TraV: A Genome Context Sensitive Transcriptome Browser
Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and...
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| Published in | PloS one Vol. 9; no. 4; p. e93677 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Public Library of Science
01.04.2014
Public Library of Science (PLoS) |
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| Abstract | Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at http://appmibio.uni-goettingen.de/index.php?sec=serv. |
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| AbstractList | Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at http://appmibio.uni-goettingen.de/index.php?sec=serv. Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at http://appmibio.uni-goettingen.de/index.php?sec=serv . Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at http://appmibio.uni-goettingen.de/index.php?sec=serv.Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at http://appmibio.uni-goettingen.de/index.php?sec=serv. |
| Audience | Academic |
| Author | Liesegang, Heiko Dietrich, Sascha Wiegand, Sandra |
| AuthorAffiliation | Abteilung für Angewandte und Genomische Mikrobiologie, Institut für Mikrobiologie und Genetik, Norddeutsches Zentrum für Mikrobielle Genomforschung, Georg-August-Universität Göttingen, Göttingen, Germany CSIR Institute of Genomics and Integrative Biology, India |
| AuthorAffiliation_xml | – name: CSIR Institute of Genomics and Integrative Biology, India – name: Abteilung für Angewandte und Genomische Mikrobiologie, Institut für Mikrobiologie und Genetik, Norddeutsches Zentrum für Mikrobielle Genomforschung, Georg-August-Universität Göttingen, Göttingen, Germany |
| Author_xml | – sequence: 1 givenname: Sascha surname: Dietrich fullname: Dietrich, Sascha – sequence: 2 givenname: Sandra surname: Wiegand fullname: Wiegand, Sandra – sequence: 3 givenname: Heiko surname: Liesegang fullname: Liesegang, Heiko |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24709941$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3389_fgene_2022_966643 crossref_primary_10_1093_femsle_fnaa103 crossref_primary_10_3389_fmicb_2018_02827 crossref_primary_10_1016_j_resmic_2018_05_009 crossref_primary_10_3389_fmicb_2017_00642 crossref_primary_10_3390_v12121359 crossref_primary_10_1038_s41598_017_05628_y crossref_primary_10_1371_journal_pone_0120759 crossref_primary_10_3390_applmicrobiol1010005 crossref_primary_10_1039_C8MT00299A crossref_primary_10_1128_JB_00192_19 crossref_primary_10_1093_database_baw146 crossref_primary_10_1093_femsle_fnw139 crossref_primary_10_3389_fmicb_2016_00737 crossref_primary_10_1186_s13742_015_0077_2 crossref_primary_10_3389_fmicb_2024_1374708 crossref_primary_10_1111_1462_2920_15964 crossref_primary_10_1186_s13059_016_0881_8 |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2014 Public Library of Science 2014 Dietrich et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Dietrich et al 2014 Dietrich et al |
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| DOI | 10.1371/journal.pone.0093677 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: HL SD. Performed the experiments: SD SW. Analyzed the data: SD SW HL. Contributed reagents/materials/analysis tools: SD SW. Wrote the paper: SD SW HL. Competing Interests: The authors have declared that no competing interests exist. |
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