Bioinformatics field helps in understanding biological data through analyzing, correlating information gathered by collecting a vast the amount of data. Even with such a vast amount of data present, many researcher complaints that they are far away from having actual data and methodologies for solving key issues at hand. This is merely due to not having enough tools to analyze and extract important bio-data from vast data available. Researchers in different fields such as agriculture, medicine and genomics, life sciences are already using Big Data technology by solving many mission-critical problems. Use of Big Data also provides headache, while processing such large sets of data. Following key factors are usually gets affected such as quality and accuracy due to uncertainty of data.
For solving such critical problem we constantly require computational tools, resources and storage facilities. To solve these problems, many companies are going for HPC models that is high performance computing. HPC model can be achieved using parallel processing such as supercomputing which used for running advanced applications. HPC models are implemented by using cloud computing. Cloud computing provides capabilities to store and process their data in VM (virtual machine) or other systems which are in its network. Cloud computing involves sharing of resources and data with systems in its network. This gives cloud computing ability to increase or decrease data stored according to the computational parameters. Hybrid computing serves the best option for storing only privileged data and ignoring the unwanted data as it is considered as temporary data.
Decisive look into the use of Bioinformatics workflow management system
Specialized workflow of Bioinformatics data can only be achieved through gathering and executing sequential steps through computing and processing data. Such management system is referred as a Bioinformatics workflow management system. Many fields, including astronomy and geology have implemented using a Bioinformatics workflow management system. Such systems are implemented by creating directed graph which will provide critical structure of computational procedures. Such graph will consist of nodes, edges and links. Here the nodes are refereed to tasks while edges to dependencies and links are abstract of data flow. The Bioinformatics workflow management system provides a user interface, so users can access and perform complex applications easily.
There are different types of workflow systems such as Scientific workflow system and Meta-workflows system. Such systems are difficult to compare and understand as each of them has different features.
Let’s try to get to the bottom of this and see, whether Bioinformatics workflow management system performs expectedly or not. The example can be given from UPPMAX and Science for Life Laboratory (SciLifeLab), in Sweden. UPPMAX and Science for Life Laboratory (SciLifeLab) provides an HPC platform to Swedish researchers for performing research in Bioinformatics using procedural tools for high performance computational work. UPPMAX has been able to serve more than hundred clients. UPPMAX has more than thousand cores and large amount of data storage systems. Clients are able to perform functions such as genome sequences very effectively and analyze sensitive specific data using critical procedure. Using Bioinformatics workflow system we can perform preprocessing with different filters and storing approaches so we can reduce, remove, and store the genomic reads also we can improve downstream analysis. With hardware being a big spoiler such workflow management system will be very important.
Why we need optimization of Data-Ware in Bioinformatics?
In a workflow system, different task is scheduled at a same time. During this task can communicate with each other. Data transfer takes place during this process. So the speed at which data transfer takes place becomes very important factor. During such large data transfer most of devices and networks connected to the cloud usually get filled up with data which is transferred at a high speed. This reduces bandwidth which is used by other tasks performing at the same time. It also reduces the availability of resources for other tasks. Parallel execution of workflows is problematic due to the same reason. To further complicate this issue, Data dependencies are a common problem during parallel execution of workflows in cloud devices.
Usually parallel processing can be achieved by converting large data into smaller chunks of data. Such chunks of data are processed independently. This technique is most useful in genomic and NSG data analysis of workflows. Even with this advantage, use of parallel processing may create data dependencies. So we need smart infrastructure so we achieve parallel processing and minimizing problems associated with it such as lesser bandwidth and data dependencies.
Before going for solving this problem, let’s have a better understanding of data-ware.
Data-ware, lot of dumb storage around
Large data are gathered while researching in Bioinformatics, to store these data, we need lots of storage. Most of the data are usually not useful anyhow, we need to find storage space for this data. The more data, we gather, we need more storage. This storage then becomes dumb as more useless data is added. In such cases, data-aware storage reduces errors for non-compliance and increases ability to handle such large amount of data. Data-ware gives us real time information on error generation applications before they arises.
Solving problem of Data Dependencies
Parallel processing is achieved by fragmenting large data into small groups and processing on a such small group of data. Such approach for data processing helps in achieving parallelism in multi-processor architecture and distributed systems. Data are present on different sites, after accessing such data leads to data dependencies. To solve this problem, we operate data on the same location and processing combined data. We can process the output of previous tasks in the same location, such process is called as recursive processing.
Such data are treated as separate instance and preprocessing is done in such instances. This allows us to make restructuring of data in the analysis process.
Providing scheduling policy
In distributed systems, limiting use of data transfer will ignore the advantages of parallel processing. As data are present on a single site, other sites will be less utilized. The use of external scheduler will serve the purpose. External scheduler will manage data, such that every site is utilized properly. This process is defined as a load balancing technique. External scheduler will check how data can be loaded on each site. Further, it will check ability of site to process data. External scheduler will perform scheduling algorithm which assigns every task required resource.
Such hardest task has to be implemented for better understanding of Bioinformatics Workflow. Bioinformatics helps us to bring limelight on so many life sciences. DNA sequencing, understanding of genes is done by gathering raw data and processing to extract key information. With the help of hybrid cloud and data-ware technology will be exploring some universal mysteries hidden in life sciences through Bioinformatics.
“Big Data is the only technology which was, is and will be helping in understanding the life sciences through Bioinformatics”.
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