Funding CRISPR: Understanding the role of government and philanthropic institutions in supporting academic research within the CRISPR innovation system
David Fajardo-Ortiz, Stefan Hornbostel, Maywa Montenegro-de-Wit, Annie Shattuck
FFunding CRISPR: Understanding the role of government, private sector actors in transformative innovation systems
David Fajardo-Ortiz , Stefan Hornbostel , Maywa Montenegro de Wit , Annie Shattuck Abstract
CRISPR/Cas has the potential to revolutionize medicine, agriculture, and the way we understand life itself. Understanding the trajectory of innovation, how it is influenced and who pays for it, is essential for such a transformative technology. The University of California and the Broad/Harvard/MIT systems are the two most prominent academic institutions involved in the research and development of CRISPR/Cas. Here we present a model of co-funding networks for CRISPR/Cas research at these institutions, using funding acknowledgments to build connections. We map papers representing 95% of citations on CRISPR/Cas from these institutions grouped by the stage that each represents in the translational research process (as a biological phenomenon, as a research tool, and development and applications of these technologies), and use a novel technique to analyse the relationships between the structures of the co-funding networks, the phase of research, and funding sources. The co-funding subnetworks were similar in that US government research funding played the decisive role in early stage research. Research at Broad/Harvard/MIT is also strongly supported by philanthropic/charitable organizations in later stages of the translational research process, clustered around certain topics. Applications for CRISPR technologies were underrepresented, which bolsters findings on the preponderance of the US private sector in developing applications, and the disproportionate number of Chinese institutions filing patents for industrial and food systems applications. These network models raise fundamental questions about the role of the state in supporting breakthrough innovations, risk, reward, and the influence of the private sector and philanthropy over the trajectory of transformative technologies.
Introduction
RISPR/Cas are a set of versatile technologies aimed to manipulate, analyse and visualize the biomolecular machinery of living organisms. [1] CRISPR has the potential to revolutionize medicine, [2-4] agriculture [5] and the way we understand life itself. [6] The impact of CRISPR genomic editing technologies has been recognized with the 2020 Nobel Prize in Chemistry awarded to Drs Emmanuelle Charpentier and Jennifer A. Doudna “for the development of a method for genome editing." [7] Applications for these technologies have been proposed in fields as diverse as pharmaceuticals, [8] crop development, [9] livestock breeding, [10] industrial biotechnology [11] and pest control. [12] As CRISPR/Cas represents one of the most potentially transformative technological breakthroughs of the last decade, it is Important for researchers, policymakers, and the public to understand innovation trajectories, who finances them, who bears the risks and rewards of innovation and for who technologies are ultimately developed. CRISPR/Cas has been analysed using myriad historical, legal, ethical, policy, and scientometric approaches. The social science and humanities discussion on CRISPR/Cas technologies ranges from ethical concerns about heritable genome editing [13] to intellectual property [14, 15] and democratization and governance of these technologies [16]. Despite the diversity of such studies, the role of financing on CRISPR/Cas research — specifically how different types of funding influence the research process — remains largely opaque. Since the Second World War, in the period known as the golden age of capitalism, government agencies have been the main source of funding for scientific research in the US academy. [17] Breakthrough technologies have emerged from long-term investments in R&D under the banner of a "public good" missions [18]. Amidst the growing influence of productivist coalitions since the 1930s – farm commodity groups, land-grant administrators, agribusiness firms, and federal agricultural agencies – [19] it is government agencies that have largely taken the lead in actively shaping markets and shouldering risk of early stage transformative research investments. [8] Private sector actors typically limited their role to lower risk forms of technology integration, development, and marketing later in the innovation process. [18] This status, during a period of corporate consolidation, is less contradictory than it first appears. A schism between "basic" and "applied" science has long been recognized as favorable to private sector interests who actively worked to carve a social division of labor that put universities in charge of basic research and positioned industry to control the commodity form. [20] Though recognized as a taxpayer subsidy to agribusiness in the agricultural sector [21], this relationship has not only withstood time but has deepened: With the passage of Bayh-Dole in 1980, Congress fundamentally shifted the incentive structure governing research and development by allowing publicly funded institutions to own nventions resulting from federally sponsored research, and to license those inventions to the private sector. [22] Early research progress on genome technologies (viral vectors, RNAi, and the different genome editing platforms) largely followed this pattern, with the US National Institutes of Health (NIH) playing a leading role in funding innovation over the past 30 years. [23] Scientometrics studies on the impact of US government institutions like the National Science Foundation and the NIH show that these institutions still function as global driving forces of innovation writ large [24, 25]. However, there has been a clear decline in US government support to science in recent years when measured as a percentage of gross domestic product, [26] while simultaneously a second golden age in the economic power of philanthropic and charitable organizations is taking place in the United States. [27] The emerging active role of philanthropic foundations as patrons of science has serious implications for the governance of science and technology. For example, Anne-Emanuelle Birn has documented the capacity of philanthropy to change the global research agenda on health from a focus on the social determinants of health to sophisticated technological solutions, with mixed results. [28] Similarly, the participation of philanthropic organizations in the area of agriculture and food sciences is currently promoting the development and implementation of “silver bullet” technologies that allow the incorporation of farmers into the commercial value chains. [29] Moreover, an investigation on the role of science philanthropy in US research universities showed how this type of organization concentrates their efforts in the translation of knowledge from basic research to the development of applications, much like other private sector actors. [30] Understanding the different types of funding to the different levels of research from basic science to the development of technologies to their application in specific industries can provide us fundamental information on the influence of these different organizational actors on the development and implementation of CRISPR/Cas technologies. The University of California System and the Broad/Harvard/MIT system are the two most prominent academic institutions involved in the research and development of CRISPR/Cas technologies. The impact of these two research systems on the invention and development of CRISPR/Cas technologies has been well documented. [14, 23, 31] Therefore, these institutions are an excellent case study to examine the evolution of these technologies, the funding networks that support them, and the relationships between innovation, financing, production, and property rights over these technologies. In the present investigation we aim to map and critically analyse the organization of the co-funding networks of the research on CRISPR/Cas technologies performed at the UC and the Broad/Harvard/MIT systems. It is also important to consider that while the University of California is public institution, the Broad/Harvard/MIT system is made up of private institutions. This distinction is relevant for the study of university research funding, as differences have been reported in terms of the ability to access government and philanthropic funding between public and private universities. [32-34] Doing so raises fundamental questions about the role of the state in supporting breakthrough innovations, risk, reward, and democratic influence over the trajectory of transformative technologies.
Methodology A search of peer-reviewed papers was performed in the Web of Science [35] on May 25, 2020. The research criteria are listed in Table 1. A very similar number of papers was found for the UC system and the Broad/Harvard/MIT system (920 and 922 respectively, See table 1). For each system we selected a number of top-cited papers that concentrated approximately 95% of the citations received. That is, we wanted to focus our explorative analysis on the most influential papers from each institutional system. Roughly 40% of top-cited papers accumulated 95% of citations while the remaining 60% of papers received just 5% of citations (Table 1).
Table 1.
Search criteria, number of papers found in the Web of Science (WoS), number of papers selected and number of papers forming the co-funding network models.
Broad/Harvard/MIT system
Search criteria: ORGANIZATION-ENHANCED: (Broad Institute or Massachusetts Institute of Technology (MIT) or Harvard University) AND TOPIC: (Cpf1 or Cas12a or CRISPR or "clustered regularly interspaced short palindromic repeats" or Cas9). Refined by: DOCUMENT TYPES: (ARTICLE) Total of papers found in the WoS (by April 25, 2020) 922 Total of papers selected from WoS with at least 25 citations (Receiving 95.2% of citations) 364 Unrelated papers identified during the classification process 3 Papers with no reporting funding 8 Papers in the network model 355
University of California System
Search criteria: RGANIZATION-ENHANCED: (University of California System) AND TOPIC: (Cpf1 or Cas12a or CRISPR or "clustered regularly interspaced short palindromic repeats" or Cas9). Refined by: DOCUMENT TYPES: (ARTICLE) Total of papers found in the WoS (by April 25, 2020) 920 Total of papers selected from WoS with at least 15 citations (Receiving 94.8% of citations) 400 Unrelated papers identified during the classification process 16 Papers with no reporting funding 9 Papers in the network model 375 2.
A bimodal network model of papers and co-funding organizations was built for each institutional system by using the information reported in the acknowledgment section of the papers. It is important to mention that some of the selected papers did not report any source of funding while other papers were unrelated to CRISPR/Cas as a research topic and therefore were not included in the co-funding network models (Table 1). 3.
The papers in the bimodal network models were classified in different levels of research as follows in table 2:
Table 2 . Research levels of the papers in the network models A. Biological phenomenon Papers investigating CRISPR/Cas as bacterial immune systems; the interactions of their component in bacteria or archaea, and/or the molecular, ecological or evolutionary interactions of these systems with the bacteriophage viruses. B. Research Tool In these papers, CRISPR/Cas is not the central object of investigation but is an instrument to identify or analyse the role of specific genes in normal or pathological biological processes. C. Developments or improvements of CRISPR/Cas technologies These papers report discoveries of alternative CRISPR genome editing systems which could be more efficient, more versatile or easier to use; investigations aimed to overcome the technical difficulties to apply the technology n different organisms; or investigations reporting molecular mechanisms that can be used to modulate the activity of Cas enzymes. D. Applications of CRISPR/Cas to organisms in working systems Papers reporting applications of CRISPR/Cas in biomedicine, food systems, environmental systems, or industrial biotechnology. E. Social studies of science papers Papers reporting the ethical or societal implications of CRISPR technologies. 4.
A correspondence analysis of the content of the papers was performed by using the software KH Coder. [36] In order to determine if papers previously classified in the same type of research tend to have a similar distribution of terms. 5.
The co-funding organizations in the bimodal network models were classified as follows: A) United States government agencies. Any public source of funding in the US including federal, state and local agencies except those belonging to the United States Armed Forces. B) Institutions belonging to the United States Armed Forces. Even though these institutions are US government agencies, they follow a distinctive logic in which research efforts either aim to increase the strategic technological advantage over rivals and enemies [37] or to satisfy the particular needs of the armed forces like the health needs of the military personnel. [38] C) Philanthropic or charitable organizations. That is, non-profit organizations that are tax-exempt under 501(c)(3) requirements in the USA. [39] D) For-profit organizations. That is, organizations aimed at earning profit through their activities and that are concerned with their own economic interests. E) Academic institutions, professional organizations, or medical research centres. Funding sources from countries other than the USA are classified in equivalent categories: governmental or intergovernmental agencies, philanthropic or charitable organizations, for-profit organizations and academic organizations. 6.
The co-funding network models were visualized and analysed with Cytoscape. [40] Subnetworks of specific levels of research and/or types of sources of research funding were built and compared in terms of their density or number of sources per paper.
Results The network models co-funding network model was built for each institutional system. The bimodal network model of the Broad/Harvard/MIT system was formed by 738 funding sources, 355 papers and 2,267 funding acknowledgements (Fig. 1) while the bimodal network model of the UC system was formed by 714 funding sources, 375 papers and 1,750 funding acknowledgements (Fig 2). The funding acknowledgements are the links that connect the funding sources to the papers. Therefore, the funding acknowledgments are the more relevant elements in the network model in order to understand the structural differences between the funding of the CRISPR/Cas research in the studied institutional systems and the different research levels. In that sense, a first observation is that even though the numbers of papers and funding sources are similar, these two network models exhibit clear differences in terms of density: there are an average of 4.7 funding acknowledgements per paper in the case of the UC system while the Broad/Harvard/MIT system has 6.4 funding acknowledgements per paper, which suggests that the funding of top cited CRISPR/Cas research in the Broad/Harvard/MIT system is more diversified than in the University of California System.
Figure 1.
The co-funding network model of top cited CRISPR/Cas research in the Broad/Harvard/MIT system. This is a bimodal network model made of papers and funding sources. “Filled” nodes are nstitutions while "empty” nodes are papers. The colour and shape of the nodes indicates the type of organization these nodes represent as it is shown in the figure legend. The grey links connecting papers with the funding sources represent the funding acknowledgements.
Figure 2.
The co-funding network of top-cited CRISPR /Cas research in the UC system. This is a bimodal network model made of papers and funding sources. “Filled” nodes are institutions while "empty” nodes are papers. The colour and shape of the nodes indicates the type of organization these nodes represent as it is shown in the figure legend. The grey links connecting papers with the funding sources represent the funding acknowledgements.
Papers in the network model by research level and type of funding source
The results of the classification of papers in the network models are summarized in Table 3 (a detailed description of the research levels is provided in table 2 in the methodology section). Around half of the papers in the network models of both institutional systems reported the use of RISPR/Cas as a research tool (Table 3). However, there are differences between both institutions as the UC system tends to be comparatively more oriented towards basic biological research than the Broad/Harvard/MIT system while the latter focuses more on the development of improved CRISPR/Cas technologies (Table 3).
Table 3.
Number of papers in the network models by type of research
Broad/Harvard/MIT system
Biological phenomenon 14 (3.9%) Research tool 206 (58%) Developments or improvements of the technology 105 (29.6%) Applications of the technology 29 (8.2%) Social studies of science 1 (0.3%) Total of papers in the network model 355 (100%)
University of California System
Biological phenomenon 68 (18.1%) Research tool 189 (50%) Developments or improvements of the technology 69 (18.4%) Applications of the technology 48 (12.8%) Social studies of science 1 (0.3%) Total of papers in the network model 375 (100%) A complementary strategy to gather evidence on the accuracy of the classification – see table 1 in the methodology – was to perform a correspondence analysis of the content of the papers so that we could see if papers of the same type of research tend to stick together in the plot. That is, we wanted to know if a statistical analysis of the distribution of terms among the papers performed by the application KH Coder [32] could distinguish between the different types of research in a way comparable to that of human-made classification. The resulting plot suggests that the correspondence analysis of the content of the papers distinguished well between the three most numerous types of investigations: papers reporting developments or improvements of the technology, the use of CRISPR/Cas as a research tool and the study of CRISPR/Cas as a biological phenomenon (S1 Fig). However, the papers related to the different areas of application were dispersed around the plot probably due to their smaller number and the diversity of their content (S1 Fig). On the other hand, the correspondence analysis plot displaying the papers grouped by type of investigation and institution suggest that papers from the same research level but from different institutional systems have a similar content (S2 Fig). he UC system and the Broad/Harvard/MIT system do not display observable differences in terms of the number of papers supported by the different types of funding sources (Table 4). For both systems around half of the papers reported financial support from entities outside the United States while around 90 percent of the papers were funded by US institutions. For both systems US governmental agencies were by far the main type of funding source followed by US academic entities (which include universities, research medical centers, and professional organizations) and in third place US philanthropic or charitable organizations (Table 4).
Table 4.
Number of papers for type of funding sources in the co-funding network models. Note that these numbers overlap, as one paper can be funded by several organizations.
Broad/Harvard/MIT system
International funding sources 179 (50.4%)
International philanthropic or charitable 64 (18%) International governmental or intergovernmental 133 (37.5%) International academic entities 53 (14.9%) International for-profit 14 (3.9%) US funding sources 341 (96%) US governmental agencies 318 (89.6%) US academic entities 203 (57.2%) US philanthropic or charitable organizations 189 (53.2%) US for-profit 16 (4.5%) US armed forces 62 (17.5%)
Total of papers in the network model 355 (100%)
University of California System
International funding sources 165 (44%)
International philanthropic or charitable 53 (15.2%) International governmental or intergovernmental 124 (33%) International academic entities 51 (13.6%) International for-profit 15 (4%) US funding sources 343 (91.5%) US governmental agencies 318 (84.8%) US academic entities 190 (50.7%) US philanthropic or charitable organizations 157 (41.8%) US for-profit 23 (6.3%) US armed forces 34 (9.1%)
Papers in the network model 375 (100%)
Table 5 lists the main funding sources of CRISPR research for each institutional system. The top four reported funding sources (The US National Institutes of Health, the National Science Foundation, the Howard Hughes Medical Institutes and the NIH National Cancer Institute) are the same for both institutions and they are ranked in the same order. There are two philanthropic (The Simons and the allee Foundations) and one charitable organization (The New York Stem Cell Foundation) among the top ten funding sources for CRISPR/Cas research at the Broad/Harvard/MIT system while only one charitable organization is among the top ten financiers of the CRISPR/Cas research at the UC system. Interestingly, the financing of CRISPR/Cas research at the UC system seems to be much more centralized than in the case of the Broad/Harvard/MIT system as the number of papers per source financed drop more sharply than in the case of the UC system as the rank progresses (Table 5) which suggests a greater reliance on public funding for CRISPR/Cas research at the University of California.
Table 5 . Top ten research funding sources for CRISPR/Cas research at the University of California and Broad/Harvard/MIT systems.
University of California System
Broad/Harvard/MIT S ystem
Main funding sources
Number of funded papers in giant component
Main funding sources
Number of funded papers in giant component
National Institutes of Health NIH 250 National Institutes of Health NIH 291 National Science F oundation NSF 97 National Science Foundation NSF 62 Howard Hughes Medical Institute HHMI 65 Howard Hughes Medical Institute HHMI 60 National Cancer Institute NCI 28 National Cancer Institute NCI 45 National Institute of General Medical Sciences NIGMS 28 US Department of Defence DOD 43 California Institute for Regenerative Medicine 27 Simons Foundation 41 Burroughs Wellcome Fund 20 Vallee Foundation 39 US Department of Energy DOE 17 New York Stem Cell Foundation 36 National Natural Science Foundation of China 15 National Institute of Mental Health NIMH 35 S Department of Defence DOD 14 National Institute of General Medical Sciences NIGMS 32 National Human Genome Research Institute NHGRI 32
Structural difference between subnetworks corresponding to different funding sources and research levels
Figures 3 and 4 show the subnetworks generated corresponding to the different research levels and types of funding sources, and the intersections between these. A rate near 1 funding acknowledgement per paper is related to very sparse co-funding subnetworks (in fact, a rate of 1 means no co-funding at all) while a rate near 2 or above means that the co-funding subnetwork is dense. The co-funding subnetworks showed structural differences between the different types of funding sources and research levels in terms of density, i.e., funding acknowledgement per paper (Fig 3 and 4). The co-funding subnetworks showed a similar intensity of support from US governmental agencies for CRISPR research in both institutional systems but only the research at the Broad/Harvard/MIT system is strongly supported by philanthropic/charitable organizations and by international sources and foreign governmental agencies (Fig 3 and 4). Moreover, differences in the financing of specific types of funding to specific types of research were identified. Particularly, a dense network of philanthropic/charitable organizations can be observed supporting research on the technical improvement of the CRISPR/Cas technologies at Broad/Harvard/MIT while a sparser and smaller network of philanthropic/charitable organizations supports the same type of research at the UC system (Fig 3 and 4). Figure 3 shows a dense subnetwork of governmental agencies supporting the different stages of the CRISPR/Cas research performed at the UC system: from the study of CRISPR systems as biological phenomena to its use as a research tool, the continuous development of CRISPR technologies, and the application of CRISPR as a component of biomedical therapies. However, the participation of the other types of funding sources was minimal in the case of the UC system except the punctual co-funding efforts of charitable/philanthropic organizations and foreign governmental agencies subnetworks to some specific research levels (Fig 3). In the case of the Broad/Harvard/MIT system, the models illustrate consistent support of subnetworks of US governmental agencies, foreign organizations and charitable/philanthropic organizations for all the different levels of research, from the most basic research types to the development, improvement, and application of CRISPR/Cas technologies (Fig 4). It is interesting to note that sub-networks of US military institutions and programs, philanthropic organizations, and international governmental agencies have a concentrated participation in the co-funding of the technical improvement of RISPR/Cas technologies in the case of the Broad/Harvard/MIT system (Fig 4). This may imply that these types of organizations tend to strategically concentrate their funding efforts in specific academic institutions and research levels possibly in order to maximize their control over the development of these technologies. Overall, both systems are strongly supported by a diversity of funding sources (Fig 3 and 4).
Figure 3.
Subnetworks, number of papers (N) and grant acknowledgements per paper (A/N) by type of funding source and research levels in the co-funding network model of CRISPR/Cas research at the University of California System. The numbers can overlap as one paper can be funded by two or more types of agencies.
Figure 4.
Subnetworks, number of papers (N) and grant acknowledgements per paper (A/N) by type of funding source and research levels in the co-funding network model of CRISPR/Cas research at the Broad/Harvard/MIT system. The numbers can overlap as one paper can be funded by two or more types of agencies.
Discussion
The use of the funding acknowledgment sections as a source of data for scientometrics studies has been extensively discussed in recent years. [35, 41-44] However, despite the enormous potential of using this source of data to investigate the impact of funding entities on the development of science nd technology, some key considerations must be taken into account. Firstly, it is important to consider that our investigation was based on information provided by the Web of Science (WOS) which began systematically collecting acknowledgments information in 2008 [42] becoming the de facto standard source for this type of investigation. In that sense, a comparative analysis of the three main bibliometric databases (WOS, PubMed and Scopus) found that the WOS outperforms the other databases in terms of the proportion of articles with funding information. [43] Secondly, social sciences, humanities, and non-English language journals are underrepresented in both WOS and Scopus. [35] In the case of the WOS, specifically, the Arts & Humanities Citation Index (AHCI) content is not indexed for funding acknowledgement data and there are problems covering this information for non-English language papers. [43] Third, a loss of funding information of 12% has been reported for the WOS. [44] That is, 12% of the funding sources in the acknowledgment section of the papers was not captured in the Web of science database. Finally, the main challenge regarding the use of this source is that authors do not adequately report their sources of funding, misspell funding bodies, or put erroneous grant numbers. [45, 46] In the present investigation, even though the Web of Science has systematized financing data, the name of each reported organization was carefully reviewed and errors were corrected. Previous studies reporting the use of funding acknowledgment sections focused on analysing the impact of specific funding sources by using traditional metrics such as the number of citations per paper. As far as we know, this is the first time that the funding acknowledgements have been used to build and analyse the relationships between the structures of the co-funding networks, the research level, and the type of funding sources. Our results suggest that even though US government agencies extensively supported all the levels of CRISPR/Cas research in both institutional systems, subnetworks of US military agencies, philanthropic organizations and international governmental agencies have concentrated participation in co-funding the research level C “developments or improvements of CRISPR/Cas technologies” in the case of the Broad/Harvard/MIT system but not in the case of the UC system. Together, our present and previous investigations in combination with studies on the distribution of CRISPR/Cas patents by country, [45] technical category [45] and institutions [46] provide us a global picture of the impact of a plethora of organizational actors in the development and application of CRISPR/Cas technologies. In this picture, leading academic institutions — whether public or private — are supported by government agencies and perform the most risky and innovative investigations for the “upstream” stages of CRISPR/Cas research as a biological phenomenon, as a research tool, and in improving on or discovering new variants of the genome editing system. [47, 48] In a second stage of incremental innovation which ride the wave of success of the original invention of CRISPR/Cas as a genome editing technology, networks of philanthropic, US military, and nternational organizations support the development or improvement of CRISPR/Cas technologies” (see research level C in table 2) in the Broad/Harvard/MIT system which could be related to the observed larger number of citations received per paper and higher number of patents awarded to the Broad/Harvard/MIT system in comparison with the University of California. [46] As supported by our earlier study [23] and related IP research, a third stage of CRISPR/Cas research, related to the application of these technologies, is taking place disproportionately in Chinese academic institutions supported by Chinese governmental agencies [19, 45] and US for-profit companies. [46] Our results raise questions about the role of philanthropy in influencing predominantly publicly funded research trajectories, about democracy in publicly funded innovation, the role of the public in shouldering the risk and cost of long-term technology development, and the privatization of reward from that public investment. That is, society as a whole finances the radical innovation process through government agencies, assuming most of the risks of investing in new areas of knowledge and technology, while private actors participate in later stages, leading to the socialization of risk and the privatization of rewards. [18, 49] Critical studies on the governance of the innovation process in biotechnology, particularly the governance of CRISPR/Cas technologies, have bypassed the important role of US charitable and philanthropic organizations as powerful actors that can redirect the trajectory of the development and application of genomic technologies in favour of specific interests or sectors of society. In that sense it is fundamental to further the study of the interaction between transformative innovation and philanthropy. A first strategy would be to measure the impact of philanthropic grants by analysing the expenditures of the sponsored projects. A consortium of 33 US public research universities is moving in that direction by connecting the information on their sponsors, the grants, project expenditures and final products like papers and patents. [50] Unfortunately, neither the University of California nor the Broad/Harvard/MIT system participate in this initiative. Another strategy would be to gather the views of researchers, administrators, and philanthropic foundations on the impact of such grants in the development of CRISPR/Cas technologies. Any strategy to deepen knowledge about the role of philanthropic foundations in the development of genomic editing technologies necessarily requires a commitment to transparency on the part of the various participating actors.
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S1 figure.
Correspondence analysis bi-plot of distinctive terms and papers reporting CRISPR/Cas investigation performed at the University of California System and the Broad/Harvard/MIT system. The round blue dots represent distinctive terms while the red squared dots are individual papers. The papers are named in the plot according to the type of research and nstitutional system as follow: “UC” stands for University of California System while “Br” stands for Broad/Harvard/MIT system; “BP” stand for biological phenomenon; RT stands for research tool; TI stands for developments or improvements of the technology; BA stands for biomedical applications; FEA stands for food and environmental systems applications, and IBA stands for industrial biotechnological applications.