A novel method for next-generation sequence data analysis using PLSA topic modeling technique

The cultivation of spiritual values to learners is a very vital step in shaping the characteristics and character of the human person, because the internalization of spiritual values is closely related to the cultivation of religious values, and is part of the formation of adolescent character as the next generation of religion and nation. This research is to find out the process of planting and internalizing spiritual value education conducted at The Senior high school of Mafaza Institute located in the Maguntapan Bantul area of Yogyakarta, while the type in this study is qualitative with a descriptive approach, while the subject in this study is teachers or teachers at this institution as well as some M.A. Mafaza students. While the data collected passes through the process of interviews, observations and documentation which then the author of the data analysis to draw conclusions. The results of this study show the following: 1). The process of planting and internalizing values and spiritual in this institution is carried out with an understanding of the material in the classes. 2). Then as a follow-up in the form of application in programs and activities such as requiring mandatory congregational prayers, sunnah worship and others, there are also activities such as the loving mosque and the the Adiwiata program. So from there it is seen that all aspects in value and spiritual, namely the aspect of Aqidah and Worship become the center of attention in planting to their students. So that it covers everything, either in the pattern of vertical relationship (Servant-Godnya) or horizontal (Servants and Environment).

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Cloud-Based Tools for Next-Generation Sequencing Data Analysis

2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS) ◽

10.1109/snams.2018.8554515 ◽

2018 ◽

Author(s):

Qanita Bani Baker ◽

Wesam Al-Rashdan ◽

Yaser Jararweh

Keyword(s):

Data Analysis ◽

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing ◽

Sequencing Data Analysis

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BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Applied Sciences ◽

10.3390/app8091471 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1471 ◽

Cited By ~ 7

Author(s):

Seo-Joon Lee ◽

Gyoun-Yon Cho ◽

Fumiaki Ikeno ◽

Tae-Ro Lee

Keyword(s):

Dna Sequencing ◽

Medical Informatics ◽

Compression Ratio ◽

Sequence Data ◽

Lossless Compression ◽

World Health ◽

Smart Devices ◽

Next Generation ◽

And Storage ◽

Efficient Transmission

Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for DNA sequencing, the management of such large volumes of data is still an issue. Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for the efficient transmission and storage of the immense amounts of DNA sequence data that are being generated by Next Generation Sequencing (NGS). Also, security and reliability issues exist in public sequence databases. For methods, compression ratio comparisons were determined for genetic biomarkers corresponding to the five diseases with the highest mortality rates according to the World Health Organization. The results showed an average compression ratio of approximately 12 for all the genetic datasets used. BAQALC performed especially well for lung cancer genetic markers, with a compression ratio of 17.02. BAQALC performed not only comparatively higher than widely used compression algorithms, but also higher than algorithms described in previously published research. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics based on smart devices for both researchers and healthcare users.

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