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With the advancement in technology and development of High Throughput System (HTS), the amount of genomic data generated per day per laboratory across the globe is surpassing the Moore’s law. The huge amount of data generated is of concern to the biologists with respect to their storage as well as transmission across different locations for further analysis. Compression of the genomic data is the wise option to overcome the problems arising from the data deluge. This paper discusses various algorithms that exists for compression of genomic data as well as a few general purpose algorithms and proposes a LZW-based compression algorithm that uses indexed multiple dictionaries for compression. The proposed method exhibits an average compression ratio of 0.41 bits per base and an average compression time of 6.45 secs for a DNA sequence of an average size 105.9 KB.

In today’s world, 96% of all goods depend on chemicals. Chemical industry plays vital role in supply chain. Chemical supply chain consists of multiple stakeholders including raw material suppliers to end user customers. Based on regulations, several product documents are needed to be supplied with the chemicals till the end of the life cycle. Blockchain based document traceability offers a viable solution to create a decentralized distributed shared platform for a secure, immutable, transparent, permanent, trustworthy, and accountable system for all the stakeholders involved. In this paper, an overview of the document traceability, current challenges and envisage how Blockchain and smart contracts address those challenges are presented. Based on the analysis, it is proposed to use using Hyperledger Fabric, an open-source private blockchain to meet the document traceability requirements such as security, privacy, scalability, authentication, and authorization. The proposed Blockchain architecture provides a feasible solution to build and deploy an end-to-end decentralized application in the chemical supply chain industry for document traceability.

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The healthcare informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use bioinformatics employs computational tools and techniques to study and analyze large biological databases and to understand disease and study of inherent genetic information molecular structure by relating them with healthcare data. This amassing of healthcare information will enable the biologist and scientist to improve health as drug discovery. This paper touches on big data in healthcare and analyzes of those big data in healthcare for the better improvement of healthcare system, bioinformatics data stored in secured manner. Finally, the paper looks on the helpful result, the beneficence of each of them in amelioration of healthcare system. To achieve this health amelioration in bioinformatics, we use Hadoop as tools which collect and analyze the huge amount of data in healthcare system.