Building a bioinformatic workflow to study methane metabolism in a capture metagenomics dataset of peatland soil
(2020) BINP50 20201Degree Projects in Bioinformatics
- Abstract
- Methane metabolism in the peatland is an important step within global carbon circulation. It is essential to understand the functional composition of the microbial community related to methane metabolism in the peatland ecosystem. In this study, a bioinformatic workflow was built to analyze a capture metagenomic dataset of peatland soil at three depth groups (5cm, 10cm, 15cm) and visualize the final abundance data into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Two annotation servers: MG-RAST and GhostKOALA were used for functional annotation in the workflow. From the annotated results, the GhostKOALA server was found to be more suitable for our dataset because it annotated significantly higher abundance and unique of genes... (More)
- Methane metabolism in the peatland is an important step within global carbon circulation. It is essential to understand the functional composition of the microbial community related to methane metabolism in the peatland ecosystem. In this study, a bioinformatic workflow was built to analyze a capture metagenomic dataset of peatland soil at three depth groups (5cm, 10cm, 15cm) and visualize the final abundance data into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Two annotation servers: MG-RAST and GhostKOALA were used for functional annotation in the workflow. From the annotated results, the GhostKOALA server was found to be more suitable for our dataset because it annotated significantly higher abundance and unique of genes related to methane metabolism. Furthermore, the abundance of several methane metabolism functional genes was found significantly different at different depth groups, which indicated that different depth samples may have different compositions of methane metabolism functional genes. (Less)
- Popular Abstract
- Does different soil depths have different methane functional communities?
Methane (CH4) is known as one major greenhouse gas in the atmosphere. The northern peatland is a large global natural source of CH4. Therefore, it is essential to understand
the methane metabolism in the peatland ecosystem. One way to achieve that is by understanding the functional composition of the microbial community related to methane metabolism in the peatland.
In this study, a bioinformatic workflow was built to analyze a capture metagenomic dataset of peatland soil at three depth groups (5cm, 10cm, 15cm). The workflow processed raw sequencing data into final annotation results. Two annotation servers: MG-RAST and GhostKOALA were used for functional... (More) - Does different soil depths have different methane functional communities?
Methane (CH4) is known as one major greenhouse gas in the atmosphere. The northern peatland is a large global natural source of CH4. Therefore, it is essential to understand
the methane metabolism in the peatland ecosystem. One way to achieve that is by understanding the functional composition of the microbial community related to methane metabolism in the peatland.
In this study, a bioinformatic workflow was built to analyze a capture metagenomic dataset of peatland soil at three depth groups (5cm, 10cm, 15cm). The workflow processed raw sequencing data into final annotation results. Two annotation servers: MG-RAST and GhostKOALA were used for functional annotation in the workflow. The Kyoto Encyclopedia of Genes and Genomes methane metabolism pathway (KEGG map 00680) is a map contains all reactions, genes and enzymes related to methane metabolism. The final abundance data would be integrated and visualized into KEGG map 00680.
From the annotated results, the GhostKOALA server was found to be more suitable for our dataset because it annotated significantly higher number of the raw data and included more unique genes related to methane metabolism. There was no significant difference identified at the total abundance of methane metabolism functional genes between different depth groups. However, several specific methane metabolism genes had significantly different abundance between different depth groups. This indicated that different depth samples may have different compositions of methane metabolism functional groups.
Master’s Degree Project in Bioinformatics 30 credits 2020
Department of Biology, Lund University
Advisor: Dag Ahrén
Department of Biology, Lund University (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9031778
- author
- Li, Songjun
- supervisor
-
- Dag Ahrén LU
- organization
- course
- BINP50 20201
- year
- 2020
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 9031778
- date added to LUP
- 2020-11-10 12:31:51
- date last changed
- 2020-11-10 12:31:51
@misc{9031778,
abstract = {{Methane metabolism in the peatland is an important step within global carbon circulation. It is essential to understand the functional composition of the microbial community related to methane metabolism in the peatland ecosystem. In this study, a bioinformatic workflow was built to analyze a capture metagenomic dataset of peatland soil at three depth groups (5cm, 10cm, 15cm) and visualize the final abundance data into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Two annotation servers: MG-RAST and GhostKOALA were used for functional annotation in the workflow. From the annotated results, the GhostKOALA server was found to be more suitable for our dataset because it annotated significantly higher abundance and unique of genes related to methane metabolism. Furthermore, the abundance of several methane metabolism functional genes was found significantly different at different depth groups, which indicated that different depth samples may have different compositions of methane metabolism functional genes.}},
author = {{Li, Songjun}},
language = {{eng}},
note = {{Student Paper}},
title = {{Building a bioinformatic workflow to study methane metabolism in a capture metagenomics dataset of peatland soil}},
year = {{2020}},
}