11,535 research outputs found

    Coding for Optimized Writing Rate in DNA Storage

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    A method for encoding information in DNA sequences is described. The method is based on the precision-resolution framework, and is aimed to work in conjunction with a recently suggested terminator-free template independent DNA synthesis method. The suggested method optimizes the amount of information bits per synthesis time unit, namely, the writing rate. Additionally, the encoding scheme studied here takes into account the existence of multiple copies of the DNA sequence, which are independently distorted. Finally, quantizers for various run-length distributions are designed.Comment: To appear in ISIT 202

    Coding for Optimized Writing Rate in DNA Storage

    Get PDF
    A method for encoding information in DNA sequences is described. The method is based on the precisionresolution framework, and is aimed to work in conjunction with a recently suggested terminator-free template independent DNA synthesis method. The suggested method optimizes the amount of information bits per synthesis time unit, namely, the writing rate. Additionally, the encoding scheme studied here takes into account the existence of multiple copies of the DNA sequence, which are independently distorted. Finally, quantizers for various run-length distributions are designed

    Coding for Optimized Writing Rate in DNA Storage

    Get PDF
    A method for encoding information in DNA sequences is described. The method is based on the precision-resolution framework, and is aimed to work in conjunction with a recently suggested terminator-free template independent DNA synthesis method. The suggested method optimizes the amount of information bits per synthesis time unit, namely, the writing rate. Additionally, the encoding scheme studied here takes into account the existence of multiple copies of the DNA sequence, which are independently distorted. Finally, quantizers for various run-length distributions are designed

    Next generation sequencing in cancer: opportunities and challenges for precision cancer medicine

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    Over the past decade, testing the genes of patients and their specific cancer types has become standardized practice in medical oncology since somatic mutations, changes in gene expression and epigenetic modifications are all hallmarks of cancer. However, while cancer genetic assessment has been limited to single biomarkers to guide the use of therapies, improvements in nucleic acid sequencing technologies and implementation of different genome analysis tools have enabled clinicians to detect these genomic alterations and identify functional and disease-associated genomic variants. Next-generation sequencing (NGS) technologies have provided clues about therapeutic targets and genomic markers for novel clinical applications when standard therapy has failed. While Sanger sequencing, an accurate and sensitive approach, allows for the identification of potential novel variants, it is however limited by the single amplicon being interrogated. Similarly, quantitative and qualitative profiling of gene expression changes also represents a challenge for the cancer field. Both RT-PCR and microarrays are efficient approaches, but are limited to the genes present on the array or being assayed. This leaves vast swaths of the transcriptome, including non-coding RNAs and other features, unexplored. With the advent of the ability to collect and analyze genomic sequence data in a timely fashion and at an ever-decreasing cost, many of these limitations have been overcome and are being incorporated into cancer research and diagnostics giving patients and clinicians new hope for targeted and personalized treatment. Below we highlight the various applications of next-generation sequencing in precision cancer medicine

    Development and Validation of Targeted Next-Generation Sequencing Panels for Detection of Germline Variants in Inherited Diseases.

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    Context.-The number of targeted next-generation sequencing (NGS) panels for genetic diseases offered by clinical laboratories is rapidly increasing. Before an NGS-based test is implemented in a clinical laboratory, appropriate validation studies are needed to determine the performance characteristics of the test. Objective.-To provide examples of assay design and validation of targeted NGS gene panels for the detection of germline variants associated with inherited disorders. Data Sources.-The approaches used by 2 clinical laboratories for the development and validation of targeted NGS gene panels are described. Important design and validation considerations are examined. Conclusions.-Clinical laboratories must validate performance specifications of each test prior to implementation. Test design specifications and validation data are provided, outlining important steps in validation of targeted NGS panels by clinical diagnostic laboratories

    Efficiently modeling neural networks on massively parallel computers

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    Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks

    A Tutorial on Coding Methods for DNA-based Molecular Communications and Storage

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    Exponential increase of data has motivated advances of data storage technologies. As a promising storage media, DeoxyriboNucleic Acid (DNA) storage provides a much higher data density and superior durability, compared with state-of-the-art media. In this paper, we provide a tutorial on DNA storage and its role in molecular communications. Firstly, we introduce fundamentals of DNA-based molecular communications and storage (MCS), discussing the basic process of performing DNA storage in MCS. Furthermore, we provide tutorials on how conventional coding schemes that are used in wireless communications can be applied to DNA-based MCS, along with numerical results. Finally, promising research directions on DNA-based data storage in molecular communications are introduced and discussed in this paper
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