Mining RNA-seq data for infections and contaminations.
RNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression oth...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3760913?pdf=render |
| _version_ | 1811232640636813312 |
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| author | Thomas Bonfert Gergely Csaba Ralf Zimmer Caroline C Friedel |
| author_facet | Thomas Bonfert Gergely Csaba Ralf Zimmer Caroline C Friedel |
| author_sort | Thomas Bonfert |
| collection | DOAJ |
| description | RNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression other than the host cell and are little equipped to address the problems arising from redundancies and gaps among sequenced microbe and virus genomes. We show that screening of sequencing reads for contaminations and infections can be performed easily using ContextMap, our recently developed mapping software. Based on mapping-derived statistics, mapping confidence, similarities and misidentifications (e.g. due to missing genome sequences) of species/strains can be assessed. Performance of our approach is evaluated on three real-life sequencing data sets and compared to state-of-the-art metagenomics tools. In particular, ContextMap vastly outperformed GASiC and GRAMMy in terms of runtime. In contrast to MEGAN4, it was capable of providing individual read mappings to species and resolving non-unique mappings, thus allowing the identification of misalignments caused by sequence similarities between genomes and missing genome sequences. Our study illustrates the importance and potentials of routinely mining RNA-seq experiments for infections or contaminations by microbes and viruses. By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained. |
| first_indexed | 2024-04-12T11:06:17Z |
| format | Article |
| id | doaj.art-0299a3e75fa44b7ba956df60d5c3f329 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T11:06:17Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0299a3e75fa44b7ba956df60d5c3f3292022-12-22T03:35:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0189e7307110.1371/journal.pone.0073071Mining RNA-seq data for infections and contaminations.Thomas BonfertGergely CsabaRalf ZimmerCaroline C FriedelRNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression other than the host cell and are little equipped to address the problems arising from redundancies and gaps among sequenced microbe and virus genomes. We show that screening of sequencing reads for contaminations and infections can be performed easily using ContextMap, our recently developed mapping software. Based on mapping-derived statistics, mapping confidence, similarities and misidentifications (e.g. due to missing genome sequences) of species/strains can be assessed. Performance of our approach is evaluated on three real-life sequencing data sets and compared to state-of-the-art metagenomics tools. In particular, ContextMap vastly outperformed GASiC and GRAMMy in terms of runtime. In contrast to MEGAN4, it was capable of providing individual read mappings to species and resolving non-unique mappings, thus allowing the identification of misalignments caused by sequence similarities between genomes and missing genome sequences. Our study illustrates the importance and potentials of routinely mining RNA-seq experiments for infections or contaminations by microbes and viruses. By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.http://europepmc.org/articles/PMC3760913?pdf=render |
| spellingShingle | Thomas Bonfert Gergely Csaba Ralf Zimmer Caroline C Friedel Mining RNA-seq data for infections and contaminations. PLoS ONE |
| title | Mining RNA-seq data for infections and contaminations. |
| title_full | Mining RNA-seq data for infections and contaminations. |
| title_fullStr | Mining RNA-seq data for infections and contaminations. |
| title_full_unstemmed | Mining RNA-seq data for infections and contaminations. |
| title_short | Mining RNA-seq data for infections and contaminations. |
| title_sort | mining rna seq data for infections and contaminations |
| url | http://europepmc.org/articles/PMC3760913?pdf=render |
| work_keys_str_mv | AT thomasbonfert miningrnaseqdataforinfectionsandcontaminations AT gergelycsaba miningrnaseqdataforinfectionsandcontaminations AT ralfzimmer miningrnaseqdataforinfectionsandcontaminations AT carolinecfriedel miningrnaseqdataforinfectionsandcontaminations |