My pleasure to have PacBio with us. We've got Jim Gibson here. Maybe Jim, just to kick it off, quick recap of one Q. Just talk a little bit about some of the momentum you have coming out of the quarter, and I know there's been some purchasing delays. Some stuff in the background, but how are you feeling about the rest of the year? Yeah. Thanks, Tycho, and thanks again for having us here. Q1 was definitely a mixed quarter. It was a quarter where we had some fantastic things happen. First, we had another record consumable quarter. Ultimately that's the company we're going to be, a consumable company supporting some really great customers. We saw some great indications of that strategy play out in Q1. Some examples of that, we saw our hospital and clinical business, which is really mid-sized hospitals and different clinics in Europe and America, increase their shipment volume 100% year-over-year. That's really part of our strategy as we sort of penetrate some of these markets where rare disease and carrier testing are important differentiators for us. Two, we signed the Basecamp deal, which is the largest deal the company's done in its history, to do 100,000 metagenomic study, really supporting AI foundational model development, which is something we feel incredibly excited about, as we believe we have the best data for that type of build with different types of customers. Where did we see some mixed things? We saw mixed things in Vega. We ran a specific Flying Solo campaign to try to capitalize on one of our competitors ending a product. It was successful from the standpoint of we got a number of customers in Asia-Pac and others to bring on Vega as their new platform of choice. 85% of those customers were new to the platform. We didn't see any uptick in the Americas related to that promotion, which we directly related to the fact that we're still not seeing academic and government picking up for us. Even with the lower price, with the promotion, we saw no activity. We saw lots of interest, people weren't willing to pull the trigger until they had the money in their account. Number three, and this is also mixed as well, is we saw a lot of excitement for our SPRQ-Nx chemistry release, which has happened now. We are commercially launched with that. One of the things that happened there is we had 15 customers successfully use it in early access and pay us to do that with fantastic results. We had some of our largest customers in China and Asia-Pac hold on their typical consumable orders, waiting for the SPRQ-Nx release. We were slightly under. Fourth is we had some headwinds with the conflict in the Middle East. We left about $1 million of revenue on ships because we couldn't deliver it in time. We have since gotten that delivered. That was Q1. Just thinking about guidance, you took it down $5 million. I guess, what's the current thinking on A&G for the rest of the year, academic and government? Yeah. When we came into the year, we were fairly certain we would not see an uptick on the Revio demand in academic and government. That was typically 50% of our demand historically. We effectively removed that from this year's guide. I think the second piece of information we got in Q1, which was newer to us, is Vega was an instrument that we were able to sell successfully to academic and government last year, and we had continued to believe that that would be a sale we could make in academic and government, but we didn't see that materialize in Q1 as much as we thought. When we lowered the revenue guide for the rest of the year, it's really thinking about, hey, is academic and government also going to be slow for us on the Vega side, even though it's a much lower price point? That's what we removed from the guide for the rest of the year. You mentioned the placements 85% to new customers on Vega. I guess, how are you thinking about utilization and maybe just the profile of the customers that are new? These are customers that are brand new to long-read, typically. Brand new to PacBio, brand new to long-read. These are customers that aren't really utilizing these things in a high rate of fashion compared to Revio. I think we're seeing 10%, 15% utilization. These are people doing individual experiments, research labs, other folks really kind of cracking into long-read, which is exactly what we created the box for. This is for people that are new, really trying to figure out the use cases. We do believe there's a big potential to move out of lower utilization customers into higher utilization customers when we release SPRQ-Nx on Vega, kind of in the second half of this year. That allows for a few things. It allows for a much lower sample volume, opens up a whole new set of samples for people that are using Vega. In addition, it's going to provide a lot more data output. So whole genomes, for instance, become a possibility at 30x on Vega in the future. So we think that's going to be a demand driver from folks that aren't just experimenting, forgive my pun, with long-read, but are actually really using it to drive some of their workflows in the second half of the year. Then Revio, I think 50% going to new customers. Are you seeing the sales cycle there shorten at all as the technology matures in the market? Conversely, is it getting extended with all the academic issues? Yeah. There's the actual sales cycle. I think the actual sales cycle is still long for academics because the funding isn't, and most of the academic funding quite honestly disappeared. I think for our customers that are commercial and hospital and clinics, it's stayed the same to slightly shortened because the use cases are clearer for them now. A lot of the folks that are the 50% that aren't new to long-read are people that are actually at many times expanding their fleets. They're used to our workflows, so their sales cycle is actually shortening somewhat from historical averages as they expand their fleets. The other 50%, the new to PacBio, it's right within what we've seen the last two or three years, getting a new customer to think about long-read at 2,500 genomes a year. You mentioned Flying Solo. I guess, how should we think about instrument ASP normalization for the remainder of the year? Yeah. For Vega, it will return to normal in Q2. It was only a one-quarter promotion. We saw a fairly significant drop in ASPs on Vega, and we expect that to normalize in Q2. We won't have any decreased revenue related to pricing. For Revio, once again, it really depends on the type of customers. When it's customers that are expanding their fleet and really sort of committing to higher consumables, it's that high $480s, if you will. For customers that aren't doing that aren't maybe committing as much, it's the $500-ish. It will be kind of in line with how we exited the year with ASPs for Revio, we believe, for the rest of the year. You commented on the strength on the consumable side, 9% growth there. I guess just talk a little bit about what's driving that. Yeah. There's two things driving consumable growth. Well, there was three components, and two of them were offsetting. The first is academics continue to use their Revios, so we've not seen a decrease. Even though the funding for new capital has dried up, they are still using their boxes and are running experiments. Which is great. On the other side, we have absolutely seen an increase in consumable consumption with the hospitals and clinics that we're working with as they move from the lab into mainline tests. We saw that at an outsized way in Europe, which is really kind of one of our leading indicators for this strategy, which is as these hospitals and clinics move from the lab into becoming mainline tests, and they can do a lot of test consolidation, their consumption is increasing dramatically. We saw, I think I mentioned briefly, consumables in China froze waiting for SPRQ-Nx. That's all been unlocked with the release of that product. I think moving forward, where we're excited when we see the consumables increasing for the rest of the year is now that SPRQ-Nx is on the market. We believe price elasticity is incredibly important to sample demand. We believe we'll see a lot more demand coming from some of these higher utilization customers that are really supporting their larger customers and price matters. That's why we feel their second half is so heavily weighted with our consumable growth. I guess just tying that all together on margins, you were 37% in the first quarter. You've guided 41%-44% for the year. How do we get comfortable with that trajectory? Yeah. 37% had a few things that we hadn't expected. Number one was the impact of the Flying Solo promotion. That took off 150 basis points, so that in and of itself will normalize in Q2. Number two was we knew we'd be coming into this year with some headwinds related to DRAM specifically. One of the things we did was we pre-bought a lot of DRAM at a higher price to secure inventory, so a little bit more of that hit us in Q1 than we anticipated because we secured more of that higher priced memory. That shouldn't be a drag on us going forward. Number three, we had some one-time issues around warranty. It's not a repeatable issue. We had to cover some things, and that was a one-time charge. We still feel confident with our gross margin guide of slightly above last year's exit rate, because it's really weighted on the fact that two things happen in the second half of the year. SPRQ-Nx becomes a much more meaningful portion of our product mix, which has a much higher gross margin for us as we go from one use per chip to three uses per chip. That saves both our customer money and saves us a lot on the gross margin line. Number two is as the insourcing of our Vega fully happens by the end of the year, so we're not paying an outside manufacturer profit on our Vega boxes anymore. We do believe that that will more than offset the headwinds that we're seeing with DRAM to allow us to increase gross margin this year. SPRQ-Nx, you've mentioned that a few times. You're now shipping worldwide sub-$300 HiFi genomes for large projects and $345 per genome list. I guess, how do we get comfortable with the reuse of the SMRT Cells? Talk through kind of the mechanisms for labs to actually capture those economics. There's a couple ways we are comfortable with the reuse. One, we had the early access program, which was two uses. We spent a lot of time on that. Number one, we were comfortable with the science, comfortable enough where we were able to charge for that early access program, which you typically do not do in our industry. We actually saw the results of that with higher data output and higher yield than we had expected than our current SPRQ-Nx chemistry. Number two was we'd been successfully doing three uses in our labs for a while and are now producing that at the same spec that our current single use has. From the standpoint of product functionality, we are actually seeing better performance than our current on-market single-use SMRT Cells. Number two is the companies that we're going to be selling to, the best customers for that are high utilization customers. These are customers that already are used to high volume sample prep, automation, and other things. These are our highest utilization customers. Part of the reason that's important is because you only have a finite amount of time to use the SMRT Cell. Once you put it on the deck, you have effectively think of it as a week, a working week to run three uses on that. In order to realize the full economics, you have to be prepared enough to have those samples loaded and ready to go. That's different than a lot of our lower utilization customers typically do. We feel confident that the customers that are selecting these and buying these are customers that are running a lot of samples, know how to use the automation, and have already worked with us on the workflow. I guess how are they adapting their workflows? You talked about, I think in the beta phase, 25% higher output per SMRT Cell. How are they adapting their workflows for the new chemistry? A, it's a simplified workflow. We've actually spent a lot of time making it simpler for our customers to extract the data. Number two is we spent a lot of time working with them on the automation and the software so that the conversion is fairly easy for them. In fact, it's so easy, it's a link that comes in an email that they can load themselves, and they're up and running on SMRT Cells. A, if they're using current single-use technology today successfully, they will have no problem using multi-use, and the only actual piece of it that's different from a Technology standpoint for the customer is the software, and that's just a straight-up update that you bring off of our system. We feel very confident that our users can successfully bring three uses into their workflow, especially in a week, and be really satisfied with the outputs. I guess how rapidly do you expect a Revio install base to transition to the kit? That's a good question. I think like I'd earlier highlighted the fact, I think the higher utilization customers are the ones that we feel are the most benefited by the switch to multi-use because they already have a lot of samples in the pipe. I think when we released the original SMRT chemistry, we had full conversion to that in about a quarter. I think our high utilization customers should be able to convert very quickly if they're the right customer segment and have the right sample volume to SPRQ-Nx. By the end of the year, I would expect most of those customers will have converted to SPRQ-Nx. The customers that are comfortable with single use because they're running their systems at lower utilization won't have to switch or do anything. I think the real question at the end of the year is what's the percentage switch? That's the piece we're still not sure, and we're hoping to get some visibility in Q3 on is like, what is the speed of that switch? Quite honestly, what's the elasticity impact on the sample use? That's something we don't know yet and won't actually know until we get up and running in Q3 for a while, and may not even know until the exit of Q4. How about, I guess, driving upgrades? You've talked about Vega being the tool to introduce customers to HiFi. To what degree are those customers eventually adding a Revio? As we think about SMRT Next, will that force more customers to upgrade? Yeah. The main premise behind the Vega was bringing folks new to long-read to start publishing and doing research on it. We're definitely seeing that play out. One of the things we didn't know was would it cannibalize Revio, i.e. would people that want Revio buy a Vega and vice versa? That did not happen. The other thing we're not really seeing a tremendous of is people upgrading from the Vegas to the Revios right now. They're just very different use cases we're learning as we've got the boxes out there. The people that buy a Revio want the throughput of the Revio, they want the 90 GB of yield. The folks that are running the Vega are comfortable with what it does right now, and that's why they're buying it. Now, what'll be interesting is when we do the SMRT Next chemistry on Vega, and we get more data and more sample types and different movie times, do you potentially start to see people buying it, then want to actually use the same thing in Revio? I think that's when we'll get a better indication of are people going to start upgrading when they buy the Vega? Got it. AI, you've mentioned AI enhanced sequencing to improve and expand methylation calling accuracy yields. Can you just walk through the DeepConsensus algorithm you've developed in collaboration with Google and how it deepens the insights users are getting? Yeah. We're getting way out of my wheelhouse. Maybe I'll take a step back and comment more broadly. I think we've been getting, like every company has, a lot of questions about, hey, how are companies going to utilize or potentially look at AI? I think we're taking sort of a very fundamentalist view about this, which is we think where our data is the best because it's the most complete and the most accurate, that people will get the quickest progress and the quickest results from our data sets. I think the partnership with Google DeepMind is showing that. Meaning if you give really smart people with really big access to compute and memory, they can come up with some incredible conclusions very quickly because of these complete data sets. I think what the Google DeepMind partnership is showing is when you hand these folks the data we can produce, they can come up with amazing results. I think how I look at the partnership with this is it's more proof points, just like Basecamp's going to be fundamentally doing with our data that the most complete, most accurate data is the data that you want to do your training on, and then ultimately base your conclusions on. I guess with the integration of the AI methylation calling into SMRT Next, how do you think about compute overhead per lab? Do they have to add capabilities? Right now, the Revios all ship with the compute you need to do all of the base calling and all of the other things you need. I think what's interesting is we think into the future, and we look at our ultra-high-throughput sequencing platform. One of the things we're talking about in designing is the compute isn't going to be on the system anymore. We're going to be leveraging what we think is a pretty basic setup for most of the larger labs, which is centralized compute. I think what we're really leaning into is how do we get the most information off of our tool, compress it in the most efficient fashion, so you don't need a lot of compute local, and then push that complete data set into these data centers, whether localized or cloud, so they can start coming to these incredible inference conclusions that support their research. We're moving away in many respects from the computers. There's this concept of what is it, edge computing or centralized? We believe that we will need less compute on the edge, i.e. the equipment, because we'll be basing it more on centralized data centers, whether it's in the labs themselves or whether then they're pushing the compressed data out to AI research centers, data centers, other places. We think that's the natural path this is going to go. We are leaning heavily into figuring out how to leverage that, and ultimately, we think that's the huge unlock for the company is all of these companies are being forced to figure out how to take huge data sets, compress it, and analyze it as effectively as possible. Before it was just us, some of the movie companies, and some of the other folks that were worried about all this data getting compressed. Now it's everybody. We think we're going to be the beneficiaries of a lot of that research. I guess by enabling deeper insights into methylation signals relevant to cancer tissue, is this kind of a bridge to open up new clinical markets? How do you think about the clinical path from here going forward? Yeah. I think we're incredibly excited about what we can do down the clinical path. One of the things internally that we've known for a while, and we're seeing evidence from it, is we believe we have an incredibly differentiated technology, first for just rare disease testing straight up. We're seeing that in Europe, and that's part of the reason we're having such significant growth in Europe, is how differentiated we are in rare disease testing and identifying things that other technologies can't. We believe that the carrier testing market is a similar testing market for us, i.e., germline. We're seeing a lot of tailwind from some of the PureT arget testing we're doing. We're working in collaboration with some of the commercial labs to test the variants that exome testing can't capture or short-read can't capture. We're seeing a lot of tailwinds from that, especially in the U.S. Three is, we know we've got a number of partnerships, a lot of the pediatric cancers where you can't currently use technologies to identify what variant of the cancer it is, but you can see it with our technology. We're getting a lot of positive results working with that. What we're really leaning into and where we think we're going to get the biggest beneficiary from the clinic is rare disease carrier testing, pediatric cancers, and ultimately, as customers start to realize that as whole genome sequencing becomes much more cost effective compared to exome sequencing, what we believe and what many of our customers have told us is we're just going to do whole genome sequencing. We're just going to do the first test using whole genome sequencing. We can go back and query that data over time if we need answers. They're waiting for, they tell us two things, cost parity on the sequencing, which we believe we're presenting to them with SPRQ-Nx, ultimately, our ultra-high-throughput sequencing platform that we'll be talking about and releasing hopefully in the coming future. That will be the thing that really allows us to take off and support the clinical and the commercial markets in testing. Yeah. We would cover GeneDx, and they're certainly seeing that whole genome transition. Pull-through on Revio dipped a bit to 29,000 per system. I guess, how do we think about SPRQ-Nx impacting pull-through? Yeah. It's a great question. How have we modeled it? We've modeled it with effectively our pull-through remaining consistent through the year because we believe that price elasticity, though it'll decrease the absolute dollar per sample, it'll increase through units. We're sort of forecasting pull-through remaining in the same range for the rest of the year. I think it could go higher than that if we see sample volume increase over that. Right now, we think that the increase in sample volume will more than offset the decrease in price per genome. We're predicting kind of in that $225-$245-ish pull-through range for the rest of the year. Let's hit on Basecamp. Biggest deal you guys have done, 100,000 samples. How do we think about that phasing in and impacting revenues back half of this year, 2027, 2028? The deal itself is targeted to be done in 2027. What we're doing in 2026 is we're effectively getting it up and running. We're planning on running about 10,000 of these metagenomic samples in the second half of the year. We're running their samples now. We've received the samples from Basecamp and have already started that and started delivering that data to them. The majority of the volume and the revenue comes in 2027, so that's when you'll see sort of a material increase on our service line, if you will, related to the Basecamp revenue. We're excited about that deal from multiple fronts. I think first is, as you said, it's the largest deal we've ever done by far. I think historically when we've won these deals, they're one 5,000, 10,000 sample deals a year. Because of the economics around SPRQ-Nx and our ultra-high-throughput box, ultimately, we could price in a way that made us competitive with everybody else and get them access to long-read. Because Basecamp is really a data first company, and their premise is, and the results they're seeing in their AI models is the best data wins, that's why they chose us. We think it's a huge positive proof point for the best data win hypothesis that we have going right now. From a revenue standpoint, the majority of it's in 2027. From a proof of concept and us being able to show the value at a human genome sequencing level with all these different metagenomic samples, that's going to start this year in 2026. As you mentioned, they're a data forward company. They're leveraging your data for the generative biology EDEN model. I guess, why is the structural context of preserved HiFi sequencing important to kind of build out these type of models? Yeah. What the people a lot smarter than me tell me about the needs for this type of data is historically, and if you've looked at the different types of data between short-read and long-read, is the more complex and complete the data sets, A, the less samples you need, and B, and there's no shocker here, the better the answers you get. I think historically, there's been a huge promise around genomics that if you got enough short-read data, you could make some of these incredible leaps using machine learning and other things. It hasn't really come to fruition yet, because as people tried to stitch this mosaic of multi-omics information together, they were too widely spaced, and there was too many gaps. When you talk to the different data scientists that aren't necessarily biologists, but come at this from a pure data standpoint, they'll tell you collectively, and they've told me the same thing every time, is the more complete the data, i.e., almost all of the genome, and the more layers of that data, the more you understand the structural foundation of what you're trying to figure out. You're not doing an inference, you're just analyzing the data. I think that's the difference is when you build these training models, when you say you're making an inference is you're extrapolating forward, and the more gaps in that extrapolation, obviously, the more chances it'll be wrong. If there's a lot fewer gaps in that extrapolation, you're not extrapolating, you're just matching. We believe that our data set allows you to do that match. Is it a match to a disease type, a phenotype, or an effect? We believe our data's the best for doing that. I guess along those lines, you added Chris Gibson to the board. How does his experience at Recursion and other ventures help with building the data tools and analytics you guys need? Well, part of what I was stealing is from Christopher Gibson, yes, you got me. No, I'm not related to him. Yeah, this has been his hypothesis for years, I think part of the reason he was excited to join the board is to help us really capitalize on this advantage we have. I think what he's learned through Recursion is he's learned kind of the hard way, what it's like to take a company that's at the bleeding edge and trying to do things that companies have never really done before, quite honestly, take on much better capitalized competitors and sort of crack into that market. He brings this sort of scrappy business acumen combined with an incredible knowledge about how data can affect biology, and he's brought that to our board, and I think that's something that's incredibly helpful for us. Even though the company's been around for 25 years trying to prove this, the next two-ish years are when we actually believe we'll start seeing tremendous amounts of evidence proving our hypothesis, and Chris is uniquely positioned to help us capitalize on that. Maybe just going back to the clinical markets for a sec. You've got the joint workflow with Covaris for FFPE tissue samples. It unlocks massive archives of oncology samples for HiFi. I guess, how do you think about that expanding the addressable market, and what early interest are you seeing from oncology researchers? Well, it's a great question. I think part of the thing, we've been behind in cancer. Obviously, it's one of the hugest areas of growth for clinical testing is cancer, and much like we've been waiting for people to understand the value of whole genome sequencing, we've been waiting for people to understand the value of the methylation signatures in others and some of the different cancers and the different phenotypes are discovering. We look at this as a hugely beneficial partnership, finally getting people to understand the benefit of HiFi and long-read in cancer detection, not just in the MRD like they're doing now with certain pieces of it, but with this much more in-depth ability to analyze and prove. We're very excited by that. We've been waiting for people to be able to unlock a lot of these solid tumor samples in a way that we can look at. We believe that much like we became what we believe is the go-to technology for rare disease testing and certain genetic variants and carrier testing, for a lot of these cancers, we believe we're going to be one of the only technologies that allow people to see and analyze these datasets and samples. Clinical customers are transitioning from pilot phases to sustained production scale sequencing. What's the timeline for a typical clinical customer to move from validation to full-scale LDT commercialization on Revio? I've been told, nine months to a year is kind of what I've been hearing now. I think sometimes it can go a little faster, sometimes a little longer. I think what we have seen in the smaller hospitals and clinics we've been working with, a lot of them purchased our systems and started looking at those things early last year and kind of got up and running into production at the end of the year, and we're seeing the results of it this year. Some of these bigger labs go a little slower, quite honestly, as they want to make sure it's battle tested. We're really hoping some of these bigger labs switch into production in the second half of the year. Some of the anticipated growth we're seeing in consumables is our expectation that these customers, these bigger labs, switch from the R&D to production in the second half of the year. I guess competitive landscape. Sequencing's always been intense. You've got emerging short-read and long-read players always vying for budget dollars. How do you differentiate the value proposition and prevent customers from going with a good enough alternative? Yeah, it's a great question, and I think what I'm learning, I'm back in the industry after a long time being away, is how many new customers are constantly entering, especially on the short-read side. I think we're wrestling with attention. It's getting people's attention for all the different solutions. What we find is the customers that are already educated on data types and long-read data, there's just two of us. Those aren't difficult battles to get attention. It becomes proving which is the better data type for your problem and what are you looking for. With the customers that kind of don't know what they want yet, it's an impact. There's a lot of noise out there, different technologies. You've got Illumina's TruPath, which isn't long-read, but they're using it to sort of combat Roche, and you've got a lot of that. We are fighting for that fixed set of capital dollars, as you highlighted at the beginning of your talk. It hasn't necessarily impacted us yet because the customers we're working with typically want a long-read solution. I think what we are seeing some probable impact is, as we want to get folks new to long-read. I think where we get their attention, quite honestly, is the ones that are effectively trying to get into the game of AI or data or anything else. They'll start looking first for what's the best dataset. They won't necessarily start from the question of what's the technology all the researchers have used. We're hoping they start asking the question, what's the best dataset I can get that will solve my question? We believe that will drive more attention to our platform, especially with Basecamp and some of the other companies that we're talking to over the next year or two. Great. We're at time. I think we'll leave it at that. Thank you. Great. Thank you, everyone.