Different platforms use varied chemistry to detect the signal that comes out after the nucleotides are read by the machine.
The sequencing reads (stretch of nucleotides of a specified length) are what we get as an output. Post sequencing QC is checked through software provided with the sequencer for quality scores, duplicates, and other metrics. NGS sequencing can be of numerous types- whole genome, transcriptome, amplicon, exome, Chip, and many more. Downstream analysis includes converting these raw reads into interpretable data and then choosing to go for either de novo assembly or mapping the reads to a reference genome and identifying differential gene expression, mutations, or splice variants.
It is indeed a huge feat that we have gone from HGP to sequencing thousands of different organisms and reducing the cost to around $1000. The huge exabyte (1018) data output coming out from these sequencing machines has given rise to a platform for data sharing among the scientific community. This provides one with the opportunity to develop software and programs to analyse the data. On the other hand, the challenge of data storage is still being addressed.
Currently, the industry is flooded with different sequencing platforms and the choices may become overwhelming.
Thus, there needs to be informed decision-making at each and every step – beginning with how the sample is going be extracted, processed, sequenced- whether single or paired-end, how much to sequence, keeping in mind the cost and the aim of the experiment, which sequencing platform to choose for the experiment, data storage facility and computational ability to make sense of the data and turn it into a scientific reality. All these require an amalgamation of well-trained scientists, bioinformaticians, and clinicians to come together and implement their expertise.
Figure 3: Chemistry of different sequencing platforms