Kübra Ermiş Tekkuş

Mutations in hepatocyte nuclear factor-1 beta (HNF1B) are the most commonly identified genetic cause of renal malformations. Heterozygous mutations are associated with renal cysts and diabetes syndrome. Various renal developmental abnormalities and maturity-onset diabetes of the young could be the presenting factors of these mutations. A 10-year-old boy who was evaluated for bilateral cystic kidneys and chronic kidney disease from the newborn period was diagnosed with HNF1B-related glomerulocystic disease by DNA sequencing. The differential diagnosis of autosomal dominant polycystic kidney disease was a diagnostic pitfall. The genetic screening of the family revealed his mother, sister, and brother to have the same mutation. Therefore, genetic diagnosis and counseling are important for cystic kidney diseases not only for formulating the diagnosis and early management plan but also for the diagnosis of potential asymptomatic cases in the family.

Objective Systemic autoinflammatory diseases (SAIDs) may not always present with typical clinical findings of a monogenic disease. We aimed to genetically screen and diagnose these clinically unclassified patients by next-generation sequencing (NGS) analysis. Method A total of 64 patients who had clinical findings of a periodic fever syndrome but did not meet the clinical diagnostic criteria for any SAID or had clinical findings for more than one monogenic SAID were identified as “clinically unclassified SAIDs.” NGS panel analysis, including 16 genes, was performed in these patients. Patients, who could not be classified as one of the defined SAID after the result of the NGS gene analysis, were identified as “undefined SAID.” Results The most common autoinflammatory symptoms in unclassified SAID patients were abdominal pain (60.9%), arthralgia (48.4%), urticarial rash (43.8%), myalgia (40.6%), oral aphthae (28.1%), and conjunctivitis (20.3%), respectively. In the result of the NGS gene panel screening, pathogenic, likely pathogenic variants, or VUS (variants of uncertain significance) were detected in 36 of 64 patients in at least one gene in the NGS panel. A total of 15 patients were diagnosed with a monogenic SAID according to both phenotypic and genotypic data; 12 patients as FMF, two patients as FCAS, and one patient as TRAPS, respectively. A total of 49 patients who did not meet the classification criteria including genetic results for a monogenic SAID were followed as undefined SAID. Conclusions The classification criteria described for SAIDs so far unfortunately do not cover all patients with signs of periodic fevers. The NGS gene panel appears to be a useful diagnostic tool for some of the patients with clinically unclassified SAID findings. Key Points • The classification criteria described for SAIDs do not cover all patients with signs of periodic fevers • The use of the undefined SAID nomenclature will benefit clinicians for diagnosis and initiating early treatment • The NGS panel appears to be a useful diagnostic tool in patients with clinically unclassified SAIDs

Emergence of new biomedical technologies, like next-generation sequencing (NGS) which is producing vast amounts of genomic data every day, is driving a big data revolution in biology. The dramatic increase in the volume, as well as the production rate of genomic data, has now made the data analysis new bottleneck for scientific discovery. Naturally, the need for highly-parallel data processing frameworks is greater than ever. It is also important for these frameworks to have certain design characteristics such as flexibility, portability, and reproducibility. Processing of sequencing data usually involves many different programs, each of which performs a specific step in the overall pipeline. Flexibility ensures that the pipelines can support a variety of use cases or data types without the need to modify existing pipelines or create new ones. Portability gives user the freedom to choose computational resources as he/she deems fit. Reproducibility across computing environments, which warrants credibility of the results, is a particularly important feature in the face of the sheer volume of data and complexity of the pipelines. There exist several platforms that offer graphical user interfaces for designing and execution of complex pipelines (e.g. Galaxy, GenePattern, GeneProf). Unfortunately, none of these platforms supports parallelism or portability across computing environments. To address these and additional shortcomings discussed in this paper, we have created DolphinNext, an easy-to-use graphical user interface for creating and deploying complex workflows for parallel processing of high throughput genomic data. DolphinNext relies on Nextflow which is a framework enabling scalable and reproducible workflows using software containers. The central idea behind the creation of DolphinNext is to facilitate building and deployment of complex pipelines using a graphically-enabled modular approach.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Bu çalışmanın amacı Borsa İstanbul’da işlem gören teknoloji, mali, hizmetler ve sanayi olmak üzere dört temel sektör bazındaki getiriler üzerinde Ramazan ayının herhangi bir oynaklık etkisi yaratıp yaratmadığını 2000-2017 dönemleri arasında incelemektir. Literatürde hisse senetleri üzerinde hareketli takvim etkisine dair çeşitli çalışmalar mevcut olup Ramazan ayının etkisi bu çerçevede incelenebilir, çünkü bu ayda oruç tutma, artan dini aktiviteler veya borsa işlemlerinin bir çeşit kumar olduğuna dair artan negatif algı etkili olabilmektedir. Bu çerçevede, Ramazan ayına ait olası etkileri araç değişken kullanıldığı regresyon ve GARCH modelleri ile tahmin edilmiştir. Bulgulara göre söz konusu ayda hisse senedi piyasası getirilerinin değişikliğe uğramadığı ve bu ayda oynaklığın arttığı gözlemlense de hizmet sektörü haricinde bulgular istatistiksel olarak anlamlı değildir.

Background The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) are transforming biological research. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for a highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility. To address these shortcomings, workflow frameworks that provide a platform to develop and portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. To simplify development, maintenance, and execution of complex data processing pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful NextFlow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis.