Dataset Information

Unraveling chloroplast transcriptomes with ChloroSeq, an organelle RNA-Seq bioinformatics pipeline.

ABSTRACT: Online sequence repositories are teeming with RNA sequencing (RNA-Seq) data from a wide range of eukaryotes. Although most of these data sets contain large numbers of organelle-derived reads, researchers tend to ignore these data, focusing instead on the nuclear-derived transcripts. Consequently, GenBank contains massive amounts of organelle RNA-Seq data that are just waiting to be downloaded and analyzed. Recently, a team of scientists designed an open-source bioinformatics program called ChloroSeq, which systemically analyzes an organelle transcriptome using RNA-Seq. The ChloroSeq pipeline uses RNA-Seq alignment data to deliver detailed analyses of organelle transcriptomes, which can be fed into statistical software for further analysis and for generating graphical representations of the data. In addition to providing data on expression levels via coverage statistics, ChloroSeq can examine splicing efficiency and RNA editing profiles. Ultimately, ChloroSeq provides a well-needed avenue for researchers of all stripes to start exploring organelle transcription and could be a key step toward a more thorough understanding of organelle gene expression.

SUBMITTER: Smith DR

PROVIDER: S-EPMC5862312 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Unraveling chloroplast transcriptomes with ChloroSeq, an organelle RNA-Seq bioinformatics pipeline.

Smith David Roy DR Sanitá Lima Matheus M

Briefings in bioinformatics 20171101 6

Online sequence repositories are teeming with RNA sequencing (RNA-Seq) data from a wide range of eukaryotes. Although most of these data sets contain large numbers of organelle-derived reads, researchers tend to ignore these data, focusing instead on the nuclear-derived transcripts. Consequently, GenBank contains massive amounts of organelle RNA-Seq data that are just waiting to be downloaded and analyzed. Recently, a team of scientists designed an open-source bioinformatics program called Chlor ...[more]

PMID: 27677960

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Project description:BACKGROUND:The genus Cuscuta is a group of parasitic plants that are distributed world-wide. The process of parasitization starts with a Cuscuta plant coiling around the host stem. The parasite's haustorial organs then establish a vascular connection allowing for access to the phloem content. The host and the parasite form new cellular connections, suggesting coordination of developmental and biochemical processes. Simultaneous monitoring of gene expression in the parasite's and host's tissues may shed light on the complex events occurring between the parasitic and host cells and may help to overcome experimental limitations (i.e. how to separate host tissue from Cuscuta tissue at the haustorial connection). A novel approach is to use bioinformatic analysis to classify sequencing reads as either belonging to the host or to the parasite and to characterize the expression patterns. Owing to the lack of a comprehensive genomic dataset from Cuscuta spp., such a classification has not been performed previously. RESULTS:We first classified RNA-Seq reads from an interface region between the non-model parasitic plant Cuscuta japonica and the non-model host plant Impatiens balsamina. Without established reference sequences, we classified reads as originating from either of the plants by stepwise similarity search against de novo assembled transcript sets of C. japonica and I. balsamina, unigene sets of the same genus, and cDNA sequences of the same family. We then assembled de novo transcriptomes from the classified read sets. We assessed the quality of the classification by mapping reads to contigs of both plants, achieving a misclassification rate low enough (0.22-0.39%) to be used reliably for differential gene expression analysis. Finally, we applied our read classification method to RNA-Seq data from the interface between the non-model parasitic plant C. japonica and the model host plant Glycine max. Analysis of gene expression profiles at 5 parasitizing stages revealed differentially expressed genes from both C. japonica and G. max, and uncovered the coordination of cellular processes between the two plants. CONCLUSIONS:We demonstrated that reliable identification of differentially expressed transcripts in undissected interface region of the parasite-host association is feasible and informative with respect to differential-expression patterns.

Dataset Information

Unraveling chloroplast transcriptomes with ChloroSeq, an organelle RNA-Seq bioinformatics pipeline.

Publications

Unraveling chloroplast transcriptomes with ChloroSeq, an organelle RNA-Seq bioinformatics pipeline.

Similar Datasets

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