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Journal articles on the topic 'Read data'

Author: Grafiati

Published: 6 September 2023

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1

Li, Donghe, Wonji Kim, Longfei Wang, Kyong-Ah Yoon, Boyoung Park, Charny Park, Sun-Young Kong, et al. "Comparison of INDEL Calling Tools with Simulation Data and Real Short-Read Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 5 (September 1, 2019): 1635–44. http://dx.doi.org/10.1109/tcbb.2018.2854793.

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2

Liao, Jianwei, Jun Li, Mingwang Zhao, Zhibing Sha, and Zhigang Cai. "Read Refresh Scheduling and Data Reallocation against Read Disturb in SSDs." ACM Transactions on Embedded Computing Systems 21, no. 2 (March 31, 2022): 1–27. http://dx.doi.org/10.1145/3495254.

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Read disturb is a circuit-level noise in flash-based Solid-State Drives (SSDs), induced by intensive read requests, which may result in unexpected read errors. The approach of read refresh (RR) is commonly adopted to mitigate its negative effects by unconditionally migrating all valid data pages in the RR block to another new block. However, routine RR operations greatly impact the I/O responsiveness of SSDs, because the processing on normal I/O requests must be blocked at the same time. To further reduce the negative effects of read refresh, this article proposes a read refresh scheduling and data reallocation method to deal with two primary issues with respect to an RR operation, including where to place data pages and when to trigger page migrations. Specifically, we first construct a data reallocation model to match the data pages in the RR block and the destination blocks for addressing the issue of where to place the data. The model considers not only the read hotness of pages in the RR block, but also the accumulated read counts of the destination blocks. Moreover, for addressing the issue of when to trigger data migrations, we build a timing decision model to determine the time points for completing page migrations by considering the factors of the intensity of I/Os and the disturb situation on the RR block. Through a series of simulation experiments based on several realistic disk traces, we illustrate that the proposed RR scheduling and data reallocation mechanism can noticeably reduce the read errors by more than 10.3% , on average, and the long-tail latency by between 43.9% and 64.0% at the 99.99th percentile, in contrast to state-of-the-art methods.

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3

Eisenstein, Michael. "Startups use short-read data to expand long-read sequencing market." Nature Biotechnology 33, no. 5 (May 2015): 433–35. http://dx.doi.org/10.1038/nbt0515-433.

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4

Shumate, Alaina, Brandon Wong, Geo Pertea, and Mihaela Pertea. "Improved transcriptome assembly using a hybrid of long and short reads with StringTie." PLOS Computational Biology 18, no. 6 (June 1, 2022): e1009730. http://dx.doi.org/10.1371/journal.pcbi.1009730.

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Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana, Mus musculus, and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie.

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SHIMADA, Y. "How to Read Blood Gas Data." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 64, no. 12 (December 1, 1994): 560–63. http://dx.doi.org/10.4286/ikakikaigaku.64.12_560.

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6

Zheng, Yuanqing, and Mo Li. "Read Bulk Data From Computational RFIDs." IEEE/ACM Transactions on Networking 24, no. 5 (October 2016): 3098–108. http://dx.doi.org/10.1109/tnet.2015.2502979.

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7

Moretti. "Introduction to “Learning to Read Data”." Victorian Studies 54, no. 1 (2011): 78. http://dx.doi.org/10.2979/victorianstudies.54.1.78.

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8

Berners-Lee, Tim, and Kieron O’Hara. "The read–write Linked Data Web." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1987 (March 28, 2013): 20120513. http://dx.doi.org/10.1098/rsta.2012.0513.

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This paper discusses issues that will affect the future development of the Web, either increasing its power and utility, or alternatively suppressing its development. It argues for the importance of the continued development of the Linked Data Web, and describes the use of linked open data as an important component of that. Second, the paper defends the Web as a read–write medium, and goes on to consider how the read–write Linked Data Web could be achieved.

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9

Stöcker, Bianca K., Johannes Köster, and Sven Rahmann. "SimLoRD: Simulation of Long Read Data." Bioinformatics 32, no. 17 (May 10, 2016): 2704–6. http://dx.doi.org/10.1093/bioinformatics/btw286.

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10

Tan, Yuxiang, Yann Tambouret, and Stefano Monti. "SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/780519.

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The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

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11

Greer, S. U., and H. P. Ji. "Structural variant analysis for linked-read sequencing data with gemtools." Bioinformatics 35, no. 21 (April 2, 2019): 4397–99. http://dx.doi.org/10.1093/bioinformatics/btz239.

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Abstract Summary Linked-read sequencing generates synthetic long reads which are useful for the detection and analysis of structural variants (SVs). The software associated with 10× Genomics linked-read sequencing, Long Ranger, generates the essential output files (BAM, VCF, SV BEDPE) necessary for downstream analyses. However, to perform downstream analyses requires the user to customize their own tools to handle the unique features of linked-read sequencing data. Here, we describe gemtools, a collection of tools for the downstream and in-depth analysis of SVs from linked-read data. Gemtools uses the barcoded aligned reads and the Megabase-scale phase blocks to determine haplotypes of SV breakpoints and delineate complex breakpoint configurations at the resolution of single DNA molecules. The gemtools package is a suite of tools that provides the user with the flexibility to perform basic functions on their linked-read sequencing output in order to address even more questions. Availability and implementation The gemtools package is freely available for download at: https://github.com/sgreer77/gemtools. Supplementary information Supplementary data are available at Bioinformatics online.

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12

Morad, Tomer Y., Gil Shomron, Mattan Erez, Avinoam Kolodny, and Uri C. Weiser. "Optimizing Read-Once Data Flow in Big-Data Applications." IEEE Computer Architecture Letters 16, no. 1 (January 1, 2017): 68–71. http://dx.doi.org/10.1109/lca.2016.2520927.

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13

Singer, Joshua, Emma Thomson, Joseph Hughes, Elihu Aranday-Cortes, John McLauchlan, Ana da Silva Filipe, Lily Tong, et al. "Interpreting Viral Deep Sequencing Data with GLUE." Viruses 11, no. 4 (April 3, 2019): 323. http://dx.doi.org/10.3390/v11040323.

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Using deep sequencing technologies such as Illumina’s platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data.

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14

Nguyen, Son Hoang, Minh Duc Cao, and Lachlan J. M. Coin. "Real-time resolution of short-read assembly graph using ONT long reads." PLOS Computational Biology 17, no. 1 (January 20, 2021): e1008586. http://dx.doi.org/10.1371/journal.pcbi.1008586.

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A streaming assembly pipeline utilising real-time Oxford Nanopore Technology (ONT) sequencing data is important for saving sequencing resources and reducing time-to-result. A previous approach implemented in npScarf provided an efficient streaming algorithm for hybrid assembly but was relatively prone to mis-assemblies compared to other graph-based methods. Here we present npGraph, a streaming hybrid assembly tool using the assembly graph instead of the separated pre-assembly contigs. It is able to produce more complete genome assembly by resolving the path finding problem on the assembly graph using long reads as the traversing guide. Application to synthetic and real data from bacterial isolate genomes show improved accuracy while still maintaining a low computational cost. npGraph also provides a graphical user interface (GUI) which provides a real-time visualisation of the progress of assembly. The tool and source code is available at https://github.com/hsnguyen/assembly.

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15

Moriyama, Takuya, Seiya Imoto, Shuto Hayashi, Yuichi Shiraishi, Satoru Miyano, and Rui Yamaguchi. "A Bayesian model integration for mutation calling through data partitioning." Bioinformatics 35, no. 21 (March 29, 2019): 4247–54. http://dx.doi.org/10.1093/bioinformatics/btz233.

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Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

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16

Schröder, Jan, James Bailey, Thomas Conway, and Justin Zobel. "Reference-Free Validation of Short Read Data." PLoS ONE 5, no. 9 (September 22, 2010): e12681. http://dx.doi.org/10.1371/journal.pone.0012681.

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17

Yue, Jia-Xing, and Gianni Liti. "Long-read sequencing data analysis for yeasts." Nature Protocols 13, no. 6 (May 3, 2018): 1213–31. http://dx.doi.org/10.1038/nprot.2018.025.

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18

Marchetti, Mirco, and Dario Stabili. "READ: Reverse Engineering of Automotive Data Frames." IEEE Transactions on Information Forensics and Security 14, no. 4 (April 2019): 1083–97. http://dx.doi.org/10.1109/tifs.2018.2870826.

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19

Krawitz, Peter, Christian Rödelsperger, Marten Jäger, Luke Jostins, Sebastian Bauer, and Peter N. Robinson. "Microindel detection in short-read sequence data." Bioinformatics 26, no. 6 (February 9, 2010): 722–29. http://dx.doi.org/10.1093/bioinformatics/btq027.

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20

Wadi,et al., L. Kais. "Read Data Of PLC Using Tranciver GSM." International Journal of Computing and Network Technology 4, no. 3 (September 1, 2016): 133–41. http://dx.doi.org/10.12785/ijcnt/040303.

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21

Wadi,et al., L. Kais. "Read Data Of PLC Using Tranciver GSM." International Journal of Computing and Network Technology 4, no. 3 (September 1, 2016): 133–41. http://dx.doi.org/10.12785/ijcts/040303.

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22

Page, Andrew J., and Jacqueline A. Keane. "Rapid multi-locus sequence typing direct from uncorrected long reads using Krocus." PeerJ 6 (July 31, 2018): e5233. http://dx.doi.org/10.7717/peerj.5233.

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Genome sequencing is rapidly being adopted in reference labs and hospitals for bacterial outbreak investigation and diagnostics where time is critical. Seven gene multi-locus sequence typing is a standard tool for broadly classifying samples into sequence types (STs), allowing, in many cases, to rule a sample out of an outbreak, or allowing for general characteristics about a bacterial strain to be inferred. Long-read sequencing technologies, such as from Oxford Nanopore, can produce read data within minutes of an experiment starting, unlike short-read sequencing technologies which require many hours/days. However, the error rates of raw uncorrected long read data are very high. We present Krocus which can predict a ST directly from uncorrected long reads, and which was designed to consume read data as it is produced, providing results in minutes. It is the only tool which can do this from uncorrected long reads. We tested Krocus on over 700 isolates sequenced using long-read sequencing technologies from Pacific Biosciences and Oxford Nanopore. It provides STs for isolates on average within 90 s, with a sensitivity of 94% and specificity of 97% on real sample data, directly from uncorrected raw sequence reads. The software is written in Python and is available under the open source license GNU GPL version 3.

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23

Wang, Siye, Ziwen Cao, Yanfang Zhang, Weiqing Huang, and Jianguo Jiang. "A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data." Wireless Communications and Mobile Computing 2020 (February 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6937912.

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The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.

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24

Gajewicz, A. "Development of valuable predictive read-across models based on “real-life” (sparse) nanotoxicity data." Environmental Science: Nano 4, no. 6 (2017): 1389–403. http://dx.doi.org/10.1039/c7en00102a.

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25

Kocic, Ljubisa. "Data variation in fractal interpolation." Filomat, no. 17 (2003): 79–84. http://dx.doi.org/10.2298/fil0317079k.

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A fractal interpolant of the proper interpolation data is fully determined by the functional equation of Read- Bejraktarevic type and a free vertical scaling vector ? such that ||?|| < 1. In this note, it is shown how insertion of the data impacts the related Read-Bejraktarevic equation and the interpolant. Some examples support the theory.

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26

He, Yan, and Liang Feng Zhang. "Verifiable Summation of Read-Once Formula Specified Data." IEEE Access 8 (2020): 22434–44. http://dx.doi.org/10.1109/access.2020.2970067.

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27

Cuntze, G., T. Hughes, S. Magnusson, W. Nichtl-Pecher, D. Norton, and M. Pechtold. "Magnetoresistive read heads for high-density data applications." IEEE Transactions on Magnetics 37, no. 5 (2001): 3839–43. http://dx.doi.org/10.1109/20.952755.

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28

Albers, C. A., G. Lunter, D. G. MacArthur, G. McVean, W. H. Ouwehand, and R. Durbin. "Dindel: Accurate indel calls from short-read data." Genome Research 21, no. 6 (October 27, 2010): 961–73. http://dx.doi.org/10.1101/gr.112326.110.

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29

Meyer, Karlene Nicole, and Michelle R. Lacey. "Modeling Methylation Patterns with Long Read Sequencing Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, no. 4 (July 1, 2018): 1379–89. http://dx.doi.org/10.1109/tcbb.2017.2721943.

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30

Horne, Ross, and Vladimiro Sassone. "A verified algebra for read–write Linked Data." Science of Computer Programming 89 (September 2014): 2–22. http://dx.doi.org/10.1016/j.scico.2013.07.005.

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31

Elyanow, Rebecca, Hsin-Ta Wu, and Benjamin J. Raphael. "Identifying structural variants using linked-read sequencing data." Bioinformatics 34, no. 2 (November 3, 2017): 353–60. http://dx.doi.org/10.1093/bioinformatics/btx712.

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32

De Coster, Wouter, Svenn D’Hert, Darrin T. Schultz, Marc Cruts, and Christine Van Broeckhoven. "NanoPack: visualizing and processing long-read sequencing data." Bioinformatics 34, no. 15 (March 14, 2018): 2666–69. http://dx.doi.org/10.1093/bioinformatics/bty149.

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33

Petrin, A. B. "Ultimate thermomechanical read rate from AFM data storage." Russian Microelectronics 35, no. 6 (December 2006): 382–91. http://dx.doi.org/10.1134/s1063739706060060.

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34

Kuss, Hans-Joachim. "Solid Data read from the Sand of Egypt." German Research 23, no. 2-3 (May 2001): 12–14. http://dx.doi.org/10.1002/1522-2322(200105)23:2/3<12::aid-germ12>3.0.co;2-c.

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35

Kodama, Koichi, Takehiro Kamiya, Masakatsu Ichimura, and Mitsuhiro Nakamura. "Digital Archives for Nuclear Emulsion Data." EPJ Web of Conferences 208 (2019): 13003. http://dx.doi.org/10.1051/epjconf/201920813003.

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Digital archives for nuclear emulsion data of past experiments, such as in cosmic-ray and accelerator physics, is being studied and prepared. Significant progress of HTS, which is an automatic read-out system for tracks recorded in emulsion, is achieving a read-out speed of about 1m2/hour and opens a possibility to read all tracks recorded in emulsion of past experiments. Current status of our first trial with RUNJOB emulsion plates is reported. Till now, the top-most 10 plates had been scanned by HTS and preliminary data is presented.

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36

Fang, Cong Cong, and Xiao Jing Yang. "The Read-Write Operation on Floating Point Data Program Design between MCU and KingView." Applied Mechanics and Materials 462-463 (November 2013): 891–95. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.891.

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By employing the ASCII type communication protocols between MCU and KingView provided by WellinTech Inc, basing on the analysis of COM port setting and command format of read-write operation between MCU and the KingView, this paper design a read-write operation on floating point data program with C programming language and realize the real-time communication between MCU and KingView successfully, which improved the accuracy and scope of data transmitted between them. Data definition and some key subprograms like serial ports initialization, read floating point data from MCU, write floating point data to MCU are provided in the paper. It has high portability and application value.

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37

Lee, Juhee, Yuan Ji, Shoudan Liang, Guoshuai Cai, and Peter Müller. "On Differential Gene expression Using RNA-Seq Data." Cancer Informatics 10 (January 2011): CIN.S7473. http://dx.doi.org/10.4137/cin.s7473.

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Motivation RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements. Results We present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study. Availability A public domain R package is available from http://odin.mdacc.tmc.edu/~ylji/BMDE/ .

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38

Ma, Jian Hui, Zhi Xue Wang, Gang Wang, Yuan Yang Liu, and Yan Qiang Li. "A Research and Implement of Data Storage and Management Method Based on the Embedded MCU Data Flash." Advanced Materials Research 756-759 (September 2013): 1984–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1984.

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This paper designed method for non-volatile data storage using MCU internal data Flash, certain data Flash sector is divided into multiple data partitions, different data partition storage data copies in different historical time, the current data partition storagethe latest copy of the data; In the data read operation, first calculate the latest data copying Flash storage location, then directly reads the address. In the data write operation, first judge if the data writing position is already erased, if not,write data in next partition, while copy the other data in the current partition to the next partition; if the write position has been erased, write data directly to the current partition. This method is similar to EEPROM data read and write, easy to operate, and give a simple application interface, and can avoid the sector erase operation, to improve storage efficiency, while increasing the service life of the MCU's internal data Flash.

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39

Stafford, Susan G. "Data, data everywhere but not a byte to read: Managing monitoring information." Environmental Monitoring and Assessment 26-26, no. 2-3 (July 1993): 125–41. http://dx.doi.org/10.1007/bf00547491.

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40

Petri, Alexander J., and Kristoffer Sahlin. "isONform: reference-free transcriptome reconstruction from Oxford Nanopore data." Bioinformatics 39, Supplement_1 (June 1, 2023): i222—i231. http://dx.doi.org/10.1093/bioinformatics/btad264.

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Abstract Motivation With advances in long-read transcriptome sequencing, we can now fully sequence transcripts, which greatly improves our ability to study transcription processes. A popular long-read transcriptome sequencing technique is Oxford Nanopore Technologies (ONT), which through its cost-effective sequencing and high throughput, has the potential to characterize the transcriptome in a cell. However, due to transcript variability and sequencing errors, long cDNA reads need substantial bioinformatic processing to produce a set of isoform predictions from the reads. Several genome and annotation-based methods exist to produce transcript predictions. However, such methods require high-quality genomes and annotations and are limited by the accuracy of long-read splice aligners. In addition, gene families with high heterogeneity may not be well represented by a reference genome and would benefit from reference-free analysis. Reference-free methods to predict transcripts from ONT, such as RATTLE, exist, but their sensitivity is not comparable to reference-based approaches. Results We present isONform, a high-sensitivity algorithm to construct isoforms from ONT cDNA sequencing data. The algorithm is based on iterative bubble popping on gene graphs built from fuzzy seeds from the reads. Using simulated, synthetic, and biological ONT cDNA data, we show that isONform has substantially higher sensitivity than RATTLE albeit with some loss in precision. On biological data, we show that isONform’s predictions have substantially higher consistency with the annotation-based method StringTie2 compared with RATTLE. We believe isONform can be used both for isoform construction for organisms without well-annotated genomes and as an orthogonal method to verify predictions of reference-based methods. Availability and implementation https://github.com/aljpetri/isONform

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41

Liu, Si. "All in One: Design, Verification, and Implementation of SNOW-optimal Read Atomic Transactions." ACM Transactions on Software Engineering and Methodology 31, no. 3 (July 31, 2022): 1–44. http://dx.doi.org/10.1145/3494517.

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Distributed read atomic transactions are important building blocks of modern cloud databases that magnificently bridge the gap between data availability and strong data consistency. The performance of their transactional reads is particularly critical to the overall system performance, as many real-world database workloads are dominated by reads. Following the SNOW design principle for optimal reads, we develop LORA, a novel SNOW-optimal algorithm for distributed read atomic transactions. LORA completes its reads in exactly one round trip, even in the presence of conflicting writes, without imposing additional overhead to the communication, and it outperforms the state-of-the-art read atomic algorithms. To guide LORA’s development, we present a rewriting-logic-based framework and toolkit for design, verification, implementation, and evaluation of distributed databases. Within the framework, we formalize LORA and mathematically prove its data consistency guarantees. We also apply automatic model checking and statistical verification to validate our proofs and to estimate LORA’s performance. We additionally generate from the formal model a correct-by-construction distributed implementation for testing and performance evaluation under realistic deployments. Our design-level and implementation-based experimental results are consistent, which together demonstrate LORA’s promising data consistency and performance achievement.

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42

Yuan, Hao, Calder Atta, Luke Tornabene, and Chenhong Li. "Assexon: Assembling Exon Using Gene Capture Data." Evolutionary Bioinformatics 15 (January 2019): 117693431987479. http://dx.doi.org/10.1177/1176934319874792.

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Exon capture across species has been one of the most broadly applied approaches to acquire multi-locus data in phylogenomic studies of non-model organisms. Methods for assembling loci from short-read sequences (eg, Illumina platforms) that rely on mapping reads to a reference genome may not be suitable for studies comprising species across a wide phylogenetic spectrum; thus, de novo assembling methods are more generally applied. Current approaches for assembling targeted exons from short reads are not particularly optimized as they cannot (1) assemble loci with low read depth, (2) handle large files efficiently, and (3) reliably address issues with paralogs. Thus, we present Assexon: a streamlined pipeline that de novo assembles targeted exons and their flanking sequences from raw reads. We tested our method using reads from Lepisosteus osseus (4.37 Gb) and Boleophthalmus pectinirostris (2.43 Gb), which are captured using baits that were designed based on genome sequence of Lepisosteus oculatus and Oreochromis niloticus, respectively. We compared performance of Assexon to PHYLUCE and HybPiper, which are commonly used pipelines to assemble ultra-conserved element (UCE) and Hyb-seq data. A custom exon capture analysis pipeline (CP) developed by Yuan et al was compared as well. Assexon accurately assembled more than 3400 to 3800 (20%-28%) loci than PHYLUCE and more than 1900 to 2300 (8%-14%) loci than HybPiper across different levels of phylogenetic divergence. Assexon ran at least twice as fast as PHYLUCE and HybPiper. Number of loci assembled using CP was comparable with Assexon in both tests, while Assexon ran at least 7 times faster than CP. In addition, some steps of CP require the user’s interaction and are not fully automated, and this user time was not counted in our calculation. Both Assexon and CP retrieved no paralogs in the testing runs, but PHYLUCE and Hybpiper did. In conclusion, Assexon is a tool for accurate and efficient assembling of large read sets from exon capture experiments. Furthermore, Assexon includes scripts to filter poorly aligned coding regions and flanking regions, calculate summary statistics of loci, and select loci with reliable phylogenetic signal. Assexon is available at https://github.com/yhadevol/Assexon .

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Lima, Leandro, Camille Marchet, Ségolène Caboche, Corinne Da Silva, Benjamin Istace, Jean-Marc Aury, Hélène Touzet, and Rayan Chikhi. "Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data." Briefings in Bioinformatics 21, no. 4 (June 24, 2019): 1164–81. http://dx.doi.org/10.1093/bib/bbz058.

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Abstract Motivation Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. Results In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. Benchmarking software https://gitlab.com/leoisl/LR_EC_analyser

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Chen, Hong Jun, Tao Tan, and Xue Qin Wu. "Research of Cloud Storage and Data Read-Write Technology." Applied Mechanics and Materials 347-350 (August 2013): 3555–59. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3555.

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Cloud storage is a promising market. In this paper, cloud storage technology and cloud data read-write technology is researched. Cloud storage has a four-layer structure model. Cloud storage uses disaster recovery and backup technology to improve the availability and reliability of the system. The request of data read-write in cloud storage is a temporary URL, which is constructed according to the algorithm. Send read-write request through push sequentially, and write by transfer between the primary chunks with the copies. The efficiency of cloud storage depends on the number of serves and clients. In order to improve the efficiency of data reading and writing, there is proposed three envisaged.

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Hetzel, Sara, Pay Giesselmann, Knut Reinert, Alexander Meissner, and Helene Kretzmer. "RLM: fast and simplified extraction of read-level methylation metrics from bisulfite sequencing data." Bioinformatics 37, no. 21 (October 2, 2021): 3934–35. http://dx.doi.org/10.1093/bioinformatics/btab663.

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Abstract Summary Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzing single-read patterns. Over the past years, various metrics have been established to explore this layer of information. However, limited compatibility with alignment tools, reference genomes or the measurements they provide present a bottleneck for most groups to routinely perform read-level analysis. To address this, we developed RLM, a fast and scalable tool for the computation of several frequently used read-level methylation statistics. RLM supports standard alignment tools, works independently of the reference genome and handles most sequencing experiment designs. RLM can process large input files with a billion reads in just a few hours on common workstations. Availability and implementation https://github.com/sarahet/RLM. Supplementary information Supplementary data are available at Bioinformatics online.

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Alexy, Tommy, and Rian Ferdian. "Multimeter dengan Sistem Penayangan Data Berbasis Web dan Kacamata Data." CHIPSET 4, no. 01 (April 30, 2023): 13–22. http://dx.doi.org/10.25077/chipset.4.01.13-22.2023.

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This study aims to create a multimeter system that is able to use with database system and see with data glasess . Multimeter is a measuring instrument used for measurements on electronic compositions. Electricity is indispensable today but if not careful electricity will be very dangerous. One of the dangers of electricity is the occurrence of electric arcs, electric arcs occur due to the presence of dielectric materials that result in electrode charge transfer. Practical learning of electronics is directly related to electricity so careful observation is needed, especially when measuring the three main quantities in the flow of electricity. This system is expected to help users in observing electrical components such as electrical voltage (Voltage / V) and electrical resistance (Ohm / Ω). The system uses a zmpt101b sensor used for voltage readings using a mean system that is taking some electric current data in a certain period of time and taking an average reading of the current read by the sensor, the voltage sensor is used for voltage readings using a mean system that is taking some voltage data in a certain period of time and taking an average voltage read by the sensor, in reading resistance researchers use the voltage divider formula to find out how much is in a resistor that is not known how much resistance. The information obtained will be displayed on the website so that users can more easily sort the readings on the amount of electricity read by the multimeter.

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Comandella, Daniele, Stefania Gottardo, Iria Maria Rio-Echevarria, and Hubert Rauscher. "Quality of physicochemical data on nanomaterials: an assessment of data completeness and variability." Nanoscale 12, no. 7 (2020): 4695–708. http://dx.doi.org/10.1039/c9nr08323e.

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"Data Dessimination to Read only Mobile clients." International Journal of Modern Trends in Engineering & Research 3, no. 10 (November 7, 2016): 273–74. http://dx.doi.org/10.21884/ijmter.2016.3112.kr4kv.

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Sriraman, Harini, Aswathy Ravikumar, and V. Pattabiraman. "ReAD: Reliability aware data." Materials Today: Proceedings, April 2022. http://dx.doi.org/10.1016/j.matpr.2022.03.440.

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Hufnagel, David E., Matthew B. Hufford, and Arun S. Seetharam. "SequelTools: a suite of tools for working with PacBio Sequel raw sequence data." BMC Bioinformatics 21, no. 1 (October 1, 2020). http://dx.doi.org/10.1186/s12859-020-03751-8.

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Abstract Background PacBio sequencing is an incredibly valuable third-generation DNA sequencing method due to very long read lengths, ability to detect methylated bases, and its real-time sequencing methodology. Yet, hitherto no tool was available for analyzing the quality of, subsampling, and filtering PacBio data. Results Here we present SequelTools, a command-line program containing three tools: Quality Control, Read Subsampling, and Read Filtering. The Quality Control tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple statistics and publication-quality plots describing the quality of the data including N50, read length and count statistics, PSR, and ZOR. The Read Subsampling tool allows the user to subsample reads by one or more of the following criteria: longest subreads per CLR or random CLR selection. The Read Filtering tool provides options for normalizing data by filtering out certain low-quality scraps reads and/or by minimum CLR length. SequelTools is implemented in bash, R, and Python using only standard libraries and packages and is platform independent. Conclusions SequelTools is a program that provides the only free, fast, and easy-to-use quality control tool, and the only program providing this kind of read subsampling and read filtering for PacBio Sequel raw sequence data, and is available at https://github.com/ISUgenomics/SequelTools.

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