scholarly journals BAGEL: A computational framework for identifying essential genes from pooled library screens.

2015 ◽  
Author(s):  
Traver Hart ◽  
Jason Moffat
Keyword(s):  
Mammalian Cells ◽  
State Of The Art ◽  
Gene Knockout ◽  
High Sensitivity ◽  
Major Advance ◽  
Computational Framework ◽  
New Methods ◽  
Gold Standard Reference ◽  
Order Of Magnitude ◽  
Prior State

Background: The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed. Results: We offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude. Conclusions: Using BAGEL, we identify ~2,000 fitness genes in pooled library knockout screens in human cell lines at 5% FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens with highly variable reagent quality.

scholarly journals Measuring error rates in genomic perturbation screens: gold standards for human functional genomics

2014 ◽  
Author(s):  
Traver Hart ◽  
Kevin R. Brown ◽  
Fabrice Sircoulomb ◽  
Robert Rottapel ◽  
Jason Moffat
Keyword(s):  
Mammalian Cells ◽  
Large Scale ◽  
Cellular Proliferation ◽  
Gene Knockout ◽  
Error Rates ◽  
Measuring Error ◽  
Technological Advancement ◽  
Loss Of Function ◽  
False Discovery Rates ◽  
Gold Standard Reference

AbstractTechnological advancement has opened the door to systematic genetics in mammalian cells. Genome-scale loss-of-function screens can assay fitness defects induced by partial gene knockdown, using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for the development of specific anticancer therapies. Unfortunately, progress in this field has been hampered by offtarget effects of perturbation reagents and poorly quantified error rates in large-scale screens. To improve the quality of information derived from these screens, and to provide a framework for understanding the capabilities and limitations of CRISPR technology, we derive gold-standard reference sets of essential and nonessential genes, and provide a Bayesian classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens. Our results indicate that CRISPR technology is more sensitive than RNAi, and that both techniques have nontrivial false discovery rates that can be mitigated by rigorous analytical methods.

Multi-Resolution Autoregressive Graph-to-Graph Translation for Molecules

2019 ◽  
Author(s):  
Wengong Jin ◽  
Regina Barzilay ◽  
Tommi S Jaakkola
Keyword(s):  
Drug Discovery ◽  
State Of The Art ◽  
Molecular Graph ◽  
Biochemical Properties ◽  
Large Margin ◽  
Previous State ◽  
Translation Methods ◽  
Atom Level ◽  
Precursor Molecules ◽  
Prior State

The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.

scholarly journals Small footprint optoelectrodes using ring resonators for passive light localization

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Vittorino Lanzio ◽  
Gregory Telian ◽  
Alexander Koshelev ◽  
Paolo Micheletti ◽  
Gianni Presti ◽  
...  
Keyword(s):  
State Of The Art ◽  
Three Dimensional ◽  
Network Activity ◽  
Ring Resonators ◽  
Spatiotemporal Resolution ◽  
Optical Ring Resonators ◽  
Light Localization ◽  
Order Of Magnitude ◽  
Small Footprint

AbstractThe combination of electrophysiology and optogenetics enables the exploration of how the brain operates down to a single neuron and its network activity. Neural probes are in vivo invasive devices that integrate sensors and stimulation sites to record and manipulate neuronal activity with high spatiotemporal resolution. State-of-the-art probes are limited by tradeoffs involving their lateral dimension, number of sensors, and ability to access independent stimulation sites. Here, we realize a highly scalable probe that features three-dimensional integration of small-footprint arrays of sensors and nanophotonic circuits to scale the density of sensors per cross-section by one order of magnitude with respect to state-of-the-art devices. For the first time, we overcome the spatial limit of the nanophotonic circuit by coupling only one waveguide to numerous optical ring resonators as passive nanophotonic switches. With this strategy, we achieve accurate on-demand light localization while avoiding spatially demanding bundles of waveguides and demonstrate the feasibility with a proof-of-concept device and its scalability towards high-resolution and low-damage neural optoelectrodes.

New Trends in Asymptotic Approaches: Summation and Interpolation Methods

2001 ◽  
Vol 54 (1) ◽  
pp. 69-92 ◽  
Author(s):  
Igor V. Andrianov ◽  
Jan Awrejcewicz
Keyword(s):  
Applied Sciences ◽  
Solid Mechanics ◽  
State Of The Art ◽  
Asymptotic Methods ◽  
Perturbation Series ◽  
Review Article ◽  
Point Of View ◽  
Advantages And Disadvantages ◽  
New Methods ◽  
Local Character

In this review article, we present in some detail new trends in application of asymptotic techniques to mechanical problems. First we consider the various methods which allows for the possibility of extending the perturbation series application space and hence omiting their local character. While applying the asymptotic methods very often the following situation appears: an existence of the asymptotics ε → 0 implies an existence of the asymptotics ε → ∞ (or, in a more general sense, ε → a and ε → b). Therefore, an idea of constructing a single solution valid for a whole interval of parameter ε changes is very attractive. In other words, we discuss a problem of asymptotically equivalent function constructions possessing for ε → a and ε → b a known asymptotic behavior. The defined problems are very important from the point of view of both theoretical and applied sciences. In this work, we review the state-of-the-art, by presenting the existing methods and by pointing out their advantages and disadvantages, as well as the fields of their applications. In addition, some new methods are also proposed. The methods are demonstrated on a wide variety of static and dynamic solid mechanics problems and some others involving fluid mechanics. This review article contains 340 references.

scholarly journals A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Moses Effiong Ekpenyong ◽  
Mercy Ernest Edoho ◽  
Udoinyang Godwin Inyang ◽  
Faith-Michael Uzoka ◽  
Itemobong Samuel Ekaidem ◽  
...  
Keyword(s):  
State Of The Art ◽  
Close Association ◽  
Contact Tracing ◽  
Genome Diversity ◽  
Cognitive Approach ◽  
Computational Framework ◽  
Machine Learning Methods ◽  
Global Initiative ◽  
Future Direction ◽  
Better Than

AbstractWhereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics.

Two Orders of Magnitude Improvement in the Detection Limit of Droplet-Based Micro-Magnetofluidics with Planar Hall Effect Sensors

2021 ◽  
Vol 6 (1) ◽  
pp. 47
Author(s):  
Julian Schütt ◽  
Rico Illing ◽  
Oleksii Volkov ◽  
Tobias Kosub ◽  
Pablo Nicolás Granell ◽  
...  
Keyword(s):  
Detection Limit ◽  
Hall Effect ◽  
State Of The Art ◽  
Limit Of Detection ◽  
Research Field ◽  
High Throughput Analysis ◽  
Planar Hall Effect ◽  
Biologically Relevant ◽  
Sensing Platform ◽  
Order Of Magnitude

The detection, manipulation, and tracking of magnetic nanoparticles is of major importance in the fields of biology, biotechnology, and biomedical applications as labels as well as in drug delivery, (bio-)detection, and tissue engineering. In this regard, the trend goes towards improvements of existing state-of-the-art methodologies in the spirit of timesaving, high-throughput analysis at ultra-low volumes. Here, microfluidics offers vast advantages to address these requirements, as it deals with the control and manipulation of liquids in confined microchannels. This conjunction of microfluidics and magnetism, namely micro-magnetofluidics, is a dynamic research field, which requires novel sensor solutions to boost the detection limit of tiny quantities of magnetized objects. We present a sensing strategy relying on planar Hall effect (PHE) sensors in droplet-based micro-magnetofluidics for the detection of a multiphase liquid flow, i.e., superparamagnetic aqueous droplets in an oil carrier phase. The high resolution of the sensor allows the detection of nanoliter-sized superparamagnetic droplets with a concentration of 0.58 mg cm−3, even when they are only biased in a geomagnetic field. The limit of detection can be boosted another order of magnitude, reaching 0.04 mg cm−³ (1.4 million particles in a single 100 nL droplet) when a magnetic field of 5 mT is applied to bias the droplets. With this performance, our sensing platform outperforms the state-of-the-art solutions in droplet-based micro-magnetofluidics by a factor of 100. This allows us to detect ferrofluid droplets in clinically and biologically relevant concentrations, and even in lower concentrations, without the need of externally applied magnetic fields.

scholarly journals A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 126 ◽  
Author(s):  
Lina Aboulmouna ◽  
Shakti Gupta ◽  
Mano Maurya ◽  
Frank DeVilbiss ◽  
Shankar Subramaniam ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Metabolic Regulation ◽  
Metabolic Flux ◽  
Optimal Parameter ◽  
Gene Knockout ◽  
Model Parameters ◽  
Mouse Bone Marrow ◽  
Optimization Approach ◽  
Substrate Uptake ◽  
Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

scholarly journals High-Fidelity Simulated Players for Interactive Narrative Planning

2018 ◽  
Author(s):  
Pengcheng Wang ◽  
Jonathan Rowe ◽  
Wookhee Min ◽  
Bradford Mott ◽  
James Lester
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
High Fidelity ◽  
Interactive Narrative ◽  
Interaction Data ◽  
Convolutional Networks ◽  
Novel Approach ◽  
Adaptation Policies ◽  
Narrative Planning ◽  
Prior State

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

scholarly journals A reference translatome map reveals two modes of protein evolution

2021 ◽  
Author(s):  
Aaron Wacholder ◽  
Omer Acar ◽  
Anne-Ruxandra Carvunis
Keyword(s):  
Model Organism ◽  
Great Majority ◽  
High Sensitivity ◽  
Ribosome Profiling ◽  
Computational Framework ◽  
Protein Coding ◽  
Biologically Relevant ◽  
Protein Coding Genes ◽  
Representative Subset ◽  
Evolutionarily Conserved

Ribosome profiling experiments demonstrate widespread translation of eukaryotic genomes outside of annotated protein-coding genes. However, it is unclear how much of this "noncanonical" translation contributes biologically relevant microproteins rather than insignificant translational noise. Here, we developed an integrative computational framework (iRibo) that leverages hundreds of ribosome profiling experiments to detect signatures of translation with high sensitivity and specificity. We deployed iRibo to construct a reference translatome in the model organism S. cerevisiae. We identified ~19,000 noncanonical translated elements outside of the ~5,400 canonical yeast protein-coding genes. Most (65%) of these non-canonical translated elements were located on transcripts annotated as non-coding, or entirely unannotated, while the remainder were located on the 5' and 3' ends of mRNA transcripts. Only 14 non-canonical translated elements were evolutionarily conserved. In stark contrast with canonical protein-coding genes, the great majority of the yeast noncanonical translatome appeared evolutionarily transient and showed no signatures of selection. Yet, we uncovered phenotypes for 53% of a representative subset of evolutionarily transient translated elements. The iRibo framework and reference translatome described here provide a foundation for further investigation of a largely unexplored, but biologically significant, evolutionarily transient translatome.

New Instrumentation in Argonne's Hvem-Tandem Facility: Expanded Capability for in Situ Ion Beam Studies+

1995 ◽  
Vol 396 ◽  
Author(s):  
Charles W. Allen ◽  
Loren L. Funk ◽  
Edward A. Ryan
Keyword(s):  
Scientific Community ◽  
State Of The Art ◽  
Ion Irradiation ◽  
Ion Beam ◽  
Voltage Electron ◽  
User Facility ◽  
Order Of Magnitude ◽  
New Instrumentation ◽  
Better Than

AbstractDuring 1995, a state-of-the-art intermediate voltage electron microscope (IVEM) has been installed in the HVEM-Tandem Facility with in situ ion irradiation capabilities similar to those of the HVEM. A 300 kV Hitachi H-9000NAR has been interfaced to the two ion accelerators of the Facility, with a spatial resolution for imaging which is nearly an order of magnitude better than that for the 1.2 MV HVEM which dates from the early 1970s. The HVEM remains heavily utilized for electron- and ion irradiation-related materials studies, nevertheless, especially those for which less demanding microscopy is adequate. The capabilities and limitations of this IVEM and HVEM are compared. Both the HVEM and IVEM are part of the DOE funded User Facility and therefore are available to the scientific community for materials studies, free of charge for non-proprietary research.

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