Abstract
In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.
TopBackground
What Is Big Data?
When a large set of datasets becomes too complex or difficult to deal, such types of data are usually termed as Big Data. The challenges related with this field are Capture, Curation, Storage, Search, Sharing, Transfer, Analysis and Visualization of the data. Traditional systems like RDBMS (Relational Database Management Systems) failed to handle Big Data. In order to get meaningful information, to interconnect the different pieces of various types of data and to get value from Big Data, new technologies and tools were developed (Jacobs, 2009).
Key Terms in this Chapter
Genomics: Genomics is a discipline in Genetics that applies recombinant DNA, DNA Sequencing methods and Bioinformatics to sequence, assemble and analyze the function and structure of genomes (the complete set of DNA within a single cell of an organism) ( Wikipedia, 2016i ).
DDBJ Sequence Read Archive (DRA): DDBJ Sequence Read Archive (DRA) is an archive database for output data generated by Next Generation Sequencing (NGS) machines including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System and others ( Wikipedia, 2016c ).
FASTA Format: FASTA Format is a text based format for representing either nucleotide sequences or peptide sequences in which nucleotides or amino acids are represented using single letter codes ( Wikipedia, 2016f ).
Contigs: The sequence obtained by the overlapping of reads ( Wikipedia, 2016b ).
FASTQ Format: FASTQ Format is a text based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding Quality Scores. Both the sequence letter and Quality Score are each encoded with a single ASCII character for brevity ( Wikipedia, 2016g ).
Standard Flowgram Format (SFF): Standard Flowgram format (SFF) is a binary file format used to encode results of pyrosequencing from the 454 Life Sciences platform for high-throughput Sequencing ( Wikipedia, 2017 ).
European Nucleotide Archive: The European Nucleotide Archive (ENA) is a repository providing free and unrestricted access to annotated DNA and RNA sequences. It also stores complementary information such as experimental procedures, details of sequence assembly and other metadata related to sequencing projects ( Wikipedia, 2016e ).
Gene Ontology: Gene Ontology (GO) is a major Bioinformatics initiative to unify the representation of gene and gene product attributes across all species ( Wikipedia, 2016h ).
Transcriptome: The transcriptome is the set of all messenger RNA molecules in one cell or a population of cells. It differs from the exome in that it includes only those RNA molecules found in a specified cell population and usually includes the amount or concentration of each RNA molecule in addition to the molecular identities (Wikipedia, 2016L).
Cloud Computing: Cloud Computing is a kind of Internet based computing that provides shared processing resources and data to Computers and other devices on demand ( Wikipedia, 2016a ).
Denovo Assembly: It is a method of assembling the reads into a transcriptome without a reference genome ( Wikipedia, 2016d ).
Sequence Read Archive: The Sequence Read Archive (SRA, previously known as the Short Read Archive) is a Bioinformatics database that provides a public repository for DNA Sequencing data, especially the “short reads” generated by High throughput Sequencing which are typically less than 1,000 base pairs in length ( Wikipedia, 2016k ).
K-mer: The term k-mer typically refers to all the possible substrings of length k that are contained in a string. In Computational Genomics, k-mers refer to all the possible subsequences (of length k) from a read obtained through DNA Sequencing ( Wikipedia, 2016j ).