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Grant proposal for knowledge graphs and transposable elements.
writing
proposal
pre-proposal
Commits
9b423abc
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9b423abc
authored
1 year ago
by
Bakker, Sibbe
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README.md
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README.md
pre-proposal.qmd
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pre-proposal.qmd
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README.md
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9b423abc
...
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@@ -17,14 +17,14 @@ get the message across in less words, even better. Finding the balance between
conciseness and comprehensiveness is the challenge! The pre-proposal should
include the following components:
*
[
] Title
*
[
] Keywords
*
[
] Motivation: relevance and background of the research topic
*
[
] Problem statement: urgency of problem, main objective of the research
*
[
x
] Title
*
[
x
] Keywords
*
[
x
] Motivation: relevance and background of the research topic
*
[
x
] Problem statement: urgency of problem, main objective of the research
project
*
[
] State of the art and knowledge gap: Why is this research required?
*
[
] Objectives/hypotheses: What is your focus?
*
[
] Approach: methodology how do you aim to conduct the research?
*
[
] Main anticipated results and expected outcomes
*
[
] Possible conclusions and/or implications
*
[
] Three to five literature references
*
[
x
] State of the art and knowledge gap: Why is this research required?
*
[
x
] Objectives/hypotheses: What is your focus?
*
[
x
] Approach: methodology how do you aim to conduct the research?
*
[
x
] Main anticipated results and expected outcomes
*
[
x
] Possible conclusions and/or implications
*
[
x
] Three to five literature references
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pre-proposal.qmd
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9b423abc
---
title: "
Is more data really needed to know more about _Aspergillus fumigatus_?
"
subtitle: "A
FAIR data approach
"
title: "
FAIR data to understand fungal epidemics
"
subtitle: "A
look at Asperillus fumigatus
"
author: "Sibbe Bakker"
date: "`r Sys.Date()`"
bibliography: "BibDataBase.bib"
csl: numeric.csl
link-citations: true
# toc: true
format:
wordcount-html: default
wordcount-pdf: default
...
...
@@ -16,7 +15,7 @@ keywords:
- Aspergillus fumigatus
- FAIR data
- fungicide resistance
-
machine learning
-
epidemiology
---
```{=html}
...
...
@@ -24,58 +23,53 @@ keywords:
- You should use the key words for sections in this text.
-->
```
```{r, eval=TRUE, echo=FALSE}
```{r, eval=TRUE, echo=FALSE
, results='asis'
}
# Additionally adding keywords for pdf and word output.
if (!knitr::is_html_output()) {
rmarkdown::metadata$keywords
paste0("*Key words* --- ",
paste0(rmarkdown::metadata$keywords, collapse = ", ")) |>
knitr::asis_output()
}
```
<!-- Introduction -->
<!-- Basic introduction -->
Fungi are an increasing concern for public health; for the first time the world
health organisation (WHO) included fungi in their 'priority pathogens' list
[@worldhealthorganizationwhoWHOFungalPriority2022;
@parumsEditorialWorldHealth2022]. These pathogenic fungi exhibit increasing
resistance to antifungal medicins, complicating treatment of these infections
[@fisherTacklingEmergingThreat2022].
health organisation (WHO) included fungi in their 'critical priority
pathogens' list [@worldhealthorganizationwhoWHOFungalPriority2022;
@parumsEditorialWorldHealth2022]. These pathogenic fungi, such as
_Aspergillus fumigatus_, _Candida albicans_ &c. exhibit increasing
resistance to anti-fungal medicines, complicating treatment of these infections
[@fisherTacklingEmergingThreat2022]. There is evidence that the increase
<!-- Advanced introduction -->
One important
aspect in
understand
ing disease outbreaks is
_geophylogeny_,
the study of how genotypes vary across geographical patterns
One important
tool to
understand
where microbes come from is _phylogeography_,
the study of how genotypes vary across geographical patterns
[@chenApproachesChallengesInferring2023]. For viruses, such as the flu, tools,
like nextstrain [@hadfieldNextstrainRealtimeTracking2018] are available to
record the geophylogeny observed by different researchers.
record the phylogeography observed by different researchers. With better
phylogeographic data, pathways of resistance may be better understood
[@fisherTacklingEmergingThreat2022].
<!-- Knowledge gap -->
For fungal priority pathogens, the geophylogeny is hard to determine, because
the metadata that is needed to put the genomes in a place and time is not
recorded well by researchers.
recorded well by researchers.
<!-- Method to address question -->
How *A. fumigatus* develops these resistance mechanisms and in what environments
this happens most quickly is not fully understood.
To fully understand
the *A. fumigatus*, data from various experiments and publications must be
combined. To combine the data, the findable, interoperable, accessible and
resuable (FAIR) principles will be used to store the data. Data that is already
collected by other researchers using a novel data collection/standardisation
toolchain that will be written for mycologist during the work. Developments in
machine learning technologies will leveraged aid researchers in metadata
standardisation. The improved metadata will made accessible to the researchers
in the domain. Then this database can be used for epidemiological studies.
<!-- This is me, Chris -->
To develop a pipeline for fungal phylogeography, the case of _A. fumigatus_
will be examined first, since me and my collaborators have expertise on this
fungus. Public whole genome data from _A. fumigatus_ will be compiled and the
metadata will be extracted and made standard. Next, the annotated WGS data be
placed on epidemiology sites like nextstrain. After this is completed, the
pipeline for _A. fumigatus_ can be generalised to other fungi.
<!-- Perspective and impact -->
Besides the obvious benefit of re-using data from the literature, which saves
money, a frame work will be developed to better understand the epidemiology of
fungi
Additionally, developing a method to collect FAIR data from
mycologists will be an innovation on its self, as this will also be useful in
other areas of mycology, such as candidiasis.
fungi. Such understanding will be able to directly pave the way to finding
paths of anti-fungal resistance, and being able to mitigate them
[@fisherTacklingEmergingThreat2022]. Besides the human pathogens, the system
described here may also be adapted to collect data from plant pathogens.
# References
...
...
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