Building Infrastructure and Workflows for Clinical Bioinformatics
Received: 27-Mar-2021 Accepted Date: Apr 12, 2021 ; Published: 19-Apr-2021
Citation: Chong R (2021). Building Infrastructure and Workflows for Clinical Bioinformatics. EJBI. 17(4): 17
This open-access article is distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC) (http://creativecommons.org/licenses/by-nc/4.0/), which permits reuse, distribution and reproduction of the article, provided that the original work is properly cited and the reuse is restricted to noncommercial purposes. For commercial reuse, contact submissions@ejbi.org
Editor’s Note
Next procreation sequencing (NGS) assays operate on large amounts of data, making bioinformatics examines an essential part of all NGS assays. These assays have been advancing both in size, from small gene panels to clinical whole genome arranging, as well as complexity of examination, and number and types of analytes.
Clinical molecular laboratories executing NGS testing must be in conformity with accreditation checklist requirements provided by the College of American Pathologists (CAP) and regulations listed by the clinical Laboratory Improvement revisions. The Association for Molecular Pathology (AMP) and CAP provided suggestions for validation of clinical bioinformatics pipelines including their design and development. However, factors about computational infrastructure and pipeline frameworks are few across all these recommendations
One of the main purposes for this is that there is lack of regularization in implementation of bioinformatics pipelines for clinical NGS assays and there is multiple reasons for this. The range of assays obtainable in each laboratory is vast, and these assays differ in technology, design, target analytes, and cettain restrictions of detection. Even if the same assay is implemented at various institutions, there is a lot of unregularity due to major differences in hardware and bioinformatics pipeline orchestration. Computing paradigms and technology vary extremely across institutions, which makes flexibility and standardization very difficult. The availability of skilled bio informatics staff also can subscribe to these differences.
A laboratory might not have the pilable to design complex pipeline modules in the absence of accomplished staff and might adopt lessflexible out-of-the-box vendor contributed solutions. Processing and examination of genomic data is an example of a big data application requiring complex interdependent data examines workflows. Such bioinformatics assignments take the primary and secondary input data through several computationally-intensive transforming steps using variable software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly demanding.
The interdisciplinary nature of bioinformatics and genomics data examination calls for a bioinformatics pipeline that advances collaboration and reflects the way you can most efficiently and dependable process and analyze genomic data – now and into the future. Setting up a new next-generation arranging (NGS) pipeline or simply adding components to an existing pipeline needs careful thought and planning.