Singular Genomics Systems, Inc. (OMIC) CEO Drew Spaventa on Q1 2022 Results – Earnings Call Transcript
Singular Genomics Systems, Inc. (NASDAQ:OMIC) Q1 2022 Earnings Conference Call May 10, 2022 4:30 PM ET
Philip Taylor – Investor Relations
Drew Spaventa – Founder and Chief Executive Officer
Dalen Meeter – Head, Finance
Eli Glezer – Founder and Chief Scientific Officer
Conference Call Participants
Matt Sykes – Goldman Sachs
Michael Ryskin – Bank of America
John Sourbeer – UBS
Tom Stevens – Cowen
Good afternoon, ladies and gentlemen, and welcome to the Singular Genomics’ First Quarter 2022 Earnings Conference Call. [Operator Instructions] It is now my pleasure to turn the floor over to your host, Philip Taylor. Sir, the floor is yours.
Thank you, operator. Presenting today are Singular Genomics’ Founder and Chief Executive Officer, Drew Spaventa; and Head of Finance, Dalen Meeter. Earlier today, Singular Genomics released financial results for the 3 months ended March 31, 2021. A copy of the press release is available on the company’s website.
Before we begin, I would like to inform you that comments and responses to your questions during today’s call reflect management’s view as of today, May 10, 2022, only and will include forward-looking statements and opinion statements including predictions, estimates, plans, expectations and other information. Actual results may differ materially from those expressed or implied as a result of certain risks and uncertainties. These risks and uncertainties are more fully described in our press release issued earlier today and in our filings with the Securities and Exchange Commission.
Our SEC filings can be found on our website or on the SEC’s website. Investors are cautioned not to place undue reliance on forward-looking statements. We disclaim any obligation to update or revise these forward-looking statements. Please note that this conference call will be available for audio replay on our website at singulargenomics.com on the Events page of the News and Events section on our Investors page.
With that, I will turn the call over to CEO, Drew Spaventa.
Hello and thank you for joining Singular Genomics’ first quarter 2022 conference call. My prepared remarks today will touch on a few key areas, a brief reminder of our mission, commercial progress and opportunities, partnership activities and our newly published sequencing and applications note in progress on our product roadmap. Lastly I will provide a brief preview of what you can expect from Singular at AGBT.
Here at Singular Genomics, our approach has always been to prioritize the needs of our customers and to advance sequencing to meet their needs. This philosophy remains at the forefront of how we think about our technology, our products, our business and how we pursue our mission to accelerate genomics for the advancement of science and medicine. The incredibly talented team of scientists, engineers, operators and commercial staff at Singular are more focused than ever to execute on this mission as we are about to commence our first customer shipments later this quarter.
The G4 is the most powerful, fastest and most flexible benchtop sequencer available today. It can produce more data output per hour than any other competitive system by a significant margin. The G4 offers unmatched speed, also leading peers by a significant margin. The speed of our novel rapid SBS chemistry developed in-house is a fundamental differentiator of our technology today and our technology roadmap going forward. The G4’s flexibility is unmatched. Each of the 4 flow cells offers 4 individually addressable lanes. No other offering comes close to providing this level of flexibility and modularity, which allows customers to scale up or down to optimize their sequencing run capacity as they need. In addition to these fundamentally differentiated performance metrics, it is imperative to deliver gold standard accuracy and data quality. Now with 7 external preproduction units placed in third-party labs over the past year, the G4 has consistently demonstrated industry gold standard accuracy levels of up to 99.9% for Q30 for 75% to 90% of base reads. Core to our mission is to provide this industry-leading performance at attractive pricing.
We provide direct cost savings across the pricing spectrum in terms of cost per gigabase, cost per read or cost per experiment depending on the setting and customer profile. To sum it up, compared to other competitive systems, the G4 can achieve a data output rate that is 2x to 4x higher, over 2x faster runtimes, unmatched 4 flow cell and 16 lane flexibility, gold standard accuracy and highly competitive pricing. For customers, this combination will enable new, more desirable workflow patterns through daily runs, flexible run sizes and simultaneous run type variation. It can also replace several alternative or redundant sequencers and enable applications limited by pooling our batching. This is a value proposition we are proud of and excited to bring to market.
Turning to our commercial progress, we continue to build our sales funnel and have added to our system order book since the last earnings call held in March. Shipments of the G4 system and F2 flow cells remained on track for this quarter. Interest and interactions with prospective customers is accelerating. Our robust engagement with potential users across end markets reinforces the exciting opportunity in front of us. We are actively scaling our commercial team, adding experienced sales reps and customer support personnel in key regions around the U.S. in anticipation of serving our customer and future installed base.
Offering a premium user experience is a critical component to our growth strategy. We are continuing to push forward with our plans to scale the team through 2022. We are also offering customers the ability to engage with our technology through our in-house customer care lab. This offering to prospective new customers is a courtesy service to help validate customer applications on a G4 via sample optimization and testing in advance of purchasing. We intend to increase the capability of this lab and offering over time in line with customer feedback and demand.
I would now like to share more information on how we are qualifying the commercial opportunity in the field and describe how differentiated G4 attributes allow us to segment major customer groups and key applications to best serve their unique needs. As we think about our target customers, three profile standout, academic core labs, clinical and research commercial labs and emerging growth labs.
I will go into detail on how the G4 positions well within these three profiles. Number one, academic labs. These labs are often serving multiple PIs and run a wide range of applications with different requirements, including RNA-Seq single cell targeted panels, exome and whole genomes. They often don’t have a steady flow of experiments and are more highly price sensitive given grant funding constraints. The flexibility of 1, 2, 3 or 4 flow cells, each with individually addressable lanes, coupled with a 19-hour less run time is ideal for their needs. The four flow cell flexibility scales up or down to accommodate changing volumes and allows them to optimize sequencing runs based on the needs of their applications and experiments. The significantly faster turnaround time alleviates the requirement to wait and batch life-size samples or runs. And we can offer favorable pricing across all G4 consumable kits, down to single digit dollars per gigabase for many of these users.
Number two, clinical and research commercial labs. These labs consist of small and large commercial organizations utilizing both clinical and research sequencing applications such as targeted panels, RNA-Seq, exome and rapid genomes. Clinical applications in this setting are often in the form of validated lab developed tests or LDTs in a CLIA lab. The G4 fits well into these routine sequencing environments where turnaround time is constrained. The flexibility of the platform alleviates the need once again to wait and batch like size samples or runs. The power in terms of gigabase throughput, combined with attractive pricing provides a more scalable solution relative to other benchtop offerings in this segment for a number of clinical and commercial labs, the G4 serves as the first alternative option for those currently using high-throughput sequences.
Number three, emerging growth labs. As we have noted on prior calls, the amount of capital that has flowed into healthcare over the last 2 years is more than the previous 10 years combined. A significant portion of that capital is making its way into new company creation and businesses that are built on NGS. These companies are running both research and clinical LDT-based applications. They are looking for a sequencing provider that can scale up with their needs over time in a cost-effective way. They don’t want to spend $1 million in a piece of capital equipment. The G4 can scale up with their sequencing needs over time, providing scalable throughput options comparable to instruments ranging from MiSeq through NextSeq all the way up to NovaSeq, SP or S1, ultimately providing high throughput level volumes and pricing on a benchtop sequencer.
We realized there are considerations beyond the power, speed and flexibility, that factor into customers’ purchasing decisions. For some labs, largely academic core labs, costs are among the most important consideration given funding limitations. Our pricing strategies provide attractive economics for all users across the pricing spectrum, cost per gigabase, cost per read and cost per experiment. We believe it’s important to look at pricing beyond simply cost per gigabase. Oftentimes, pricing comparisons are quoted solely in the context of the highest number of cycles on a 100% fully utilized flow cell. This is often not what happens in customer labs. Full flow cell utilization is not always practical based on customer sequencing and application needs, required turnaround times and sample flow.
As a result, customers end up paying more for their run. Alternatively, they wait days, if not weeks, to batch samples and run only after a flow cell is more fully utilized. This is not practical for many customers. The G4 scales down cost efficiently due to the fore flow cell modularity. This alleviates inefficiencies from both ends reducing the need to wait to pull samples and reducing the need for low flow cell utilization. Because of this, all customer types, low, mid and high volume users can realize cost savings with the G4 through operational efficiencies. In summation, we look the G4 as a superior sequencing platform in terms of core KPIs with favorable pricing and strong value propositions in some of the largest and fastest-growing markets.
Now, I’ll turn to our recent highlights and partnership activities. Integrating with customers’ existing workflows as a plug-and-play solution has been a priority. We have already made significant progress with 9 partners previously announced, including some of the most widely used library prep providers. We continue to expand our partner network to work with innovative and collaborative companies to accelerate our mission. As such, we are excited to announce that we have entered into two new partnerships with library prep solution providers, Bio-Rad Laboratories and Quantabio. With Bio-Rad, we are validating it’s SEQuoia library prep kits for RNA-Seq, with Quantabio we are validating its kits, a product line for both DNA and RNA sample prep. In addition, we’re proud to announce our partnership with market leading bioinformatics solutions provider, NVIDIA. With NVIDIA, we’re validating G4’s workflows with their Parabricks secondary analysis platform with the goal of providing accelerated secondary analysis and data handling.
Next, I will provide a few updates on our recently disclosed G4 sequencing data and application note. If you recall from our last quarterly earnings call, we highlighted a technical report where the G4 demonstrated state-of-the-art accuracy in whole human genome sequencing. We are pleased to expand the portfolio of available data and share some of the highlights from our most recently published application notes to further demonstrate the performance and versatility of the G4 in one of the most widely run applications RNA-Seq.
The study covered RNA samples on the G4 compared to Illumina NextSeq 2000. Samples were individually loaded on to different flow cells on the G4 to highlight the sequences reproducibility, both within the system and compared to the NextSeq. The G4 process need on a library using 2,100 base care sequencing. Each sample produced 25 million reads for RNA-Seq analysis for both the G4 and the NextSeq 2000. The overall comparison was nearly identical across all secondary RNA-Seq analysis metrics substantiating that the G4 meets customers’ needs on quality and accuracy, coupled with the potential to produce results at a much faster rate given the shorter sequencing run times.
In addition to our progress on standard sequencing applications, we continue to make headway on our specialized applications kits, HD-Seq for rare variant detection and extended rate sequencing or XR-Seq. We continue to advance our development of HD-Seq and are now demonstrated greater than Q50 accuracy in paired reads 150 base formats with greater than 100 million reads per flow cell. We recently published a technical paper on XR-Seq novel library prep method to enable longer molecule reconstruction from standard paired reads 150 base cycle data.
The study demonstrated how this technique can be used to reconstruct 1 to 3 kilo based long fragments from short-read technology. We believe it has the potential to fill unmet needs in areas such as immunology for therapeutic antibody and T cell discovery, protein engineering and vaccines for infectious disease. We expect the XR-Seq method to aid in the characterization of complex DNA libraries with high accuracy and throughput ultimately expanding the utility of the G4. Transitioning now to the PX system, we are assembling our first beta instruments now. Later this year, we are planning to open a Technology Access Program, or TAP, for early PX collaborators. The TAP will offer early customers and thought leaders the ability to collaborate with us to develop assays and applications in advance of the commercial launch in late 2023.
Turning to AGBT, we are looking forward to meeting in person at the conference in June. We anticipate having a G4 system onsite for demos, hosting KOL discussions and networking events, showcasing new technical developments and application nodes and providing additional data and highlights from our specialized application chips. In addition, at AGBT, we are excited to provide a sneak preview of one of the disruptive innovations that our development team has been working on, the MAX read flow cell. We believe the MAX read flow cell will redefine how customers think about cost and flexibility for short-read applications in the 30 to 100 base care range, a configuration that is applicable to applications such as short read counting for NIPT, proteomics and single cell RNA-Seq.
And one final note. As highlighted last quarter, we launched our Scientific Advisory Board. We recently issued a press release noting a distinguished group of academic and industry experts on the SAB; David Barker, Lawrence Fong, David Ledbetter, Elaine Mardis and Daniel Shoemaker. We are thrilled to work with this exceptional group of industry thought leaders who advise in the company’s product and service offerings and R&D pipeline.
With that, I will now turn the call over to Dalen to go over the details of our first quarter financial results and operational progress.
Thank you, Drew. I’ll start by covering the Q1 2022 financials and then provide brief remarks on our operational progress, including our infrastructure build-out and manufacturing capacity planning to support future growth.
Operating expenses for the first quarter of 2022 totaled $22 million compared to $10.3 million for the first quarter of 2021. These totals included non-cash stock-based compensation expense of $3.6 million in Q1 2022 and $1.1 million in Q1 2021. The year-over-year increase in total operating expenses was driven primarily by our product pipeline and R&D roadmap, scaling headcount and infrastructure to support the G4 launch and the costs associated with being a public company.
Net loss for the first quarter of 2022 was $22 million or $0.31 per share compared to $23.9 million or $2.05 per share in the first quarter of 2021. The year-over-year decrease in net loss and net loss per share was driven primarily by the change in fair value of convertible notes and warrants in Q1 2021, which were converted to common stock with the IPO and no longer outstanding in Q1 2022. This was partially offset by higher operating expenses, as previously noted. In addition, the year-over-year decrease in net loss per share was driven by the increase in weighted average share count used to calculate net loss per share because of the common stock issued in connection with the IPO.
Our weighted average share count for the quarter used to calculate net loss per share was approximately 71 million. Ending cash, cash equivalents and short-term investments, excluding restricted cash, totaled $316 million. Looking ahead through 2022, we continue to expect investment across the business to increase as we scale up manufacturing, add headcount sales, customer service and support and marketing and progress the product roadmap and future innovations in R&D. We expect our Q2 weighted average share count used to calculate net loss per share to be approximately 70 million.
Turning to commercial. We are encouraged by the progress our sales team is making in developing the sales funnel. We are still early in our launch. And we will plan to share more information after we begun to ship products and have more visibility into how the sales funnel is transitioning to orders. We are scaling our manufacturing headcount across both instruments and consumables in line with anticipated production volumes. We believe that our dedicated manufacturing site in San Diego is sufficient to meet our capacity needs for the near and medium-term.
We expect our longer-term manufacturing and lab space needs will be accommodated with our new 78,000 square foot headquarters facility. And finally, we are pleased with our history of execution and progress to date. We’ve always been capital-efficient. Since inception, we’ve raised approximately $450 million and have spent less than $140 million. We remain well capitalized to support the G4 launch activities with $316 million of cash and investments on hand. Our existing resources are forecasted as sufficient to support our activities over the next 3 years. With initial orders in, we are ramping up our commercial organization and we will continue to make the necessary investments to support our commercial goals and scale the business.
Thank you and back to Drew for closing remarks.
Thank you, Dalen. We continue to advance the technical capabilities of the G4 with expanded library prep provider integration, improved accuracy and advanced specialized applications to further differentiate it as the most versatile and powerful benchtop sequencer in the market. Interactions with current and potential customers across end user profiles indicate that G4 provides compelling value beyond the alternatives. We are excited to leverage the Customer Care Lab to advance our increasing funnel opportunities and continue to grow the order book.
The G4 is positioned favorably in this large and growing market. Singular Genomics is in a great position. We are well situated with two differentiated technology platforms well-funded with $316 million of cash and investments, providing roughly 3 years of runway for us to scale our operations and products, execute on our exciting technology roadmap and generate valuable customer relationships.
In the near-term, we are laser-focused on getting G4 manufacturing scale and identifying market segments and customers where the G4 offers a differentiated value proposition. Our exceptional team continues to execute across the organization. We are on track to commence our first customer shipments later this quarter. And we are confident that those early customers will get real value for their investments and will have an exceptional customer experience as they put our platforms to work.
Joining me for Q&A, we have Eli Glezer, Founder and CSO; and Dalen Meeter, Head of Finance.
Now let’s open it up for questions. Operator?
Thank you. [Operator Instructions] And the first question is coming from Matt Sykes with Goldman Sachs. Your line is live.
Hey, good afternoon. Thanks for taking my questions. Drew, maybe I’ll start with where you just finished on the customer service part. Just given the critical nature of service and support in the early portion of your launch, could you maybe talk a little bit about the infrastructure that you put in place from a service and support standpoint? I mean, needing to properly balance in terms of the headcount and resources you put towards it, but knowing that the service and support element is so important to the beginning part. Can you just talk about kind of the investments you’ve made in the plan that you have for that?
Yes, absolutely happy too. So early on with an initial launch, it really is bringing multiple parts of the organization together. So we’ve tried to get ahead of it by hiring and building out a dedicated the CS&S team. But largely, it’s a collaborative effort with R&D to really bear hubs the customers, the early ones. And another component of that is the sample testing and what you heard in the call is we’re setting up a customer care lab where we can scale that. So for your first handful of customers, you really want to make sure you understand their assays, you understand how they are going to use the sequencer. You do ample testing in-house first and you work with them to make sure that once you drop a box in, they are going to get the expected type of data quality and results that they are expecting and that you’ve already done yourself in your own lab. And within that whole process, you’re building out the customer sales and support infrastructure, which is CS&S/SAS technicians to really shadow closely the first few units to make sure that we can absorb all the learnings we need to kind of learn and iterate forward.
So taking a step back, this is something that has been on the forefront of our kind of mind for quite some time. During the last year of EAP sites and placing these units gave us a number of really good learning experiences. And we’re taking those learnings forward to build out dedicated personnel to kind of handle and scale that. But it’s very much something that you have to take head on. And it’s going to be more work for the first few up to dozens of systems as we start to learn kind of what Install looks like, what customer service looks like, understanding the nature of the early technology and third-party hands.
So we feel like we’re very prepared. But it’s definitely a focus area for us.
Got it. Thanks, very helpful. And then, just two quick follow-ups. I’ll ask them both upfront and get back in the queue. But it sounds like we will get more color later, but just anything you can give us on order backlog in sort of the early stages of this quarter would be really helpful or at least maybe some feedback that you’ve gotten from customers? And then secondly, Drew, you talked about the emerging growth labs and totally appreciate the level of funding that we’ve seen over the past couple of years. So looking forward, it seems like a different environment and maybe cost consciousness might be a little bit more on the minds of these types of customers. How does the cost element and value proposition of G4 RESONATE with these customers? And has that come up in the conversations with those customers? Thanks very much.
Yes. They are both good questions. I guess I’ll take the first one, which is kind of – the short answer is we added to the order book. We’re not providing guidance yet. But at the time and point, when we’re able to, with confidence, provide more color around things like order book and installs, we absolutely will, it’s just still early for us. In terms of the emerging growth segment specifically, we directly haven’t heard or seen too many people that at this point are rethinking purchasing decisions or rethinking their investment into their own businesses to advance their science. And I think that it’s going to take some time. I mean, taking a step back, the figure that we quoted on the earnings call just a few moments ago, the number or the amount of capital that’s flown into the space over the last 2 years, a lot of that’s going to need to be put to work. And people are going to need to spend that capital to advance their efforts internally. That being said, I think we’re going to have to be adaptable to how customers want to step into a new technology or spend money and that might be different solutions. It might be reagent rental. It might be a capital lease. It might be just having to walk people through and show them how there’s real cost savings holistically with one of our sequencers and their specific use case over the alternative. But the need is still there and these companies are well capitalized. They are going to have to invest in it and a lot of that investment is going to need to be in sequencing to advance their own efforts.
Great. Thank you.
The next question is coming from Michael Ryskin with Bank of America. Your line is live.
Great. Thanks for taking my question, guys. Following up on the previous question, I want to start with the valuation process for some of these customers. There has been some preliminary data published. You’ve got your own data sets out there comparing the G4 with some comparator systems. But I want to go through the process that your customers and your potential customers are going through when they are making that purchasing decisions. Can you just walk us through the process, what data points are they looking at sort of how long is that validation process go and sort of just if you could give us some case studies there? Thanks.
Yes. So it really does vary by customer type. If you’re talking about kind of academic labs, it’s going to be a longer sales cycle in general. And there’s probably going to be more back and forth over a longer period of time to really make sure that you have buy-in. Academic cores typically have budgeting cycles and you really want to start conversations early and get ahead and know who has budget for a new system and really have a more tailored approach to engaging and bringing them forward. And for the academic cores, cost is really a big part of it. So you have to understand the nuances of other applications. You have to walk them through how they would use the applications on your system. You’re going to have to provide data, whether it’s publicly available or other data that’s not publicly available yet that we would engage with the customer and really get them confident that the system can perform. And once they are confident the system can perform, they are going to have to see a cost savings relative to alternatives typically. Now, there is some academic cores that have specific value propositions, where the speed of the flexibility specifically makes it a very different dynamic for them to purchase. And again, those are where we want to, for a large part focus our efforts, places where our competitive advantage really highlights the need to adopt our system versus others. As we’re thinking about other customers, whether they are emerging growth customers or commercial labs, typically with a private entity, it’s a quicker buying decision. But there’s still that hurdle where you need to get them comfortable. The sequencer is going to meet their needs. So we offered and will continue to offer sample testing as needed. And that’s something that, again, was not a surprise or a reaction. We’re kind of building in that infrastructure to have capacity to test sample that high volumes for a highest number of customers.
Now, of course, you hope that you’re qualifying your customers and understanding who’s a high potential buyer if you do spend the time and work to evaluate those samples versus somebody that just wants to see free data. But that’s part of the engagement process that we’re trying to make sure we improve that. So long story short, it really varies. Some customers are very quick. There’s a new technology. I’ve seen the technical paper. I know the team, I want to buy. And then there’s people all over the spectrum from that very quick decision that doesn’t meet me much all the way to the customer that needs many, many months of education of interaction of testing samples to the point when they get to a buying decision.
Okay. Alright, that’s helpful. And a follow-up on that, if I may. In the last couple of months, obviously, there has been a lot of noise by some existing competitors by some new competitors with product entry to the market. I am just wondering, with some of these products that are already on the market and some of these products, for example, like chemistry X, that’s projected to be a future launch at some point, right? And you have your own F2 flow cell launch and then there is the F3. So, I want to ask about future innovation. How much does that come into conversations when you are discussing the G4 with customers, is the selling point on the F2 flows, or is F3 something that has to come into play? Is the timing on that, still unchanged by end of year? And again, sort of as you are going through the comparisons, are you comparing with existing staff with future stats, just sort of could you level set where the discussions are from a chemistry and from a consumables perspective? Thanks.
Yes. No, it’s a good topic. There were a lot of sub-questions in there. I would say that, again, it varies based on customers. Some people look at the G4 and the F2 right now and their comparator is a P1 or a P2. And if you are looking at an F2 versus a P1 or P2, it’s a very simple buying decision. It’s going to be much cheaper on a cost per rear, cost per gigabase. It’s going to be faster and more flexible when it scales down. There are other users that are using P3s and they are really at the high-volume usage in of the NextSeq or users or potential customers that are using the low end of the NovaSeq. For those customers, they really do want to wait and understand that is the F3 going to be on time, that’s really how I want to use this sequencer. And then within that subset of customers, some are willing to get in line early and know that the roadmap is going to be there and others are candidly saying we want to wait until we have an F3 and then we are a customer. I think the roadmap is actually very important. There is going to be continued competition in the space. In terms of your question on Chemistry X, we still don’t really know enough to kind of handicap where or what that means, especially for our segment, which is really bench-top kind of mid to low end of the high throughput. I think the rumors that we have heard and again there are simply rumors that Chemistry X most likely makes its first appearance at the very high end, which again is not our target market. But back to the question on roadmap, absolutely, the ability to continually push our cycle times faster and our run time faster and to push the density or the throughput of our system higher are top of mind. And we certainly intend to continue to improve on those metrics over time. And then the last part is we still are on track for an F3 launch in Q4 this year. So, everything is on track in terms of the product roadmap for the near-term.
Great. Thanks so much. I appreciate all the detail there guys.
Okay. Next, we have John Sourbeer with UBS. Your line is live, John.
Good evening. And I appreciate the update on the sales funnel. The company had previously highlighted that revenues aren’t recognized until after G4s have completed installation and validation. It sounds like you are going to be shipping units in the second quarter. Should we expect revenues in 2Q, or is this more of a second half event?
Yes. Hey John, this is Dalen. Yes, like you highlighted, the primary focus right now is shipping, placing systems in customer labs, getting them up and running. Just as really trying to get as many units out into the field and start driving that consumable pull-through. Like you highlighted on the last call, the revenue recognition is going to follow the specific terms and conditions for each of the orders. Those initial shipments, there is going to be a customary acceptance or validation process that we are going to engage with the customer on to complete. And that’s going to be a gating item for revenue recognition. That’s not uncommon for a new product launch, right. Longer term, we are going to structure the Ts and Cs to allow for revenue recognition upon shipment. But assuming we are shipping here in June, that acceptance process we are targeting to take about 30 days or so. It could take a little bit longer and revenue recognition will follow that. So, that could end up triggering revenue recognition on Q2 shipments in Q3.
I appreciate that. And I guess just on the continued build-out on the commercial organization, any update on the search for Head of Chief Sales Officer, Head of the Commercial Organization and this continued build-out on that team?
No specific update in that position. We did initiate a search quite some time ago. And we are in the process right now, hope we would have more information on that soon, but very encouraged by the existing team. As we have previously noted, we do have a very strong set of sales leaders across the country at this point. And the team continues to grow in terms of a leader, we have been very happy with the quality of potential candidates out there and expect to be moving forward with the CCO position fairly shortly.
And then lastly, strong cash on hand and the company has been very prudent on the cash burn, any changes just from the update that was provided last quarter and on the burn for this year and outlook for the next year?
Yes. I will let Dalen comment on any changes for this year. But I guess I will just add one thing in. We were very fortunate to access the market and put a very strong cash position on our balance sheet. And while we absolutely want to not ignore the macro situation and make sure that we have as much flexibility as possible to extend runway, very much the next 12 months to 18 months to 24 months are an opportunity for us to invest that cash into R&D, into product, into commercial and to really grow our business and further advance our technology. So, there is a lot of thought that’s going into this. And we are trying to be very dynamic and really maximize flexibility. In terms of the rest of this year, I will let Dalen talk about kind of how we are thinking about cash.
Hey John. Yes, on the last call, we talked about 2022 operating expenses could potentially be about double in 2021. We are probably tracking a little bit below that, but nothing materially different from our comments there. Yes, I would just say we have always been really capital efficient, like we said in the prepared remarks. We are going to continue to be very disciplined in terms of how we invest our capital. We feel really good about our position and our forecasts are basically using our existing resources through the next 3 years. So, we feel really good about the position we are in.
Thanks for taking the questions.
Okay. Next, we have Tom Stevens with Cowen. Your line is live. Okay. It looks like Tom’s line has disconnected. [Operator Instructions] Okay. Next, we have Tom Stevens with Cowen. Tom, your line is live.
Hi. Yes. Just to follow-up on John’s question. So, I believe you said you exited last year with about 40 sales people on staff. What’s your target for headcount this year? And then if you could maybe give us some color on target headcount for your kind of services team given your kind of ramping into this year?
Yes. Sure. So, I am not sure I will have to punt over to Dalen on what we have said for total headcount. I know we have said we plan to grow the operations team to about 100 by the end of this year. And we plan to grow the commercial team, and that’s sales support and marketing to about 40 by the end of this year. Outside of that, I don’t know if we have guided further on headcount. Dalen, do you know?
No, that’s it. Tom, we ended the quarter at total headcount of about 240.
Got it. That’s useful. And then just to kind of follow-up on your high accuracy kit coming towards the end of the year. So, actually there are some competitors out there. You have also kind of gone after this route. How does your kind of key kit compare to kind of coming competitors in the high accuracy arena? And where is your kind of key differentiator there?
Yes. So, Eli Glezer is with us, and I will have him answer that.
Yes. Tom, the key differentiator there is that we sequenced both trends of a double-stranded DNA molecule. And that allows us to achieve not only inherent high accuracy of sequencing, but also get around other air modes that can be introduced by DNA damage by polymerase, miscopying and so on. And so it really provides a unique advantage for applications where you want to detect rare variants and really get a true variance that are genomic in nature as opposed to just resulting from library prep steps or just general DNA damage.
Got it. Thanks for that. And then just the last one, soo given you kind of wrapped up your access program, I was wondering if you could kind of give an overall kind of spread of the metrics you saw. So, it would be there are seven in total, we know about five of them. If you give any metrics overall, so maybe the range of accuracy across the access partners and kind of the key learnings from the final two, would be really helpful.
Yes. So, maybe I will provide kind of a few overall metrics, and then I will let Eli talk about any additional learnings. It was a very broad set of applications. So, I would say, if you think about kind of the core performance metrics of sequencing, you start with accuracy. And we were consistently at our target spec above 75% Q30 for all of the different applications. In fact, for a number of them, we were quite a bit above Q30. And I think you have to remember that Q30 is a prospective estimate. What really matters is retrospective accuracy. And if you look at the accuracy metrics we had for each of those sites. And I believe there is a table on the slide on our website. We were consistently above 99.7% and even at 99.8%, which is higher than Q30 for all of the EAP sites. In terms of cycle time, there is actually a nice progression. We improved our cycle time throughout the year last year. So, whereas we started with our first few sites, we were closer to four minutes. We were able to get the cycle times down to our target specs, which under three minutes, and that’s also on that table. So, accuracy and cycle time or run time consistently within or beating spec. And then the last one would probably be the throughput or the number of reads and actually don’t have this in front of me. I am talking from memory. But I believe that we also demonstrated pretty consistent improvements actually getting above our spec on the F2. So, the F2, we set our specs rather conservative. We had 150 million reads per flow cell, so across four flow sales at 600 million reads. And what we were showing on the EAP is that we were actually able to achieve not only above 150 million reads, but on some of the EAPs, we were getting over 200 million reads on the F2 flow cells. And I think that that really probably concludes the core KPIs or other minor things like insert lanes and GC bias and other things like that that we worked on with the individual partners. And I think the fact that we were able to advance several of those to purchasing decisions speaks to the quality of the data and the experience and the confidence they have that it meets their need. In terms of learnings, there were a ton of learnings. And one of the initial questions on the support component of it was a big one. What is it going to take to make sure that the customer experience for our initial users is really gold standard and it’s understanding the way the sequencer is going to behave. It’s understanding what onboarding is going to look like. And all of those things we have now had experience with over the last year. There were definitely some learnings on the actual user experience. There were some learnings on the robust reliability of the machines. There were learnings across the board. I don’t know, Eli, if you have anything specific that you would want to say that is a key learning that we are bringing forward on machines?
I would say we had kind of general progression in development as we were going through the two beta sites first and the five early access sites. So, we started off with relatively easy applications like RNA-Seq and then progressed to full paired N150 reads with index reads additionally. So, kind of working through that and putting us in a position where we are comfortable about hitting our commercial specs, like Drew mentioned, 150 million reads per flow cell in the F2, flow cell every flow cell will have double that 300 million reads. And then overall, run times, accuracy and some of the secondary metrics that Drew mentioned in terms of GC coverage and in certain length that we can handle in the sequencing. So, yes, lots of good progression and confidence building throughout that early access period.
Right. Thank you. And just one more if I could tie up. So, given the recent kind of IT battle between Illumina’s and BGI, I just want to make sure you guys use four kind of chemistry and not the two color. And then on that kind of [indiscernible] product you have been announced at AGBT values for the G4 not for the coming PX?
Yes. To the first question, yes, we are a four-color novel chemistry that was developed from the ground up internally, so very different composition of matter, different polymerases than anything else out there. And we actually call it rapid SBS chemistry, given the fact that we are able to push the cycles so fast. And we actually have a roadmap to go even faster. And then on the second question, MAXRead, yes, MAXRead is something that really, again, was born out of deep understanding of how a lot of customers are using sequencers. And one of the pain points is essentially that the cost per read doesn’t get that much cheaper for short reads versus long reads. And we saw that as an opportunity if you can deliver lower cost or a higher number of short reads at a lower cost, you are really offering something differentiated that’s a pain point. If someone is doing a 50 or 70 base reads versus a 300 base reads, they are not getting a third of the price for that read. It’s not even close. So, how do you allow people to see real cost savings for short reads and it’s the technology we are working on and there is actually a configuration that we are looking to share in a technical paper where we can produce up to about 1 billion reads on a single flow cell for short reads. And that dramatically will increase the number of short reads that you can do on a G4, which we think will be a big differentiator for a lot of the short-read applications.
[Operator Instructions] Okay. We have no further questions in queue. Thank you, ladies and gentlemen. This does conclude today’s conference call. You may disconnect your phone lines at this time and have a wonderful day. Thank you for your participation.