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Explore the biogeographic characteristics of the microbiome in diverse datasets and predict its geographical sources

Zhang, Yali (2022) BINP51 20212
Degree Projects in Bioinformatics
Popular Abstract
Urban microbial communities, where do they come from?

Microorganisms are everywhere, but where do they come from? If you live in a city, what about the microorganisms around you?

In the environment where humans live, although invisible to our eyes, thousands of microorganisms live with us. Like human communities, these microorganisms form small communities with different distribution characteristics. With the air currents, goods transportation, and even human travel and tourism, microorganisms invisible to our eyes embark on a journey. These microorganisms constantly exchanged between the individual and the environment hide many secrets. For example, when a detective investigates a stolen treasure, by predicting the source of the... (More)
Urban microbial communities, where do they come from?

Microorganisms are everywhere, but where do they come from? If you live in a city, what about the microorganisms around you?

In the environment where humans live, although invisible to our eyes, thousands of microorganisms live with us. Like human communities, these microorganisms form small communities with different distribution characteristics. With the air currents, goods transportation, and even human travel and tourism, microorganisms invisible to our eyes embark on a journey. These microorganisms constantly exchanged between the individual and the environment hide many secrets. For example, when a detective investigates a stolen treasure, by predicting the source of the microbes at the scene, then tracking microbes that do not belong to the location makes it possible to find where the thief came from.

In this study, I improved an algorithm, the "Microbiome Geographic Population Structure" (mGPS), that can predict the source of a sample by the characteristics of microorganisms in it. I also built an interface for it, so that more scientists can use mGPS without learning the computer language.

In addition, curious about the microbial community in cities where more than half of the population now lives, I studied microbial samples collected from different locations in the city, such as subway stations and monuments. Not only does it show a map of the distribution of microorganisms in the city, but it also traces the global spread of antimicrobial resistance during 2016-2017 using mGPS.

Finally, using samples collected from the monument, I applied the mGPS interface and found that despite both being in the city, the microorganisms collected from the monument and the metro station tell us very different information about the geography. Focus on Tel Megiddo, this study is the first to explore its microorganisms, telling the differences in the microorganisms community in the different sites and materials here.

Master’s Degree Project in Bioinformatics 45 credits 2022
Department of Biology, Lund University

Advisor: Eran Elhaik
Department of Biology, Lund University, Lund, Sweden (Less)
Please use this url to cite or link to this publication:
author
Zhang, Yali
supervisor
organization
course
BINP51 20212
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
9085354
date added to LUP
2022-06-08 11:44:09
date last changed
2023-03-29 09:27:45
@misc{9085354,
  author       = {{Zhang, Yali}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Explore the biogeographic characteristics of the microbiome in diverse datasets and predict its geographical sources}},
  year         = {{2022}},
}