Micro binfie podcast

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.

We've been busy attending in-person conferences such as IMMEM XIII and ASM NGS so we thought we'd give you some of our reflections. We discuss waste water surveillance, hybrid conferences and metadata amongst other things.

For the first time ever all 3 MicroBinfies are together in person to record an episode. We are joined by Torsten Seemann for a conversation about how what we do in research can get lost in translation when applied to public health. We discuss what we did with SARS-CoV-2 genomics and somehow end up chatting about geography and language. Hope you enjoy.

Over the past few weeks scientists have been swapping Twitter for Mastodon. Our very own Nabil-Fareed Alikhan talks about his experience with setting up and running a Mastodon server called https://mstdn.science which is one of the places where scientists have moved over to. We are joined by Emma Hodcroft to get an independent scientists view on the whole thing.

In the MicroBinfie podcast, Andrew and Nabil discuss the migration of academics from Twitter to a new platform called Mastodon, with Nabil playing a significant role in this shift. According to Nabil, Mastodon is a free and open web application designed for micro-blogging. It enables integration and communication between servers, allowing the users to follow, reply, or read content from other servers.

The migration happened after Elon Musk bought Twitter and made significant changes that concerned people about freedom of speech and democracy. In response, Nabil and Duncan set up their own Mastodon instance called https://mstdn.science initially planning to create a social network for bioinformaticians, microbial genomics people, and tech-savvy microbiologists. Expected to have only 50-100 users, many more scientists, including Nobel Laureates, journals, and scientists from other disciplines, joined, and Nabil's instance now has almost 2000 users.

Meanwhile, other instances around science, like genomic.social or ecoevo.social, also saw a surge in sign-ups. In terms of resources, Nabil and Duncan's virtual server have almost 2000 users costing around £100 per 1000 users, depending on how much interaction and following goes on.

The Mastodon network replicates content from other instances, spawning many jobs, even if a user's account doesn't change much. Nabil does not limit which instances of Mastodon communicate with his site but does block domains serving unwanted or unsafe content. Even though the Mastodon network can crash and burn, Nabil thinks it could still work in the long run.

The podcast contributors suggest that Twitter's recent changes have left some users feeling dissatisfied, leading them to Mastodon, which is a decentralized social media platform. Some dodgy servers have been blocked by Mastodon for moderation, and people have moved from Twitter to Mastodon as a total replacement for Twitter. Mastodon has become a "sign" for fed-up users.

According to Emma, who recently moved from Twitter to Mastodon, Mastodon is a hedge against Twitter's unknown future. Mastodon's decentralized platform allows for a shift of power towards content and interaction, not available in a centrally controlled platform. Mastodon may not replace Twitter as a one-for-one replacement, but it fits certain use cases, such as a place for academics to complain about papers.

Mastodon's success is not dependent on Twitter's fate but rather on what "crazy ideas" Twitter comes up with in the future, Emma argues. While Mastodon may never be quite the same as Twitter, it could be even better.

We go through bug reports and issues and give insights into how bioinformaticians dig into them. We suggest the underlying problems and possible solutions and also provide tips on how to file better bug reports.

Often the hard part of bioinformatics isnt the analysis, its getting all of the software you need setup and installed. Come with us on this journey and avoid dependancy hell.

In the MicroBinfie podcast, the hosts discuss the struggles of installing, managing, and dealing with dependencies with bioinformatics software. In the past, software installations were a nightmare, and it was common to edit lines of code and manage dependencies manually, causing conflicts like diamond dependency. To ease this process, the hosts suggest using containers, virtual machines, and local environments. They stress the importance of adhering to semantic versioning guidelines and understanding the end-users' perspective for proper documentation, testing, and clarity regarding dependencies. Additionally, software maintenance is critical for its longevity and usability.

The hosts also discuss software dependency management with different chip architectures and operating systems. The M1 Apple architecture's differences from traditional computer processors cause compatibility issues and slow down emulation, leading to difficulties in informatics. Using separate Conda environments for each project or Mamba as a package manager can solve dependency-related problems that can cause significant issues. However, Mamba may take shortcuts and create conflicts with specific programs. Other package managers like Homebrew and APT are also discussed.

The episode also covers the benefits of using Docker and Singularity to manage software packages on a local machine. Docker is useful for databases, web servers, and complicated pipelines, while Singularity is perfect for more complex software and plays better with HPC. The hosts provide tips on using containers or virtual machines in a team environment, passing containers instead of binary files, and using Docker and Singularity as tools to ease the process. Overall, the episode offers practical advice to streamline the workflow of researchers who manage software packages.

The MicroBinfie podcast discusses the top programming languages for bioinformatics. Andrew, Lee, and Nabil agree that Python is a great starting point for its consistency and rigor. Its strict syntax is ideal for teaching programming fundamentals that are essential in any language. In contrast, Perl encourages multiple ways of doing the same thing, creating confusion and difficulties in keeping track of things.

The hosts caution against starting with trendy languages that are constantly changing. Instead, stick with more established languages like Python, which have established libraries and concepts that will help you advance more easily. Trendy languages come and go like changing tides, making them riskier choices. Additionally, they highlight the importance of understanding databases and their primary keys and unique fields. SQL is useful, particularly in dealing with large datasets. It is consistent across flavors and unlikely to go away soon. It takes a lot of skill to optimize queries to work in milliseconds.

The hosts emphasize that the language you choose to learn depends on your individual goals and environment. For instance, Lee suggests that you should look to who is in your space and what they are using and who is willing to help you. Once you understand the programming concepts, it is easier to transfer them to other languages, and it is just a question of understanding the syntax.

Andrew, Lee, and Nabil also discuss their own trajectories of learning programming languages, revealing that it takes a long time to become an expert in a language, and it is something that needs to be appreciated. They highlight the difference between just learning the basics of a language and really getting into the depths of it and the frameworks and libraries.

The hosts also mention languages that are important to pick up, like SQL and bash scripting, and languages that are popular for web development, like JavaScript. However, they caution that JavaScript and Java are not the same thing and that JavaScript has a reputation for being a weird language.

When asked what language they would choose for a task, Nabil says he would use Perl, Lee mentions R for stats, while Andrew admits that he has to relearn R every time he comes back to it and therefore prefers Perl for quick scripts. They also discuss their love-hate relationship with R, mentioning that while it has useful libraries like GGplot and GGtree, its syntax is difficult to work with and has separate paradigms of approaching the same problem.

The hosts conclude by acknowledging that there is no one-size-fits-all approach to learning programming languages. One should choose based on their goals, environment, and personal preferences. Python is a useful language to learn, even if one is not interested in bioinformatics. Additionally, they note that the fundamentals of databases and how they work are crucial to understand and utilized across fields.

90 Public health bioinformatics on the cloud with WDL + Terra.bio by Microbial Bioinformatics

Today on the @microbinfie podcast, we talk about WDL with @sevinsky and @DannyJPark. We learn what widdle means to Andrew and his kids. Joel takes a shot at Lyve-SET and you'll never guess what happens next.

In the MicroBinfie podcast, we discuss the workflow description language (WDL) commonly used to describe bioinformatics pipelines in a portable and cross-environmental way. The starting point is the presumption that tools are already containerized, and WDL helps to bind them together. The guests highlighted that this standardizes bioinformatics in the field, making it more reproducible and scalable. It also helps remove the need for excessive CIS admin work, enabling researchers to spend more time on scientific questions. Despite having many workflow languages available, WDL is unique in its formal specification and its orthogonality to the common implementations that are used in executing those things.

In the second part of the podcast discussion, guests Joel and Danny talked about workflow languages and public health bioinformatics. They highlighted the challenge of version control to quality management and its effects on the field of bioinformatics. They spoke about the origins of the community, StaphB, which comprises state-level public health bioinformaticians. The community discusses various challenges and contributes to creating links between academia and state public health departments.

WDL is a workflow language used for bioinformatics work that the hosts use. Danny shared his story of how they came to use Whittle and how they realized it was the perfect language for portability of pipelines. On the other hand, Joel talked about how they chose WDL for its applicability to public health and the support it received from its creators, particularly the Broad Institute. They both agreed that the choice of workflow language was driven by the environment they could work in and which language was best suited to their needs.

In conclusion, the discussion focused on the vital role of workflow languages such as WDL in bridging the gap between bioinformatics and public health. The choice of workflow language was critical and would depend heavily on the environment in which the language was used. Finally, they expressed their support for WDL and how it had helped them streamline their bioinformatics workflows.

This is an extended directors cut of our chat with Dr Henk den Bakker about Sepia. Its a summer holiday bonus.

Some URLs

Get Sepia here: https://github.com/hcdenbakker/sepia

Some information on the food safety informatics group at UGA: https://www.denglab.site/

Rust: https://www.rust-lang.org/

Kalamari: https://github.com/lskatz/kalamari

CAMI: https://www.nature.com/articles/nmeth.4458