Genomic data is the information regarding the DNA and the genome of an organism.
This data is used in bioinformatics for storing and processing of genome.
The rise of technologies like the Next Generation Sequencing (NGS) has helped in collecting a lot of genomic data but has created a problem of abundance.
There is so much data accumulated that there is an urgent need for advanced data processing and analytics.
Additionally, there is a great advancement in precision medicine and that has made the combining of genomic data with clinical data critical.
Of late, there are new tools that are making the task of genomic data analysis simpler.
Read on, to know more about them and how they are helping in genome data analysis.
Analyzing Genomic Data
In genomic data analysis, there are processes that convert the sequence of nucleic acid into valuable insights.
Depending on the sequencing technology used, the raw data from the sequence samples are converted into shorter sequences in the range of 200 to 1000 nucleotide base pairs.
That means one NGS experiment can potentially produce billions of such short sequences and that literally converts to many GB of data.
The raw sequence data that is so produced is stored in FASTA format and is stored in the database as a Sequence Read Archive, SRA files.
The most common and the largest database used to store is the International Nucleotide Sequence Database Collection, INSDC.
The Phred algorithm is used to determine if the data accumulated is of good quality or not.
The raw data sequence is not in any particular order and hence needs to be orderly sequenced.
To do that it is aligned against the reference genome, this is then stored in FASTA format.
The way the reference genome is identified is still not conclusive despite the advancement in technologies.
During the alignment, every individual raw sequence and its genomic position is matched.
After this process, notes are added to the genomic sequences to indicate regions like exons, genes and regulatory.
The notes or the annotations are in specialized formats and it contains specific mention of the regions.
The main intention of having files in this specific format during analysis is to give top priority to sequence regions that have greater clinical or biological significance.
Sequence Alignment Algorithms
The sequence alignment algorithms were developed in the 1970s and in the following years, it was refined.
The refinements led to the creation of FASTA and BLAST algorithms which is considered the most efficient.
The alignment algorithm used here for a particular study depends on certain parameters.
These parameters include biases in sequence reactions, sequence read length, the reference genome available, the computational resources and the available efficiency of the data analysis.
The Sequence Alignment Map and the Binary Alignment Maps are the most common format of files to store the aligned sequences.
There are many tools that can be used to manage these files effectively.
They are also helpful in post-processing of the DNA sequence read alignments.
Creating Valuable Insights
Gaining valuable insights from the data generated from the NGS is the final stage.
Depending on the goal of the experiments, specific methods, and tools that aid in accomplishing it is used.
But having said that, the most common goal of an NGS experiment is to identify and categorize the genome variants.
These genomic variants have sequences that are very different from the reference genome.
This is described in a different format called the Variant Call Format, VCF.
To analyze this set of data also known as the variant analysis, a larger pool of data than what’s generally used is needed.
Not just that, this analysis also calls for a set of specialized tools that helps variant detection and computational analysis of molecular variants to find out about the drug responses.
It also needs tools for finding structural rearrangements, detecting genomic structural variation and also downstream analysis.
Role of NSG in Life Sciences Research
NSG technologies play a crucial role in the life sciences as there is the great value of genomic data and analysis in this branch of science.
There is a great push by the World Economic Forum, WEF to move the genomic experiments into clinical research and also make genomics mainstream in the health industry.
There is research being conducted on integrating clinical data with genomic data.
The genomic data analysis is complex as well as quite modular hence it is challenging to create integration tools for it.
But there are a few tools that are already doing ontology-based integration of database of genomic and clinical data.
Instead of making tools that integrate after the final stage of the genomic experiments, the emphasis is being laid on building tools that can integrate with the NGS data pipelines.
Many companies already have developed these solutions so that they can be used in all types of research including clinical.
These apps help in easy implementation of solutions but the major drawback is that it is tied to the NGS technology the service provider offers.
Looking into the Future
The tools and technology mentioned herein are branching out to new venues based on the need for such analysis.
As genomic data analysis is maturing as a compelling discipline, these intelligence platforms are slowly yet surely developing into a cohesive toolbox.
Going forward, it can help analyze existing data and unravel complex genetic mysteries thorough simple computations.
The gene sequencing patterns and data generation on those lines can help understand the biological basis of genetically transmitted diseases.
This can be a game-changer in genetic disease research.
The deep potential that lies has offered plenty of excitement and expectations among those involved in genome analysis.
Apart from helping us understand the underlying cause of genetic diseases, such data and studies can help in finding solutions to Huntington’s disease, down syndrome, etc.
Even if this means to make life easier than what it is at the moment, data and information processing such as this becomes a great deal.
These are good times for those involved in research in genomic data.
It is also gaining more insights into the mutations and other molecular mechanisms.
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