the time-stamped HPO entries, indicating good performance with high sensitivity and specificity. Our model shows stable performance with regard to changes in the underlying annotation data, is fast and scalable for implementation in routine pipelines. Availability and implementation: http://biomina.be/apps/pbrit/; https://bitbucket.org/medgenua/pbrit. Supplementary information: Supplementary data are available at Bioinformatics online.

Mutations in two large pedigrees highlight the role of ZNF711 in X-linked intellectual disability.

Intellectual disability (ID) affects approximately 1-2% of the general population and is characterized by impaired cognitive abilities. ID is both clinically as well as genetically heterogeneous, up to 2000 genes are estimated to be involved in the emergence of the disease with various clinical presentations. For many genes, only a few patients have been reported and causality of some genes has been questioned upon the discovery of apparent loss-of-function mutations in healthy controls. Description of additional patients strengthens the evidence for the involvement of a gene in the disease and can clarify the clinical phenotype associated with mutations in a particular gene. Here, we present two large four-generation families with a total of 11 males affected with ID caused by mutations in ZNF711, thereby expanding the total number of families with ID and a ZNF711 mutation to four. Patients with mutations in ZNF711 all present with mild to moderate ID and poor speech accompanied by additional features in some patients, including autistic features and mild facial dysmorphisms, suggesting that ZNF711 mutations cause non-syndromic ID.

GOParGenPy: a high throughput method to generate gene ontology data matrices.

BACKGROUND: Gene Ontology (GO) is a popular standard in the annotation of gene products and provides information related to genes across all species. The structure of GO is dynamic and is updated on a daily basis. However, the popular existing methods use outdated versions of GO. Moreover, these tools are slow to process large datasets consisting of more than 20,000 genes. RESULTS: We have developed GOParGenPy, a platform independent software tool to generate the binary data matrix showing the GO class membership, including parental classes, of a set of GO annotated genes. GOParGenPy is at least an order of magnitude faster than popular tools for Gene Ontology analysis and it can handle larger datasets than the existing tools. It can use any available version of the GO structure and allows the user to select the source of GO annotation. GO structure selection is critical for analysis, as we show that GO classes have rapid turnover between different GO structure releases. CONCLUSIONS: GOParGenPy is an easy to use software tool which can generate sparse or full binary matrices from GO annotated gene sets. The obtained binary matrix can then be used with any analysis environment and with any analysis methods.

Genome sequencing of the plant pathogen Taphrina deformans, the causal agent of peach leaf curl.

UNLABELLED: Taphrina deformans is a fungus responsible for peach leaf curl, an important plant disease. It is phylogenetically assigned to the Taphrinomycotina subphylum, which includes the fission yeast and the mammalian pathogens of the genus Pneumocystis. We describe here the genome of T. deformans in the light of its dual plant-saprophytic/plant-parasitic lifestyle. The 13.3-Mb genome contains few identifiable repeated elements (ca. 1.5%) and a relatively high GC content (49.5%). A total of 5,735 protein-coding genes were identified, among which 83% share similarities with other fungi. Adaptation to the plant host seems reflected in the genome, since the genome carries genes involved in plant cell wall degradation (e.g., cellulases and cutinases), secondary metabolism, the hallmark glyoxylate cycle, detoxification, and sterol biosynthesis, as well as genes involved in the biosynthesis of plant hormones. Genes involved in lipid metabolism may play a role in its virulence. Several locus candidates for putative MAT cassettes and sex-related genes akin to those of Schizosaccharomyces pombe were identified. A mating-type-switching mechanism similar to that found in ascomycetous yeasts could be in effect. Taken together, the findings are consistent with the alternate saprophytic and parasitic-pathogenic lifestyles of T. deformans. IMPORTANCE: Peach leaf curl is an important plant disease which causes significant losses of fruit production. We report here the genome sequence of the causative agent of the disease, the fungus Taphrina deformans. The genome carries characteristic genes that are important for the plant infection process. These include (i) proteases that allow degradation of the plant tissues; (ii) secondary metabolites which are products favoring interaction of the fungus with the environment, including the host; (iii) hormones that are responsible for the symptom of severely distorted leaves on the host; and (iv) drug detoxification enzymes that confer resistance to fungicides. The availability of the genome allows the design of new drug targets as well as the elaboration of specific management strategies to fight the disease.