What are the steps involved in analyzing NGS data

Next-generation sequencing (NGS) is a high-throughput method. It is capable of generating immense volumes of data through massively parallel sequencing. Whole genome sequencing, whole exome sequencing, RNA-seq, and ChIP-seq are applications of NGS technology.

NGS achieves this by collecting millions of nucleotide base clusters in parallel. The explosion of data arising from the lightning-fast sequencing reactions demands the presence of tools and platforms that can perform the analysis of the massive volumes of NGS seq data rapidly and accurately.

Due to the differences in experimental goals, WGS, WES, RNA-seq, and ChIP-seq differ from each other during the experimental design and data analysis steps. However, since all are applications of NGS, a few steps remain common among all.

To explain the basic steps involved in the analysis of NGS data, we shall focus on the genetic variant analysis of WES for now.

How to assess the quality of NGS raw data?

NGS data analysis begins with assessing the quality of raw seq data. The most common standard format of raw data from NGS is FASTQ. Four lines comprise each read in this format. Several variations of the quality score encoding now exist including Phred 33, Phred64, and Sanger. Each one uses different characters to represent quality scores. The very first challenge comes from the various representation of quality scores. It makes reading the actual quality score much easier unless one knows which version has been used in the particular FASTQ file. The quality scores can tell you the probability of the sequencer calling a base incorrectly.

Knowing the FASTQ file format can help you preprocess the data. This step is critical in removing the inconsistencies of data quality and improve the initial quality of seq data. The results of the analysis will depend upon the quality of the raw data. Preprocessing can enhance the quality of the raw data for subsequent analysis steps.

What is seq alignment or a mapping?

After preprocessing, the next step is mapping. Mapping or sequence alignment aligns the raw data to a reference transcriptome or genome. It facilitates the analysis of sequence data against a reference genome. It eliminates the need for a de novo assembly by leveraging an already present complete genome in a database.

NGS seq alignment has become fast and highly accurate due to the almost complete automation of the analysis process. The research team can pick a sequencing data analysis software that can achieve mapping with high accuracy against a reference genome (or transcriptome) depending upon the type of NGS data. In the event of WES sequence alignment or mapping, the team can utilize a reference transcriptome in case of an already sequenced genome/transcriptome of an organism. Additionally, WES reads enable the analysis of nucleotide variation between the reference sequence and the transcript. The mapping accuracy influences the accuracy of the variant identification.

Why is evaluating mapping quality essential for a WES experiment?

Checking the mapping quality is a part of the best practices for NGS data analysis. After mapping, more biases in the data might surface. Using the correct sequencing data analysis software can give you access to the quality of the mapping in the comprehensive NGS data analysis report.

You can improve the mapping quality by processing the mapped reads. It is similar to the previous step of preprocessing the raw data. However, in this case, it is about removing duplications in the mapped reads. Duplication of the mapped reads can be of PCR artifacts. The post-alignment processing is necessary for the improvement in accuracy and quality of the further variant analysis. Your choice of sequencing data analysis software should allow you access to post-alignment processing.

What is NGS data analysis?

Once the mapping or alignment is complete, it’s time for NGS data analysis. The algorithm used will depend on the specific goal of an experiment. For example – if you are conducting a WES seq data analysis, your sequencing data analysis software will use the reference database to map the reads. The variant analysis includes variant calling and the prediction of the effects of genetic variants in the samples.

You can determine frameshift mutations, single nucleotide polymorphisms (SNPs), chromosome rearrangement, and IN/DEL mutations in a set of genes that may be causing the variation.

You can determine the influence of each variation on the phenotype. For example – synonymous mutations (in amino acid sequences resulting from a transcript) can result in minimal effects. However, larger changes like chromosome rearrangements or recombination can result in deleterious effects on the gene function.

How to visualize NGS data?

Data visualization was a challenge even less than a decade ago. Now, there is multiple next-generation sequencing data analysis softwares that allows the visualization of WES data. Apart from WES, now you can turn to commercial platforms for the visualization of WGS and RNA-seq data. Since WES targets the exons only, WES analysis uses probes to target around 1% of the genome. These only include the genes that code for proteins. The interspersing regions comprise 99% of the genome that these probes do not target. The correct analysis, mapping, and visualization are critical since mutations in these regions can potentially lead to severe phenotypic aberrations or diseases. WES can detect the exons that may contain harmful or even potentially lethal mutations. Speak to the commercial NGS seq analysis platform or SaaS provider you are working with to find out more about the sequencing and analysis of WES data.

What is the advantage of using sequencing data analysis software for NGS data analysis?

One of the most significant advantages of using a sequencing data analysis software is its easy accessibility. The user does not have to learn command languages like Java or Python to use the tools. The user can access the tools across the cloud in a pay-as-you-use format. They come with easy-to-use user interfaces or APIs. There is simply no rendezvous with computer programming or command languages that can make the analysis, mapping quality assessment, and visualization difficult for the users of old-school, device-based bioinformatics tools.