Pathogen Inoculation and Rating Strategies for Studying Maize Diseases
- ↵1Correspondence: tjamann{at}illinois.edu
Abstract
Maize is a globally important staple that is used as food for human and animal consumption, fuel, and other industrial applications. Pathogens affect all stages of the plant life cycle and every plant organ, and lead to significant yield losses. An integrated strategy incorporating cultural and chemical management practices, as well as development of resistant plant varieties, is needed to prevent yield losses due to plant diseases. Large numbers of breeding material must be screened to develop pathogen-resistant maize varieties. Inoculation methods must be high-throughput to accommodate the large screening experiments. Additionally, there needs to be an extensive understanding of the plant–pathogen interaction to use a targeted biotechnology-based approach, which takes advantage of knowledge of the system to engineer resistance. To evaluate germplasm for breeding and biotechnology approaches, inoculation methods must replicate natural infection, and disease severity must be rated consistently to accurately screen germplasm or gather data on pathogens of interest. Here, we review inoculation and rating methods for Gibberella ear rot, seedling blight caused by Globisporangium ultimum var. ultimum, and Goss's wilt that are efficient and high-throughput. We also introduce fluorescence microscopy techniques for leaf samples infected with Exserohilum turcicum, the causal agent of northern corn leaf blight. These pathogens all cause significant yield losses, and in particular, Gibberella ear rot is associated with the accumulation of harmful mycotoxins. Understanding how pathogens cause disease and how plants defend against attack is a major goal of maize pathology studies and critical for developing integrated management strategies.
INTRODUCTION
Maize is a staple food crop for humans and livestock. Additionally, it is an input for ethanol production and is used for a variety of industrial processes (Ranum et al. 2014). Maize is highly impacted by a variety of diseases each year, leading to reductions in yield. In the United States and Ontario (Canada), an estimated average of 6.13% of maize yield was lost each year due to diseases between 2016 and 2019 (Mueller et al. 2020). Globally, annual losses are estimated to be >5% (Savary et al. 2019), and from 2001 to 2003, the estimated global loss of maize each year was 8% (Oerke 2006). Pathogen ranges are expanding with the changing climate, and an increase in unpredictable weather could lead to more favorable environments for disease proliferation (Juroszek and von Tiedemann 2013; Hooda et al. 2016; Miedaner and Juroszek 2021). In this review, we describe various diseases that affect maize, as well as how these diseases are managed in the field. We also discuss how plants defend against the pathogens that cause these diseases. An important tool in the fight against maize pathogens is conducting inoculations to study these diseases in controlled and field environments. We introduce a variety of inoculation techniques used to study different maize pathosystems. Finally, we describe a fluorescence microscopy technique that can be used to explore plant–fungal interactions.
DISEASE PRESSURE THROUGHOUT THE MAIZE LIFE CYCLE
Diseases occur throughout all stages of the plant life cycle and all plant tissues. At the seedling stage, there is a complex of diseases referred to as seedling blights, root rots, and damping off, caused by oomycete and fungal pathogens. These diseases reduce seed germination and pose a risk by reducing crop establishment and eventually yield (Munkvold and White 2016). A rise of disease incidence might be observed in the coming years as planting dates are shifting earlier to maximize yields, particularly at higher latitudes with cooler springs, as damping off pathogens favor cold and wet conditions (Broders et al. 2007; Schmidt et al. 2020). Additionally, pathogen strains not controlled by fungicides have been identified (Brantner and Windels 1998; Robertson et al. 2014; Matthiesen et al. 2016; Bickel and Koehler 2021).
During the vegetative growth phase, foliar diseases typically produce lesions that reduce yields due to lowered photosynthetic capacity in the leaves (Silveira et al. 2019; Ludwig Navarro et al. 2024). Major fungal foliar diseases and their causal agents include gray leaf spot (Cercospora zeae-maydis), northern corn leaf blight (NCLB; Exserohilum turcicum), and southern corn leaf blight (SCLB; Cochliobolus heterostrophus) (Ward et al. 1999; Pratt and Gordon 2005; Munkvold and White 2016). SCLB rose to national recognition in the United States due to an epidemic stemming from the widespread use of maize varieties hypersusceptible to a strain of C. heterostrophus called race T (Ullstrup 1972). This hypersusceptiblity was due to a male sterility gene used in breeding stocks for hybrid maize production. Bacterial foliar diseases and their causal agents include Goss's wilt (Clavibacter nebraskensis) and bacterial leaf streak (Xanthomonas vasicola pv. vasculorum), both of which can significantly reduce yield (Vidaver and Mandel 1974; Coutingo and Wallis 1991; Mueller et al. 2020; Ortiz-Castro et al. 2020).
Stalk rots degrade the pith when grain fill begins, decreasing yield through the loss of harvestable ears on fallen, or lodged, plants and slowing harvest when lodging is extensive due to the weakened stalks (Dodd 1980b; Munkvold and White 2016). Two significant stalk rots and their causal agents include anthracnose stalk rot (Colletotrichum graminicola) and Gibberella stalk rot (Fusarium graminearum); both diseases are highly influenced by environmental and nutritional factors (White 1978; Dodd 1980a; Smith and White 1988). Additionally, ear rots are a concern during grain fill. Ear rots are caused by fungal pathogens and include Fusarium ear rot (Fusarium verticillioides and other species), Gibberella ear rot (Fusarium graminearum), and Aspergillus ear rot (Aspergillus spp.) (Sutton 1982; Logrieco et al. 2002; Munkvold and White 2016). In addition to reducing harvestable grain quantities, these fungal pathogens also produce harmful mycotoxins (Mueller et al. 2020). Mycotoxins can cause acute sickness, but chronic health problems, such as cancer, are also common following mycotoxin exposure (Placinta et al. 1999; Richard 2000, 2007). Chronic exposure to mycotoxins can reduce animal productivity, further harming production of animal products (Bryden 2012).
DISEASE MANAGEMENT AND HOST RESISTANCE
Integrated disease management uses a combination of cultural, biological, and chemical methods to reduce disease and prevent yield losses. Cultural methods, like crop rotation and tillage (soil preparation), are forms of pathogen control that do not involve chemicals but rather changes to where, when, and how fields are planted and maintained. Some pathogen species can overwinter on plant debris, providing inoculum for the next season. Tillage and crop rotation can be used to prevent the buildup of inoculum in fields. Unfortunately, rotation may not be agronomically or practically feasible in some situations where growers only plant a limited diversity of crops. Rotation may also be ineffective in areas where a disease is well established, against pathogens that spread large distances, or against those that can persist in the soil for years (Ward and Nowell 1998; Munkvold and White 2016). Delayed planting can reduce losses from seed rots and seedling blights (damping off) by avoiding the cold and damp conditions those pathogens thrive in, but this strategy might not work for especially aggressive species, and planting later can reduce overall yields (Broders et al. 2007; Matthiesen et al. 2016; Baum et al. 2019).
Fungicides are an effective form of control for fungal and oomycete diseases and are now commonly used on maize. Fungicide use increased in the early 2000s, likely due to the improved economic viability of applying fungicides (Munkvold et al. 2001; Paul et al. 2011; Mallowa et al. 2015; Matthiesen et al. 2016; Esker et al. 2018; Wise et al. 2019). Bacterial pathogens are difficult to target with chemical controls, as researchers have not found effective or practical chemicals or application methods (Mehl et al. 2015; Longhi et al. 2022). Chemical controls are important, particularly for newly emerged diseases or quickly evolving pathogens, but there is increasing public concern about antimicrobial compounds used in agriculture and their environmental costs (Berger et al. 2017; Ren et al. 2017; Parums 2022). There is growing interest in biocontrols, the use of living organisms to control pest populations, to manage bacterial and fungal diseases, but only a few biocontrols have been commercially released. The most widely used biocontrol in maize is Afla-Guard. Alfa-Guard protects against aflatoxin accumulation caused by Aspergillus ear rot, through the distribution of atoxigenic strains of Aspergillus in a field that outcompete the toxigenic strain (Mao et al. 1997; Dorner 2010; Bathke et al. 2022). Biocontrols that reduce mycotoxin accumulation for other ear rots would be useful for growers, but none are currently available.
Host resistance is another strategy used to avoid yield losses from pathogens, while allowing growers to reduce chemical inputs without compromising profits (Comito et al. 2013; Esker et al. 2018). Maize varieties with enhanced resistance can be developed through plant breeding or biotechnology-based approaches that take advantage of the knowledge of how plants defend themselves. Plant immunity consists of a two-tiered system (Yuan et al. 2021; Hudson et al. 2024). The initial defense is referred to as pattern-triggered immunity (PTI) and the secondary defense is effector-triggered immunity (ETI) (Jones and Dangl 2006; Bent and Mackey 2007). PTI involves the detection of microbe-produced pathogen-associated molecular patterns (PAMPs) through extracellular pattern recognition receptors (PRRs) that can induce defenses. These defenses include callose deposition, which blocks pathogen movement, or reactive oxygen species production, which kills host cells and pathogens (Boller 1995; Felix et al. 1999; Gómez-Gómez et al. 1999). Typically, PAMPs are highly conserved and essential to pathogen survival. Examples of PAMPs include flagellin and chitin, both of which are integral structural components of bacterial and fungal pathogens, respectively (Gómez-Gómez et al. 1999; Jones and Takemoto 2004). Most pathogens have evolved methods to avoid PRR recognition by producing secreted molecules, referred to as effectors, which allow the pathogen to still infect the plant. Infection occurs by effectors preventing plant recognition of PAMPs or other effectors, thereby preventing downstream defense responses. One distinct example is observed in pepper, where Xanthomonas campestris pv. vesicatoria produces an elicitor that blocks PTI-induced callose deposition (Brown et al. 1995). Plants, in turn, have evolved intracellular receptors, typically referred to as resistance (R) genes, which recognize effectors and lead to ETI (Jia et al. 2000; Dangl and Jones 2001; Deslandes et al. 2003). Effectors can prevent recognition by physically blocking the recognition of PAMPs, by degrading R gene products, or through a variety of other methods (Bent and Mackey 2007). A pattern forms, referred to as the “zigzag model,” where pathogens and plants constantly evolve methods to cause and avoid infection (Jones and Dangl 2006). Understanding how the plant immune system works can enable host resistance to be developed and deployed to make maize resistant to pathogen attack.
Phenotypically, host resistance is described in two categories, qualitative and quantitative, but it exists on a continuum (Poland et al. 2009; Gou et al. 2023). Typically, qualitative resistance is controlled by one large-effect gene, most commonly an R gene, effective against only certain strains of a pathogen species, while quantitative resistance is typically controlled by many small-effect genes and confers resistance to a whole pathogen species (Niks et al. 2015; Nelson et al. 2018). Durability, or resistance that is effective with widespread and prolonged usage in environments that are conducive to disease development (Johnson 1981), is typically associated with quantitative resistance (Johnson 1984; Cowger and Brown 2019). This association is likely because the small-effect genes that govern quantitative resistance are involved in many defense processes, thus preventing the rapid selection of pathogen variants that can evade recognition. There are exceptions to this generality, with cases of highly durable R genes (i.e., those associated with qualitative resistance), as durability is the result of pathogen evolutionary potential, the nature of the disease, and the specificity of the host resistance genes (McDonald and Linde 2002; Parlevliet 2002; Palloix et al. 2009; Poland et al. 2009; Wiesner-Hanks and Nelson 2016; Gou et al. 2023).
Maize is a model organism, and there are an abundance of resources available for studying plant–microbe interactions in maize. Many mapping populations have been developed in maize, including the nested association mapping (NAM) and multiparent advanced generation intercross (MAGIC) populations, which enable high-resolution mapping and the identification of disease resistance genes from genetically diverse sources (Yu et al. 2008; Dell'Acqua et al. 2015). These multiparental populations have increased resolution compared to biparental populations, and have high-density genotypic data available. Seed for the NAM and MAGIC populations are available for researchers to request and use in their own studies (Yu et al. 2008; Dell'Acqua et al. 2015). Genetic resources are available through the Maize Genetics and Genomics Database (Maize GDB; https://maizegdb.org/), the Germplasm Resources Information Network (GRIN; https://www.ars-grin.gov/), and the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/). These genetic resources allow researchers to conduct studies to identify germplasm that is highly resistant or susceptible to their pathogen of interest, and to genetically map genes underlying resistance. These resources can also be used to understand pathogenesis. Significant advances in the understanding of plant–microbe interactions have been made using maize as a model. These advances include the first cloned major plant R gene, Hm1, which confers resistance to a race of Cochliobolus carbonum, the fungus that causes the northern corn leaf spot, and the first cloned host susceptibility gene, URF13, which led to the 1970 and 1971 SCLB epidemic caused by C. heterostrophus (Ullstrup 1972; Dewey et al. 1987; Johal and Briggs 1992; Levings and Siedow 1992).
INOCULATION AND PHENOTYPING CONSIDERATIONS
Performing inoculations is foundational to understanding host resistance and plant–pathogen interactions, as inoculations enable phenotypic data that quantify the host response to be gathered. Both greenhouse and field inoculation experiments can be conducted to collect disease severity data, but choosing which one to use is dependent on the hypothesis being tested, the required size of the experiment, the type of data that will be gathered, and the pathogen of interest. Many studies require screening a large population of hundreds to thousands of individual plants, which requires using a field to accommodate the experiment (Vales et al. 2005). To conduct large field-scale experiments, high-throughput inoculations and phenotyping must be performed to screen genotypes efficiently and effectively, which can be a significant undertaking (Myles et al. 2009).
Greenhouse or growth chamber experiments provide increased environmental and biosafety control. These safeguards are useful for studying disease-related phenotypes commonly confounded with environmental factors, or if the spread of the pathogen in the environment is a concern. Experiments in controlled environments allow researchers to perform more repetitions in a year than the field would permit and to carry out technical assays not easily conducted in field settings. For example, stalk rot disease evaluations are laborious and involve bisecting stalks at the end of the maize life cycle to measure disease progression through the length of the plant. These manipulations can be difficult to perform in large field experiments. Experiments in controlled environments also enable fine-grained examination of the infection process. Greenhouse assays that involve inoculation and evaluation of stalk rot severity when the plants are young can be conducted more quickly, at higher densities, and more frequently throughout the year than if they were performed in the field (Sun et al. 2018). Unfortunately, it is challenging to study tar spot in both controlled environment and field experiments, due to the obligate biotroph nature of the causal agent, Phyllachora maydis. Obligate biotrophs require a living plant to survive and multiply, making them difficult to culture on media for inoculum preparation. Recent advances toward inoculum preparation and inoculation protocols for studying tar spot have been made, with the caveat that inoculum must be used within 1 h after collection for consistent results (Góngora-Canul et al. 2023; Solórzano et al. 2023).
To evaluate disease phenotypes accurately, inoculations should mimic the natural infection process. Consideration of what natural environmental conditions contribute to disease spread, and which tissues the pathogen colonizes, should be examined before developing inoculation procedures. In the case of the bacterial disease Goss's wilt, C. nebraskensis can infect the plant through wounds and hydathodes (Mallowa et al. 2016; Mullens and Jamann 2021). In this scenario, spray inoculations could be used to evaluate resistance at the hydathodes, while cut inoculations would be useful to evaluate resistance within the vascular system (Mullens and Jamann 2021).
Accurately and efficiently evaluating disease severity is essential for understanding pathogenesis. Direct measurements, including taking lesion lengths or quantifying area of disease, can be accurate and unbiased, but they can also be inefficient for large experiments and are impractical for difficult to quantify phenotypes. Visual rating scales provide an alternative, high-throughput method for collecting disease severity data. Rating with a percentage scale generates high-resolution data, due to the number of rating classes between 0% and 100%. Percentage scales have been shown to increase the precision and accuracy of ratings performed by both inexperienced and experienced raters (Forbes and Korva 1994; Nutter and Esker 2006; Poland and Nelson 2011; Chiang et al. 2017). Ordinal scales, where there are a reduced number of classes compared to percentage scales, are easier for new raters to learn, but lower-resolution data are produced and can be influenced by raters who lack precision or temporal reproducibility in their ratings (Nutter 1993; Nutter and Guan 2001; Nutter and Esker 2006; Poland and Nelson 2011). Ordinal scales can be effectively applied, but the number of raters, how the data will be analyzed, the interval between classes, and whether to use a quantitative or qualitative scale must be considered so that consistent and reliable data are produced (Bock et al. 2009, 2024; Del Ponte 2023). Diagrams with representative disease levels for each class, or photos of different rating classes, can increase precision and accuracy while reducing the rater bias to which both percentage and ordinal scales are susceptible (Nutter 1993; Bock et al. 2009, 2010; Braga et al. 2020). Researchers must consider what information is needed, how they will analyze the data, and how the results will be used to choose an appropriate scale.
Visual ratings performed by humans can be labor- and time-intensive; therefore, high-throughput techniques to perform disease ratings are being explored. Unpiloted aerial vehicles (UAVs) can efficiently determine the phenotype of plants in field experiments with images in visible and hyperspectral ranges, and then analysis methods, such as machine learning, can be used to process the images to detect diseases and quantify disease severity (West et al. 2010; Mahlein 2016; DeChant et al. 2017; Singh et al. 2021). Such a method was implemented for studying tar spot by measuring vegetative indices, which are calculated from visible and infrared reflectance by UAV, and then using regression models to translate the indices into disease severity ratings (Oh et al. 2021). There are drawbacks to UAV-based phenotyping, including the high cost of equipment and variable accuracy due to environmental factors and how the image processing system is trained. Additionally, there is a large time investment to develop the statistical models and refine downstream data processing. As these methods improve and become more accessible, the use of UAVs to determine the phenotype of plants in the field will likely be used more broadly, as they can expand the ability to screen large experiments efficiently and with high precision. Additionally, the reduction in labor would allow more measurements to be conducted to increase temporal precision and track disease development.
Regardless of the method being used to evaluate disease in large experiments, data must be collected and organized in an efficient manner. Data should be stored in a format that is consistent with the Findable, Accessible, Interoperable, and Reusable (FAIR) guidelines and ideally collected in a digital format to reduce transcription errors (Wilkinson et al. 2016). Applications, such as the Field Book app, enable high-throughput data collection and output data that can easily be uploaded to a database (Rife and Poland 2014).
INOCULATIONS FOR MAIZE PATHOLOGY RESEARCH
Seedling Blight Greenhouse Inoculations
Seedling blight, root rot, and damping off cause significant yield reductions each year by reducing seed germination and stunting surviving plants (Mueller et al. 2016; Munkvold and White 2016). Multiple laboratories have developed seedling blight assays that layer sand–cornmeal inoculum in potting media to examine the effect of relevant fungal species on grain and leguminous crops (Strausbaugh et al. 2004; Bilgi et al. 2008; Shrestha et al. 2021). Our associated protocol focuses on Pythium root rot caused by the oomycete pathogen Globisporangium spp., and uses a similar sand–cornmeal inoculum (Hall et al. 2025). Previous studies did not mix the inoculum with the soil, thereby only exposing part of the root system to the pathogen. Our protocol differs from prior reports by mixing the inoculum with the potting media to place the entire root system under disease pressure, which we found can be influential for total root ratings and measurements. We also attempt to mimic flooding conditions that enhance Globisporangium infection (Kirkpatrick et al. 2006). Measurements taken include stand counts, which are the number of germinated and surviving seedlings; root lengths and mass; and disease severity rated on a percentage scale to allow for consistent ratings (Forbes and Korva 1994).
Ear Rot Field Inoculations
Fungal ear rot pathogens can infect the maize ears both through the silks and through wounds produced by pests, which could result in different mechanisms of resistance depending on the infection route (Sutton 1982; Schaafsma et al. 1997; Nerbass et al. 2016). To differentiate between these two forms of resistance, maize can be inoculated through the silk channel or by creating wounds in the ears and adding inoculum (Reid et al. 1992; Chungu et al. 1996; Reid and Hamilton 1996; Schaafsma et al. 1997; Nerbass et al. 2016). Our associated protocol for ear rot screening is high-throughput and uses a modified backpack sprayer with an attached hog vaccinator to mimic silk infection and wound infection, followed by rating ears postharvest with a percentage scale (Lipps et al. 2025).
Bacterial Foliar Disease Field Inoculations
Goss's bacterial wilt and blight (C. nebraskensis) is a bacterial foliar disease that causes flecks that appear water-soaked and permeable to light, gray lesions, and wilt that can reduce maize yields by >40% in susceptible varieties (Vidaver and Mandel 1974; Pataky 1988; Carson 1991; Jackson et al. 2007). Most research on C. nebraskensis has focused on the wounding inoculations conducted by pinpricking maize leaves or wounding with sandpaper and introducing inoculum with spray bottles or sponges (Calub et al. 1974; Mallowa et al. 2016; Soliman et al. 2018; Mullens and Jamann 2021). A protocol for another bacterial foliar disease, Stewart's wilt, describes efficient field inoculations with the use of a “clapper” that wounds leaves and simultaneously introduces inoculum (Blanco et al. 1977; Chang et al. 1977). The clapper is made of pins and a sponge soaked in inoculum that are attached to two wooden boards, which are “clapped” over the whorl. One drawback to this method is the need to manually resaturate the sponge in inoculum every few plants, which increases labor requirements and reduces the efficiency and consistency of introducing the same amount of inoculum to each plant. Our associated protocol adapts these methods and makes the clapper more efficient by using a reservoir and tubing to continually deliver inoculum to the sponge (Mullens et al. 2025). The base of the clapper is a modified grabber with a handle to reduce strain on the operator. One week after inoculation, disease severity is rated on a percentage scale twice to create a single, averaged rating, or up to three times to create an area under disease progress curve.
FLUORESCENCE MICROSCOPY TO VISUALIZE PLANT–FUNGAL INTERACTIONS
Foliar fungal diseases are destructive, causing large global yield losses due to lowered photosynthetic efficiency (Silveira et al. 2019). NCLB (E. turcicum f.sp. zeae), one of the most damaging foliar fungal diseases, produces elliptical lesions on maize leaves, causing yield losses of ∼3% each year globally (Pratt and Gordon 2005; Savary et al. 2019). Protocols for E. turcicum inoculum production and evaluation of resistance in field and greenhouse conditions have been described previously (Chung et al. 2010; Fang et al. 2022). Here, we focus on microscopic visualization of plant–fungal interactions, which can be used to study the mechanisms of resistance. Confocal fluorescence microscopy in diverse systems has been well described by other researchers (Nelson et al. 2024), and can be used to reduce background and to examine thick samples. Fluorescence microscopy is one of the most cost-effective and easy to learn microscopy methods. Many fluorescence microscopy methods have been described for E. turcicum and other fungal pathogens, and we improved on these by increasing the efficacy of clearing and staining the leaf while retaining leaf structure (Hood and Shew 1996; Chung et al. 2010; Kotze et al. 2019; Navarro et al. 2020). In our associated protocol, infected leaf samples are soaked in KOH overnight for 16 h, and then autoclaved to clear leaf pigments. The leaf samples are then stained with an aniline blue solution, which stains glucans present in plant and fungal cell walls, to visualize fungal structures (Pokhrel et al. 2025). During visualization, the number of fungal conidia, the asexual spores of E. turcicum, and appressoria, the structures formed after conidia have germinated to push through the leaf surface, can be quantified. In addition, the efficacy of mesophyll and xylem penetration and colonization can be assessed. These quantifications allow for statistically rigorous comparisons of different fungal strains and host genotypes. This method can also be a resource to characterize pathogenesis, define interactions based on differing levels of resistance, and understand the plant–pathogen interactions that define the pathosystem.
CONCLUSION
There are myriad motivations for inducing disease through inoculations, including characterizing plant–pathogen interactions, defining susceptibility of lines or cultivars, and testing the efficacy of chemical or biological controls. Ultimately, the research that uses these methods includes breeding for disease resistance to reduce yield losses and to protect against mycotoxin contamination of grain. These methods also enable research on understanding how pathogens infect maize, which can be useful for deploying chemical and cultural controls, and genetic resistance, both of which ensure growers have effective management strategies available. It is necessary for inoculation methods to replicate natural infection consistently and efficiently. Inoculation and phenotyping methods, including rating scales and visualization through microscopy, must be high-throughput to effectively conduct large experiments. We describe protocols to study seedling blight, ear rot, Goss's wilt, and NCLB that enable high-throughput inoculations and phenotyping in both greenhouse and field settings.
COMPETING INTEREST STATEMENT
The authors declare no competing interests.
AUTHOR CONTRIBUTIONS
Conceptualization: P.S., S.M., and T.J. Writing—original draft: P.S. and T.J. Writing—review and editing: P.S., S.M., and T.J.
Footnotes
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From the Maize collection, edited by Candice N. Hirsch and Marna D. Yandeau-Nelson. The entire Maize collection is available online at Cold Spring Harbor Protocols and can be accessed at https://cshprotocols.cshlp.org/.
REFERENCES
*Reference is also in this subject collection.
