Quality control (QC) of genome wide association study (GWAS) result files has become increasingly difficult due to advances in genomic technology. The main challenges include continuous increases in the number of polymorphic genetic variants contained in recent GWASs and reference panels, the rising number of cohorts participating in a GWAS consortium, and inclusion of new variant types. Here, we present GWASinspector, a flexible R package for comprehensive QC of GWAS results. This package is compatible with recent imputation reference panels, handles insertion/deletion and multi-allelic variants, provides extensive QC reports and efficiently processes big data files. Reference panels covering three human genome builds (NCBI36, GRCh37 and GRCh38) are available. GWASinspector has a user friendly design and allows easy set-up of the QC pipeline through a configuration file. In addition to checking and reporting on individual files, it can be used in preparation of a meta-analysis by testing for systemic differences between studies and generating cleaned, harmonized GWAS files. Comparison with existing GWAS QC tools shows that the main advantages of GWASinspector are its ability to more effectively deal with insertion/deletion and multi-allelic variants and its relatively low memory use.
 
  Our package is available at The Comprehensive R Archive Network (CRAN) https//CRAN.R-project.org/package=GWASinspector. Reference datasets and a detailed tutorial can be found at the package website at http//gwasinspector.com/.
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.
  Illumina DNA methylation bead arrays provide a cost-effective platform for the simultaneous analysis of a high number of human samples. However, the analysis can be time-demanding and requires some computational expertise. shinyÉPICo is an interactive, web-based, and graphical tool that allows the user to analyze Illumina DNA methylation arrays (450k and EPIC), from the user's own computer or from a server. The tool covers the entire analysis, from the raw data to the final list of differentially methylated positions and differentially methylated regions between sample groups. It allows the user to test several normalization methods, linear model parameters, including covariates, and differentially methylated CpGs filters, in a quick and easy manner, with interactive graphics helping to select the options in each step. shinyÉPICo represents a comprehensive tool for standardizing and accelerating DNA methylation analysis, as well as optimizing computational resources in laboratories studying DNA methylation.
 
  shinyÉPICo is freely available as an R package at the Bioconductor project (http//bioconductor.org/packages/shinyepico/) and GitHub (https//github.com/omorante/shinyepico) under an AGPL3 license.
 shinyÉPICo is freely available as an R package at the Bioconductor project (http//bioconductor.org/packages/shinyepico/) and GitHub (https//github.com/omorante/shinyepico) under an AGPL3 license.
  The inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date.
 
  We present EM-stellar, a platform that is hosted on Google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-Stellar we show that the performance of any DL method is dependent on the properties of the images being segmented.  https://www.selleckchem.com/products/plx5622.html  It also follows that no single DL method performs consistently across all performance evaluation metrics.
 
  EM-stellar (code and data) is written in Python and is freely available under MIT license on GitHub (https//github.com/cellsmb/em-stellar).
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.
  A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation. To automatically extract information from biomedical literature, existing biomedical text-mining approaches typically formulate the problem as a cross-sentence n-ary relation-extraction task that detects relations among n entities across multiple sentences, and use either a graph neural network (GNN) with long short-term memory (LSTM) or an attention mechanism. Recently, Transformer has been shown to outperform LSTM on many natural language processing (NLP) tasks.
 
  In this work, we propose a novel architecture that combines Bidirectional Encoder Representations from Transformers with Graph Transformer (BERT-GT), through integrating a neighbor-attention mechanism into the BERT architecture. Unlike the original Transformer architecture, which utilizes the whole sentence(s) to calculate the attention of the current token, the neighbor-attention mechanism in our method calculates its attention utilizing only its neighbor tokens. Thus, each token can pay attention to its neighbor information with little noise. We show that this is critically important when the text is very long, as in cross-sentence or abstract-level relation-extraction tasks. Our benchmarking results show improvements of 5.44% and 3.89% in accuracy and F1-measure over the state-of-the-art on n-ary and chemical-protein relation datasets, suggesting BERT-GT is a robust approach that is applicable to other biomedical relation extraction tasks or datasets.
 
  the source code of BERT-GT will be made freely available at https//github.com/ncbi-nlp/bert_gt upon publication.
 the source code of BERT-GT will be made freely available at https//github.com/ncbi-nlp/bert_gt upon publication.