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Mining RNA-seq data for infections and contaminations.

Bonfert T, Csaba G, Zimmer R, Friedel CC - PLoS ONE (2013)

Bottom Line: 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.By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.

View Article: PubMed Central - PubMed

Affiliation: Institute for Informatics, Ludwig-Maximilians-Universität München, Munich, Germany.

ABSTRACT
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.

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Related in: MedlinePlus

Comparison of abundance calculated by GRAMMy and coverage determined by ContextMap on the microbial community data set.Results are shown for all taxa identified by GRAMMy with a relative abundance of at least 0.1%. The green line indicates a linear fit to the data.
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pone-0073071-g006: Comparison of abundance calculated by GRAMMy and coverage determined by ContextMap on the microbial community data set.Results are shown for all taxa identified by GRAMMy with a relative abundance of at least 0.1%. The green line indicates a linear fit to the data.

Mentions: GRAMMy correctly identifies 7 of the 9 microbes with an estimated abundance of %, but also assigns a very low abundance to P. pentosaceus (0.4%). Remarkably, the relative frequency estimated by GRAMMy and the coverage calculated by ContextMap are highly correlated (correlation coefficient 0.995), in particular for microbes with high coverage (Figure 6). This indicates that coverage as determined by ContextMap provides a reliable estimation of the relative frequencies identified by GRAMMy. As ContextMap is much faster than GRAMMy, it can thus be used to replace GRAMMy for applications where GRAMMy is too inefficient.


Mining RNA-seq data for infections and contaminations.

Bonfert T, Csaba G, Zimmer R, Friedel CC - PLoS ONE (2013)

Comparison of abundance calculated by GRAMMy and coverage determined by ContextMap on the microbial community data set.Results are shown for all taxa identified by GRAMMy with a relative abundance of at least 0.1%. The green line indicates a linear fit to the data.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3760913&req=5

pone-0073071-g006: Comparison of abundance calculated by GRAMMy and coverage determined by ContextMap on the microbial community data set.Results are shown for all taxa identified by GRAMMy with a relative abundance of at least 0.1%. The green line indicates a linear fit to the data.
Mentions: GRAMMy correctly identifies 7 of the 9 microbes with an estimated abundance of %, but also assigns a very low abundance to P. pentosaceus (0.4%). Remarkably, the relative frequency estimated by GRAMMy and the coverage calculated by ContextMap are highly correlated (correlation coefficient 0.995), in particular for microbes with high coverage (Figure 6). This indicates that coverage as determined by ContextMap provides a reliable estimation of the relative frequencies identified by GRAMMy. As ContextMap is much faster than GRAMMy, it can thus be used to replace GRAMMy for applications where GRAMMy is too inefficient.

Bottom Line: 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.By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.

View Article: PubMed Central - PubMed

Affiliation: Institute for Informatics, Ludwig-Maximilians-Universität München, Munich, Germany.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus