Next-generation sequencing provides researchers with the opportunity to see the genetic blueprint responsible for directing the functionalities of living organisms.
Next-generation sequencing technologies are capable of producing an immense number of data, and their analysis requires more fast and more sophisticated algorithms.
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Machine Learning and Data Science Limitations
The complexity and escalation of the NGS data pose problems; sharing, storing, archiving, and analyzing data as large as 1 TB per sample is an issue. The current sequencing platforms are capable of producing 13 quadrillion DNA bases limit of NGS technologies is evaluated to be 13 quadrillion DNA bases per year and is hard to manage.
However, this limitation is overcome by the development of many machine learning algorithms, NGS software, and big data analytics. Big data analytics, a new trend in research, promises the development of significant approaches for the analysis of complex NGS data using customized next-generation sequencing software.
Both machine learning and data science (like deep learning) are emerging as the latest and the most efficient approaches to speed up the sequencing and analysis process. The development of multiple algorithms like indexes, hash tables, and spaced-seed has led to the optimization of the NGS data analysis.
Machine learning technologies have improved the process of identifying novel gene functions, and many regulatory regions, and helped with cancer research, animal, and human studies.
The current knowledge of big data and machine learning algorithms applied in the development of sophisticated next-generation sequencing software has identified the hidden patterns in sequencing, analysis, and annotation of NGS data.
The era of Big Data and Machine Learning
Now, in the era of big data, the primary concern of modern-day research is the transformation of this data into valuable knowledge. It is considered a major challenge in the field of computational biology/bioinformatics.
Gene expression and regulation, including the analysis of splice junctions, and RNA binding proteins, are now more easily investigated using machine learning and big data science approaches.
One of the big data and machine-learning-based next-generation sequencing software, the Apache-based Hadoop framework, provides an excellent environment for large-scale NGS data analysis.
The Hadoop framework contains several machine learning modules, such as MapReduce, Seal, Myrna, and many others, to tackle the NGS data management and analysis in a parallel fashion.
Impact of Machine Learning and Data Science Apps
Current applications of machine learning and data science are impacting the process of genetic and clinical research. The recent advancements in machine learning and data science are making precision medicine more accessible to researchers who are interested in learning more about the role of heredity in health.
In omics research, the genomic, transcriptomic, and proteomic data are used to solve many problems (previously thought to be unsolvable) in bioinformatics using deep learning algorithms.
Next-generation sequencing has emerged as a buzzword in the research market, which includes modern DNA sequencing platforms. Sequencing the DNA of an individual using these technologies is now a matter of a day, as compared to the classical Sanger sequencing technique.
Machine learning is playing a significant role in interpreting the genetic variations present within a genome of an individual.
Specific algorithmic working on some pre-identified patterns in large genetic sets is translated into computer models, which help understand the impacts of genetic variations affecting specific cellular processes.
A DNA sequence is a biological text or blueprint, and it can be analyzed using artificial neural networks (ANN). These networks can successfully identify transcription factors, binding sites, and splice sites present in the NGS genomic data.
Ancient DNA is fascinating. The advanced NGS technologies are powerful enough to extract DNA from ancient bones and many other remnants and provide useful information about the past. Modern contamination (sequencing human DNA or some other organism) is an issue.
Although people use advanced statistical analysis methods such as “deamination pattern interference,” it’s only feasible for a sample containing a lot of DNA and a reference genome, which is not available in many cases.
Machine learning and deep learning solve this problem by finding DNA motifs (patterns) for modern and ancient DNA. Later, these motifs can be used to differentiate between both DNAs.
Overall, both machine learning and big data science aim to improve the older versions of next-generation sequencing software and analytics, which could provide filtering, mapping, and analysis of vast datasets in a shorter time.
The NGS data produced by the NGS platforms is not completely error-free. Therefore, customized next-generation sequencing software and algorithms need to be accurate, apart from being faster in terms of operation.
The current advancements in big data analytics and machine learning approaches have provided promising capabilities for faster and more accurate data analysis.
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