Sepsis is a potentially fatal emergency medical condition that reflects the presence of the body’s systematic inflammation. Around 260 biomarkers have been determined to sepsis. The gold standard of blood culturing has remained the best technique for finding sepsis etiology up to this data. However, some vital drawbacks, such as laboriousness, have encouraged a global attempt to find new techniques. This study aimed to optimize a method from which earlier sepsis diagnosis compared to the gold standard could be resulted. DNA was extracted from both spiked and non-spiked whole blood samples. After quality control of the DNA elutions, library preparation and nanopore sequencing using the MinION device were carried out. Basecalling and demultiplexing were done using Guppy GPU and barcoded FASTQ-files were analyzed using What’s-In-My-Pot and Kraken2 taxonomy classification programs. Three different DNA extraction methods were compared from which the second and third methods opted as the optimized methods. Although spiked species were not found in the used taxonomy classification databases, their respective families were spotted. Kraken2 program indicated a relationship between the read percentage of the families and the spiking level of initial blood samples. On the other hand, What’s-In-My-Pot did not show such a trend and only the highest spiking concentration had indicated the families within the reads. A possible justification for not finding the species within the reads is the patchiness of the two databases. Despite the failure in determining the species within FASTQ-files, the whole experiment has gathered valuable experiences for future studies. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-21682 |
| Date | January 2022 |
| Creators | Ghiasvand, Mohammad |
| Publisher | Högskolan i Skövde, Institutionen för biovetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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