{"id":2907,"date":"2024-06-13T21:04:16","date_gmt":"2024-06-13T13:04:16","guid":{"rendered":"https:\/\/opentrons.com.cn\/?post_type=knowledge2&p=2907"},"modified":"2024-09-03T11:37:50","modified_gmt":"2024-09-03T03:37:50","slug":"zdhngswkzbdzs","status":"publish","type":"knowledge2","link":"https:\/\/opentrons.com.cn\/en\/knowledge2\/zdhngswkzbdzs\/","title":{"rendered":"Automated NGS library preparation e-book"},"content":{"rendered":"\n

Advantages of automated NGS library preparation<\/strong><\/p>\n\n\n\n

Valuable NGS data depends on high-quality library preparation; and insightful sequencing relies on unbiased library coverage and good yields. Unfortunately, library preparation is time-consuming, labor-intensive, and error-prone. Faced with these challenges, high-quality library preparation requires time and money, time and money that scientists rarely have in abundance. As a result, researchers found that their ambitious goals were limited by library preparation bottlenecks. Now, after more than a decade of optimization and improvement, advances in laboratory automation promise to alleviate this limitation. Automation brings many benefits to NGS library preparation. This chapter discusses the three most important benefits: improved data quality, increased throughput, and reduced costs. We will discuss each factor, focusing on aspects that may surprise those new to automation and highlighting those that have the greatest impact on the research goals.<\/p>\n\n\n\n

\"\u624b\u52a8\u79fb\u6db2\"<\/figure>\n\n\n\n

Improving Data Quality: Accuracy and Precision Accuracy and precision are critical to achieving ideal results at every step of the library preparation workflow. In all tasks, reagents and samples must be dispensed, combined, and transferred in precise amounts to drive sensitive chemical reactions and deliver high-quality results. Most laboratories may need to reduce the volume to 1 \u03bcl during NGS library preparation. At this volume, automated systems and skilled human technicians performed comparably, with actual pipetting volumes within 5% of the target, i.e., 0.95-1.05 \u03bcl, termed the coefficient of variation (CV). Human technicians and automated systems do differ in pipetting variability and error rates, as discussed below.<\/p>\n\n\n\n

Reduce variability and errors Automated systems minimize variability and eliminate errors, which technicians may experience difficulty when working with complex protocols or large numbers of samples. Library preparation protocols can involve countless individual steps in a multi-hour long process, and given the scale of most NGS experiments, pipetting tasks are repeated countless times throughout the project (see Chapter 2 for more discussion).<\/p>\n\n\n\n

Inevitably, even skilled human technicians make pipetting errors due to inattention. Often, the only solution is to redo the entire procedure. However, robotic systems can perform pipettings tirelessly and flawlessly. Moves the right liquid to the right hole every time. By eliminating duplicate runs, you save expensive reagents and valuable time (see below for more information on cost savings). Additionally, by eliminating errors, automation can provide more reliable library and downstream data.<\/p>\n\n\n\n

Reproducibility and Consistency Analysis needs to be reproducible and consistent not only to ensure that its results are correct, but also to give researchers confidence in their results. Automation supports repeatability and consistency of analysis better than manual processes. One of the key factors affecting assay reproducibility is pipetting variability. Automation eliminates variability in the experimenter's technique because the robot performs each step the same way every time. Additionally, this enables better downstream analysis.<\/p>\n\n\n\n

Achieve higher throughput in less time In addition to improving data quality, automation can increase lab throughput, generate more libraries faster and free up valuable hands-on time. Each manual procedure takes 3-4 hours. In comparison, an automated system can complete a library in less than 3 hours, with less than 15 minutes of hands-on time, and the automated system can work during off-hours. With faster turnaround times, automation can shorten experiment times and deliver impactful data faster. With higher throughput, automation can facilitate more ambitious experiments and open new avenues for experimental design.<\/p>\n\n\n\n

With less hands-on time, researchers can devote more energy to experimental design and data analysis, leveraging their valuable skills.<\/p>\n\n\n\n

Reducing costs Automated systems can require a large initial investment, although some modern systems are less expensive (see Chapter 3 for tips on comparing automation options). But as efficiency and performance improve, many labs will recoup their initial investment and actually save on overall costs. Replacing tedious manual pipetting with efficient robots can reduce costs in many ways. Automation saves wasted samples, reagents, and consumables by eliminating the need to rerun after an erroneous procedure. Additionally, automation can save the indirect costs of lengthy experiments by reducing turnaround time. Although the short-term investment and learning curve may seem large, the result of these improvements is a net savings for most laboratories.<\/p>\n\n\n\n

Estimating expected return on investment (ROI) A laboratory\u2019s automation ROI can be calculated as the difference between its current manual workflow costs and the potential cost of an automated system. These costs will depend on some laboratory specifics, such as the price of specific reagents or the cost of hands-on time. However, the biggest impact on ROI is lab throughput, as it can amplify costs and savings. To quantify costs, it helps to distil samples, reagents, consumables, labor, and machinery into a single metric, cost per sample (CPS). Labs can use their CPS as a baseline to extrapolate their current and future throughput and compare with automation costs. To calculate your CPS, add up the lab's testing charges for a given period. Next, add in the labor hours spent on manual procedures. These hours can be quantified using full-time equivalents (FTEs). FTE assigns hourly wages to the work being performed. This can be the technician's actual salary or a reasonable estimate. You can also quantify FTE in hours rather than dollars to focus on time savings. These costs are then divided by the number of samples run during the period. The quotient will be your CPS in dollars or dollars and FTE hours per sample.<\/p>\n\n\n\n

The next step in calculating ROI is to quantify your CPS using automation. Simply add the reagent and FTE costs for equivalent sample volumes using the new system. Then add the costs associated with the automation system itself, such as the purchase price (see Chapter 3 to make sure you understand the sometimes hidden costs). Divide this total by the number of samples you intend to run and you will get a comparable CPS. An automated system with a good ROI will provide an affordable machine with minimal operating costs and provide the cost-reducing results described above. The more samples you run, the greater these net savings are, primarily by saving more hands-on time and reducing more wasted analysis on errors.<\/p>\n","protected":false},"excerpt":{"rendered":"

Advantages of automated NGS library preparation Valuabl […]<\/p>\n","protected":false},"featured_media":2908,"template":"","meta":{"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"knowledge2_cat":[90],"knowledge2_app":[],"class_list":["post-2907","knowledge2","type-knowledge2","status-publish","has-post-thumbnail","hentry","knowledge2_cat-dianzishu"],"yoast_head":"\n\u81ea\u52a8\u5316NGS\u6587\u5e93\u5236\u5907\u7535\u5b50\u4e66 - Opentrons\u4e2d\u56fd\u5b98\u7f51<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/opentrons.com.cn\/en\/knowledge2\/zdhngswkzbdzs\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u81ea\u52a8\u5316NGS\u6587\u5e93\u5236\u5907\u7535\u5b50\u4e66 - 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