97 Advances in sequencing technologies

We discuss recent advancements in genome sequencing technologies, based on what we've been hearing at conferences and within the community. The Microbial Bioinformatics podcast brought together three experts, Andrew, Lee, and Nabil, to discuss the latest advances in sequencing technologies. The team explored the new developments in the market, including a cutting-edge instrument from Element Biosciences that captured Nabil's attention. Andrew analyzed the adaptive sequencing feature in Illumina that enables the checkout of unwanted reads. The discussion highlighted how the computing power of sequencing labs has developed due to advancements in computers, with gaming computers being repurposed to aid in data analysis. Illumina's complete long-read solution and NextSeq's kits were also topics of discussion. Moreover, the team also discussed the increasing popularity of pacbio with its hi-fi sequencing capabilities to achieve more high fidelity readings. The experts then discussed how longer reads pave the way for 4th generation sequencing while also acknowledging the challenges posed by software tools catering to the new technology. While the developments in sequencing technology seem exciting, Nabil cautioned the panel to not forget the importance of quality over quantity. In the second part of the episode, the team moved on to analyze the limitations of sequencing software, particularly regarding its long-read handling capabilities. Andrew explained how sequencing software is hard-coded to operate up to 300 paired-ended reads, and exceeding this limit often leads to software crashes. Lee asked if there was a constant limit in the source code of Spades or SKESA to limit the software's ability to handle larger datasets. Andrew answered the query by explaining that developers may have set some limits on the memory or stack size of the software, leading to issues when processing larger datasets. The team concluded by noting that the hard-coding and data processing limitations shouldn't be considered permanent obstacles as software development is a continuous process. As sequencing technologies advance, software solutions must also advance to handle increasingly complex genetic datasets better.