Clinical features of UK Biobank subjects carrying protein truncating variants in genes implicated in schizophrenia pathogenesis

The SCHEMA consortium has identified ten genes in which protein truncating variants (PTVs) confer substantial risk of schizophrenia. Exome-sequenced participants in the UK Biobank who carried PTVs in these genes were studied to determine to what extent they demonstrated features of schizophrenia or had neuropsychiatric impairment. Following automated quality control and visual inspection of reads, 251 subjects were identified as having well-supported PTVs in one of these genes. The frequency of PTVs in CACNA1G was higher than had been observed in SCHEMA cases, casting doubt on its role in schizophrenia pathogenesis, but otherwise rates were similar to those observed in SCHEMA controls. Numbers were too small to allow formal statistical analysis but in general carriers of PTVs did not appear to have high rates of psychiatric illness or reduced educational or occupational functioning. One subject with a PTV in SETD1A had a diagnosis of schizophrenia. one with a PTV in HERC1 had psychotic depression and two subjects seemed to have developmental disorders, one with a PTV in GRIN2A and one with a PTV in RBCC1. There seemed to be somewhat increased rates of affective disorders among carriers of PTVs in HERC1 and RB1CC1. Carriers of PTVs did not appear to have subclinical manifestations of schizophrenia. Although PTVs in these genes can substantially increase schizophrenia risk, their effect seems to be dichotomous and most carriers appear psychiatrically well.This research has been conducted using the UK Biobank Resource.

A Scalable and Modular Automated Pipeline for Stitching of Large Electron Microscopy Datasets

Serial section Electron Microscopy can produce high throughput imaging of large biological specimen volumes. The high-resolution images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. A high fidelity volume assembly is required to correctly reconstruct neural anatomy and synaptic connections. It involves seamless 2D stitching of the images within a serial section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP(Assembly Stitching and Alignment Pipeline) that is scalable and parallelized to work with distributed systems. The pipeline is built on top of the Render [18] services used in the volume assembly of the brain of adult Drosophila melanogaster [2]. It achieves high throughput by operating on the meta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. The modularity of ASAP allows for easy adaptation to new algorithms without significant changes to the workflow. The software pipeline includes a complete set of tools to do stitching, automated quality control, 3D section alignment, and rendering of the assembled volume to disk. We also implemented a workflow engine that executes the volume assembly workflow in an automated fashion triggered following the transfer of raw data. ASAP has been successfully utilized for continuous processing of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex [1, 25]. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis

Use of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Here, we developed HTSQualC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSQualC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSQualC for conducting batch analysis of HTS datasets with 322 samples with an average ~ 1 M (paired end) sequence reads per sample. HTSQualC accomplished the QC analysis in ~ 3 h in distributed mode and ~ 31 h in shared mode, thus underscoring its utility and robust performance. In addition to command-line execution, we integrated HTSQualC into the free, open-source, CyVerse cyberinfrastructure resource as a GUI interface, for wider access to experimental biologists who have limited computational resources and/or programming abilities.

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

Background Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward. Results In this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data. Conclusions eQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC.

ALGORITHM FOR MEASURING CUTTER WEAR IN TURNING BY EVALUATING DYNAMIC SYSTEM STABILITY RESERVE

The article considers introducing automated quality control systems to the industrial enterprises, which is very important today. There are many factors that negatively affect the reliability of products used in different industries (transport, agriculture, etc.). One of the important factors at present is the quality of turning parts, which, in turn, is influenced by the state of the cutting tool. Practical research shows that a greater number (35%) of cutting tool breakdowns happen due to its wear, and the time spent on changing the cutter is, on average, 10% of the working time of mechatronic machine tool systems. To prevent breakage of the cutter and damage of products associated with wear, it is necessary to determine in a timely manner the moment of the beginning of critical wear of the cutting tool. Thus, a need arose for the development of an automated system for recognizing the initial phase of critical wear of a cutter during turning on a CNC machine in real time, and it is essential to create a reliable algorithm and software and mathematical support. The implementation of the considered algorithm for determining the critical wear of the cutter in turning according to the stability margin of the dynamic system and its software implementation make it possible to use the tool resource and practically eliminate the rejects associated with late replacement of the cutter during turning, as well as significantly reduce financial costs under conditions of mass production to which the suboptimal consumption of the working resource of the tool leads. The proposed decision helps to increase the turning efficiency

A deep database of medical abbreviations and acronyms for natural language processing

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6–14% increase in abbreviation coverage; 28–52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations.

BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data

Background Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples. Results Here we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs. Conclusions Researchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.