Genomic research has undergone a massive transformation because of next-generation sequencing (NGS) technology, which makes has made the whole procedure more time efficient with improved quality. The standard NGS workflow, on the other hand, is less innovative, requiring multiple manual stages in addition to issues with cost, throughput, and unpredictability.
Traditional techniques for NGS sample preparation and data processing can be very labor-intensive and have lots of room for error. Many of these issues are resolved through automation, which also improves precision by decreasing sample-to-sample variability.
With the aid of NGS informatics tools, it is necessary to develop optimized automation systems that will enable researchers to increase the number of parallel reactions that can be carried out, significantly decrease the amount of sample processing time, increase the number of samples each researcher can process, and decrease the amount of sample-to-sample variability in order to meet the challenges of NGS research.
The specific areas where an integrated NGS workflow can be fully automated are covered in detail in this article. However, let's first define NGS informatics.
What is NGS informatics?
NGS informatics refers to the field of using computational and statistical methods to analyze and interpret data produced by next-generation sequencing technologies. NGS is a method of deoxyribonucleic acid (DNA) sequencing that allows scientists to quickly and cheaply determine the sequence of a large number of DNA fragments.
NGS informatics involves developing algorithms and software tools to process and analyze the large amounts of data generated by NGS experiments, as well as developing methods for storing, managing, and sharing this data. Some common applications of NGS informatics include finding mutations in DNA that may be associated with diseases, identifying genes that are expressed in different tissues or at different times, and studying the evolution and diversity of species.
There are many software tools that are commonly used for NGS informatics. Some examples include:
• Alignment tools: These tools are used to align the short DNA sequences produced by NGS experiments to a reference genome or transcriptome. Examples include Bowtie, Burrows-Wheeler Alignment (BWA) tool, and Spliced Transcripts Alignment to a Reference (STAR.)
• Variant calling tools: These tools are used to identify differences (variants) between an individual's genome and a reference genome. Examples include the genome analysis toolkit (GATK), VarScan, and FreeBayes.
• Expression analysis tools: These tools are used to quantify and analyze the expression of genes in different samples. Examples include Cufflinks, RNA-Seq by expectation maximization (RSEM), and Kallisto.
• Data visualization tools: These tools are used to visualize and explore NGS data, such as alignments, variant calls, and gene expression levels. Examples include the University of California Santa Cruz (UCSC) Genome Browser and Integrative Genomics Viewer (IGV).
• Workflow management tools: These tools are used to automate and manage complex NGS analysis pipelines. Examples include Snakemake and Nextflow.
• Data management tools: These tools are used to store, manage, and share NGS data. Examples include the Sequence Read Archive (SRA) and the Gene Expression Omnibus (GEO).
It is anticipated that ongoing technological developments will lead to more automation in the NGS library preparation to enhance research in healthcare. Additionally, it is anticipated that market participants will keep creating software tools to streamline and automate NGS workflows in order to meet the demands of healthcare organizations and university research facilities.
According to the BIS Research report, the global NGS informatics market was valued at $1.38 billion in 2021 and is projected to reach $6.71 billion by the end of 2032. The market is expected to grow at a CAGR of 15.21% during the forecast period 2022-2032.
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How does NGS informatics help in NGS workflow automation?
1. Commercial data analysis, data interpretation, and reporting tools: The primary, secondary, and tertiary data analysis processes make up the majority of the NGS workflow. The main analysis program, which is integrated into the sequencing system, is in charge of giving base calls, i.e., the process of assigning nucleobases to chromatogram peaks, light intensity signals, or electrical current changes resulting from nucleotides passing through a nanopore, and producing the quality ratings of the called bases in real time.
To make it easier to align the generated DNA and RNA sequencing data with a reference genome, find gene variations, and enable data visualization, secondary data analysis is used. While the sequencing equipment can handle some of the secondary analysis and some of the primary analysis, the tertiary analysis is the most important stage and requires additional software solutions.
The secondary analysis entails the transformation of the sequenced genetic information into clinically useful findings. Software solutions for the tertiary interpretation of NGS data are provided by several industry participants, including Agilent Technologies, Inc., Sophia Genetics, Fabric Genomics, Golden Helix, Qiagen N.V., and Genomatix.
2. Storage and computing tools: There are several storage and computing tools that are commonly used for NGS informatics to manage and analyze large amounts of NGS data. Some examples include:
• Cloud storage and computing platforms: These platforms provide scalable, on-demand storage and computing resources for storing and processing NGS data. Examples include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
• Distributed file systems: These systems allow researchers to store and access large amounts of data across a distributed network of computers. Examples include Hadoop Distributed File System (HDFS) and GlusterFS.
• High-performance computing (HPC) clusters: These clusters are used to perform resource-intensive NGS data analysis tasks using multiple computers working in parallel. Examples include Torque and Slurm.
3. Laboratory information management systems (LIMS): A laboratory information management system (LIMS) is a software tool used to manage and track samples, data, and processes in a laboratory. LIMS can be used in a variety of settings, including research labs, hospitals, and industrial labs. Some common features of LIMS include:
• Sample tracking: LIMS can be used to track the location, status, and history of samples within a laboratory.
• Data management: LIMS can be used to store and manage data generated by laboratory experiments, such as NGS data, chromatography data, and microscopy data.
• Process management: LIMS can be used to manage and track processes within a laboratory, such as a sample processing and analysis, quality control, and instrument maintenance.
• Collaboration: LIMS can be used to enable collaboration between lab members, allowing them to share data, collaborate on projects, and track the progress of work.
LIMS can be a valuable tool for improving the efficiency and accuracy of laboratory work and for ensuring compliance with regulations such as Good Laboratory Practice (GLP).
NGS informatics is a field that involves using computational and statistical methods to analyze and interpret data produced by next-generation sequencing (NGS) technologies. NGS informatics involves developing algorithms and software tools to process and analyze large amounts of NGS data, as well as developing methods for storing, managing, and sharing this data.
Some common applications of NGS informatics include finding mutations in DNA that may be associated with diseases, identifying genes that are expressed in different tissues or at different times, and studying the evolution and diversity of species. NGS informatics plays a key role in automating NGS workflows and improving the efficiency and reproducibility of NGS data analysis. It is expected that NGS informatics will continue to be an important field as NGS technology advances and expands in the coming years.
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