scholarly journals Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e808 ◽  
Author(s):  
Richard Wilton ◽  
Tamas Budavari ◽  
Ben Langmead ◽  
Sarah J. Wheelan ◽  
Steven L. Salzberg ◽  
...  
Nano Research ◽  
2021 ◽  
Author(s):  
Olga A. Krysiak ◽  
Simon Schumacher ◽  
Alan Savan ◽  
Wolfgang Schuhmann ◽  
Alfred Ludwig ◽  
...  

AbstractDespite outstanding accomplishments in catalyst discovery, finding new, more efficient, environmentally neutral, and noble metal-free catalysts remains challenging and unsolved. Recently, complex solid solutions consisting of at least five different elements and often named as high-entropy alloys have emerged as a new class of electrocatalysts for a variety of reactions. The multicomponent combinations of elements facilitate tuning of active sites and catalytic properties. Predicting optimal catalyst composition remains difficult, making testing of a very high number of them indispensable. We present the high-throughput screening of the electrochemical activity of thin film material libraries prepared by combinatorial co-sputtering of metals which are commonly used in catalysis (Pd, Cu, Ni) combined with metals which are not commonly used in catalysis (Ti, Hf, Zr). Introducing unusual elements in the search space allows discovery of catalytic activity for hitherto unknown compositions. Material libraries with very similar composition spreads can show different activities vs. composition trends for different reactions. In order to address the inherent challenge of the huge combinatorial material space and the inability to predict active electrocatalyst compositions, we developed a high-throughput process based on co-sputtered material libraries, and performed high-throughput characterization using energy dispersive X-ray spectroscopy (EDS), scanning transmission electron microscopy (SEM), X-ray diffraction (XRD) and conductivity measurements followed by electrochemical screening by means of a scanning droplet cell. The results show surprising material compositions with increased activity for the oxygen reduction reaction and the hydrogen evolution reaction. Such data are important input data for future data-driven materials prediction.


2014 ◽  
Author(s):  
Richard Wilton ◽  
Tamas Budavari ◽  
Ben Langmead ◽  
Sarah J Wheelan ◽  
Steven Salzberg ◽  
...  

Motivation: In computing pairwise alignments of biological sequences, software implementations employ a variety of heuristics that decrease the computational effort involved in computing potential alignments. A key element in achieving high processing throughput is to identify and prioritize potential alignments where high-scoring mappings can be expected. These tasks involve list-processing operations that can be efficiently performed on GPU hardware. Results: We implemented a read aligner called A21 that exploits GPU-based parallel sort and reduction techniques to restrict the number of locations where potential alignments may be found. When compared with other high-throughput aligners, this approach finds more high-scoring mappings without sacrificing speed or accuracy. A21 running on a single GPU is about 10 times faster than comparable CPU-based tools; it is also faster and more sensitive in comparison with other recent GPU-based aligners.


2021 ◽  
Author(s):  
Ilker Tezsevin ◽  
M.C.M. van de Sanden ◽  
Süleyman Er

<p>It is a present-day challenge to design and develop oxygen permeable solid oxide fuel cell (SOFC) electrode and electrolyte materials that operate at low temperatures. Herein, by performing high-throughput density functional theory (HT-DFT) calculations, oxygen vacancy formation energy, <i>E</i><sub>vac</sub>, data for a pool of all-inorganic ABO<sub>3</sub> and A<sup>I</sup><sub>0.5</sub>A<sup>II</sup><sub>0.5</sub>BO<sub>3</sub> cubic perovskites is generated. Using <i>E</i><sub>vac</sub> data of perovskites, the area-specific resistance (ASR) data, which is related to both oxygen reduction reaction activity and selective oxygen ion conductivity of materials, is calculated. Screening a total of 270 chemical compositions, 31 perovskites are identified as candidates with properties that are in between state-of-the-art SOFC cathode and oxygen permeation components. In addition, an intuitive approach to estimate <i>E</i><sub>vac</sub> and ASR data of complex perovskites solely by using the easy-to-access data of simple perovskites is shown, which is expected to boost future explorations on perovskite material search space for genuinely diverse energy applications.</p>


2020 ◽  
Author(s):  
Emil D. Jensen ◽  
Francesca Ambri ◽  
Marie B. Bendtsen ◽  
Alex A. Javanpour ◽  
Chang C. Liu ◽  
...  

SummaryDirected evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection, and amplification. While powerful, systematic searches through large sequence-spaces is a labor-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers >13-fold compared to reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study, is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.


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