HighThroughput Sequencing Pipeline Tool Analysis Staphylococcus Aureus Justin

High-Throughput Sequencing Pipeline Tool Analysis - Staphylococcus Aureus Justin Fay Bioinformatics Department, University of Nebraska at Omaha, NE 68182 ABSTRACT Persister cells are defined as being tolerant to antibiotics. Wood, Knabel, and Kwan (2013) define these cells and explain that, while they are fairly rare occurring in roughly 1% of bacterial cells - they can be very detrimental, causing antibiotic resistant infections, unable to be cleared by antibiotics when they are present. They also go on to describe that toxin-antitoxin pairs are generally responsible for these persister cell formations. Conlon et. al. (2016) have also found out that these persister cells can also be associated with ATP (adenosine triphosphate) depletion in Staphylococcus aureus cells. Staphylococcus aureus, or simply “staph”, is a bacterial human pathogen linked to many types of infections. Since persister cells resist antibiotics, the cause of these persister cells can be found in the DNA sequences. This is where high-throughput sequencing comes into the picture. High-throughput sequencing is defined as having a quick and inexpensive method of both sequencing and analyzing large sequences and genomes. In high-throughput sequencing, beginning with a sequence, there are several steps needed to go from sequencing a genome all the way to detecting for variants and single-nucleotide polymorphisms (SNP’s). Pabinger et al. (2014) describe several of the tools associated with high-throughput and Next-Generation Sequencing (NGS) and several of the necessary tools needed to go through the pipeline. The general pipeline used is to: Material/Method In this project, I compared the tools used at each step of the high-throughput sequencing pipeline used to detect single-nucleotide polymorphisms (SNP’s). There are 11 sequences I utilized from my mentor, Dr. Kate Cooper, on this project (pertaining to Staphylococcus aureus) and I used those in the sequencing pipeline. I evaluated different tools by using different ones at different steps of the pipeline. By doing this, I was able to see how close or far the outputs are by only changing one factor. It is also important to note that, with each online tool comes its own sets of parameters. I kept track of the parameters I am using and did everything I could to keep them consistent between tools. In the beginning of this project, I gathered data by running the same sequence through a different series of tools. Again, I ran the same data through several times while only changing one tool at one step. The next time through, I changed a tool at a different step while keeping the rest of the tools consistent. This provided plenty of data since there are many combinations of tools for how many steps there are. After gathering enough data, I analyzed the results and how significantly similar or different the results are. Finally, after analyzing the results, I looked into the specific algorithms of each tool to determine why the results are different and attempted to construct the best possible pipeline to obtain the best set of results. The ultimate end goal was to determine which tools are optimal for accomplishing their relevant tasks and to assemble the pipeline that obtains the highest quality data. I obtained some data, both good and bad, and assembled a poster to help explain the results. Many tools have errors Tool names -error descriptions The tools completed their designated jobs Different algorithms provided different results The ideal tool for the job should depend on what each researcher is after Examples Quality control is vital Must be able to interpret final output (variant files) Conclusion and Future Directions 1. First sequence a genome to be used. Research 2. Then for the sequence being used, quality control testing will need to be done to ensure that nothing with the sequence is contaminated in any way. 3. If it is, parts of it may need to be trimmed and then retested for quality. Results Research Image to be put here: currently in progress 4. After the quality of the sequence is determined to have no issues, it will need to be mapped to a genome. 5. If there is no genome that the sequence can be mapped to, one will need to be assembled. There are several tools online for mapping and improving genome assemblies. 6. After obtaining a genome, the sequence will then need to be mapped the genome. • The tool used is vastly dependent on what the researcher is after. The researcher should also note which tools are more user friendly, even when using the Linux command line. Some errors were pretty self-explanatory and easy to fix while others were much more difficult and required extensive research to fix. Some were even unable to be fixed. Researchers should also note the time allotted for their research, as there were a few tools that required extensive periods of time to fix. References 7. Next, quality control should be performed again to ensure there are no problems that have occurred in the process. 8. Finally, after confirming there are no issues, variant and SNP calling will be done to find any variations. Ideally put in graphical form in final poster Description of Image The University of Nebraska does not discriminate based on race, color, ethnicity, national origin, sex, pregnancy, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, marital status, and/or political affiliation in its programs, activities, or employment. C. , & S. (2017, February 15). Pathogen genomics of methicillin resistant staphylococcus aureus and leishmania. Retrieved September 1, 2018, from https: //aran. library. nuigalway. ie/handle/10379/6315 Conlon, B. P. , Rowe, S. E. , Gandt, A. B. , Nuxoll, A. S. , Donegan, N. P. , Zalis, E. A. , . . . Lewis, K. (2016, April 18). Persister formation in Staphylococcus aureus is associated with ATP depletion. Retrieved September 21, 2018, from https: //www. nature. com/articles/nmicrobiol 201651 Pabinger, S. , Dander, A. , Fischer, M. , Snajder, R. , Sperk, M. , Efremova, M. , . . . Trajanoski, Z. (2014, March). Retrieved September 21, 2018, from https: //www. ncbi. nlm. nih. gov/pmc/articles/PMC 3956068/ Wood, T. K. , Knabel, S. J. , & Kwan, B. W. (2013, December 01). Bacterial Persister Cell Formation and Dormancy. Retrieved September 20, 2018 from https: //aem. asm. org/content/79/23/7116. full