scholarly journals An open-source platform to distribute and interpret data from multiplexed assays of variant effect

2019 ◽  
Author(s):  
Daniel Esposito ◽  
Jochen Weile ◽  
Jay Shendure ◽  
Lea M Starita ◽  
Anthony T Papenfuss ◽  
...  
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Massively Parallel ◽  
Sequence Variant ◽  
Sequence Variants ◽  
Public Repository ◽  
Single Experiment ◽  
Multiplex Assays ◽  
Reporter Assays ◽  
Variant Effect

AbstractMultiplex Assays of Variant Effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here we present MaveDB, a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first of these applications, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

scholarly journals MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel Esposito ◽  
Jochen Weile ◽  
Jay Shendure ◽  
Lea M. Starita ◽  
Anthony T. Papenfuss ◽  
...  
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Massively Parallel ◽  
Sequence Variant ◽  
Sequence Variants ◽  
Public Repository ◽  
Single Experiment ◽  
Multiplex Assays ◽  
Reporter Assays ◽  
Variant Effect

Abstract Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB (https://www.mavedb.org), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

scholarly journals MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

2020 ◽  
Author(s):  
Ammar Tareen ◽  
Anna Posfai ◽  
William T. Ireland ◽  
David M. McCandlish ◽  
Justin B. Kinney
Keyword(s):  
Neural Network ◽  
Information Theory ◽  
Computational Analysis ◽  
Massively Parallel ◽  
Information Compression ◽  
Unified Approach ◽  
Multiplex Assays ◽  
Reporter Assays ◽  
Variant Effect ◽  
Python Package

AbstractMultiplex assays of variant effect (MAVEs), which include massively parallel reporter assays (MPRAs) and deep mutational scanning (DMS) experiments, are being rapidly adopted in many areas of biology. However, inferring quantitative models of genotype-phenotype (G-P) maps from MAVE data remains challenging, and different inference approaches have been advocated in different MAVE contexts. Here we introduce a conceptually unified approach to the problem of learning G-P maps from MAVE data. Our strategy is grounded in concepts from information theory, and is based on the view of G-P maps as a form of information compression. We also introduce MAVE-NN, a Python package that implements this approach using a neural network backend. The capabilities and advantages of MAVE-NN are then demonstrated on three diverse DMS and MPRA datasets. MAVE-NN thus fills a major need in the computational analysis of MAVE data. Installation instructions, tutorials, and documentation are provided at https://mavenn.readthedocs.io.

scholarly journals The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?

2020 ◽  
Author(s):  
Bernard Mulvey ◽  
Tomas Lagunas ◽  
Joseph D. Dougherty
Keyword(s):  
Effect Size ◽  
Genetic Variants ◽  
Large Scale ◽  
De Novo ◽  
Disease Risk ◽  
Massively Parallel ◽  
Single Experiment ◽  
Biological Variables ◽  
Small Effect Size ◽  
Specific Implementation

AbstractNeuropsychiatric phenotypes have been long known to be influenced by heritable risk factors. The past decade of genetic studies have confirmed this directly, revealing specific common and rare genetic variants enriched in disease cohorts. However, the early hope for these studies—that only a small set of genes would be responsible for a given disorder—proved false. The picture that has emerged is far more complex: a given disorder may be influenced by myriad coding and noncoding variants of small effect size, and/or by rare but severe variants of large effect size, many de novo. Noncoding genomic sequences harbor a large portion of these variants, the molecular functions of which cannot usually be inferred from sequence alone. This creates a substantial barrier to understanding the higher-order molecular and biological systems underlying disease risk. Fortunately, a proliferation of genetic technologies—namely, scalable oligonucleotide synthesis, high-throughput RNA sequencing, CRISPR, and CRISPR derivatives—have opened novel avenues to experimentally identify biologically significant variants en masse. These advances have yielded an especially versatile technique adaptable to large-scale functional assays of variation in both untranscribed and untranslated regulatory features: Massively Parallel Reporter Assays (MPRAs). MPRAs are powerful molecular genetic tools that can be used to screen tens of thousands of predefined sequences for functional effects in a single experiment. This approach has several ideal features for psychiatric genetics, but remains underutilized in the field to date. To emphasize the opportunities MPRA holds for dissecting psychiatric polygenicity, we review here its applications in the literature, discuss its ability to test several biological variables implicated in psychiatric disorders, illustrate this flexibility with a proof-of-principle, in vivo cell-type specific implementation of the assay, and envision future outcomes of applying MPRA to both computational and experimental neurogenetics.

Fundamental study on clarification of brain molecular pathology of Nasu-Hakola disease

Impact ◽  
2019 ◽  
Vol 2019 (8) ◽  
pp. 24-26
Author(s):  
Jun-ichi Satoh
Keyword(s):  
Multiple Sclerosis ◽  
Human Brain ◽  
Clinical Research ◽  
Neurodegenerative Diseases ◽  
Molecular Pathology ◽  
Large Scale ◽  
Molecular Pathogenesis ◽  
Novel Therapies ◽  
Brain Pathology ◽  
Fundamental Study

Brain pathology expert Dr Jun-ichi Satoh, from the Department of Bioinformatics and Molecular Neuropathology of Meiji Pharmaceutical University in Tokyo, is drawing on his expertise on neurology and neuroimmunology to delve into some of the more complex diseases impacting the human brain. His knowledge and expertise have allowed him to direct his research interests to study neurodegenerative diseases, such as Alzheimer's disease (AD), and neuroinflammatory diseases, such as multiple sclerosis (MS), and the analysis of their molecular pathogenesis by using a bioinformatics approach. His current focus is on Nasu-Hakola disease (NHD), a disease whose rarity has posed significant barriers towards performing large-scale clinical research in order to understand what exactly causes this disease and develop effective novel therapies.

scholarly journals Acoustoelectronic nanotweezers enable dynamic and large-scale control of nanomaterials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peiran Zhang ◽  
Joseph Rufo ◽  
Chuyi Chen ◽  
Jianping Xia ◽  
Zhenhua Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Condensed Matter ◽  
Dynamic Control ◽  
Massively Parallel ◽  
Large Field ◽  
Graphene Flakes ◽  
Field Dynamic ◽  
Scale Control ◽  
Orientation Pattern ◽  
Resolution Single

AbstractThe ability to precisely manipulate nano-objects on a large scale can enable the fabrication of materials and devices with tunable optical, electromagnetic, and mechanical properties. However, the dynamic, parallel manipulation of nanoscale colloids and materials remains a significant challenge. Here, we demonstrate acoustoelectronic nanotweezers, which combine the precision and robustness afforded by electronic tweezers with versatility and large-field dynamic control granted by acoustic tweezing techniques, to enable the massively parallel manipulation of sub-100 nm objects with excellent versatility and controllability. Using this approach, we demonstrated the complex patterning of various nanoparticles (e.g., DNAs, exosomes, ~3 nm graphene flakes, ~6 nm quantum dots, ~3.5 nm proteins, and ~1.4 nm dextran), fabricated macroscopic materials with nano-textures, and performed high-resolution, single nanoparticle manipulation. Various nanomanipulation functions, including transportation, concentration, orientation, pattern-overlaying, and sorting, have also been achieved using a simple device configuration. Altogether, acoustoelectronic nanotweezers overcome existing limitations in nano-manipulation and hold great potential for a variety of applications in the fields of electronics, optics, condensed matter physics, metamaterials, and biomedicine.

Crack Growth and Propagation in Metallic Alloys

1995 ◽  
Vol 409 ◽  
Author(s):  
W. C. Morrey ◽  
L. T. Wille
Keyword(s):  
Large Scale ◽  
Critical Strain ◽  
Cleavage Plane ◽  
Crack Tip ◽  
Metallic Alloys ◽  
Propagation Direction ◽  
Dynamics Simulation ◽  
Parallel Computer ◽  
Massively Parallel ◽  
Tip Position

AbstractUsing large-scale molecular dynamics simulation on a massively parallel computer, we have studied the initiation of cracking in a Monel-like alloy of Cu-Ni. In a low temperature 2D sample, fracture from a notch starts at a little beyond 2.5% critical strain when the propagation direction is perpendicular to a cleavage plane. We discuss a method of characterizing crack tip position using a measure of area around the crack tip.

scholarly journals Beyond spatial scalability limitations with a massively parallel method for linear oscillatory problems

2017 ◽  
Vol 32 (6) ◽  
pp. 913-933 ◽  
Author(s):  
Martin Schreiber ◽  
Pedro S Peixoto ◽  
Terry Haut ◽  
Beth Wingate
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Weather Prediction ◽  
Finite Difference Methods ◽  
Scaling Limit ◽  
Performance Model ◽  
Massively Parallel ◽  
Large Set ◽  
Problem Size ◽  
Single Node

This paper presents, discusses and analyses a massively parallel-in-time solver for linear oscillatory partial differential equations, which is a key numerical component for evolving weather, ocean, climate and seismic models. The time parallelization in this solver allows us to significantly exceed the computing resources used by parallelization-in-space methods and results in a correspondingly significantly reduced wall-clock time. One of the major difficulties of achieving Exascale performance for weather prediction is that the strong scaling limit – the parallel performance for a fixed problem size with an increasing number of processors – saturates. A main avenue to circumvent this problem is to introduce new numerical techniques that take advantage of time parallelism. In this paper, we use a time-parallel approximation that retains the frequency information of oscillatory problems. This approximation is based on (a) reformulating the original problem into a large set of independent terms and (b) solving each of these terms independently of each other which can now be accomplished on a large number of high-performance computing resources. Our results are conducted on up to 3586 cores for problem sizes with the parallelization-in-space scalability limited already on a single node. We gain significant reductions in the time-to-solution of 118.3× for spectral methods and 1503.0× for finite-difference methods with the parallelization-in-time approach. A developed and calibrated performance model gives the scalability limitations a priori for this new approach and allows us to extrapolate the performance of the method towards large-scale systems. This work has the potential to contribute as a basic building block of parallelization-in-time approaches, with possible major implications in applied areas modelling oscillatory dominated problems.

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