Eukaryotic Molecular Biology Databases: An Overview

2018 ◽  
pp. 1-7 ◽  
Author(s):  
Kumar Girish
Keyword(s):  
High Performance ◽  
Life Science ◽  
Science Studies ◽  
Biological Database ◽  
Biological Databases ◽  
Science Data ◽  
Molecular Databases ◽  
Experimental Technology ◽  
Protein Information Resource ◽  
Swiss Institute

A biological database is a big, structured body of continuous information, generally connected with computerized software intended to update, query, and recover information elements deposited within the framework. A straightforward database could be a single folder comprising several data, each carrying the same number of data. Such famous databases are GenBank from the National Center for Biotechnology Information, SwissProt from the Swiss Institute of Bioinformatics and PIR from the Protein Information Resource. Biological databases are bibliotheques of life science data, gathered from science studies, published literature, high-performance experimental technology, and computational analysis. Here we brefly described some recently published molecular databases.

scholarly journals Biological Databases- Integration of Life Science Data

2012 ◽  
Vol 04 (05) ◽  
Author(s):  
Nishant Toomula, ◽  
Arun Kumar ◽  
Sathish Kumar D ◽  
Vijaya Shanti Bheemidi
Keyword(s):  
Life Science ◽  
Biological Databases ◽  
Science Data

scholarly journals FCC – An automated rule-based processing tool for life science data

2013 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Simon Barkow-Oesterreicher ◽  
Can Türker ◽  
Christian Panse
Keyword(s):  
Life Science ◽  
Science Data ◽  
Rule Based

scholarly journals Toward High Throughput Core-CBCM CMOS Capacitive Sensors for Life Science Applications: A Novel Current-Mode for High Dynamic Range Circuitry

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3370 ◽  
Author(s):  
Saghi Forouhi ◽  
Rasoul Dehghani ◽  
Ebrahim Ghafar-Zadeh
Keyword(s):  
High Performance ◽  
Life Science ◽  
Dynamic Range ◽  
Current Mode ◽  
Capacitive Sensor ◽  
Oxide Semiconductor ◽  
Capacitance Measurement ◽  
Biological Research ◽  
Cmos Process ◽  
Capacitive Sensors

This paper proposes a novel charge-based Complementary Metal Oxide Semiconductor (CMOS) capacitive sensor for life science applications. Charge-based capacitance measurement (CBCM) has significantly attracted the attention of researchers for the design and implementation of high-precision CMOS capacitive biosensors. A conventional core-CBCM capacitive sensor consists of a capacitance-to-voltage converter (CVC), followed by a voltage-to-digital converter. In spite of their high accuracy and low complexity, their input dynamic range (IDR) limits the advantages of core-CBCM capacitive sensors for most biological applications, including cellular monitoring. In this paper, after a brief review of core-CBCM capacitive sensors, we address this challenge by proposing a new current-mode core-CBCM design. In this design, we combine CBCM and current-controlled oscillator (CCO) structures to improve the IDR of the capacitive readout circuit. Using a 0.18 μm CMOS process, we demonstrate and discuss the Cadence simulation results to demonstrate the high performance of the proposed circuitry. Based on these results, the proposed circuit offers an IDR ranging from 873 aF to 70 fF with a resolution of about 10 aF. This CMOS capacitive sensor with such a wide IDR can be employed for monitoring cellular and molecular activities that are suitable for biological research and clinical purposes.

Refined JST Thesaurus Extended with Data from Other Open Life Science Data Sources

2017 ◽  
pp. 35-48 ◽  
Author(s):  
Tatsuya Kushida ◽  
Yuka Tateisi ◽  
Takeshi Masuda ◽  
Katsutaro Watanabe ◽  
Katsuji Matsumura ◽  
...  
Keyword(s):  
Life Science ◽  
Data Sources ◽  
Science Data

scholarly journals Human-readable and machine-readable Persistent Identifiers

2015 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Life Science ◽  
Fundamental Problem ◽  
Science Data ◽  
Important Paper ◽  
Simple Rules ◽  
Machine Readable

Yesterday Julie McMurry and co-authors published a preprint 10 Simple rules for design, provision, and reuse of persistent identifiers for life science data. This is an important paper trying to address a fundamental problem: how can we make persistent ...

scholarly journals Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1221 ◽  
Author(s):  
Phillip A. Richmond ◽  
Wyeth W. Wasserman
Keyword(s):  
High Throughput ◽  
High Performance ◽  
Life Science ◽  
Genomic Analysis ◽  
High Throughput Analysis ◽  
Time Period ◽  
Research Organizations ◽  
Efficacy Measures ◽  
Training Resources ◽  
Short Time

Researchers in the life sciences are increasingly faced with the task of obtaining compute resources and training to analyze large, high-throughput technology generated datasets. As demand for compute resources has grown, high performance computing (HPC) systems have been implemented by research organizations and international consortiums to support academic researchers. However, life science researchers lack effective time-of-need training resources for utilization of these systems. Current training options have drawbacks that inhibit the effective training of researchers without experience in computational analysis. We identified the need for flexible, centrally-organized, easily accessible, interactive, and compute resource specific training for academic HPC use.  In our delivery of a modular workshop series, we provided foundational training to a group of researchers in a coordinated manner, allowing them to further pursue additional training and analysis on compute resources available to them. Efficacy measures indicate that the material was effectively delivered to a broad audience in a short time period, including both virtual and on-site students. The practical approach to catalyze academic HPC use is amenable to diverse systems worldwide.

scholarly journals Recent developments of ion sources for life-science studies at the Heavy Ion Medical Accelerator in Chiba (invited)

2016 ◽  
Vol 87 (2) ◽  
pp. 02C107 ◽  
Author(s):  
A. Kitagawa ◽  
A. G. Drentje ◽  
T. Fujita ◽  
M. Muramatsu ◽  
K. Fukushima ◽  
...  
Keyword(s):  
Life Science ◽  
Science Studies ◽  
Heavy Ion ◽  
Ion Sources ◽  
Recent Developments
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