In the given research project, the objective is to conduct a comparison of guanine-cytosine (GC) composition percentages among five species. The intention is to perform statistical analysis such as ANOVA tests, homogenity, posthoc test, t-tests and more to compare and understand the difference in variation based on the species DNA sequences. The guanine-cytosine composition is an essential genomic trait that can provide insights to the genetic makeup and evolution of organisms. Through comparison assessments of guanine-cytosine composition percentages across the species, researchers can gain a better understanding identifying regions of genomic variation.
To calculate the guanine-cytosine composition percentage, computational methods were used to analyze the genomic sequences. I used utliization tools such as Pandas, Biopython Statsmodels, SciPy, and Matplotlib to extract genomic sequence and create visuals to gain a better understanding on the differences in the statistical analysis among species. The resulting guanine-cytosine composition percentages were used for further analysis for a ANOVA analysis.
To compare the GC composition percentages between five species, employment of statistical analysis techniques such as one-way ANOVA analysis tests were conducted. If and when the discrepancy was ever too significant for variability, a post-hoc test should be involved to identify the pairwise differences between species. Below are the species represented by it's JPEG images along with their corresponding guanine-cytosine percentage content:
Figure 1.1 shows the species with their corresponding guanine-cytosine percentage content.
To present my findings, I implemented data visualization tool Seaborn to create visually appealing plots. I generated bar charts, box plots, and scatter plots to illustrate the differences in the guanine-cytosine composition percentages between five species. These visualizations help in understanding the distribution and variation of guanine-cytosine composition the given species.
Figure 1.2 represents the distribution of guanine-cytosine content in it's genomic sequence.
To present the findings, data visualization tool utilized was Seaborn to create visually appealing bar chart to illustrate the differences in the guanine-cytosine composition percentages between the species. These visualizations help in understanding the distribution of guanine-cytosine content in the five species, and providing the standard deviation that'll help understand the variability in the percent values in the dataset. The calculations indicated that the standard deviation measured a variability of 12.531. The percentage values helps represent the variablity in the speciesc processes and charcteristics. The guanine-cytosine content in a species is reponisble for influencing stablity of DNA, gene expression, protein coding, and moelcular or cellular processes.
Figure 1.3 represents the variation in analytical results from the ANOVA test.
To present the findings, the data visualization tool of Tabulate was used to create a table to compare the result of the one-way anaylsis of variance (ANOVA) test. The variation was split into three sources; between groups, within groups, and total. The variation was split into three sources to investigate the relative contributions of between and within groups. The source of variation 'Between Groups' sum of squares was 627.882, representing dissimilarities amount the groups being compared, while the source of variation 'Within Groups' had a sum of squares 0.229591, representing variability unexplained after defining the differences between the groups. The residual variability could be due to random ractors, measurement errrors, or uncontrollable factors. The degrees of freedom represent the number of independent groups or data sets considered in the analysis test.
Based on the studies, It was anticipated upon observing differences in the GC composition percentages that the percentage differed greatly when considering factors such as sequencing errors, assembly errors, duplication or deletion of genes, and it's biological variation. Zebrafish is a vertebrate organism with a relatively higher GC content compared to Mycobacterium tuberculosis, which is a bacterial pathogen known for its low GC content. I expect to see statistically variations reflecting the genomic characteristics of these two organisms.
To reproduce my analysis and visualizations, follow these steps:
- Install Python (version 3.0.0) and the required libraries listed in the
requirements.txtfile. - Clone the repository:
git clone https://github.com/jobinjohn/SpeciesGC-.git - Navigate to the project directory:
cd SpeciesGC- - Run the analysis script:
python analysis.py - The results and visualizations will be generated in the specified output directory.
The results showed patterns within the amount of guanine-cytosine composition among species exhibiting higher content comparative to bacterial species, such as Mycobacterium Tuberculosis. The ANOVA analysis confirmed statistically significant differences between groups, and post-HAC tests with GC discrepancies. These variations highlighted the biological diversity in genomic structure and function, which emphasized the role of GC content that influenced DNA stabilization, gene expression, and cellular processes. This research underlines the importance of computational and statistical approaches in understanding complex genomic traits.