Micro binfie podcast

Today we are talking about systematics, and specifically SeqCode; a nomenclatural code for prokaryotes described from sequence data.

Joining us to talk about it are co-authors on the recent publication. Marike Palmer and Miguel Rodriguez. Marike Palmer is a Postdoctoral researcher in the School of Life Sciences at the University of Nevada Las Vegas and Miguel Rodriguez is an Assistant Professor of Bioinformatics at the University of Innsbruck in the departments of Microbiology and the Digital Science Center(DiSC).

An honest discussion about the up and downsides of doing a postdoc in front of an audience of first year PhD students. Guests Dr Emma Waters, Dr Heather Felgate and Dr Muhammad Yasir are joined by Dr Andrew Page.

It was recorded in front of a live audience of PhD students at the Microbes, Microbiomes and Bioinformatics doctoral training program in the Quadram Institute in Norwich UK.

Emma starts the conversation by sharing that she enjoys research and solving problems with different tools. The thrill of discovery and exploration that comes with the postdoc position is something she loves.

Heather echoes Emma's thoughts and believes that she is happy where she is, rather than chasing after a higher paying job in the industry. She appreciates the flexibility that academia offers, which has enabled her to balance her family and personal life.

The conversation takes a turn when PhD students ask if any of the postdocs regret the decision of choosing academia despite the evident pay gap between the industry and academia. Emma points out that although she may have earned more in the industry, she is happy where she is, and finds satisfaction in helping people through her work. Chasing profits in the industry would not offer her that kind of gratification.

Yasir shares his success story of sequencing 600 samples of the SAR-CoV-2 virus in Pakistan, and how it contributed towards the fight against the pandemic. He credits the freedom and flexibility of academia that allows him to collaborate with colleagues from all over the world.

In conclusion, Andrew advises students to explore their options and to keep their careers open-ended. He suggests that if they are after a higher paycheck, they should consider the bioinformatics data science path that offers more earning opportunities in the industry. The postdocs stress the importance of following what makes one happy in life, rather than chasing big salaries.

This is a panel discussion on mobile genetic elements, guest chaired by Dr Muhammad Yasir with guests Dr Emma Waters, Dr Heather Felgate and Dr Andrew Page. We cover AMR, Salmonella Typhi and Staphylococci and outbreaks and the role of MGEs.

It was recorded in front of a live audience of PhD students at the Microbes, Microbiomes and Bioinformatics doctoral training program in the Quadram Institute in Norwich UK.

We talk about KRAKEN the taxonomic classification software and the software suite around it and are joined by Jennifer Lu and Natalia Rincon from Johns Hopkins University Center for Computational Biology.

Dr. Jennifer Lu and Natalia Rincon from the Kraken software development team were interviewed on the MicroBinfie podcast. They discussed the various versions of Kraken and the tools developed around it. They began by explaining the original Kraken, which uses an exact camera matching process and a camera size of 31 based on jellyfish. Kraken Unique is an additional version of Kraken that includes an additional column called unique camera counting, which determines how many unique cameras are covered by each read, providing an additional way to verify microbial identification. Kraken two was developed to accommodate larger databases by using a probabilistic data structure and minimizers to map cameras to a shorter sequence size.

They then talked about how Kraken is useful for microbiome analysis, including detecting pathogens. However, the accuracy of the results depends heavily on the availability of genomic data in the database, which emphasizes bacterial and viral data. For infectious pathogen detection, Kraken one unique is combined with Bracken to approximate the abundance of species present.

The developers emphasized the importance of users being aware of available genomic data in the database because the results can only be as accurate as the data. They also talked about how Kraken is used widely in bioinformatics and can be used for various scenarios beyond metagenomics. For example, they use Kraken to treat a single genome as a metagenome as part of quality control analysis. In cases where there are conflicting taxa in the reads, Kraken results show it, making it useful in determining the presence of contamination in samples.

The Kraken team also talked about how they use Kraken for contamination work to detect contamination in pathogen genomes. They compare all eukaryotic pathogen genomes against bacteria, human genomes, and databases of vertebrates and plants to filter out any contaminants. They have found in some instances where contaminating sequences from hosts such as chicken or cow were present in eukaryotic pathogen genomes.

Moving forward, the Kraken team intends to maintain all Kraken repositories, enhance its accuracy, speed, and usefulness, and develop new scripts and downstream analysis for the Kraken Tools suite. They acknowledge the need to make the database smaller as more genomes become available and are exploring ways of indexing and sketching to achieve this.

In conclusion, Kraken has been an essential software for metagenomic analysis, and it remains a continually improving tool for pathogen detection and classification. The Kraken team advises users to keep in mind the importance of accurate data for effective pathogen detection and classification.

We are talking about KRAKEN - the taxonomic classification software and in the hot seat are Dr Jennifer Lu and Natalia Rincon from Johns Hopkins University Center for Computational Biology.

The MicroBinfie podcast welcomed Dr. Jennifer Lu and Natalia Rincon to discuss Kraken, a taxonomic classification software. Developed in 2013-2014, Kraken easily identifies and assigns sequencing reads to a specific species, genus, or general bacteria. Its efficiency in classifying millions or billions of reads puts it ahead of other classification methods such as Melan, Mega Blast, and Chime. The tool is known for its ease of use and accuracy.

Following the success of Kraken's metagenomic analysis, Florian Breitweiser developed Kraken Unique, which provides more information than the standard Kraken. C Another edition to the Kraken family is Bracken, developed by Jennifer Lu, which estimates abundance, and Nat Rincon contributes to the newest editions, which analyze diversity metrics.

Kraken's exact camera matching technology identifies reads and classifies taxonomy IDs, with two outputs: a long text file for every read and a Kraken report that provides a breakdown of reads for each taxonomy ID. The interpretation of the Kraken report relies on the sample and its taxon. Even if there are few reads available, taxons can still be meaningful. For beginners, Kraken simplifies the classification process by providing pre-built databases.

There was an interesting discussion about the origin of the Kraken name. It is derived from a mythological creature that relied on Jellyfish, a camera counting tool used to build the Kraken databases. Derek Wood developed the original concept of Kraken.

The hosts found a true pathogen in a sample, which was significant for downstream analysis. The number of reads in some samples was very few, and some unclassified reads could also be uninformative or indicate contamination. Being developed for Illumina reads, Kraken's accuracy in classifying Nanopore reads is likely to be affected due to the higher error rate. The Kraken database achieves exact matching of k-mers and fits all genome information into a small space. Tools spawned out of the Kraken world are widely used due to their high accuracy, speed, and simplicity in the classification of taxonomy.

Kraken provides an additional column in the report to count the number of unique k-mers to validate the results. The developers worked closely with others to test new Nanopore chemistries due to the frequent changes in the chemistry that affected the accuracy of the reads.

Kraken databases contain vector sequence information, and vectors are given their taxonomy ID as "synthetic sequences." The software mixes Pearl and C++, with Pearl processing inputs and C++ managing heavy memory stuff by building and compacting sequences and writing bytes. Dr. Jennifer Lu appreciates the simplicity and accuracy of the classification algorithm, and Nat Rincon takes pride in being part of the Kraken community.

Ed Feil is a professor of bacterial evolution at the University of Bath, and Natacha Couto, a data scientist at the Center of Genomic Pathogen Surveillance at the University of Oxford. We delve into the concept of multi-locus sequence typing (MLST) in bacterial population genetics.

They highlight how the MLST method allows for defining strains based on partial sequences that range up to 500 base pairs. The method measures differences between loci for each strain, offering an allele number while assigning similar numbers to identical sequences. The cumulative sequence number represents the unique identification, which is subsequently referred to as the sequence type (SST).

MLST has revolutionized the field by facilitating digital storage and comparison of epidemiological databases, proving particularly useful in investigating transmission events and dissemination of certain strains. Although there are other methods such as Pulse Field Gen Electrophoresis (PFGE) that offer higher resolution when looking for similarities between different strains, MLST remains a versatile and widely used method.

They also talk about the shortcomings of MLST and the need for continued improvements in population genetics research. They mention the development of the Eburst program, which uses a circular model, rather than the traditional dendrogram tree structure, to better visualize MLST data and understand the clonal expansion of populations. They also discuss how the original MLST schemes may not have included the best genes for all bacterial species as the genes were chosen before genome sequencing became widely available.

Ed and Natacha further elaborate on the concept of clonality among bacterial species. Ed suggests that bacterial population structures have no consistent pattern, with some organisms being well-behaved, while others have a lot of allele shuffling. However, clones have existed since day one, and their presence is still seen today. Natacha adds that although MLST has flaws, it leaves behind the nomenclature for the lineages or clones, which is a lasting legacy.

Nabil-Fareed notes that while most reference labs have moved on to genomics, some people still use MLST. He adds that the pipeline is the same for any organism, and the process is efficient in the end.

The discussion concludes with the hosts thanking the guests and promising more exciting topics in their next episode. Overall, the hosts highlight the significance of understanding the limitations of MLST and the scope for further research in bacterial population genetics.

The hosts of the MicroBinfie podcast invite Dr Natacha Couto (University of Oxford) and Professor Ed Feil (University of Bath) as special guests to discuss the concept of "One Health". One Health is a comprehensive approach that seeks to manage the problem of antimicrobial resistance (AMR) by addressing the use of antibiotics in healthcare, agriculture, and the environment. It aims to improve health outcomes across all sectors to create a better planet. However, the diagrams often used to represent One Health are misleading as they do not take into account the complexity of the transmission of AMR. Therefore, there is a need for a quantitative study to understand and identify the ecological and biological barriers to AMR transmission. Visual aids such as these diagrams are not always accurate and should be approached with caution; scientists should be mindful of the implicit confirmation bias in visually-appealing graphics.

AMR determinants are found in various settings, including animals, the environment, and humans, due to the derived nature of most antibiotics from natural compounds on Earth. Studies have shown that the presence of AMR determinants is not limited to hospitals; they can be found in the environment and surroundings of hospitals. However, they caution that sampling methods can skew results, and it is essential to use a quantitative approach to understand the transmission of AMR across different sectors.

The One Health approach requires understanding the drivers of resistance and virulence and looking beyond human pathogens. Plants, insects, and animals form part of the broader virome and represent systems that are harder to study. There is no clear answer on where to focus resources as both resistant and commensal strains can be important to study. Context is essential when it comes to virulence as the consensual bacteria can become dangerous pathogens in certain situations.

They note that environmental factors play a significant role in disease outbreaks, and understanding the habits of hosts like deer or pheasants, on whom ticks feed on, is crucial. Approaches like outbreak analysis that work in hospitals cannot be used in environmental settings. Disease cannot be studied as if it occurs in a vacuum. Covid-19 has shown how host switches can have severe consequences, but spillover events usually fizzle out before causing any harm. Understanding environmental factors like habitat changes may help tackle disease outbreaks better in the future.

While tools like sequencing and analysis may be equivalent, questions investigated in different settings are vastly different. It is essential to comprehensively understand social science factors such as people's compliance level and risk perception when studying transmission in human communities. In conclusion, the issue of antimicrobial resistance is complex and requires a multidimensional approach involving different perspectives and fields of study.

We celebrate having 100 episodes! We look back at the history of our podcast and then talk about what the future might hold.

Then: Lee gets his revenge by having Andrew and Nabil pronounce words local to him.

We very briefly mentioned this paper: https://www.nature.com/articles/s41586-022-05543-x

Nabil was trying to remember this particular site and remembered it after recording: https://phagesdb.org/phages/

At the 8th Microbial Bioinformatics Hackathon in Bath we talked to a live panel with Kristy Horan, Torsten Seemann, Finlay Maguire and Andrew Page about bioinformatics from the frontlines.

We apologise for the poor audio quality, it was recorded in a room with 20 people in the background so at points it got a bit loud, however we felt you might enjoy the discussion regardless.

We interview Frank Ambrosio. He is embarking on a lifestyle of nomadic bioinformatics, living his best life.

* https://www.linkedin.com/in/francis-ambrosio/

In this episode of the MicroBinfie podcast, Frank Ambrosio, a bioinformatician working for Theiagen as a traveling bioinformatician, joins co-hosts Andrew Nabil and Lee to talk about his journey into bioinformatics.

Frank shares how he transitioned from being a lab technician and microbiologist to analyzing his own data and pursuing a master's program in bioinformatics at Georgia Tech. He also discusses his experience working at the CDC, where he gained exposure to different laboratories working on tuberculosis, biodefense research and development, surveillance-oriented production laboratories for strep genomes, and the division of HIV/AIDS prevention.

Frank gives tips for aspiring bioinformaticians, recommending that early career scientists focus on applying for contracting agencies at the CDC to gain valuable experience and eventually become full-time employees. He also suggests starting with a virtual machine and a cloud-based IDE like Google Cloud and VS Code for ease of use and reliability.

The conversation then moves onto Frank's nomadic lifestyle as a traveling bioinformatician, and his desire to connect with the public health community worldwide. Frank shares his recent experience meeting collaborators in Mozambique and the importance of building personal connections with colleagues in public health for collaboration and support.

Frank concludes by discussing his approach to routines while traveling and how he uses his Google calendar to plan out his days and weeks. He emphasizes the importance of flexibility and adaptability as a traveling bioinformatician, and his eagerness to continue meeting new people and building connections in the public health community.

Moving on to Frank's lifestyle as a digital nomad bioinformatician, he explains how he enjoys enhanced flexibility, better quality of life, and the ability to work anywhere in the world. However, he also highlights that this lifestyle model could be challenging, particularly for those who prefer greater stability and predictability.

Nabil wonders how possible it would be for bioinformaticians to engage in mentoring and education while working as digital nomads. Frank acknowledges the concerns but highlights that he has been fortunate enough to maintain his mentor relationships remotely. He talks about how working with someone on a project can facilitate a stronger and more rewarding mentor-mentee relationship.

The hosts note that flexibility is not new to bioinformatics and that technological advancement is making it easier to find intelligent people worldwide to join in the missions of organizations like the CDC. Frank reflects on his future, reserving the potential to remain with his current institution, Theiagen. He remains optimistic about the potential of these digital collaborations and is open to new opportunities to help the global bioinformatics community.