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Purpose: In parent-child trios with genome sequencing data, we investigated inherited biallelic deletions to identify known and novel genetic disorders. Methods: We developed a copy-number variations analysis pipeline based on autosomal genome sequencing read depth of Genomics England 100,000 Genomes Project data from 11,754 parent-child trios and additional 18,875 non-trios. A control cohort of 15,440 cancer patients provided independent deletion frequencies. Results: Autosomal recessive (AR) modeling detected 34 distinct rare deletions that were homozygous in the proband and heterozygous in both parents. These inherited biallelic deletions were only detected in 52 trios. These “knockout” regions included 37 genes, having among them 8 with an Online Mendelian Inheritance in Man AR annotation. Deletions of NPHP1, followed by OTOA, both within segmental duplications, were the only recurrent findings explaining phenotypes in a total of 10 and 3 patients, respectively. Recurrent heterozygous NPHP1 deletions were detected in 0.3%-0.5% of controls. We reviewed “knockout” patients for the remaining 29 genes without disease associations and identified SLC66A1 as a likely novel cause for AR rod-cone dystrophy in 4 families. Conclusion: A tailored copy-number variations analysis of genome sequencing trio data shows that biallelic inherited gene deletions are rare, with NPHP1 biallelic deletions causing nephronophthisis the leading finding. We propose SLC66A1 as a novel cause for AR retinopathy.

Abstract Background Sequencing of miRNAs isolated from exosomes has great potential to identify novel disease biomarkers, but exosomes have low amount of RNA, hindering adequate analysis and quantification. Here, we have assessed several steps in developing an optimized small RNA (sRNA) library preparation protocol for next-generation sequencing (NGS) miRNA analysis from urinary exosomes. Methods A total of 24 urinary exosome samples from donors were included in this study. RNA was extracted by column-based methods. The quality of extracted RNA was assessed by spectrophotometric quantification and Bioanalyzer software analysis. All libraries were prepared using the CleanTag small RNA library preparation protocol and the effect of our additional modifications on adapter-dimer presence, sequencing data and tagged small RNA library population was also analyzed. Results Our results show that good quality sequencing libraries can be prepared following our optimized small RNA library preparation protocol from urinary exosomes. When the size selection by gel purification step was included within the workflow, adapter-dimer was totally removed from cDNA libraries. Furthermore, the inclusion of this modification step within small RNA library protocol augmented the small RNA mapped reads, with an especially significant 37% increase in miRNA reads, and the gel purification step made no difference to the tagged miRNA population. Conclusions This study provides researchers with an optimized small RNA library preparation workflow for next generation sequencing based exosome-associated miRNA analysis that yields a high amount of miRNA mapped reads without skewing the tagged miRNA population significantly.

Abstract Background Biological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data. Results In this work we present PyCellBase, a Python package that provides programmatic access to the rich RESTful web service API offered by CellBase. This package offers a fast and user-friendly access to biological information without the need of installing any local database. In addition, a series of command-line tools are provided to perform common bioinformatic tasks, such as variant annotation. CellBase data is always available by a high-availability cluster and queries have been tuned to ensure a real-time performance. Conclusion PyCellBase is an open-source Python package that provides an efficient access to heterogeneous biological information. It allows to perform tasks that require a comprehensive set of knowledge resources, as for example variant annotation. Queries can be easily fine-tuned to retrieve the desired information of particular biological features. PyCellBase offers the convenience of an object-oriented scripting language and provides the ability to integrate the obtained results into other Python applications and pipelines.