Data acquisition in bioinformatics

What is data collection in bioinformatics?
The data of bioinformatics
The classic data of bioinformatics include DNA sequences of genes or full genomes; amino acid sequences of proteins; and three-dimensional structures of proteins, nucleic acids and protein–nucleic acid complexes..
Bioinformatics is fed by high-throughput data-generating experiments, including genomic sequence determinations and measurements of gene expression patterns.
Database projects curate and annotate the data and then distribute it via the World Wide Web.

Data acquisition is the process of sampling signals that measures real world physical conditions and converts the resulting samples into digital numeric values that can be manipulated by computer. Data processing is, broadly, “the collection and manipulation of data to produce meaningful information” [1, 2].

Data acquisition refers to the procedure of obtaining information by downloading or transferring files from one location to another. File transfer, FASTA manipulation, manipulating Excel data sheets, and data management are the four primary sections of data acquisition.

Can bioinformatics big data improve healthcare delivery?

Bioinformatics big data hold the potential to improve the healthcare delivery to those most economically and socially disadvantaged and historically underserved.
This chapter presents a bioinformatics big data life cycle model with three segments:

Segment 1
data

What is data acquisition in big data life cycle?

Data acquisition, the first segment in the big data life cycle (Fig. 2 ), describes the process of collecting and digitizing data.
The four steps of the data acquisition segment are data collection, data aggregation, data evaluation, and data tagging.

What is the interdisciplinary field of bioinformatics?

The interdisciplinary field of bioinformatics is an umbrella term that encompasses the computerized management of biological, medical, and healthcare data from the initial data collection through the entire data life cycle.

Why is aggregation important in bioinformatics?

Aggregate data is important for the advancement of bioinformatics.
Biomedical scientists are confronted with the challenge of storing, managing, and analyzing massive amounts of data.
Data aggregation is imperative to permit data use and analysis.

Workflow

COMMAND>_ is a multi-user application.
From the web interface it is possible to create users and groups and grant privileges.
Admin users have unlimited access, while normal users might be limited to work only on specific compendia and/or with a subset of functionalities.
The typical workflow can be divided into three steps: i) search and download .

The Plant Genomics and Phenomics Research Data Repository (PGP) is a data publication infrastructure to comprehensively publish multi-domain plant research data.
It is hosted at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) in Gatersleben, Germany.
The repository hosts DOI citeable datasets that are not being published in public repositories because of their volume or data scope.
PGP enables the publication of gigabyte-scale datasets and is registered as a research data repository at FAIRSharing.org, re3data.org and OpenAIRE as a valid EU Horizon 2020 open data archive.
The above features, the programmatic interface and the support of standard metadata formats, enable PGP to fulfil the FAIR data principles—findable, accessible, interoperable, reusable.
The PGP repository was created using the e!DAL software infrastructure and applies an on-premises approach to bring the infrastructure to the data (I2D).