Table 2.

Summary of data complexity in TGS and NGS.

Feature	TGS	NGS
Basecalling Complexity	More complex due to indirect signal interpretation and longer reads	Less complex due to direct imaging and shorter reads
Computational Analysis	More powerful computing resources are required for assembly and variant calling	Generally less computationally demanding
Data-File Size	Larger files per gigabase sequenced due to longer reads	Smaller files per gigabase sequenced due to shorter reads
Data-Analysis Challenges	Requires specialised algorithms to handle longer reads and higher error rates	Requires robust algorithms for high-throughput data processing
Genome Assembly	Easier for complex or repetitive genomes due to long reads More challenging due to higher error rates and potential for chimeric reads (merged from different fragments)	More challenging for complex genomes due to shorter reads Easier due to lower error rates and shorter reads providing more overlap
Variant Detection	More powerful for detecting large insertions/deletions and structural variants	Well-suited for detecting single nucleotide variants