Data Analysis for Chemistry: An Introductory Guide for Students and Laboratory Scientists

ISBN 13: 9780195162103
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This activity guides the analysis of a published scientific figure from a study that investigated physiological and genetic adaptations in the Bajau, a group of people who traditionally do freediving.

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This activity guides the analysis of a published scientific figure from a study that investigated genetic factors contributing to skin color differences, particularly within African populations.

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This activity guides the analysis of a published scientific figure from a study that investigated how gene duplication contributed to the evolution of electric fish. This multipart animation series explores the process of photosynthesis and the structures that carry it out. This activity guides the analysis of a published scientific figure from a study that compared the testosterone levels of Olympic-level elite athletes.

This activity guides the analysis of a published scientific figure from a study that modeled the impact of an infectious fungal disease on a bat population. This activity builds on information presented in the video Selection for Tuskless Elephants. Students use scientific evidence and reasoning to construct an explanation of and develop an argument for tusklessness in elephant populations.

Skip to main content. Students were asked to use available information to predict the proportion and function of genes expected to respond to abiotic stress conditions in maize seedlings and to compare transcriptome response between different abiotic stresses cold and heat and in plants from different genetic backgrounds B73 and Mo After a discussion of the major concepts of environmental effects on gene expression and an introduction of the RNA-seq data set, students formulated hypotheses regarding gene expression changes that could be answered using this data set.

They also investigated general approaches of Illumina RNA sequencing: fragmentation, adaptor ligation, indexing and multiplexing, and sequencing by synthesis. Using many available resources, including the Internet, textbooks, and help from the instructor, students constructed the schematic representation of the RNA-seq experimental flow and briefly described it in their own words.

Additionally, they discussed the ways data quality is graphically visualized in the DNA Subway software. Finally, students completed a short exercise demonstrating the principles of following steps of the RNA-seq analysis: mapping short reads back to the genome and read counting and normalization. We chose to work with the Green Line of DNA Subway, because it provides the intuitive platform for conducting some of the analysis, essential for students who lack computer programming skills and for a lab environment in which computer power and time are limited.

Although the DNA Subway Green Line allows the complete workflow of the RNA-seq analysis Tuxedo protocol to be conducted, many of the analysis steps take a long time and could not be completed in 3-h lab periods. This approach allowed students to focus on principles of read alignment and counting through a series of guided exercises in worksheet 3 and on discovering differentially expressed genes in worksheet 4.

Students worked with files containing raw gene counts for two abiotic stresses and control samples for two genotypes generated by the instructor see Data Set 1 in the Supplemental Material. The students identified questions of interest and created lists of differentially expressed genes for conditions relevant to their questions. They also discussed several approaches to visualizing RNA-seq data and used these approaches to answer the questions generated during the previous steps. Students informally presented their work to peers and the instructor to receive feedback and solve problems during the analysis.

This worksheet guides students through DE-Seq analysis in R statistical analysis software Anders and Huber, and provides necessary explanations of the steps involved. Students performed data normalization and statistical analysis of differentially expressed genes and filtered their results based on the significance level, fold difference of expression levels, and the minimal expression level in one or several samples see Data Set 2 in the Supplemental Material for an example of a DE-Seq output file.

The whole class engaged in the discussion of criteria that should be used to identify genes as differentially expressed. The students discussed and chose the questions they would like to address and approaches to data visualization and analysis that could be used to answer their questions. This worksheet aims at introducing students to various types of graphical representation of the RNA-seq data, such as scatter plots, histograms, kernel-density plots, heat maps, Venn diagrams, and genome views.

It uses examples of figures from published RNA-seq studies and asks students to interpret these graphs.

Data analysis for chemistry: an introductory guide for students and laboratory scientists

To assess student learning gains, we used the same test as a pretest and a posttest. Student scores were used to calculate normalized learning gains Hake, , a metric that takes into account differences in student knowledge and measures the fraction of the available improvement that can be gained.

A CURE presurvey and a content assessment pretest were conducted during week 2 of both courses, while a CURE postsurvey and a content assessment posttest were conducted during week 14, the last week of the courses, at least 1 wk after the lab reports were turned in. Owing to a low number of students in Applied Biotechnology, the assessment data described here refer to the students from Principles of Genetics, unless noted otherwise.

Extra-credit points were assigned for correct answers to the content assessment pre- and posttests and for completion of CURE surveys. Finally, students were asked to provide any unsolicited comments about the RNA-seq laboratory series as a part of the university-wide postcourse online student evaluations. These comments remained completely anonymous and confidential. Light conditions were the same for all stress and control conditions. Reads were filtered to allow for only uniquely mapped reads. Statistical significance of expression differences was determined using the DE-Seq package Anders and Huber, Gene ontology analysis was performed using information from the Maize Genetics Database maizegdb.

The key to successful implementation of this series of lab exercises is the choice of the data set for analysis.

Student Challenge #3: Learning Data Analysis

To provide students with the background on abiotic stress and experimental flow Figure 1 , we had students reproduce the conditions of the experiment and observe the effects of cold- and heat-stress exposure on maize seedlings Figure 3. When the stressed plants were allowed to recover after stress for 24 h, phenotypic consequences became apparent for both stress treatments.

While Mo17 plants were resistant to cold stress and showed very little, if any, phenotypic differences compared with control plants, B73 seedlings showed striking phenotypic response with dry and necrotic leaf edges and tips and severe wilting. Both Mo17 and B73 seedlings showed mild response to heat stress, with wilted and discolored leaves Figure 3. Phenotypic effects of exposure to abiotic stress observed by students. B73 seedlings show strong response to cold stress with dry necrotic leaf edges and tips, while Mo17 seedlings show only minimal response to cold. Both B73 and Mo17 seedlings show response to heat stress with wilted leaves.

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Students from different groups compiled a table summarizing the number of genes differentially expressed in response to different abiotic stress conditions in both genotypes Supplemental Table 2. Generating lists of differentially expressed genes stimulated further questions. With some guidance, students explored the approaches to visualizing data and asked deeper questions about differentially expressed genes worksheet 5. Students used a variety of approaches to visualize the data pertinent to their research questions Supplemental Table 1 and Figure 4.

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Some groups investigated the level of individual variation in transcriptional response to abiotic stress by comparing variation between replicates of the same condition and between different samples using scatter plots Figure 4, A and B. Other students asked the same question by constructing a heat map that visualized differentially expressed genes in two genotypes under stress conditions Figure 4E. Several student groups compared the stress response between maize genotypes Figures 4, C and E.

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Students also asked whether some genes responded in a similar manner to different abiotic stress conditions Figure 4F. Finally, students investigated the likely functions of the stress-response genes by comparing the proportion of genes that belong to different gene ontology categories for all maize genes and genes differentially expressed in response to stress Figure 4D. Examples of graphs students used to visualize data and answer the questions. A and B Comparison of variation between two replicates of the same condition and between stress and control conditions.

Log 2 RPKM values are graphed for all maize genes. C The conservation of stress response. Many genes up-regulated in response to cold stress in B73 are also up-regulated in response to cold stress in Mo17, while many genes show response in only one of the genotypes. D The proportion of all maize genes, genes up-regulated in response to cold, and genes down-regulated in response to cold is shown. SE is shown with error bars. E Abiotic stress exposure results in up- or down-regulation for a number of maize genes in each genotype.

The Z-normalized RPKM values for all differentially expressed genes were used to perform hierarchical clustering of the gene expression values. The genotypes B B; Mo M and conditions heat: red; control: green; cold: blue are indicated below each column. Three replicates of each condition are shown. F Genes affected by cold stress are frequently up-regulated in response to heat stress as well.

Genes up- and down-regulated for cold stress in B73 are shown, as is their response to heat stress. ND: the genes with no differential expression.

Data Analysis For Chemistry by D Brynn Hibbert, J Justin Gooding

The number of genes in each category is shown. A combination of subjective and objective assessment approaches were used to assess student learning as the result of this lab series. First, students were asked to complete a test with 22 multiple-choice questions once during the first week of the class pretest and once at the end of the last lab period posttest.

The questions were designed to test understanding of principles of gene expression regulation, major concepts of RNA-seq analysis, and data analysis skills see Table 2 for question category assignment. Interestingly, the most difficult questions from the regulation of gene expression category questions 3, 9, and 14 focused on the overall transcriptional response to stress and its magnitude and variation. The overwhelming majority of students in Principles of Genetics said that stress affects gene expression in a predictable way, primarily activating gene expression of a relatively small number of genes.

Assessment of student learning. Student learning was assessed using a test consisting of 22 multiple-choice questions. Questions were separated into three categories, and the average proportion of correct answers for the questions in these categories was calculated for two courses Principles of Genetics and Applied Biotechnology. Vertical bars show SD.

For assessment of student skills in graphical data visualization and interpretation, 27 group lab reports were assessed using the rubric that focused on the appropriateness, clarity, and quality of the figures, figure legends, and data interpretations Table 4 ; see the Supplemental Material for the rubric used. Students reported perceived learning gains higher or comparable with learning gains reported by all CURE participants in all 21 categories, with the largest gains in categories related to understanding the scientific process and skills in data analysis Table 5.

Finally, students were asked to provide comments regarding the RNA-seq data analysis lab experience in the anonymous university-wide online student evaluations of the course, and 65 students chose to provide comments. All student responses were analyzed using the constant comparative method Erickson, We believe that extending this laboratory module to four or even five lab periods by incorporating additional debriefing activities, or even minilectures provided by the instructor and aimed at explaining most common mistakes, would significantly ameliorate this problem.

We developed a series of laboratory exercises that engages students in investigating transcriptional response of maize seedlings to abiotic stress. In our experience, a connection to climate change served as a great way to excite students about plant genetics and show them the relevance of plant genetics research. Analysis of student comments in the online course evaluations suggests that students were excited to participate in the real research project and analyze the unpublished data, potentially exploring novel scientific ideas and connections Table 6 , highlighting the need for a careful choice of the RNA-seq data set.

One of the main advantages of the approach we used is the opportunity to engage students using any publicly available RNA-seq data set. The manuscripts describing these data sets usually address specific questions and leave a lot of room for additional questions that students could investigate. Furthermore, the costs for library construction and sequencing, the most expensive steps of generating RNA-seq data, continue to decrease, and the possibility of running RNA-seq experiments designed and run by students in undergraduate biology courses is already within reach for many institutions.

Most of the exercises and the general approach described here can be easily adopted for analysis of any RNA-seq data set. The series of laboratory exercises on transcriptional response to abiotic stress in maize was implemented in the introductory genetics course and in the upper-level biotechnology course and as an approach to introduce summer research students to RNA-seq analysis. Depending on time commitment and the level of the students, these exercises could be extended to incorporate quantitative reverse-transcription polymerase chain reaction qRT-PCR validation of most interesting differentially expressed genes as well as to test expression of these genes under other relevant conditions, further investigating the biological role of identified differentially expressed genes.

One of the difficulties in incorporating RNA-seq analysis into the classroom is the complexity of the tools used by the research community to map and count RNA-seq reads and to find differentially expressed genes. Many features of the Green Line are readily accessible, and the students were able to conduct analyses and interpret their data.

Unfortunately, the time required for running the applications for read mapping and counting for a large maize genome on the Green Line was too long to be effectively integrated in a time-limited lab environment. To overcome this issue and to allow students to concentrate on data analysis instead of technical details of the computer applications, we chose to provide students with the raw read counts.

Students were engaged in a series of exercises simulating these activities, including analysis of analogies aimed at helping them understand the purpose and potential limitations of each of the steps. Students used a DE-Seq R package to find differentially expressed genes and to conduct downstream analysis of these genes Anders and Huber, While the students were provided with the template scripts for DE-Seq analysis and using R to build various graphs, students had to modify these scripts to their specific questions, a task that required them to understand the purpose of each line of code.

In addition, the instructional approach described here, specifically peer-to-peer presentations and peer reviews of the lab reports, presents potential for students to develop written and oral communication skills. Although beyond the scope of this project, formal assessment of development of mathematical and communication skills as the result of implementing this laboratory series should provide interesting data on integrative development of student skills related to science.

Students must be available for their entire assigned rotation shift; as clinical rotation may be scheduled during evening hours. The granting of the CLT Associate in Applied Science degree is not contingent upon the student passing any type of external certification or licensure examination. Alumni of the program have also found employment as phlebotomists; quality control technicians in the food industry; in biological; pharmaceutical; and chemical laboratories.

In order for the student to perform the essential functions of the clinical laboratory science profession, the following technical standards are required of students entering the CLT Associate in Applied Science Program. The student must be able to handle stress and take appropriate actions when emergency situations arise.

The health report form includes a physical examination and an immunization report. NAACLS Accreditation guidelines require that a procedure be established for determining that the applicant's or student's health will permit them to meet the technical standards of the program. As needed, consultation with the New York State Education Department is suggested for details regarding the legal limitations to licensure in New York State. In addition to the Clinical Laboratory Technician CLT Program competencies already listed on this page, the following data is provided to reflect the program's effectiveness.

An Associate in Applied Science A. Many states in the U. Separate applications for state licensure must be filed within that state. Program graduation rates are defined as the number of students who began the "final half" of the program and have since graduated. Program attrition rates are defined as the number of students who began the "final half" of the program but voluntarily or involuntarily left the program.