Interpretation of variation in omics data : Applications in proteomics for sustainable agriculture
(2020)- Abstract
- Biomarkers are used in molecular biology to predict characteristics of interest and are applied in agriculture to accelerate the breeding of target traits. Proteomics has emerged as a promising technology for improved markers by providing a closer view to the phenotype than conventional genome-based approaches. However, a major challenge for biomarker development is that the identified biological patterns often cannot be reproduced in other studies. One piece of the puzzle to alleviate this problem is improved software approaches to distinguish biological variation from noise in the data.
In this work, two new pieces of software are introduced to facilitate interpretation of data from omic experiments. NormalyzerDE (Paper I) helps... (More) - Biomarkers are used in molecular biology to predict characteristics of interest and are applied in agriculture to accelerate the breeding of target traits. Proteomics has emerged as a promising technology for improved markers by providing a closer view to the phenotype than conventional genome-based approaches. However, a major challenge for biomarker development is that the identified biological patterns often cannot be reproduced in other studies. One piece of the puzzle to alleviate this problem is improved software approaches to distinguish biological variation from noise in the data.
In this work, two new pieces of software are introduced to facilitate interpretation of data from omic experiments. NormalyzerDE (Paper I) helps the user to perform an informed selection of a well-performing normalization technique, presents a new type of normalization for electrospray intensity variation biases and gives a user-friendly approach to performing subsequent statistical analysis. OmicLoupe (Paper II) provides interactive visualizations of up to two omics datasets, introduces novel approaches for the comparison of different datasets and provides the ability to rapidly inspect individual features. These pieces of software were applied together with existing methods to study three agricultural organisms. Firstly, a proteogenomic approach was used to study Fusarium head blight in oat. This study provided the deepest proteomic resource to date in this organism (Paper III) and identified proteins related to a differential resistance towards Fusarium head blight. It can contribute towards the development of commercial varieties with improved resistance towards this pathogen. Secondly, bull seminal plasma was studied to identify proteins correlated with fertility, which are also robust to seasonal variation (Paper IV). This study contributes towards ensuring maintained high fertility in livestock. Finally, potato grown at sites in northern and southern Sweden (Paper V) were studied to identify proteins linked to the different growth conditions at the two locations. This study contributes towards a better understanding of the molecular physiology in the agricultural field and the selection of varieties better adapted to the different growth conditions.
In conclusion, these results contribute towards improved analyses of omics data and to biomarkers with potential applications in accelerated breeding in the studied organisms. Together, this could provide tools for the development of a more sustainable agriculture.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a9c8da81-675d-4c24-9152-5e07fbdfb776
- author
- Willforss, Jakob LU
- supervisor
- opponent
-
- Prof. Elo, Laura, University of Turku, Finland.
- organization
- publishing date
- 2020-11-17
- type
- Thesis
- publication status
- published
- subject
- keywords
- agriculture, proteomics, omics, biomarker, normalization, batch effect, visualization, software
- pages
- 232 pages
- publisher
- Department of Immunotechnology, Lund University
- defense location
- Lecture hall Hörsalen, Medicon Village, Scheelevägen 2, Faculty of Engineering LTH, Lund University, Lund
- defense date
- 2020-12-11 09:00:00
- ISBN
- 9789178956418
- 9789178956401
- language
- English
- LU publication?
- yes
- id
- a9c8da81-675d-4c24-9152-5e07fbdfb776
- date added to LUP
- 2020-11-09 15:51:10
- date last changed
- 2020-11-17 10:28:18
@phdthesis{a9c8da81-675d-4c24-9152-5e07fbdfb776,
abstract = {{Biomarkers are used in molecular biology to predict characteristics of interest and are applied in agriculture to accelerate the breeding of target traits. Proteomics has emerged as a promising technology for improved markers by providing a closer view to the phenotype than conventional genome-based approaches. However, a major challenge for biomarker development is that the identified biological patterns often cannot be reproduced in other studies. One piece of the puzzle to alleviate this problem is improved software approaches to distinguish biological variation from noise in the data.<br/><br/>In this work, two new pieces of software are introduced to facilitate interpretation of data from omic experiments. NormalyzerDE (Paper I) helps the user to perform an informed selection of a well-performing normalization technique, presents a new type of normalization for electrospray intensity variation biases and gives a user-friendly approach to performing subsequent statistical analysis. OmicLoupe (Paper II) provides interactive visualizations of up to two omics datasets, introduces novel approaches for the comparison of different datasets and provides the ability to rapidly inspect individual features. These pieces of software were applied together with existing methods to study three agricultural organisms. Firstly, a proteogenomic approach was used to study <i>Fusarium</i> head blight in oat. This study provided the deepest proteomic resource to date in this organism (Paper III) and identified proteins related to a differential resistance towards <i>Fusarium</i> head blight. It can contribute towards the development of commercial varieties with improved resistance towards this pathogen. Secondly, bull seminal plasma was studied to identify proteins correlated with fertility, which are also robust to seasonal variation (Paper IV). This study contributes towards ensuring maintained high fertility in livestock. Finally, potato grown at sites in northern and southern Sweden (Paper V) were studied to identify proteins linked to the different growth conditions at the two locations. This study contributes towards a better understanding of the molecular physiology in the agricultural field and the selection of varieties better adapted to the different growth conditions.<br/><br/>In conclusion, these results contribute towards improved analyses of omics data and to biomarkers with potential applications in accelerated breeding in the studied organisms. Together, this could provide tools for the development of a more sustainable agriculture.<br/>}},
author = {{Willforss, Jakob}},
isbn = {{9789178956418}},
keywords = {{agriculture; proteomics; omics; biomarker; normalization; batch effect; visualization; software}},
language = {{eng}},
month = {{11}},
publisher = {{Department of Immunotechnology, Lund University}},
school = {{Lund University}},
title = {{Interpretation of variation in omics data : Applications in proteomics for sustainable agriculture}},
url = {{https://lup.lub.lu.se/search/files/86529764/jakob_willforss_thesis_web.pdf}},
year = {{2020}},
}