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Systematic Omics Analysis Review (SOAR) Tool to Support Risk Assessment

  • Emma R. McConnell,

    Affiliation Oak Ridge Institute for Science and Education, Research Triangle Park, NC, United States of America

  • Shannon M. Bell,

    Affiliation Oak Ridge Institute for Science and Education, Research Triangle Park, NC, United States of America

  • Ila Cote,

    Affiliation National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Arlington, VA, United States of America

  • Rong-Lin Wang,

    Affiliation National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Cincinnati, OH, United States of America

  • Edward J. Perkins,

    Affiliation United States Army Corps of Engineers, Department of the Army, Department of Defense, Vicksburg, MS, United States of America

  • Natàlia Garcia-Reyero,

    Affiliation Department of Chemistry & Biochemistry, Jackson State University, Jackson, MS, United States of America

  • Ping Gong,

    Affiliation SpecPro, Inc, Vicksburg, MS, United States of America

  • Lyle D. Burgoon

    Affiliation National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, United States of America

Systematic Omics Analysis Review (SOAR) Tool to Support Risk Assessment

  • Emma R. McConnell, 
  • Shannon M. Bell, 
  • Ila Cote, 
  • Rong-Lin Wang, 
  • Edward J. Perkins, 
  • Natàlia Garcia-Reyero, 
  • Ping Gong, 
  • Lyle D. Burgoon


Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.


Government agencies and environmental consultants develop human health risk assessments to determine the potential exposure and toxicity risks of chemicals, a process which involves consideration of all of the available published scientific literature on that chemical. Experts evaluate and integrate the studies that are available, make judgments on the quality of the science, and choose appropriate studies to derive cancer or noncancer toxicity reference values. A National Academy of Science Committee reviewing the draft Integrated Risk Information System (IRIS) Toxicological Review of Formaldehyde recommended that the IRIS Program develop “clear concise statements of criteria” when choosing studies to exclude or include for toxicity reference value calculations [1].

Significant work has been done by authors such as Fostel et al [2] and Schneider et al [3] to determine the criteria that are crucial for understanding the quality and reproducibility of toxicological studies in general. However, these criteria are not designed for use with transcriptomic studies, and are not adequate to provide an assessment of the entire study. Microarrays, one of many transcriptomic tools, are vastly different than the whole-animal toxicity studies that risk assessors and assessment teams are accustomed to evaluating. In acknowledgement of the complicated and varied procedures and analysis required to perform a microarray experiment, the gene expression microarray community created the “Minimum Information About a Microarray Experiment” (MIAME) [4] standard, along with data reporting requirements that have been adopted by several journals. Though this is a community standard for transcriptomic microarrays, it does not specifically consider their application to toxicogenomic studies for the purpose of human health risk assessment.

One method of combining the need to consider next generation technology with systematic approaches and transparency is through the development of a tool for “systematic reviews” of microarray literature. Systematic review methods are becoming increasingly more common, especially in medical and public health fields which involve a plethora of stakeholders and have wide-ranging human health implications [5]. A tool for performing such reviews would allow risk assessors and assessment teams to transparently apply standard criteria for judging the studies that they find in literature searches and include in their assessments. However, there are currently no systematic review tools focused on the applicability of toxicogenomic studies for use in human health risk assessment.

The Systematic Omics Analysis Review (SOAR) tool originated from our interest in developing a distributable tool to facilitate the systematic screening of transcriptomics studies using existing community standards as criteria, so that such studies can become more widely applied to risk assessment. The Toxicological Reliability Assessment (ToxR) Tool [3], MIAME standard [4], and the Checklist for Exchange and Interpretation of Data from a Toxicology Study [2] were resources for question development. After a spreadsheet of questions was generated, multiple rounds of testing were performed by scientists to refine and determine the appropriate weight for questions, and ultimately validate user agreement across a test set of published studies.

Our current implementation of SOAR is focused on screening/identifying transcriptomic studies that can be used to support a risk assessment. These aspects could include, but are not limited to: hazard identification, mode of action analyses, weight of evidence evaluations, assumptions, and read-across. Future work will focus on implementing a series of questions to assess dose-response studies to ascertain if they are suitable for benchmark dose modeling analysis.


Source of questions

The initial questions used to develop the SOAR tool were derived from three main peer-reviewed sources: 1) MIAME, 2) ToxRTool, and 3) the Checklist for Exchange and Interpretation of Data from a Toxicology Study. The questions that pertained directly to microarray data came from MIAME [4], while general questions on information needed for repeating a toxicological study are drawn from the ToxRTool [3] and the Checklist [2]. A few questions were also written based on expert guidance because they were not included elsewhere. The ToxRTool in particular was also used as a general guide for how to design and structure this type of tool.

Development of the tool

Questions from the source materials were organized in a Google Drive Spreadsheet (see A “Preliminary Questions” section was developed to screen out manuscripts that do not have three or more biological replicates or do not pertain to a chemical exposure and are thus not relevant to chemical risk assessment. This section also asked questions that determined the type of study (in vivo, in vitro, etc) in order to tailor the questions asked in the subsequent sections (answers to these questions do not affect the score). The remaining questions were organized into five sections: 1) Test System (including separate sets of questions for in vivo and in vitro studies), 2) Test Substance, 3) Experimental Design, 4) Microarray Data, and 5) Suitability for Benchmark Dose (BMD) modeling, as seen in Table 1. Each question had a “yes” or “no” answer, with a few questions also containing a “Not Applicable” option. Initially weights were set to one for every question, with an “NA” answer causing the weight to drop to zero. After testing weights were adjusted to range from 0 to 1 depending on the importance of the information, as determined by participating microarray experts.

Table 1. Question sections included in the original version of the SOAR tool compared to the final version.

The spreadsheet format allowed for the use of drop-down response menus, automatic calculation of weighted scores for each section of questions, sections for rater comments, and automated scripts that adjust the questions that users were presented based on the type of data in the study, as well as automatic bibliographic data entry. Additionally, mouse-over comments were added to the spreadsheet to provide more information and examples of how to find the answer to the question within a published manuscript. Questions were edited first internally using a training set of four manuscripts, shown in Table 2, for which the pass/fail designation was determined a priori [6][9]. During the course of testing, some questions were re-worded for clarity, other questions were removed because the evaluation team did not find them informative, and the weights of the questions were adjusted to better reflect their importance in determining suitability for use in an assessment.

Table 2. The papers used to develop and test the SOAR tool. The first four were used only during internal development of the questions.


Seven scientists with diverse backgrounds and experience with toxicogenomic data were recruited to assist in assessing and validating the SOAR tool (see Table 3 for details on participants) over the course of four separate rounds conducted over nine weeks. During the first two rounds, the scientists were asked to focus on editing, clarifying, reformatting, or suggesting questions for removal. In the third round statistics on user agreement were calculated to focus on improving the wording and the weights of the questions, specifically where users disagreed. In the fourth and final round, six of the experts rated the same four manuscripts (n = 6; one scientist dropped out of the study before this round) to validate the tool. Because of the small sample size throughout the study, percent agreement between users on the final pass/fail outcome for a manuscript was the only statistic used.

Table 3. Experts who participated in editing and validating the SOAR tool, their affiliations, and expertise.

Papers used for testing and evaluating SOAR were identified by performing a PubMed ( literature search using the search string: microarray AND exposure. Nineteen papers were chosen that were pertinent to risk assessment of chemicals and not coauthored by the participating scientists. Papers were assigned to participants so that each paper was rated at least twice in one round and no participant rated the same paper more than once (n = 2–5 per paper per round, n = 6–7 per paper total; see Table 4 for exact sample sizes per paper per round).

At the beginning of each round, the scientists were given PDF copies of their assigned manuscripts for that round with author, affiliation, date, and journal information removed. The participants were also given PDF copies of information pertaining to the raw data (e.g. a print out of the manuscript's entry in the Gene Expression Omnibus (GEO, database), also with author and date information removed. Table 2 gives reference information for the papers used for testing. Participants were given approximately 10 days to answer all of the questions in the tool for the four papers in a round. After all participants had completed a round, feedback was collected on questions to edit, remove, or add and changes were made accordingly. The weighting was also modified and a pass/fail threshold was developed based on participant feedback.


Round 1 & 2 (General Question and Format Editing)

Significant changes were made to the tool after the first two rounds of question adjustment. The number of questions dropped from a maximum of 61 questions to a maximum of 34 questions, as shown in Table 1. Several subjective questions were removed from the tool, along with questions that did not come from a peer-reviewed source. Originally there were 11 questions from MIAME, 23 questions from ToxRTool, 5 questions from Fostel et al [2], 12 questions from the Benchmark Dose Technical Guidance document [10], and 17 questions from domain experts. Section 5 pertaining to BMD modeling was removed because it required highly specific questions about the data and a level of understanding and time commitment beyond what should be expected from a first pass screening tool. That is not to say that SOAR cannot address the applicability of dose-response transcriptomic data. In fact, the questions in SOAR deal with those aspects of toxicogenomic (specifically transcriptomic) studies that are generally applicable. Rather, we are stating that at this time those types of questions are best left to BMD specialists (we do have future work that will specifically address BMD given our experiences here; thus, further discussion of BMD is outside the scope of this particular manuscript). Additionally, after the participating scientists rated a paper that involved human subjects (papers #4, 7, and 8), it became apparent that a separate set of questions was needed specifically for human studies under the “Test System” section. Originally the “Test System” questions were broken up into in vivo and in vitro but did not consider the human subject. With guidance from the participating scientists, a section was added for “In vivo, human” test subjects.

Finally, the “Microarray Data” section was split into two different sets of questions depending on whether or not raw data were available for the study. Less information is needed about how the normalized data were processed if interested scientists can access the data in raw form. After making these revisions, the final version of the tool involved five main sections with the first section setting up the tool and the remaining four sections used to score the paper. The final version contained 11 questions from MIAME, 19 questions from the ToxRTool, 4 questions from Fostel et al [2], and 6 questions from domain experts (if a question was repeated in two of the guidance sources it was only cited as being from one of the two).

Throughout the editing process the weights of the questions were also set. It was determined that a paper would be recommended for further consideration in a human health risk assessment if it received a score of at least 80% for each section.

Round 3 (Targeted Question Editing)

Results from the third round of testing are shown in Fig. 1. Of the seven papers tested in this round, there were only two where the experts disagreed on the pass/fail outcome (i.e., there was not a unanimous pass/fail determination). For paper 13 there was no agreement between the three experts rating this paper, though further inquiry showed that this was caused by rater misunderstanding of the presented data. One scientist had incorrectly interpreted the study as being in vitro, while the other two answered as in vivo. Of the two scientists who determined it was an in vivo study, one failed it by answering “no” to the question “II.4. Is frequency and duration of exposure to the test substance explained?” while the other scientist answered “yes.”

Figure 1. Percent agreement between experts on final pass/fail result of papers tested in Round 3.

Each paper was tested through the SOAR tool by 3–5 expert experts. Paper 13 had no agreement between the three experts due to misunderstanding of the data presented in the paper. Paper 17 had 1 of 3 experts disagree.

For paper 17, two of the three experts were in agreement that the paper should fail. The third expert did not agree, making the percent agreement 66%. The main disagreement was on the answer to the question: “Are the study endpoint(s) and their method(s) of determination clearly described?” which may be considered subjective to some users.

Round 4 (Validation)

Round 4, where all scientists rated the same papers as validation, produced 100% agreement on the final outcome (pass/fail) of all 4 papers (n = 6), as shown in Table 4. Concordance was achieved only after discussing the responses of one participant. The results were reviewed when there was disagreement on the pass/fail status of a paper. Each response given by the scientist who disagreed was examined and it was discovered that the scientist had incorrectly answered a single question that caused Papers 9 and 10 to fail (question II. 4: “Is frequency and duration of exposure to the test substance explained?”). The frequency and duration information was pointed out in the manuscript to the scientist who had answered “no.” This scientist realized that they missed this information while rating the manuscript and chose to revise their response, bringing their results into concordance with the rest of the group. Though there was some other disagreement between answers to specific questions for all of the papers, none of the differences were significant enough to change the pass/fail outcome of the tool.

The final questions included in the manual are provided in Table 5. For access to the full version of the tool, see the

Table 5. The full questions included in the SOAR manual and the source of the question if it was taken from an existing publication.


The SOAR tool was designed to provide a transparent method for risk assessors and assessment teams to determine the suitability of specific, published microarray data for risk assessment purposes. The goals are similar to those of the ToxRTool but with a focus on issues of data analysis and study design specific to transcriptomic microarrays. The tool was developed through four rounds of testing with experts who have microarray experimental design and analysis experience. This repetitive testing allowed for a thorough evaluation of the wording, the appropriateness, and the weights applied to each question, as well as the general ease of use of the spreadsheet format. By the final validation round, all six experts agreed on whether the four papers would pass or fail.

The tool should be used by at least two different assessors familiar with microarray data for each manuscript being scored. If the two raters cannot agree on whether the manuscript passes or fails the tool, a third assessor should be consulted to make the final determination on the manuscript. The final round of validation was performed with this method in mind. Specific answers were examined only when an expert did not agree with the pass/fail designation of the rest of the group, as we would expect to occur in actual use. The situation discussed in the results of the validation, where one user made an honest mistake in their response that caused the papers to incorrectly fail, is a prime example of how multiple users will ensure the accuracy of the scores. Choosing to only have such comprehensive discussions when there was disagreement on the ultimate pass/fail result of the paper removed the need to discuss every question in the tool when the overall outcome was the same and benefited the users by reducing the overall length of time spent considering the literature.

Notably, there are disadvantages to taking such a broad look at the results. The main concern is that all users could make mistakes on a single paper that would result in an incorrect pass/fail designation. This could occur if the mistakes were made on the same question or on different questions. Additionally, these “mistakes” could occur in two different ways: 1) as the result of typing the incorrect response (choosing “no” for a question when the user meant to choose “yes”), or 2) as the result of differing interpretations of the questions or of the information in the manuscript being rated. If only the overall pass/fail result is examined in a case where multiple users make “mistakes,” both users may end up having incorrectly passed or failed a study. The remedy for this, which was also performed in the present study, was to have one person quickly compare the individual results from multiple users. Then, if answers differed on questions with high weights or on a significant number of questions, regardless of the final pass/fail designation, these can be brought to the attention of users.

Using repetitive testing with the same group of experts can result in the experts being trained in the meaning of the questions. By the final round their agreement in scoring may have been based on their collective understanding of the meaning of the questions and not on the innate clarity of the wording. This could mean that the tool would not produce such concordant results with new users who have less experience with the questions. In order to combat this issue, the majority of the questions were given comments in the spreadsheet with an alternate wording or clarifying details. Training would need to be provided for risk assessors to familiarize themselves with microarrays and their data, so specific training on the SOAR tool could be provided at that time.

The ultimate goal for the SOAR tool is to use natural language processing to enable computers to perform the first pass screen of all papers resulting from a literature search. If the computer gives a manuscript a “pass” then it will be sent to a human for further consideration and potential analysis. Many questions that could be considered subjective were removed by the final round of testing in an attempt to make the transition to natural language processing easier. Since this is a screening activity, we would need to ensure that the computer is more inclusive than exclusive, meaning we are more accommodating of including false positives and to build the models to ensure we have very few false negatives. There are questions on data quality that computers will not be capable of answering in the foreseeable future and these were set aside for human consideration after the first pass screening has taken place. As a result, the tool does not examine some of the more important aspects of data quality, such as overall reproducibility of the results. However, the goal is that after using the tool, risk assessors will be much better informed on the details of the paper and the study, as well as possible weaknesses and strengths so that they can make a final decision on whether or not it is appropriate to include in their assessment.

It is important to note that the results from the tool are not meant to be used as a strict cut-off; the opinion of an experienced expert should always take precedence over the result of the tool, which is intended only to make the process of identifying suitable studies more systematic and transparent. However, if agencies and risk assessors employ the SOAR tool, the information and the record created by collecting that information will be a critical step in fulfilling the need for transparent and thorough decisions on the quality of the omics studies.


The authors would like to thank Drs. Channa Keshava, Jennifer Nichols, Reeder Sams, and John Vandenberg for their helpful comments on the tool and the manuscript. Disclaimer: the views expressed in this manuscript are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA. Mention of trade names or commercial products does not constitute endorsement or recommendations for use. Permission for publishing this information has been granted by the Chief of Engineers.

Author Contributions

Conceived and designed the experiments: LDB ERM. Performed the experiments: ERM SMB IC RLW EJP NGR PG LDB. Analyzed the data: ERM LDB. Contributed reagents/materials/analysis tools: ERM SMB IC RLW EJP NGR PG LDB. Wrote the paper: ERM SMB IC RLW EJP NGR PG LDB.


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