We are analyzing https://link.springer.com/article/10.1007/s10115-023-02049-4.

Title:
Biological big-data sources, problems of storage, computational issues, and applications: a comprehensive review | Knowledge and Information Systems
Description:
Biological big data are a massive amount of data generated from multi-omics experiments, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, glycomics, epigenomics, and other omics. These data are used to study biological processes and to gain insights into how living systems work. It can also be used to develop new treatments for diseases and understand the causes of certain conditions. The storage and analysis of these data present several challenges owing to their sheer size and complexity. Storing these data efficiently requires a large amount of storage space and processing power. Furthermore, there are certain limitations in terms of the kind of insights that can be gained from multi-omics data because of their complexity. Despite these challenges, biological big data offers great potential for advancing our understanding of biology and developing new treatments for diseases. Big-data research is a rapidly growing field, with numerous applications. As the amount of data continues to increase, it is important to understand its storage, utility, limitations, and challenges. In this review article, various sources of big-data research and their storage capacities, limitations, and challenges are discussed. Factors affecting the data quality and accuracy have been reported. It will be helpful for researchers to understand the available big data in biology for their further utilization and integration into novel discovery.
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google, scholar, article, res, acids, data, database, nucleic, analysis, nucl, sequencing, big, bioinformatics, genome, human, wang, biol, httpsdoiorg, dna, expression, biology, bioinform, chapter, gene, nat, protein, research, metagenomic, bmc, mol, biological, httpsdoiorgs, plant, cell, methods, challenges, genomics, metabolomics, sci, liu, chen, systems, proteomics, proteins, zhang, sequences, information, kumar, short, nature,

Topics {✒️}

molecular-diagnostics/qiagen-launches-genereader-ngs-system-amp-presents-performance-evaluation 10 dev bukhsh singh month download article/chapter high-throughput dna sequencing—concepts single-cell rna-seq technologies multi-species genotype/phenotype database presents performance evaluation single-molecule long-read survey mass-spectrometry-based draft high-performance computing strategies pathogen-derived carbohydrate recognition explore spatio-temporal dynamics lc-ms-based metabolomics tissue-specific expression patterns variable-length dna fragments gold-standard data classification multi-drug resistant cancers tissue-specific gene expression high-performance comparative metagenomics biological big-data sources future anti-adhesion drugs high-throughput ssr characterization real-time dna sequencing usda-ars soybean genetics gut-derived microbial consortia high-throughput sequencing technologies solid-state nanopores integrated spatio-temporal portal high-confidence taxonomic assignments advance small-grains breeding multi-omics data integration large ngs datasets artificial intelligence/machine learning big-data glycomics solid-state nanopore ms/ms spectra generated single-cell rna sequencing central nervous system genome-wide expression profiling open-access database clustered piwi-interacting rnas large-scale phosphoproteome analysis high-throughput phenotyping single-cell data science nucleic acid binding european genome-phenome archive multi-omics experiments full article pdf 1093/nar/gkw1008 robinson oligonucleotide discrimination enabled

Questions {❓}

Kind T, Scholz M, Fiehn O (2009) How large is the metabolome?
Robison K (2022) 2022: a wild year for short reads in genome sequencing?

Schema {🗺️}

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