This episode features an easygoing bioinformatics expert. Our conversation with Nikhil Ram Mohan, Staff Scientist at the Stanford University School of Medicine, is an easy entry into the dry-lab work of bioinformatics and how this informs and augments wet-lab molecular biology work. He tells us about his use of digital PCR to analyze biobank samples, how his first visit to another country was his move to the U.S. for grad school, about his love of teaching, and about being a new father. Check out this interesting episode, fueled by an interesting guest!
Bioinformatics is a relatively new field of science that is very interdisciplinary in nature. Its practitioners use a mixture of biology, chemistry, physics, statistics, and computer science to develop methods and software aimed at helping integrate and understand biological and other data.
Our guest for this episode is Nikhil Ram Mohan, Staff Scientist at the Stanford University School of Medicine. He describes bioinformatics as the bridge to understanding biology. We learn about his international studies and path that brought him to this current role and field of study, and then dive into some of his recent work. Here he and his team analyze biobank samples using digital PCR (dPCR) and quantitative PCR (qPCR) and compare results from the two while correlating results with additional data available for each sample to determine if SARS-CoV-2 RNA detection and quantification in blood can serve to help predict potential for patient coinfection. Their work found that dPCR was able to detect SARS-CoV-2 in samples that were negative when evaluated by qPCR and that a series of biomarkers can help predict coinfection.
We also get to hear a bit of Nikhil’s interesting personal story, which includes his undergraduate engineering studies in India and leaving his native country for the first time when he moved to the U.S. for graduate school. We learn how he managed changes in culture, what he loves about teaching, and about him being a new father.
Visit the Absolute Gene-ius page to learn more about the guest, the hosts, and the Applied Biosystems QuantStudio Absolute Q Digital PCR System.
Cassie McCreary 00:00
All right.
Jordan Ruggieri 00:00
Well, I think we need to bring in the “spookala bookala.”
Cassie McCreary 00:03
Spook-a-la-bucula? Yeah, Spooky Scary Skeletons? OK. Hmm. Oh! it's me first? Okay, here we go.
Cassie McCreary 00:23
Welcome to Absolute Gene-ius, a new podcast series from Thermo Fisher Scientific. I'm Cassie McCreary.
Jordan Ruggieri 00:29
And I'm Jordan Ruggeri. And today we're joined on the show by Dr. Nikhil Ram Mohan.
Cassie McCreary 00:33
Nikhil earned his PhD in genetics and genomics at the University of Connecticut in 2016. He's currently a staff scientist at the Stanford University School of Medicine, where he specializes in bioinformatics. He's working on some extremely interesting research around SARS-CoV-2 coinfections to help study the virus more.
Jordan Ruggieri 00:51
He also has had a fascinating career journey; we hope you enjoy our conversation.
Jordan Ruggieri 00:56
Oh, Cassie, I can't believe that viruses and bacteria would just invade my body without permission. That makes me sick!
Cassie McCreary 01:04
Here we go, back to puns.
Jordan Ruggieri 01:10
So I know, you know, you're researching SARS-CoV-2 and bacterial coinfections. Can you tell us a little bit about this? How does that work? What exactly are you looking into?
Nikhil Ram Mohan, PhD 01:21
So when the pandemic hit, I was in the research lab in the Emergency Medicine Department. We were tasked with understanding how viral RNA was working in the host system itself. And what the presence of viral RNA in the blood meant to outcomes and whatnot. So, the first thing we really wanted to see is how the viral RNA in the blood correlated with viral RNA in the nasal pharynx because that was the point of entry for the SARS-CoV-2 virus itself. And as we went through the study, we started looking into estimating the copy number or the number of transcripts of the viral RNA, both in the nasal pharynx, as well as in the blood itself. And traditional methods only allowed us to use qPCR. And we heard about this instrument for absolute measurements using digital PCR, we wanted to explore that as well. So, what we did was basically from blood and nasal pharynx, collect samples, extract RNA, amplify the N-1 and N-2 regions of the virus genome and just estimate how much of it was present in the body. And based on what we found in the plasmids, we then did some modeling to see how well that would be associated with extra pulmonary complications and whatnot. That was one wing of the study. The other study was basically looking at bacterial coinfections, coinfections or super infections and people who had COVID who visited the ED. And we were, there again, we were trying to see if the viral RNA in blood was very well correlated with this one test, we were using that was able to detect both virus and bacteria in the subject's blood itself. And there again, that seemed to work out really well. It was pretty interesting. Actually, we saw that, for one the viral RNA, the blood RNA, or anemia, as it's been called now, was actually a really good predictor of severe disease and a host of extra pulmonary complications as well. And there was also a strong correlation between the viral loads that we detected and the presence and vital scores from the test that we were doing in the second study.
Jordan Ruggieri 03:41
So it's more along looking at if there is some correlation on the amount of virus, the amount of bacteria and how the virus actually impacts the host. Right? So research and looking at, if you have more virus, does that cause a more negative impact on the host? Or if you have both virus and bacteria does that cause a more negative impact on the host? Is that a correct assumption?
Nikhil Ram Mohan, PhD 04:05
Without necessarily going into the mechanism of how things are happening itself, we were just doing, It was a prospective study. So based on the information we had, it tried to make these associations that we were able to base on the viral loads and the 30-day mortality or the need for greater oxygen, mechanisms of implementation and stuff like that.
Jordan Ruggieri 04:30
What did the research show how about coinfections and how it impacted the host?
Nikhil Ram Mohan, PhD 04:34
So we were testing a particular methodology of detecting the viral RNA or signatures for viral and bacterial infections based on host response against the code patients that we had in our biobank. And what we saw was that coinfection rates are not very high, but it can differ based on the institution you're sampling from, if you will. Right? So it can be as low as 3% coinfection, or as high as 10% coinfection in the population that you're studying.
Jordan Ruggieri 05:10
And how do you identify which coinfections to research? Were you looking more just broadly to see or just in general, if they had also had a species of bacteria? Or did you actually go in mind thinking of, you know, “Did they catch E. coli, or do they have some type of other organisms?” Were you looking to? How did you decide that? How did you look at that?
Nikhil Ram Mohan, PhD 05:36
So it was it was not preplanned. A lot of these correlations are done based on the clinical data that we get after the fact. Right, so when the subject go into the emergency room, if a physician suspects that the subject might have signs of a bloodstream infection, as well, and they would collect blood from the subject, and then that sample would be inoculated into a blood culture tube, if you will. And then the colonies that are isolated after are then identified for what organism it is. So, all the subjects that we enrolled in the study, a subset had also had these symptoms that were flagged, and blood culture analysis done. So, we went in and looked at how many of those subjects had these potential co- infections, what those co-infections were, and how those correlated with the viral loads that we were seeing, or the vital scores from the other tests we were seeing.
Jordan Ruggieri 06:32
So Nikhil, it sounds like these, this was more researching the impact of the virus and the coinfection on a potential host. It's not necessarily diagnostic, it was just examining potential outcomes from these coinfections. Is that correct?
Nikhil Ram Mohan, PhD 06:47
Yes, definitely. So samples are collected while the patients are already there, and research was performed after the fact. Right? So you were trying to make connections for what could’ve happened based on the data that we had presented.
Jordan Ruggieri 07:01
So Nikhil, you know, you're in a really interesting spot in terms of the research that you're conducting, and in that the samples that you're looking at, and the organism that you're looking at, humans, you know, that there is a viral infection. You know that there's something already there. You're almost taking this, this already known, right? It's a positive infection and looking to generate more data to research and inform future prospective health outcomes. So, it's a really interesting spot in that, you know, most people think, "Okay, yes, I'm, you know, yes, it's there, or no, it's not there," but you're actually looking at this pool of positive data to see if you could extrapolate for future potential. Right? For future information, for future ways of looking at a particular type of infection. Is that right? Can you explain kind of that role a little bit?
Nikhil Ram Mohan, PhD 08:06
I can try. So with, with COVID at least back then when you say positive, that just meant that the virus was detected in the nasal pharynx. Right? Not a lot of people back then were looking at the virus in the blood itself. There was, there were a couple of studies that had started looking at it in the serum, and correlating it with certain outcomes, but they were doing only qPCR back then. So, we, we did look at a small pool of subjects who were positive based on the traditional sense of testing for COVID back then. But then we were looking for the viral loads in blood, right? In subjects who were positive by the nasal pharyngeal swabs. So, there was, that was how we were trying to differentiate ourselves, right? So, we were looking at how the virus was able to translocate. So, let me be clear, so we don't know if the virus was still replicating in the blood, right? We were just looking for evidence of the RNA in plasma itself. So what we were looking to see is that if we were able to find the viral RNA in plasma of the subjects that came into the ED, and whether the viral load itself could correspond to some of the severe complications, we were seeing in those subjects as well, right. So, it's a combination of using prior knowledge about the subjects’, you know, their past medical history, all of the clinical labs that are taken as part of the routine lab work, and adding an additional layer to that in the form of the viral RNA here that we were testing in the lab and trying to see how we will be able to connect the two.
Jordan Ruggieri 09:50
Makes total sense. Did you do any type of modeling with these coinfections? Or how are you able to show that, you know, these coinfections may have some impact?
Nikhil Ram Mohan, PhD 10:03
So, the test that we performed uses a suite of host response markers that's based on transcriptomics signals from the host itself. And based on the signals that we get, we can, that test actually can talk about a potential bacterial infection or potential viral infection, and also give us a severity score. We didn't necessarily do any modeling, because the N for that particular study was rather small, it was not a very large N. We saw that and we compared proportions rather than doing modeling, there was some statistical significance in the difference of proportions between those that had coinfections and those that didn't with respect to the scores that were being generated by the other test.
Jordan Ruggieri 10:52
That makes sense. I want to talk a little bit about that kind of transcriptomics approach. How did how did you approach assay design for transcriptomics and looking at those markers?
Nikhil Ram Mohan, PhD 11:05
So that was in collaboration with another third party, that was not something that we had done. So, this is an offshoot of a different lab from Stanford where they had studied this for many years at this point and come up with a set of 29 host response markers that describe all three outcome results, if you will. So that, we were really testing that platform against this data, that was not something that we did.
Jordan Ruggieri 11:31
Make sense. So you were using those 29 markers as a measure of potential outcome for the host. And those transcriptomes, are they host transcriptomes? Or are they viral?
Nikhil Ram Mohan, PhD 11:45
They're host, host response, right. So we sent,
Jordan Ruggieri 11:49
Host response. Okay.
Nikhil Ram Mohan, PhD 11:51
One of the samples that we collected was just the whole blood in tubes called PAXgene tubes, which can protect the RNA in the sample for extended periods of time. RNA is extracted from these tubes and then you use an N counter to get a measurement of the number of copies of each of those transcripts that you're looking at.
Jordan Ruggieri 12:10
Okay, so it's those 29 markers are indicative of the overall health of the host. And so you're researching, if they came in with an infection of SARS-CoV-2 or a coinfection, looking at those 29 markers to kind of gauge general health of the of the host. Correct?
Nikhil Ram Mohan, PhD 12:36
And also, to be able to detect bacterial coinfection, it's a lengthy process, right. So you need to inoculate the blood in our blood culture tube for extended periods of time. And then you need to identify what it is. Currently, antibiotic treatments are pretty empirical, based on what the best guess for infecting organism is, it has, right? So, if there isn't even a bacterial infection to begin with, it's just you're seeing the symptoms but there is no active bacterial infection. You're treating the patient with an antibiotic for no real reason. This is also a way to do a test in its current form, it still takes time, but they are trying to shorten that to develop a point of care test that can tell you if there's a bacterial infection in a specified amount of time.
Cassie McCreary 13:27
So that'd be really beneficial, right? Because like antibiotic like resistance, and everything like that is a big problem. So for just, you know, willy nilly using antibiotics, so to speak, but we have a tool like this, it can be really helpful to maybe potentially reduce that one day?
Nikhil Ram Mohan, PhD 13:42
Definitely, that's the goal, at least, right? So, this was our study was also like a proof of concept to show that that test can actually detect bacterial coinfections with high accuracy.
Jordan Ruggieri 13:56
Very interesting. We love digital PCR here at Absolute Gene-ius. So, talk a little bit about the digital PCR angle of your research. How did you use digital PCR? How was it helpful?
Nikhil Ram Mohan, PhD 14:08
For one, when we were studying the viral RNAenemia with qPCR, we were finding that of the subjects that we had tested, there was a very low positivity rate. But we didn't know if there was because of the lack of sensitivity in, sensitivity in the qPCR mechanism itself, or it was actually the case. Right. So we started testing on the digital PCR and we saw that if I remember the numbers correctly with qPCR positivity rate was about 1.4%. But with digital PCR, we saw that jumped up to about 24%. And we are getting absolute quantities, right? So, there's no need for a standard curve or anything to get the viral load. So, it just made more sense for us to go that route for that particular study.
Jordan Ruggieri 14:54
Why do you think that, you know, in terms of the sensitivity, in terms of that huge jump from 1% to 24%, How did that, ,how did that, you know, impact to the maybe the precision or the observations for the, for your study?
Nikhil Ram Mohan, PhD 15:09
Yeah. So, the challenge was that a lot of the samples that we had had fairly low viral loads to begin with. If we had relied on one person from qPCR for us to go forward in that study, we then needed a larger N for us to be able to do further analysis, right? But with a digital PCR, that helped us out because the rate limiting step there was that we were only able to sample so many subjects coming in as well. The higher sensitivity with a digital PCR platform really helped us in being able to ascertain the patterns that were actually present, which will we would have missed out relying on just the qPCR.
Jordan Ruggieri 15:53
Did you use qPCR in conjunction with digital PCR in any way? Or did you use, you know, qPCR to help maybe optimize your assays, or at least get up the road for some of those assays?
Nikhil Ram Mohan, PhD 16:05
Initially, at least for about 100 or 150 samples, we did both q and digital PCR simultaneously. What happens is when the patient, when we get the samples, the clinical microbiology lab is already making sure that those samples are positive or negative. Ours is just a research lab, right. So, we need to do the qPCR to confirm to ourselves that it's the same results that we are seeing. So we did do qPCR simultaneously with digital PCR. And that's kind of how we were able to compare the correlations between the RNAemic and non-RNAemic and the viral loads, and how those were impacted and all of that as well. So, the initial study relied a lot on us being able to do both simultaneously. And once we ensure that we were seeing concordant results, then we move forward with this digital PCR.
Jordan Ruggieri 17:02
So, I know you have a very analytical background, how did you approach using digital PCR? And how did that, you know kind of help in regard to your study?
Nikhil Ram Mohan, PhD 17:14
I think for one, just the absolute numbers help, right? It's not unlike that the qPCR data has never been used before, because all you have to do is make sure that your lab log transforming the data. But with absolute numbers, it's easier to see the minute differences as well. A Ct value from 23 to 24, might not seem as big, but you might still see a lot of differences in the absolute quantity itself. So, to be able to parse those out, having the absolute quantities from the digital PCR definitely helped out.
Jordan Ruggieri 17:50
You mentioned a few times that you you're doing some coding. Are you the one that's actually running the digital PCR? Do you, you know, shadow somebody? Or do working in conjunction with somebody? And how important is it for you to understand the, what's happening in the lab for you to write good code for the bioinformatics portions of your project?
Nikhil Ram Mohan, PhD 18:14
Let me start with the latter. I think there is a necessary interplay between the wet lab and the dry lab aspects of what we're doing. If I don't understand the experimental setup, my writing lines of code do not make really sense if I don't know what the appropriate controls are and what my comparisons against are either. As for the initial part of the question, during the peak of that study, I had to get my hands dirty and done a lot of the RNA extractions and running the digital PCR myself as well. So, there were others doing it in the lab as well. But this was time sensitive project. So in order to get information out, we just took turns and I had to actually do it myself as well.
Jordan Ruggieri 19:06
So it sounds like it's good, it's good to have that experience. If you're thinking about going into bio, or being a bioinformaticist, it's good to have that blend of both wet and dry lab experience.
Nikhil Ram Mohan, PhD 19:18
Definitely, and we got lucky that the instrument we were using for the digital PCR was a breeze to set up. The reaction mix was the same as just the qPCR mix in. Putting it on the plates they were just pretty straightforward, so I didn't have to learn a whole lot of new things to be able to do it. So that was easy.
Cassie McCreary 19:44
Taking a quick break from our conversation to tell you about Applied Biosystems™ QuantStudio™ Absolute Q™ dPCR system. This instrument enables quantification of your targets without the need for standard curves in only 90 minutes. Digital PCR can be as simple as preparing your samples, loading onto the plate, and running the instrument.
Jordan Ruggieri 20:03
Unlike other digital PCR systems, the Absolute Q dPCR instrument does not use emulsion or other droplet-based methods to compartmentalize reactions. In fact, the microfluidic array plate (MAP) technology enables consistent delivery of more than 20,000 micro-chambers. It's a great solution for anyone looking to quantify gene targets.
Cassie McCreary 20:24
And Thermo Fisher Scientific has a suite of dPCR assays for applications like AAV viral titer quantification, liquid biopsy analysis, and wastewater surveillance. You can learn more at www.thermofisher.com/absoluteq or visit the Absolute Gene-ius webpage. Again, that's www.thermofisher.com/absoluteq or visit the Absolute Gene-ius webpage.
Jordan Ruggieri 20:51
The Applied Biosystems™ QuantStudio™ Absolute Q™ dPCR system is for Research Use Only. Not for use in diagnostic procedures. Let's get back to our conversation.
Jordan Ruggieri 21:03
Cassie, it's your turn — Cassie's Career Corner.
Cassie McCreary 21:07
I need like my own little jingle for like Cassie's Career Corner. Bum Bum Bum. But anyway, now we get to talk about all the cool, like fun, colorful things that have to do with your career. And I just realized now like, we didn't really even get the chance to go into like, even just like a like 30-second overview. Like what has your career journey looked like just so our listeners are aware.
Nikhil Ram Mohan, PhD 21:27
It was actually pretty diverse. So, I'm an international, originally from India. And I did my undergraduate degree in engineering, interestingly enough with a focus on industrial biotechnology. And then I came to the United States for my master's where I started studying halophilic archaea. So, these are salt loving organisms that you see that turn hypersaline environments red in color. And then I decided to pursue studying halophilic archaea again for my PhD as well. It's been a long-drawn-out process because I've done two postdoc positions now. First one was in Boston College with Dr. Michelle Meyer, studying the oral microbiome and the non-coding RNAs in oral microbiome, and how the non-coding RNAs in certain pathogenic organisms affect pathogenicity. And then I moved to Stanford in 2019 to work with Dr. Sam Yang in the Emergency Medicine Department. It's been a diverse set of projects to look at so far. So initially, I started looking at how the host neutrophils responded to different stimuli, like bacteria or fungal, viral stimuli. And we were doing epigenetics to see if we were able to get an epigenetic pattern for those. The lab has always been interested in potentially developing diagnostic tools for reducing the turnaround time in antibiotic susceptibility testing and stuff like that. When COVID happened, the focus switch to studying COVID, how it might cause extra pulmonary complications, severity, and even long COVID. And now we're doing other projects, we're going back to the focus of reducing the turnaround time for antibiotic susceptibility testing using microscopy. There's actually a different project where we using, we use digital PCR a little bit but Neisseria gonorrhoea, as well. But that's kind of the roadmap of how I got to this point now.
Cassie McCreary 23:29
So, you attended, like your undergraduate studies, you completed those in India. You did your graduate work in the US. Did you not only make the jump from an engineering background to like pursuing, like, you know, like the genetics and things like that. But did you also make a total jump, like to a brand-new country as well all at once? That's like a lot.
Nikhil Ram Mohan, PhD 23:50
Actually. I've never been out of my country before I came to the United States for the first time.
Cassie McCreary 23:55
So what was that like? That's, that must have been overwhelming?
Nikhil Ram Mohan, PhD 24:00
It definitely was. I grew up in a city in India and I did my I did my masters and PhD at University of Connecticut, where it's in, it's in Storrs, which is a small-town college town. So, there wasn't really much around there when I got there. So it was, it was a pretty big change for me.
Cassie McCreary 24:23
How did you navigate some of the challenges that you probably faced when you were trying to make all of this adjustment at once?
Nikhil Ram Mohan, PhD 24:28
So for one, at least, when I was in the in my undergraduate institution, we didn't have a lot of opportunities to do hands on research. At that point, it was just starting out. So, a lot of the things that I learned theoretically, I'd never actually done until I came from my graduate program. Apart from that, just the way the whole exam structure itself is was very different. So when I was in India, we were told that these were the classes I had to take. And we had a schedule that was given to me. I didn't really have the option to pick and choose classes at all. Just being able to pick classes. I was like, "Oh, I want to take this and that, that and this, and that way," you know?
Cassie McCreary 25:11
And that leads me to kind of a good question, because a lot of our listeners, at least we're hoping, are a little bit earlier in their career, or they might even still be pursuing their studies themselves. Do you have any tips for like navigating like stress or like managing that? Because I mean, a PhD alone is a lot. And it's, you know, it's a lot to work through. So what would you suggest?
Nikhil Ram Mohan, PhD 25:31
I was very lucky, I had a good support system, it's essential. I was lucky enough that the, the gym at UConn was really good. And I had a good group of friends who would always want to go to the gym and play a sport or lift weights or whatever. So you are bound to get frustrated when you're in grad school. So, you just need to find a way to distract yourself in a healthy manner.
Cassie McCreary 25:57
Listening to kind of your overview of your career and everything like that, has your career gone the route that you would have expected. You've made some changes over the course of things. So is this ultimately, when you first started like your studies all the way back in like undergrad, is this kind of the trajectory you had pictured for yourself or just totally different?
Nikhil Ram Mohan, PhD 26:14
It's kind of on the same trajectory, I would say. One of the reasons I wanted to pursue a graduate career was to eventually become a professor. I was lucky to have really good instructors both in undergraduate as well as in my graduate school. And my mentors all through so far, they've built me piece by piece to where I want to be, I think. So, my current training with Sam has really helped me you know, just learn about the nitty gritties of how a lab runs itself. Ever since I've been a staff scientist here Sam has given me the opportunity to learn a lot of that.
Cassie McCreary 26:57
And what is it about like professorship that you're so interested in? Is it the mentoring component? Is it like, the research component? Is it a little bit of both?
Nikhil Ram Mohan, PhD 27:05
It's a little bit of both. And I love to teach. I always knew I wanted to teach at some point. But when I was in grad school, I was a TA for most of my grad career, and I just thoroughly enjoyed, you know, being able to interact with students and finding new and interesting ways to keep them engaged and just, you know, teach them the basics of what they're learning and all of that stuff. I think I love doing research, but I think teaching is what drew me into this.
Cassie McCreary 27:42
Awesome, well, this is like on a very minor scale for me, but like, so I tutor on the side. So I mean, obviously, I am no professor but like, I will say that, you know, being a tutor, I'm typically working with students who are feeling very challenged with a subject or something like that. And then when you finally are able to connect with them on a level and explain something in a way that works for them, because everybody's mind works differently. It's just like, it's such a rewarding feeling, isn't it?
Nikhil Ram Mohan, PhD 28:10
I agree, I completely agree. And then, towards the end of the course, when they come back and ask you questions, and they're like, "Aha, that's interesting." You know, you feel good about yourself, right? Just to be able to do that.
Cassie McCreary 28:22
Yeah, it feels good to make other people feel good. Or at least, I think.
Jordan Ruggieri 28:25
You're shaping the next generation as well, right? The next people that come up in the in the field.
Cassie McCreary 28:31
Yeah, no pressure. So, what would be your biggest piece of advice for people who are either new to the field or even to yourself back to when you were first starting out on some things like even on your career journey? I know, we talked about the importance of having like a good support system and all that, but just people who are starting out in their career even?
Nikhil Ram Mohan, PhD 28:53
Yeah. I think I would say, you know, you start with a particular interest, but I think you need to stay open to find the right path for you, at least in terms of the questions you want to answer long term, right? So as I said, I started working with halo archaea, and then the oral microbiome, and now AST. I feel like you need a diverse set of experiences to be able to really learn from all of that to you know, then be able to say, "Okay, this is exactly the question I want to answer in the future." So, if you're able to get some of that from different places, definitely take it. And in this day and age, I think interdisciplinary sciences are everything now. So, it's definitely good to have that. Don't limit yourself to just bioinformatics or just molecular biology. If you can be both a bench and wet lab scientist, go for it.
Cassie McCreary 29:57
I love that. There's a lot of different skills that you can learn from a lot of different areas and apply in many different ways. So I think that's excellent advice. You refer to yourself as an analytical computational biologist, right? That ties into like bioinformatics. And I don't know a ton, admittedly, about bioinformatics myself. And maybe this is because I'm pretty detached from the field. But like, I would assume that at times, there are probably those who struggle to understand what the kind of the world of bioinformatics is, or kind of people who are maybe adjacent to that, but they're not totally familiar trying to relate to it. So like, what is your method for explaining something of this nature to people who may not be totally familiar or as ingrained in it as you are?
Nikhil Ram Mohan, PhD 30:42
That's a tough one.
Cassie McCreary 30:45
You're like, "I don't" you're like "I tell them to Google it, and then I walk away."
Nikhil Ram Mohan, PhD 30:53
That's a tough one. Bioinformatics is just a simple tool that one uses to understand the biology of things, right? All of the sequencing, and all of that is just a tool to be able to decipher questions that we have not been able to answer before. Every day, there's a new technology that's arising and bioinformatics I think is just a way to make sense of what the technology is putting out. It started off just DNA sequencing, understanding the genome, went to the transcriptomics and understanding the gene expression patterns. As new technologies arise, bioinformatics just works as a bridge between, you know, the person who's generating the data and what the data is telling you.
Cassie McCreary 31:43
So for those who might be interested in the field and learning more about the field, what is like a day in the life of an analytical computational biologist? What does that look like?
Nikhil Ram Mohan, PhD 31:52
My current projects involve both looking at, you know, sequencing data, genomics, or metagenomics, or transcriptomics, mass spec spectra for MALDI, or LC-MS. I'm trying to get some information from microscopy images. So it's like, I feel like I'm providing the bioinformatics support for all of the projects in the lab. But I also get to be a part of every single project that happens in the lab, right? So, you kind of have to prioritize which project needs to get done when and then you're looking at different kinds of data, you're pretty much just staring at the screen all day trying to write the line of code that, you know, someday flow smoothly, someday there's just like a, you have a barrier there that just doesn't let you code anymore.
Cassie McCreary 32:42
So I would say it's two things. One, you have to have a lot of patience. And two, it sounds like every day is just a little bit different for you like you're involved in all these different projects. But I think that would probably keep things very interesting for you and very engaging at the same time?
Nikhil Ram Mohan, PhD 32:54
Definitely. I am glad that I don't have to do the same thing over and over. This way I get to learn a lot of new things as well.
Cassie McCreary 33:02
Now we move to the part of Cassie's Career Corner which, in which I ask you about what would be your best or proudest lab moment do you think so far in your career?
Nikhil Ram Mohan, PhD 33:15
I feel like you know, science is a battle most of the time. Things don't always work out the way you expect it to work out on a daily basis. So every small victory, I think I'm proud of. I've had rough times. So when things work out, you just celebrate it.
Cassie McCreary 33:33
Yeah, definitely. Okay, well, then, since you've given me the perfect segue, thank you. So, you said that things don't always work out? What would be your biggest lab oops, or mistake? Or do you not want to own up to that on the podcast? And that's okay.
Nikhil Ram Mohan, PhD 33:50
No. So oh, so then, when I was doing my PhD, there was about two and a half years into my PhD, there was some contamination in the lab that basically meant that I had to dump that project and start a whole new one. So that that would be my biggest oops. It's, you know, that got me to where I am today.
Cassie McCreary 34:19
All's well that ends well. Mistakes do happen, things like that. And then, you mentioned like your support system and things like that, and like sports and all that. Has science influenced your life outside of your career or any hobbies that you might have? I do hear you like basketball and I do hear that you speak like 10 million languages. Although when you're not looking at numbers and the data and everything else, what do you enjoy doing?
Nikhil Ram Mohan, PhD 34:47
I like to, maybe it sounds a little bit like a cliche, but when I can just focus on my son. I'm very happy just doing that.
Cassie McCreary 35:00
Oh, that's great. Excellent anything, anything you enjoy doing together, or is he still like itty bitty?
Nikhil Ram Mohan, PhD 35:05
He's not even two yet. So every day, every day is different, and you know. Right now, outside of work, that's all I do now, so.
Cassie McCreary 35:17
That's awesome. Any plans on bringing science into his life at an early stage?
Nikhil Ram Mohan, PhD 35:23
If he wants to, yeah.
Jordan Ruggieri 35:28
I think that's the right response for any two-year-old, right?
Cassie McCreary 35:32
I think that's exactly right. Yeah.
Jordan Ruggieri 35:35
That was Nikhil Ram Mohan, Staff Scientist at the Stanford University School of Medicine. Thank you so much for joining us for today's episode of Absolute Gene-ius. Stay curious, and we'll see you next time.
Cassie McCreary 35:48
Being an analytical computational biologist and working in bioinformatics makes you like a computational - biological - detective - librarian - prophet wizard?
Nikhil Ram Mohan, PhD 36:04
I like that. That's going on my Stanford profile now.
Cassie McCreary 36:09
There it is.