Introduction to Genomics Analytics Platform
The main reason behind the fact that “now it is possible to handle genome data and genomics research” is the evolution in the area of Big Data Analytics. Dr. Robert Green (Professor of Medicine at Harvard Medical) described the relation of Big Data and Genomics Analytics by these words — “Genomics is Big Data, Big Data are genomics, and this is, I think clearly one cutting edges of the ways in which Big Data are going to become integrated into the practice of Medicine”.
The genome data exist in the body of a single human can be considered as the size of a universe. The first human genome was sequenced between 1998 and 2001 which took near about $2.8 billion as cost. But due to the existence of Data analytics and different Big Data Technologies, now it takes around three days and $1000 as a cost for sequencing single human body genome.
Genomics analytics is responsible not only handling non-invasive generational diagnostic tests but sooner this technology will become an answer of the problems related to the diagnostic test and clinical screening which will result in a way to expand the work related to genomic medicine.
Talking about the use of this technique in the jargon of biomedical, with the help of genomics and data science, it is possible to find abnormalities in the genome sequencing which are causing diseases such as cancer, autism and heart disorders. Considering the case of heritable diseases which are rare and can be diagnosed using genome sequencing of individual members.
So how this technique can be achieved, what should be the steps to follow and how the platform can be adopted on it. Let’s find out in the next section.
Technological Insights – Machine learning in Genomic Perspective
Machine learning (considering Deep learning as its subset technique) is itself consists of many different methods that can be used to resolve various problems of genomics with its advantages and applications.
One of the most famous deep learning model architectures, CNN can be trained for global characterization and local characterization of genomic data. The specialization of CNN of extracting features adaptively during training becomes a big plus for it while handling genomic data. Recently it is used for the tasks like modeling the sequence specificity of protein binding and to study the functional activities of DNA sequence.
RNN, which is known for its capability to handle the speech recognition tasks, it is also used to data which is sequential and utilized for processing DNA sequence its genomics. RNN is used for developing DeepNano for base calling, DanQ for quantifying the function of non-coding DNS and most importantly an LSTM which is also convolutional is developed to predict the subcellular localization of the protein from the sequences of the proteins.
These are only some examples of handling Genomics Analytics using deep learning. There are other examples of these types of models also such as Auto-Encoders and other Emergent Deep Architectures.
To build an ML/DL model is not considered as typical task these days as there are so many frameworks, libraries and functions are already available to ease this task. But the challenge of this new world is how to productionize an ML/DL model.
There are some points which will be useful and considered while Productionizing a DL/ML model –
- Give special attention to Model Interpretation and its components.
- Use of Transfer Learning and Multitask Learning.
- Use Multi view Learning.
The risk of ML models not doing well is continuous and needs continuous monitoring and evaluation if they are performing within expected bound.
Benefits of Using Genomics Analytics
Using Genomics Analytics platform, it is possible to export the meaning from an individual’s genome data which can further be used for the comparison to other existing genome. It requires an extensive database for doing the same.
Using the patterns of the Genomics Analytics, predictive analytics, and prescriptive analytics, it can be possible to predict the measures to prescribe the diet to a person who can improve health and prevent diseases.
To edit the genome using different techniques, will give rise to a measure which can be used to suggest improvements through a broad range of solutions in agriculture that can aid farmers in increasing the crop production.
enges for Building the Genomics Analytics Platform
- Handling the necessity of the large genomic data sets which can be fed to the deep learning algorithms to analyze, comparison and prediction.
- It is easy to extract the sequencing of the genetics as there are private firms which offer these services but after passing this sequencing data to the user. These firms are free to share this individual’s data anywhere which can result in data privacy hurdle.
- Another challenge is to share this data deliberately without hurting the confidentiality and the rights of the patients.
Genomics Analytics Applications
Gene Expression – The process which gives power to the cell to handle and respond to the changes in the environment is known as Gene expression. It can be done by a cell by regulating the functions by controlling the type of proteins and the amount manufactured by a gene in an automated way. The gene can encode proteins, and these proteins can dictate cell functions. The areas of gene expressions which are influenced by the use of deep learning are –
- Gene Expression Predictions
- Gene Expression Classifications
Huge amounts of genomic data allow bioinformaticians and computational biologists to discover new DNA variants that were not detectable with smaller data sets.
Regulatory Genomics – This science is related to gene expression regulation which is a cellular operation that can regulate the level of expression of gene products (RNA or protein) when it becomes high or low. The amount of protein produced, RNA and location & timing of the activation of genes is combined handled by genes, proteins, RNA molecules, and other components. Deep learning which developed over the significance of the information of sequence can be used to tackle the following tasks –
- Mutations and Variant Calling
- Subcellular localization
- Transcription Factors and RNA-binding Proteins
- Functional Activities
- Promoters and Enhancers
A Comprehensive Approach
Organize and Analyze large genomic information efficiently with Real-time Data Processing and Data Security Solutions. To know more about Genomics Analytics we recommend taking the following steps –