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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
Published on: March 15, 2019
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Lessons learnt on the analysis of large sequence data in animal genomics
F Biscarini1,2, P Cozzi1,3, P Orozco-Ter Wengel4
1CNR-IBBA, Via Bassini 15, 20133, Milan, Italy.
Animal Genetics
|April 7, 2018
Summary
The genomics revolution generates massive datasets, posing computational challenges for animal genomics research. This review offers guidelines for processing large genomic data sets, aiding researchers with limited computing experience.
Area of Science:
- Bioinformatics
- Genomics
- Big Data in Biological Sciences
Background:
- The 'omics revolution has led to an exponential increase in biological sequence data.
- Next-generation sequencing technologies have profoundly impacted plant and animal genomics.
- Genomic data constitutes a growing portion of big data due to reduced sequencing costs.
Purpose of the Study:
- To review challenges in processing large animal genomic datasets.
- To provide practical guidelines for researchers with limited computational expertise.
- To address issues in data manipulation, visualization, and error tracking.
Main Methods:
- Review of current challenges in big data analysis within animal genomics.
- Identification of common problems in processing large-scale genomic data.
- Compilation of best practices and troubleshooting tips for researchers.
Main Results:
- Data processing and analysis lag behind data generation, creating computational burdens.
- Large datasets can compromise data quality assessment and interpretation.
- Software failures and error tracking are significant frustrations in big data analysis.
Conclusions:
- Effective processing of large genomic datasets is crucial for advancing animal genomics.
- Guidelines are needed to help researchers overcome computational hurdles.
- Addressing these challenges will improve the efficiency and reliability of genomic research.
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