scholarly journals Data storage using peptide sequences

2021 ◽  
Author(s):  
Cheuk Chi A. Ng ◽  
Wai Man Tam ◽  
Haidi Yin ◽  
Qian Wu ◽  
Pui-Kin So ◽  
...  

Abstract From the beginning of civilization, the media for storing data have been continuously evolving from such as stone tablets, animal bones and bamboo tablets to paper, with improvements on data density over time. Since the invention of electronics in the last century, the percentage of data stored in digital form has been increasing rapidly to almost 100% recently. Moreover, the amount of data generated has been increasing exponentially, from several ZB in 2008 to an expected 74 ZB in 2021, causing a much increased demand for data storage correspondingly. Most of the digital data are stored in physical media such as hard drives. In addition, many of the data are rarely accessed and are archived on reels of magnetic tapes. However, the physical thickness of the tapes and the size of magnetic domains limit the maximum data density, which is expected to reach a plateau soon. Furthermore, data in old tapes need to be copied onto new tapes regularly, as the magnetic tapes can normally last for ten to twenty years only. This process is time-consuming and expensive. Hence, next-generation media that can store digital data with a much higher data density and durability are needed.Here we report the use of peptide sequences for digital data storage, a method that has not been reported before. The data-bearing peptides are commercially synthesized, and the data retrieval process is described here. As an example, we stored one dataset consists of (i) 848 bits of ASCII formatted text in 40 peptides, and (ii) another dataset consists of 13752 bits of the “silent night” music in MIDI format together with its title in ASCII format in 511 peptides. These files are available in Supplementary Files section.

2018 ◽  
Vol 6 (3) ◽  
pp. 359-363
Author(s):  
A. Saxena ◽  
◽  
S. Sharma ◽  
S. Dangi ◽  
A. Sharma ◽  
...  

1998 ◽  
Author(s):  
Kai-Oliver Mueller ◽  
Cornelia Denz ◽  
Torsten Rauch ◽  
Thorsten Heimann ◽  
J. Trumpfheller ◽  
...  

Author(s):  
Huan Liu

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.


2007 ◽  
Vol 43 (3) ◽  
pp. 1101-1111 ◽  
Author(s):  
Sebastien Tosi ◽  
Martin Power ◽  
Thomas Conway

2010 ◽  
Vol 15 (2) ◽  
pp. 242-252 ◽  
Author(s):  
Choong Woo Lee ◽  
Bong Sik Kwak ◽  
Chung Choo Chung ◽  
M. Tomizuka

2016 ◽  
Vol 12 (10) ◽  
pp. e1005097 ◽  
Author(s):  
Edmund M. Hart ◽  
Pauline Barmby ◽  
David LeBauer ◽  
François Michonneau ◽  
Sarah Mount ◽  
...  

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