A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome, bioRxiv, 2019-03-01

Background Developing insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits. Results Overall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 x 10-08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively. We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations. Conclusions The atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at httpmrcieu.mrsoftware.orgTissue_MR_atlas.

biorxiv genetics 100-200-users 2019

Deep learning in bioinformatics introduction, application, and perspective in big data era, bioRxiv, 2019-03-01

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at httpsgithub.comlykaust15Deep_learning_examples .

biorxiv bioinformatics 100-200-users 2019

Non-homologous end joining minimizes errors by coordinating DNA processing with ligation, bioRxiv, 2019-03-01

Genome stability requires efficient and faithful repair of DNA double-strand breaks (DSBs). The predominant DSB repair pathway in human cells is non-homologous end-joining (NHEJ), which directly ligates DNA ends1–5. Broken DNA ends at DSBs are chemically diverse, and many are not compatible for direct ligation by the NHEJ-associated DNA Ligase IV (Lig4). To solve this problem, NHEJ end-processing enzymes including polymerases and nucleases modify ends until they are ligatable. How cells regulate end processing to minimize unnecessary genomic alterations6 during repair of pathological DSBs remains unknown. Using a biochemical system that recapitulates key features of cellular NHEJ, we previously demonstrated that DNA ends are initially tethered at a distance, followed by Lig4-mediated formation of a “short-range synaptic complex” in which DNA ends are closely aligned for ligation7. Here, we show that a wide variety of end-processing activities all depend on formation of the short-range complex. Moreover, using real-time single molecule imaging, we find that end processing occurs within the short-range complex. Confining end processing to the Lig4-dependent short-range synaptic complex promotes immediate ligation of compatible ends and ensures that incompatible ends are ligated as soon as they become compatible, thereby minimizing end processing. Our results elucidate how NHEJ exploits end processing to achieve versatility while minimizing errors that cause genome instability.

biorxiv molecular-biology 0-100-users 2019

 

Created with the audiences framework by Jedidiah Carlson

Powered by Hugo