Open Access Highly Accessed Research article

Combining laser microdissection and RNA-seq to chart the transcriptional landscape of fungal development

Ines Teichert, Gabriele Wolff, Ulrich Kück* and Minou Nowrousian*

Author Affiliations

Lehrstuhl für Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum, Bochum, 44780, Germany

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BMC Genomics 2012, 13:511  doi:10.1186/1471-2164-13-511

Published: 27 September 2012

Additional files

Additional file 1:

Figure S1. Read coverage and UTR predictions. Figure S2. Algorithm for modelling UTRs. Figure S3. Algorithm for improving exon-intron structures based on RNA-seq data. Figure S4. Algorithm for counting reads that map to predicted features (e.g. mRNAs) for implementation in Perl. Figure S5. Analysis of the genome-wide coverage of different genomic regions. Figure S6. Venn diagram of the number of genes without any read counts. Figure S7. Transcript analysis of selected genes using qRT-PCR and RNA-seq. Figure S8. Expression of known developmental genes. Figure S9. Phylogenetic tree of all DUF3328 proteins from Sordaria macrospora, Neurospora crassa and Neurospora tetrasperma. Figure S10. Distribution of gene expression levels. Table S1. Transcription factors among the genes with the top 500 read counts and their homologs in Neurospora crassa and Fusarium graminearum. Table S2. Comparison of results from RNA-seq and microarray analysis. Table S3. Oligonucleotides used in this study. Method S1. UTR predictions from RNA-seq data. Method S2. Annotation of novel gene models based on RNA-seq data.

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Additional file 2:

Contains normalized read counts and expression analyses for different conditions.

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Additional file 3:

Contains an analysis of the top500 genes with respect to read counts for each of the four conditions.

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Additional file 4:

Contains base counts per locus tag for eight independent RNA-seq experiments.

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