The Bioinformatics and Beyond Podcast

Dr. Justin Siegel begins this episode by explaining what enzymes are, how they have evolved, and why Dr. Siegel is motivated to try to engineer enzymes to perform functions tailored to help humanity instead of to perform functions based on how they evolved in nature. He explains the primary goal of the work discussed and relating enzyme sequence to function. Dr. Siegel also explains how his work was the first of its kind by scaling up enzyme design to hundreds of mutants instead of dozens. 

We then dig into the details of Dr. Siegel’s work. We learn details of his study such as why his team chose to study the particular enzyme that was used to create a massive set of enzyme mutants. We hear the previous difficulty of doing a study like this on only one enzyme and what enabled this increase in the scale of enzyme design. We also hear about how the use of cloud labs was introduced into the project and why. 

Next, we hear all about the cloud lab aspect of this project. Dr. Siegel explains which parts of the enzyme mutant creation process were most challenging and benefited most to be moved to cloud labs. 

Finally, we learn about how machine learning was then applied to the large set of generated enzyme mutants. Dr. Siegel explains how the generated data allowed his team to test previous hypotheses about mutant enzymes and to start trying to predict the functions of enzymes from sequence. Dr. Siegel also comments on a finding of the paper that for highly conserved residues, if you change them, you lose the function. 

Learn more about Dr. Siegel’s work by reading the corresponding publication which you can find here: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147596

Dr. Justin Siegel explains the past, present, and future of wet lab work and wet lab automation. We start by hearing a description of what it is like to work in a wet lab, covering the contrast between the excitement of seeing life changing results and the countless hours of monotony that is often involved to produce these results. 

We then begin discussing where automation will fit in to help alleviate the burden of long term monotonous work in the wet lab. We learn about the challenges of implementing automation in a lab, and hear about the dream that exists from the promise of automation versus the reality of implementing automation in an actual academic lab or in industry. We also hear Dr. Siegel’s take on the current state of implementing automation in an actual lab right now. We hear about the intricacies of implementing automation, such as discussing the pros and cons of different types of brands of robots, hearing about how lab robots can end up sitting unutilized or underutilized in academic labs, and considering practical questions that are involved when implementing automation. We end our discussion of robots that could be purchased with a discussion on Opentrons.  

Finally, we discuss cloud labs. Dr. Siegel starts by explaining what cloud labs are. Then, we hear about how a scientist would actually go about utilizing a cloud lab service. Dr. Siegel shares his thoughts on the potential promise of cloud labs and gives justification for the excitement surrounding this new approach. Dr. Siegel also shares his personal experience using cloud labs and how things like the accuracy and reliability of cloud labs can already make it a viable option for automating academic lab tasks. He also explains an unintended benefit of using cloud labs in that it allows researchers to spend more time thinking critically about the tasks that need to be done and how they will be done.

This episode continues our series of episodes on space biology and #SpaceOmics with Dr. Tejaswini Mishra. Dr. Mishra introduces The NASA Twins Study, a cornerstone scientific work where two twin astronauts were monitored, with one twin traveling to space, and one staying on earth. Dr. Mishra explains the importance of studying long term spaceflight missions, how The NASA Twins Study was set up in a particularly great way to study spaceflight impact, the many different types of data collected and analyzed, and some of the results found by the study.

During the episode, Dr. Mishra explains many of the types of data collected such as microbiome and telomere data. After covering the types of data, we explore some of the main results such as the first ever test of a vaccination in space. Dr. Mishra then explains more in depth on changes seen during spaceflight such as telomere length, gene expression, DNA damage, cognitive function, and more. We discuss how concerning the various changes that occur in space could be for astronauts, such as becoming hypoxic. Dr. Mishra also explains pointers to the kinds of things we should focus on when we go deeper into space for understanding the impact on the human body. Finally, we summarize the main messages of the paper and hear Dr. Mishra’s thoughts on the future of space research. 

The NASA Twins Study can be found at: https://science.sciencemag.org/content/364/6436/eaau8650

For people who work in the life sciences, a very common occurrence is for folks who work on the "wet" side of research, largely doing bench work, to become interested in or start wanting to transition to doing more "dry" research, like computational research in bioinformatics. In this special episode, dedicated to those thinking about transitioning from "wet" lab work to doing more "dry" lab type work, my guest Dr. Willian da Silveira explains his own transition from a full bench scientist to a full time bioinformatician. Dr. da Silveira also answers many questions from the bioinformatics subreddit on this topic. Following Dr. da Silveira's explanation of his career trajectory and his own shift from "wet" lab work to "dry" lab work, I ask a series of questions from the bioinformatics subreddit seen below, with time stamps included:

[19:00] Bioinformatics subreddit questions begin.

[20:00] What general stats and technical requisites are necessary to transition from wet lab to dry lab work?

[23:30] Is it boring to only do data analysis versus conducting lab experiments?

[27:50] Should you transition early, for example during a masters or PhD program, or can it be done later?

[33:30] Does the transition need to be forced or does it happen more often by chance?

[35:10] Is there a downside to being self-taught as a bioinformatician?

[36:20] What are the upsides of picking up bioinformatics later on, starting as a wet lab scientist first?

[40:25] How to get accepted into a bioinformatics PhD program with no formal CS education?

[42:07] What about dry lab to wet lab transitioning?

[48:07] How do you get your foot in the door when switching from the wet lab to a dry lab with little or no dry lab experience on your CV?

[50:45] If you do feel stuck, would the best route be to go ahead and pick up some formal education like a paid masters degree?

[52:48] Would it make sense to transition to dry lab work given employment and financial considerations?

Finally, to end the discussion, I ask Willian what he thinks the ideal mix of wet lab and dry lab experience might look like.

In this episode we begin discussing the biology of spaceflight with Dr. Willian da Silveira. We start by hearing the story of how Dr. da Silveira's recent high profile space omics paper (https://www.cell.com/cell/pdf/S0092-8674(20)31461-6.pdf) came to be. He first describes the NASA GeneLab and how he got involved, and how his story of this paper began with an analysis of some liver transcriptomics data. We hear about all the different types of data used in this study, including epigenetics and metabolomics data. Dr. da Silveira discusses how to try to incorporate and work with this many types of data all at the same time. He then further elaborates and explains data like epigenetics and metabolomics. 

After discussing all the different types of data, and how to try to analyze all the data together, Dr. da Silveira talks more about the biological side of some of the data, for instance discussing rodent data and human cell lines. Finally, we discuss the results of his paper and how all the data analysis point to a central hub of the impact of spaceflight, with mitochondrial stress acting as this central hub. We conclude with a discussion of the principal risks to humans when they go to space and what Dr. da Silveira sees coming for the future of space omics research.

Link to Dr. da Silveira's recent publication: https://www.cell.com/cell/pdf/S0092-8674(20)31461-6.pdf

Link to spaceflight impact review paper mentioned: https://pubmed.ncbi.nlm.nih.gov/33242416/

Dr. Hayden Metsky begins by introducing the ADAPT method for doing large-scale detection of viruses. ADAPT is a computational method that aids the design of CRISPR-based viral testing. He then discusses the motivation for ADAPT and how it relates to his previous works like CATCH. In comparing ADAPT to other work, Dr. Metsky discusses, for instance, differences between CRISPR-based testing and more traditional testing like qPCR. In discussing the challenges of designing diagnostic tests and detection assays, Dr. Metsky then describes how he breaks these challenges into three different components. 

Dr. Metsky goes on to talk about how they designed an assay based around the Cas13 enzyme. He describes how they used this approach for targeting viruses and explains how they designed a large-scale library of 20,000 pairs of target viral sequences and guide RNAs. He then explains how they used this library as training data for a machine learning model. He also explains his thought process of designing and training the convolutional neural network model that they ended up using for predicting how well the guide RNAs would work. 

As our conversation continues, Dr. Metsky points out an interesting observation that his team made while working on this project. He points out that it could be the case that it may not be best to only design diagnostics around a highly or universally conserved region. He explains that taking into account other considerations, like how well the diagnostic technology works for a particular target sequence, may produce even better results. He also points out how it can be really challenging to only consider the highly conserved or totally conserved regions because those regions are going to be the most likely to be shared by other viruses or organisms which induce false positives in tests. Dr. Metsky explains his thought process for how you take a problem like this, figure out the characteristics of the problem, and match it well to a closely related problem or other scientific works, explaining the process of figuring out how to optimize the final objective function in ADAPT. Final topics include a discussion on the speed of ADAPT and the availability of the software. 

To learn more about ADAPT, you can read the ADAPT manuscript at https://www.biorxiv.org/content/10.1101/2020.11.28.401877v2 or visit the software page at https://github.com/broadinstitute/adapt

In this episode we focus on the applications side of synthetic biology for the environmental sciences and environmental microbiology with Dr. Ilenne Del Valle and Emm Fulk. To start, we walk through the more classical omics approaches for understanding environmental microbiology, setting us up for newer synthetic biology approaches. We then discuss the main questions in environmental microbiology that synbio is well suited to help answer. We discuss a few specific problems such as quorum sensing. We learn about what quorum sensing is and how synthetic biology can be used to help understand it.

Next we discuss how a researcher would conduct an environmental microbiology study using synthetic biology. We also briefly discuss ethical considerations for these topics, such as the ethics of releasing synthetically modified organisms into the environment as part of a study. This leads to an introduction on cell free sensors as yet another strategy for performing this type of work. We then hear about horizontal gene transfer as another phenomenon that synthetic biology can be used to better understand. We end with a discussion on cryptic processes that occur in microbial communities, how synthetic biology could allow for measuring these processes, and the overall role my guests see for synthetic biology in the future. For more on these topics, see Dr. Ilenne Del Valle and Emm Fulk's recent publication: https://www.frontiersin.org/articles/10.3389/fmicb.2020.618373/full

Dr. Ilenne Del Valle and Emm Fulk introduce the topic of synthetic biology in this episode. I start by asking, "What is synthetic biology?" We then begin digging into some of the intricacies of synthetic biology by learning about biosensors; biosensors are a fundamental component of synthetic biology for translating environmental inputs and outputs. We next talk about all the different molecules that biosensors can sense that we could use for various applications. 

Our conversation continues with introductions to many additional aspects of synthetic biology. We introduce and discuss synthetic biology circuit diagrams, which is another interesting area where the computational sciences and biological sciences converge. We next introduce CRISPR-based biosensors, synbio AND gates, and other logical operations that can used in the synthetic biology world. Finally, we discuss reporters. We introduce and cover several types of reporters and how they can help indicate which inputs are present. We end with a discussion of how synthetic biology components can be tuned to work even better, and the role bioinformatics can play in the world of synthetic biology.

This episode covers the review that both guests cowrote, that can be found here for further reading: https://www.frontiersin.org/articles/10.3389/fmicb.2020.618373/full

Dr. Kyle Frischkorn explains the interplay between different sample analysis methods such as transcriptomics and proteomics. He starts by explaining some of the basics of both transcriptomics and proteomics and gives a refresher on the central dogma. This explanation covers aspects of these methods such as a high level description of what they are, how they work, and what data you get from them. He also mentions more detailed considerations such as difficulties with mapping RNA back to genomes, the power of transcriptomes and metatranscriptomes, and different types of RNA sequencing that can be performed. 

We then move on to discuss Dr. Frischkorn's recent oceanography study. He starts by explaining the motivation behind this study. He explains the importance of understanding oceanography and the role of the ocean as a primary source for all chemistry that takes place on the planet, how the fixation of nitrogen is such an important part of the planet's chemistry, and how there are very few organisms that are able to turn nitrogen gas, which is highly abundant in the atmosphere, into biologically available nitrogen. He explains a further motivation which was to help resolve some of the unknown regarding if it is better to look at, for instance, transcriptomics or proteomics to perform these kinds of studies. We learn about how transcription and the presence of proteins might not necessarily always be totally correlated with one another. Finally, we do a deeper dive into the methodology of the study and learn how the study was performed, and conclude with some of the results and conclusions of what was found. We hear about the harmony and discord between transcriptomics and proteomics, and the "choreography" of the "dance" between these two methods. Dr. Frischkorn also explains a final, third piece of data that can be gathered, which is enzyme activity assays. The study discussed can be found at https://www.frontiersin.org/articles/10.3389/fmicb.2019.00330/full

In this episode, we attempt to take a look behind the machine of science with Nature Communications senior editor Dr. Kyle Frischkorn. We begin by simply asking the question, "How do you publish in Nature?" Dr. Frischkorn breaks down several of the main hurdles to getting published in Nature. These include having striking findings in the actual research and picking the right venue from within the Nature portfolio. We learn about different aspects that could potentially help or hurt your chances and where they fit into the publishing process. Topics include having a relationship with an editor, writing a cover letter, and working in a current hot topic of science. Dr. Frischkorn gives his thoughts on how an author might decide to submit to Nature, open access, and about the advantages and any potential disadvantages of publishing in Nature. Finally, we briefly discuss life as an editor. 

Dr. Leor Weinberger begins this episode by talking about his motivation for developing a novel therapy for HIV. He explains the fundamental mismatch between the mutation and transmission of the disease and how our therapies work, which inspired him to take a novel approach to try and combat the disease. 

We discuss topics such as the potential for scientists to give up on an HIV vaccine and why it seems like there are no good general antiviral drugs. To lead up to discussing Dr. Weinberger's new therapy, we hear a bit about the history of HIV therapeutics. After walking us through this history, culminating with the development and widespread adoption of the modern day drug cocktails for HIV patients, Dr. Weinberger introduces us to a fundamentally new type of therapy he has been working on to overcome previous barriers. He explains some of the origins of this new approach and explains how this approach is based on molecular parasites that steal resources from the virus. 

This new approach, which Dr. Weinberger refers to as therapeutic interfering particles, uses defective particles that function not by poisoning the virus but by stealing from the virus. He then explains how this approach overcomes the fundamental mismatch between HIV and HIV therapies. We hear some of the story of the long road from the initial idea for this work to the status of the therapy today. Dr. Weinberger also explains a sort of happy accident that happened in a petri dish that may have enabled this research to finally move forward.

Finally, we spend some time on the lower level mechanisms of how this therapy works, such as how you could deliver this treatment into a patient. We also talk about safety and the potential for public hesitancy. We conclude with a mention about the potential for this type of approach to be used for COVID-19 therapy.

Watch Dr. Weinberger's TED Talk on this topic at https://www.ted.com/talks/leor_weinberger_can_we_create_vaccines_that_mutate_and_spread?language=en

This episode begins by asking the question, "Why is it that we don't have an HIV vaccine after 40 years, but we do have a COVID-19 vaccine after one year?" Dr. Leor Weinberger explains that the answer to this question is primarily due to the existence (or lack thereof) of natural convalescents and whether the immune system is able to beat the virus. Dr. Weinberger further explains that there have only been two "recovered" people who had HIV. These two recoveries happened through bone marrow transplants, which themselves have 50 percent survival rates. Next, Dr. Weinberger gives a history of HIV vaccine development. Then, we drill down into the specifics of why HIV has been so challenging to develop a vaccine for. Dr. Weinberger explains the role of mutation as a primary source of this difficulty, coupled with the fact that HIV inserts itself into your genome and is able to persist in the body for decades, leading to no natural convalescents. 

To conclude, we hear Dr. Weinberger's thoughts on COVID-19 vaccine specifics such as how long COVID-19 may stay with us, if we may have to get additional vaccines, and how the persistence of antibody levels factors into these considerations. We end with a discussion on the mechanisms of the COVID-19 vaccines that have been developed. For those interested, a great resource for further reading on the bioinformatics of COVID-19 vaccine mRNA can be found at https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/

In this episode, Dr. Sabrina Green discusses the clinical aspects of phage use. We start by going back to one of the first ever uses of phage for a bacterial infection before discussing how phages are used now. Dr. Green explains a wide range of details related to clinical phage use. Topics discussed include cases today where phages are used, the safety of phages for clinical use, regulatory considerations for phage therapies, the pros and cons of phages, how specific phages are matched to specific patients, and phage affordability. Dr. Green describes potential hurdles for widespread adoption such as convincing people that viruses can be good for you. For further reading see https://www.liebertpub.com/doi/10.1089/phage.2020.0007 and https://mbio.asm.org/content/12/1/e03474-20

Dr. Zahi Fayad and Dr. Robert Hirten return to continue their discussion on wearable technology. This episode revolves around the study Dr. Fayad and Dr. Hirten performed to find out if wearable devices can identify and predict COVID-19. They discuss some of the struggles they had being “largely restricted in the data they could collect”. Their findings have the potential to be ground-breaking and showed “there are significant changes in your heart rate variability over 24 hours, which allows us to diagnose someone 7 days before a nasal swab”. To finish the episode, they discuss the potential to expand the study in the future, stating “The more you make these available to the researchers, the more helpful they will be to the healthcare biomedical research community”. Read more about their study at https://www.medrxiv.org/content/10.1101/2020.11.06.20226803v1.full-text

Dr. Zahi Fayad and Dr. Robert Hirten discuss all things wearable technology. Throughout the episode we learn about the exciting potential of this technology for conducting health studies and for the general wellbeing of the population. Dr. Fayad and Dr. Hirten explain what wearables do for athletes and for the general health of the public, even predicting “there is a market for the future for scaling it up and making it bespoke”. Dr. Fayad explains why he utilises many different wearables all at once, stating “I am competing against myself, it helps me stay motivated and helps me compete against myself everyday” when exercising. They then share their excitement with us on the future of wearables in human health. Finally, they finish off with a discussion on the uses of wearables in studies for such things as early heart attack warnings and COVID detection, with “almost all of them adding to their app the option to join a (COVID) study”. Dr. Fayad and Dr. Hirten will be returning in the next episode to discuss the exciting COVID-19 wearables work they’ve been doing!

In this second part of our history of metagenomics with Matthew Schechter, we start with a description of what a metagenome contains and how you analyze this type of data. Matt explains a few high level concepts such as metagenome assembly, metagenomic assembled genomes, contigs, contig binning, and genome completeness. Matt explains how metagenomics can help answer previously unanswered questions and even generate new hypotheses like in the example of the Candidate Phyla Radiation. Matt further explains how metagenomics is unbiased when compared to 16S sequencing and what his vision is for “Metagenomics 3.0.” Further topics discussed include pangenomics, further ways metagenomics can generate new hypotheses, and metapangenomics. Read Matt's full article of the history of metagenomics at https://merenlab.org/2020/07/27/history-of-metagenomics/

In this episode we begin our history of metagenomics with Matthew Schechter. Beginning with highlights like the initial ability to see microbes with a microscope and growing microbial colonies, we work our way through the history of metagenomics leading to modern day sequencing. Matt describes a discrepancy between culturing and what is present in a sample, and how sequencing began to overcome this discrepancy. Matt covers what 16S sequencing is and where it fits in the history of metagenomics. We end with a discussion of a seminal work on reconstructing genomes from sequenced metagenomes. Read Matt's full article of the history of metagenomics at https://merenlab.org/2020/07/27/history-of-metagenomics/

Dr. Heer Mehta starts by going over several of the ways that bacteria become resistant to antibiotics. Dr. Mehta explains the connection between specific antibiotics and specific drugs, and how she uses this information to know what to look for when studying the outcomes of experimental evolution studies. She explains how her group can isolate individual mutations that arise as a pathogen becomes resistant, and determine the difference in the protein structure caused by the mutation. We then discuss specifics of how Dr. Mehta and her group have studied the mechanisms for resistance used by individual pathogens, including a potential biosecurity threat. This discussion includes an example of a pathogen evolving resistance to two antibiotics in a single experimental evolution experiment. Finally, we discuss the potential uses for this type of research to translate to clinical usefulness.

Dr. Heer Mehta first goes over some basics of what antibiotic resistance is, why it is a global concern, and some related history. Dr. Mehta explains how bacteria are able to evolve to become resistant to antibiotics. She goes on further to explain how experimental evolution is one way scientists can understand this process and potentially use as a weapon in humanity’s battle against antibiotic resistant pathogens. She explains additional tools we can use such as microscopy and genome sequencing. We walk through how experimental evolution is setup, how the full experiment proceeds, and the final results that can be obtained. Additional topics discussed include pharmacodynamics and hypermutation.

Dr. Dinler Antunes explains what the protein folding problem is and gives a bit of history on the problem. Dr. Antunes describes the types of methods for solving the problem computationally. He then discusses the rise of AlphaFold 2 for protein folding and gives his thoughts on its impact. Our discussion then shifts to the molecular docking and related problems, where the problem becomes to predict how molecules interact with each other. Dr. Antunes discusses how this problem shows up in the real world, for instance when a drug interacts with a protein. He explains the computational aspect of molecular docking and molecular dynamics. Finally, we learn about related work in relation to finding potential treatments for SARS-CoV-2.

Dr. Lauren Stadler describes the connection between environmental microbiology and bioinformatics. We start with an explanation of wastewater monitoring including how it is collected, cleaned, gotten rid of, monitored, and studied. Dr. Stadler defines and gives a bit of history on wastewater-based epidemiology. Dr. Stadler discusses how SARS-CoV-2 environmental monitoring could be widened and further developed to monitor other pathogens such as influenza. We then discuss the technical aspects of wastewater and environmental monitoring. Final topics include environmental monitoring after hurricane Harvey and engineering our drinking water. 

What if we could know how widespread COVID-19 is in our communities, in a low-cost way, that everyone automatically opts in to, with no additional effort? In this episode, Dr. Lauren Stadler tells us how her lab shifted from studying microbial communities in wastewater to monitoring levels of the SARS-CoV-2 virus in wastewater. Dr. Stadler explains how SARS-CoV-2 ends up in wastewater and how it can be used to track COVID-19 positivity rates. She discusses the incredible power and potential of this new form of community testing. For instance, she describes how the government is catching on to its power, the cost compared to individual testing, and how sequencing wastewater could give us additional information on how the virus is being transmitted. 

In this episode we revisit the “expanded” scientific skillset, discussing skills such as written and oral communication, marketing, people skills, listening, reading, networking, and literature search. Dr. Luay Nakhleh teaches us how to improve each of these skills. He explains how he continually asks himself, “How did I do?” He also explains how we can use this question to improve ourselves, in addition to feedback from others. Dr. Nakhleh then elaborates on further ways to improve skills. For instance, describing how our writing skills can be improved through a more mindful analysis of what we read. For each skill, Dr. Nakhleh attempts to define what it looks like to do that skill well. For instance, he addresses what it means to be a good listener and a good reader. Additional topics discussed include the role of social media in science and how to become an effective communicator when english is not your first language.

In this episode, we introduce and explore the “expanded” scientific skillset. Dr. Luay Nakhleh, who was recently named Dean of Engineering at Rice University, describes how communication is a requirement for leadership. He tells us the story of when he decided to grow his own communication skills, how he began improving, and the rewards of being an effective communicator. Additional skills such as marketing are discussed. For instance, Dr. Nakhleh explains how skillful marketing can help scientists spread their work and increase their impact. Further skills discussed include writing, listening, reading, people skills, networking, and literature review. For each of these skills, we attempt to highlight how they complement traditional career skills in STEM, why they are important, and how this importance can change depending on a person’s career goals.

Dr. Kathryn Kundrod explains how her work has focused on making quality testing available in more resource limited settings. She discusses the design decisions involved and the tradeoffs that can be made to balance the accuracy of a test against the accessibility and cost of a test. We learn about how resource constraints apply to all COVID-19 testing done in all countries of the world. We then dive into the technical details of designing a test with a technology like LAMP. She discusses further considerations that impact designing a new COVID-19 test, such as practical considerations like ease of regulatory approval and availability of reagents. We conclude by hearing about what the experience was like when Dr. Kundrod and her colleagues rolled out their newly designed test at Rice University. 

In this episode, we explore the landscape of COVID-19 diagnostic testing with Dr. Kathryn Kundrod. Dr. Kundrod walks us through a bit of history on how we got to where we are at with COVID-19 diagnostic testing from the start of the pandemic to now. This history includes perspectives from both the technical and regulatory sides, as well as practical considerations such as shortages in test reagents.  For those wondering about getting a test, Dr. Kundrod explains the different types of tests currently available. She discusses some of the early roadblocks to setting up widespread testing and thoughts on how things could have potentially gone more smoothly. Finally, we explore newer technologies and how close we are to getting to tests that can be used at home in a format as simple as a pregnancy test. Emerging technologies discussed include methods such as isothermal amplification with RPA and LAMP. 

Dr. Tomer Altman describes the atypical origin of the Serratus project and explains why Serratus is such a big leap forward from doing a sequence search with something like BLAST. Dr. Altman dives into the nuts and bolts of how Serratus works and how it was used to find entirely new branches of the tree of life, filled in with previously uncharacterized coronaviruses. Dr. Altman also outlines potential future uses of systems like Serratus for things like biosurveillance and human health in relation to the human microbiome. Learn more about Serratus at https://www.biorxiv.org/content/10.1101/2020.08.07.241729v1

Dr. Melissa Haendel discusses how the N3C organization she co-leads and co-founded has led the way in integrating COVID-19 clinical data to aid research efforts. She discusses how the creation of synthetic clinical data also has the potential to help COVID-19 researchers. Further topics include the current status of electronic health records in the United States and how her team overcomes heterogeneity and data quality concerns in electronic health records.

Dr. Fritz Sedlazeck discusses his own SARS-CoV-2 research works. He discusses a number of projects such as quantifying structural variants in the SARS-CoV-2 genome and converting a human sequencing center to a COVID-19 testing center. He also shares tips on how he analyzes genomic data sets.

Dr. Fritz Sedlazeck discusses detection and interpretation of structural variants within genomes. He begins by explaining the basics of what structural variants are and how they affect organisms' phenotypes. He also covers some of his own work, for instance, categorizing the effects of structural variants in tomatoes and yeast, including being able to edit the genome of a tomato to change its flavor. Finally, he explains how the methods for detecting structural variants have evolved over time.

Dr. Todd Treangen gives us an update on what's been going on lately in the field of bioinformatics. We begin with a discussion on the best ways to stay current in bioinformatics. Dr. Treangen discusses the roles of the wide variety of sources of information such as conferences and journals. In particular, he expands on the role of social media sites like twitter in science. Finally, Dr. Treangen covers some of the current bioinformatics buzz, such as the integration of machine learning and the progress on a final, fully finished human genome sequence.

A diverse population of the SARS-CoV-2 virus can exist inside of a single person who is infected with COVID-19. Dr. Todd Treangen explains how his background in analyzing microbial genomes set him up to investigate this "hidden" diversity. He also discusses related work in co-developing a COVID-19 diagnostic test and co-founding and co-leading the COV-IRT organization.

For those considering a scientific career, Dr. Krista Ternus explains the wide array of scientific career paths in depth, from academia to companies to government labs to non profits and more. We also discuss leading a lab and winning scientific funding outside of academia. Finally, Dr. Ternus shares tips on writing successful grants and how to handle failure as a scientist.

Dr. Krista Ternus explains how studying metagenomics leads to being able to answer what is in an unknown sample, and describes the multiple applications of metagenomics. Dr. Ternus also explains applications of bioinformatics in synthetic biology and biosecurity.

As more sequence data becomes available from the SARS-CoV-2 outbreak, there are a number of considerations to make when analyzing read datasets coming from genome sequencers. Dr. Krista Ternus explains common steps to take when analyzing these data sets, potential pitfalls, and interesting scientific questions that can be answered using this type of data.

What is Convalescent Plasma Therapy and how can it be used as a treatment for COVID-19? In this episode, Dr. Michael Joyner and Dr. Rickey Carter give us a lesson on the past, present, and future of convalescent plasma therapy. From its origins to its role in the fight against the ongoing COVID-19 pandemic. We get a glimpse of both the clinical side, as well as the informatics side, of studying treatments for COVID-19, as well as the role of both clinicians and bioinformaticians in the future.

Adrianne Gladden-Young has been involved in the sequencing and surveillance of past outbreaks of emerging pathogens for many years. She discusses, for instance, her previous involvement in outbreaks such as Ebola and Zika. How can we be better prepared for future outbreaks?

Adrianne Gladden-Young tells us about her work in sequencing the very first SARS-CoV-2 genome in Massachusetts. She explains the full bioinformatics process of sequencing and analyzing SARS-CoV-2 genomes, starting from collecting a sample from someone who is sick, and ending with computational analyses leading to a better understanding of how COVID-19 spreads.

One of Darwin's original drawings of an evolutionary tree had "I think" written next to it. Dr. Luay Nakhleh explains how evolution underlies much, or perhaps all, of bioinformatics analyses. This includes his own research where he computationally models the evolutionary process, with a focus on cases where evolution violates Darwin's original assumption that evolution mirrors the shape of a tree. Dr. Nakhleh explains how he tackles the problem of modeling these complex evolutionary processes, including in cancer genomics.

What is Bioinformatics? What is the Bioinformatics and Beyond Podcast? What is the difference between Bioinformatics and Computational Biology? What skill sets are required to become a Bioinformatician? How would someone take their first step into the world of Bioinformatics? What career options are out there for Bioinformaticians?