Publication detail

Reproducible analytical pipeline for using raw RNA-Seq data from non-model organisms

SCHWARZEROVÁ, J.

Original Title

Reproducible analytical pipeline for using raw RNA-Seq data from non-model organisms

English Title

Reproducible analytical pipeline for using raw RNA-Seq data from non-model organisms

Language

en

Original Abstract

Current biotechnological research of bacterial or archaeal genomes has huge potential due to the use of the next generation sequencing (NGS) platforms. NGS era unravelled huge analysis data for sufficiency description microorganisms with ecology potential in future. Nowadays, efforts lie in creating comprehensive pipelines that can be used for pre-processing analysis to enable effective following steps of high throughput data processing. This paper deals with design of data analysis pipeline for using raw RNA-Seq data that was applied to the Clostridium beijerinckii NRRL B-598. The bacterium is typical performer in the field of biofuels production thanks to its ability to produce butanol. Unfortunately, it is non-model organism as many other microorganisms which can be of great potential from ecological point of view. The proposed pipeline offers to take necessary steps in initial data processing that produces data of comparable quality to widely studied model organisms. Therefore, it can be combined with following pipelines for gene regulatory network inference, which was up to date matter of non-model organisms.

English abstract

Current biotechnological research of bacterial or archaeal genomes has huge potential due to the use of the next generation sequencing (NGS) platforms. NGS era unravelled huge analysis data for sufficiency description microorganisms with ecology potential in future. Nowadays, efforts lie in creating comprehensive pipelines that can be used for pre-processing analysis to enable effective following steps of high throughput data processing. This paper deals with design of data analysis pipeline for using raw RNA-Seq data that was applied to the Clostridium beijerinckii NRRL B-598. The bacterium is typical performer in the field of biofuels production thanks to its ability to produce butanol. Unfortunately, it is non-model organism as many other microorganisms which can be of great potential from ecological point of view. The proposed pipeline offers to take necessary steps in initial data processing that produces data of comparable quality to widely studied model organisms. Therefore, it can be combined with following pipelines for gene regulatory network inference, which was up to date matter of non-model organisms.

Keywords

RNA-Seq; Next generation sequencing; Clostridium beijerinckii NRRL B-598; Transcriptome

Released

23.04.2020

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5867-3

Book

Proceedings of the 26th Conference STUDENT EEICT 2020

Edition number

1

Pages from

225

Pages to

228

Pages count

4

URL

Documents

BibTex


@inproceedings{BUT165307,
  author="Jana {Schwarzerová}",
  title="Reproducible analytical pipeline for using raw RNA-Seq data from non-model organisms",
  annote="Current biotechnological research of bacterial or archaeal genomes has huge potential due to the use of the next generation sequencing (NGS) platforms. NGS era unravelled huge analysis data for sufficiency description microorganisms with ecology potential in future. Nowadays, efforts lie in creating comprehensive pipelines that can be used for pre-processing analysis to enable effective following steps of high throughput data processing. This paper deals with design of data analysis pipeline for using raw RNA-Seq data that was applied to the Clostridium beijerinckii NRRL B-598. The bacterium is typical performer in the field of biofuels production thanks to its ability to produce butanol. Unfortunately, it is non-model organism as many other microorganisms which can be of great potential from ecological point of view. The proposed pipeline offers to take necessary steps in initial data processing that produces data of comparable quality to widely studied model organisms. Therefore, it can be combined with following pipelines for gene regulatory network inference, which was up to date matter of non-model organisms.",
  address="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  booktitle="Proceedings of the 26th Conference STUDENT EEICT 2020",
  chapter="165307",
  howpublished="online",
  institution="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  year="2020",
  month="april",
  pages="225--228",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií"
}