Next Generation Sequencing (NGS)/Big Data

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Next Generation Sequencing (NGS)
Introduction Big Data Bioinformatics from the outside

Big Data[edit]

Data Deluge[edit]

The first problem you face is probably the large size of the NGS FASTQ files - the "data deluge" problem. You no longer only have to deal with microplate readings, or digitalized gel photos; the size of NGS data can be huge. For example, compressed FASTQ files from a 60x human whole genome sequencing can still require 200Gb. A small project with 10 - 20 whole genome sequencing (WGS) samples can generate ~4TB of raw data. Even these estimates do not include the disk space required for downstream analysis.

Storing data[edit]

Referenced from a post from BioStar[1]:

  • Very high end: enterprise cluster and SAN.
  • High end: Two mirrored servers in separate buildings or Cloud.
  • Typical: External hard drives and/or NAS with raid-5/6

Moving data[edit]

Moving data between collaborators is also non-trivial. For RNA-Seq samples, FTP may suffice, but for WGS data, shipping hard drives may be the only solution.

Externalizing compute requirements from the research group[edit]

It is difficult for a single lab to maintain sufficient computing facilities. A single lab will probably own some basic computing hardware; however, many tasks will have huge computational demands (e.g. memory for de novo genome assembly) that require them to be performed elsewhere. An institution / core facility may host a centralized cluster. Alternatively, one might consider doing the task on the cloud.

  • NIH maintains a centralized computing cluster called Biowulf.
  • Bioinformatics cloud computing is suggested[2][3] EBI has adopted a cloud-based platform called Helix Nebula.[4]

References[edit]

  1. Wo, H. (24 March 2011). "Question: Huge Ngs Data Storage And Transferring". Biostars. Biostar Genomics, LLC. https://www.biostars.org/p/6749/. Retrieved 28 April 2016. 
  2. Akhlaghpour, H. (3 July 2012). "Genomic Analysis in the Cloud". YouTube. Google. https://www.youtube.com/watch?v=ZzBCvmV-6p4. Retrieved 28 April 2016. 
  3. Schadt, E.E.; Linderman, M.D.; Sorenson, J.; Lee, L.; Nolan, G.P. (2010). "Computational solutions to large-scale data management and analysis". Nature Reviews Genetics 11 (9): 647-57. doi:10.1038/nrg2857. PMID 20717155. 
  4. Lueck, R. (16 January 2013). "Big data and HPC on-demand: Large-scale genome analysis on Helix Nebula – the Science Cloud" (PDF). Trust-IT Services. http://www.helix-nebula.eu/sites/default/files/4.%20EMBL%20Flagship.pdf. Retrieved 28 April 2016.