New gene models and alternative splicing in the maize pathogen Colletotrichum graminicola revealed by RNA-Seq analysis

BMC Genomics

Ralf Horbach²  Holger B Deising¹  Marcus Hempel²  Rayko Becher²  Ivo Schliebner² 

[1]Institute for Agricultural and Nutritional Sciences, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, D-06120 Halle (Saale), Germany

[2]Interdisciplinary Center for Crop Plant Research, Martin-Luther-University Halle-Wittenberg, Betty-Heimann-Str. 3, D-06120 Halle (Saale), Germany

关键词: Genome annotation;    RNA-Seq;    Anthracnose of corn;    Colletotrichum graminicola;   

Others  :  1139334
DOI  :  10.1186/1471-2164-15-842

 received in 2014-04-17, accepted in 2014-09-09,  发布年份 2014

【 摘 要 】

Background

An annotated genomic sequence of the corn anthracnose fungus Colletotrichum graminicola has been published previously, but correct identification of gene models by means of automated gene annotation remains a challenge. RNA-Seq offers the potential for substantially improved gene annotations and for the identification of posttranscriptional RNA modifications, such as alternative splicing and RNA editing.

Results

Based on the nucleotide sequence information of transcripts, we identified 819 novel transcriptionally active regions (nTARs) and revised 906 incorrectly predicted gene models, including revisions of exon-intron structure, gene orientation and sequencing errors. Among the nTARs, 146 share significant similarity with proteins that have been identified in other species suggesting that they are hitherto unidentified genes in C. graminicola. Moreover, 5′- and 3′-UTR sequences of 4378 genes have been retrieved and alternatively spliced variants of 69 genes have been identified. Comparative analysis of RNA-Seq data and the genome sequence did not provide evidence for RNA editing in C. graminicola.

Conclusions

We successfully employed deep sequencing RNA-Seq data in combination with an elaborate bioinformatics strategy in order to identify novel genes, incorrect gene models and mechanisms of transcript processing in the corn anthracnose fungus C. graminicola. Sequence data of the revised genome annotation including several hundreds of novel transcripts, improved gene models and candidate genes for alternative splicing have been made accessible in a comprehensive database. Our results significantly contribute to both routine laboratory experiments and large-scale genomics or transcriptomic studies in C. graminicola.

【 授权许可】

   
2014 Schliebner et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]Horbach R, Navarro-Quesada AR, Knogge W, Deising HB: When and how to kill a plant cell: Infection strategies of plant pathogenic fungi. J Plant Physiol 2011, 168:51-62.
• [2]Bechinger C, Giebel K-F, Schnell M, Leiderer P, Deising HB, Bastmeyer M: Optical measurements of invasive forces exerted by appressoria of a plant pathogenic fungus. Science 2009, 285:1896-1899.
• [3]Deising HB, Werner S, Wernitz M: The role of fungal appressoria in plant infection. Microbes Infect 2000, 2:1631-1641.
• [4]Ludwig N, Löhrer M, Hempel M, Mathea S, Schliebner I, Menzel M, Kiesow A, Schaffrath U, Deising HB, Horbach R: Melanin is not required for turgor generation but enhances cell wall rigidity in appressoria of the corn pathogen Colletotrichum graminicola. Mol Plant-Microbe Interact 2014, 27:315-327.
• [5]Mims CW, Vaillancourt LJ: Ultrastructural Characterization of Infection and Colonization of Maize Leaves by Colletotrichum graminicola, and by a C. graminicola Pathogenicity Mutant. Phytopathol 2002, 92:803-812.
• [6]Horbach R, Graf A, Weihmann F, Antelo L, Mathea S, Liermann JC, Opatz T, Thines E, Aguirre J, Deising HB: Sfp-type 4’-phosphopantetheinyl transferase is indispensable for fungal pathogenicity. Plant Cell 2009, 21:3379-3396.
• [7]Bergstrom GC, Nicholson RL: The biology of corn anthracnose. Plant Disease 1999, 83:596-608.
• [8]Breakspear A, Momany M: The first fifty microarray studies in filamentous fungi. Microbiol 2007, 153:7-15.
• [9]O’Connell RJ, Thon MR, Hacquard S, Amyotte SG, Kleemann J, Torres MF, Damm U, Buiate EA, Epstein L, Alkan N, Altmuller J, Alvarado-Balderrama L, Bauser CA, Becker C, Birren BW, Chen Z, Choi J, Crouch JA, Duvick JP, Farman MA, Gan P, Heiman D, Henrissat B, Howard RJ, Kabbage M, Koch C, Kracher B, Kubo Y, Law AD, Lebrun MH, et al.: Lifestyle transitions in plant pathogenic Colletotrichum fungi deciphered by genome and transcriptome analyses. Nat Genet 2012, 44:1060-1065.
• [10]Brent MC: How does eukaryotic gene prediction work? Nature Biotech 2007, 25:883-885.
• [11]Poptsova MS, Gogarten JP: Using comparative genome analysis to identify problems in annotated microbial genomes. Microbiol 2010, 156:1909-1917.
• [12]Tisserant E, Da Silva C, Kohler A, Morin E, Wincker P, Martin F: Deep RNA sequencing improved the structural annotation of the Tuber melanosporum transcriptome. New Phytol 2011, 189:883-891.
• [13]Cerqueira GC, Arnaud MB, Inglis DO, Skrzypek MS, Binkley G, Simison M, Miyasato SR, Binkley J, Orvis J, Shah P, Wymore F, Sherlock G, Wortman JR: The Aspergillus genome database: multispecies curation and incorporation of RNA-Seq data to improve structural gene annotations. Nucl Acids Res 2014, 42:D705-D710.
• [14]Teichert I, Wolff G, Kück U, Nowrousian M: Combining laser microdissection and RNA-seq to chart the transcriptional landscape of fungal development. BMC Genomics 2012, 6:511.
• [15]Zhao C, Waalwijk C, de Wit PJGM, Tang D, van der Lee T: RNA-Seq analysis reveals new gene models and alternative splicing in the fungal pathogen Fusarium graminearum. BMC Genomics 2013, 14:21. BioMed Central Full Text
• [16]Wang B, Guo G, Wang C, Lin Y, Wang X, Zhao M, Guo Y, He M, Zhang Y, Pan L: Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing. Nucl Acids Res 2010, 38:5075-5087.
• [17]Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP: Sequencing depth and coverage: key considerations in genomic analysis. Nat Rev Genet 2014, 15:121-132.
• [18]Kleemann J, Rincon-Rivera LJ, Takahara H, Neumann U, Ver Loren van Themaat E, van der Does HC, Hacquard S, Stüber K, Will I, Schmalenbach W, Schmelzer E, O’Connell RJ: Sequential delivery of host-induced virulence effectors by appressoria and intracellular hyphae of the phytopathogen Colletotrichum higginsianum. PLoS Pathog 2012, 8:e1002643.
• [19]Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP: Integrative Genomics Viewer. Nature Biotech 2011, 29:24-26.
• [20]Stanke M, Morgenstern B: AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucl Acids Res 2006, 33:W465-W467.
• [21]Galagan JE, Henn MR, Ma LJ, Cuomo CA, Birren B: Genomics of the fungal kingdom: insights into eukaryotic biology. Genome Res 2005, 15:1620-1631.
• [22]McGuire AM, Pearson MD, Neafsey DE, Galagan JE: Cross-kingdom patterns of alternative splicing and splice recognition. Genome Biol 2008, 9:R50. BioMed Central Full Text
• [23]Nilsen TW, Graveley BR: Expansion of the eukaryotic proteome by alternative splicing. Nature 2010, 463:457-463.
• [24]van Bakel H, Nislow C, Blencowe BJ, Hughes TR: Most “Dark Matter” transcripts are associated with known genes. PLoS Biol 2010, 8:e1000371.
• [25]Rabini M, Levin JZ, Fan L, Adiconis X, Raychowdhury R, Garber M, Gnirke A, Nusbaum C, Hacohen N, Friedman N, Amit I, Regev A: Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat Biotech 2011, 29:436-442.
• [26]Marioni J, Mason C, Mane S, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008, 18:1509-1517.
• [27]Sirbu A, Kerr G, Crane M, Ruskin HJ: RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering. PLoS One 2012, 7:e50986.
• [28]Xu X, Zhang Y, Williams J, Antoniou E, McCombie WR, Wu S, Zhu W, Davidson NO, Denoya P, Li E: Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets. BMC Bioinformatics 2013, 14(Suppl 9):S1. BioMed Central Full Text
• [29]Fang Z, Cui X: Design and validation issues in RNA-seq experiments. Brief Bioinformatics 2011, 12:280.
• [30]Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Genetics 2009, 10:57-61.
• [31]Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008, 5:621-628.
• [32]Filichkin SA, Priest HD, Givan SA, Shen R, Bryant DW, Fox SE, Wong WK, Mockler TC: Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Res 2009, 20:45-58.
• [33]Kuai L, Fang F, Butler JS, Sherman F: Polyadenylation of rRNA in Saccharomyces cerevisiae. Proc Natl Acad Sci 2004, 101:8581-8586.
• [34]Zhuang Y, Zhang H, Lin S: Polyadenylation of 18S rRNA in algae. J Phycol 2013, 49:570-579.
• [35]Mattick JS: The genetic signatures of noncoding RNAs. PLoS Genet 2009, 5:e1000459.
• [36]Mercer TR, Dinger ME, Mattick JS: Long non-coding RNAs: insights into functions. Nat Rev Genet 2009, 10:155-159.
• [37]Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ: Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 2008, 40:1413-1415.
• [38]Ebbole DJ, Jin Y, Thon M, Pan H, Bhattarai E, Thomas T, Dean R: Gene Discovery and Gene Expression in the Rice Blast Fungus, Magnaporthe grisea: Analysis of Expressed Sequence Tags. Mol Plant-Microbe Interact 2004, 12:1337-1347.
• [39]Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25:1105-1111.
• [40]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup: The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 2009, 25:2078-2079.
• [41]Anders S, Pyl PT, Huber W: HTSeq-A Python framework to work with high-throughput sequencing data. 2014. bioRxiv preprint 2014, doi: 10.1101/002824.
• [42]R Development Core Team: R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2009.
• [43]Wang L, Wang S, Li W: RSeQC: quality control of RNA-seq experiments. Bioinformatics 2012, 28:2184-2185.
• [44]Liao Y, Smyth GK, Shi W: FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30:923-930.
• [45]Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26:841-842.
• [46]Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010, 28:511-515.
• [47]Finn RD, Clements J, Eddy SR: HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 2011, 39:W29-W37.
• [48]Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate JG, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer ELL, Eddy SR, Bateman A, Finn RD: The Pfam protein families database. Nucleic Acids Res 2012, 40:D290.
• [49]Mituyama T, Yamada K, Hattori E, Okida H, Ono Y, Terai G, Yoshizawa A, Komori T, Asai K: The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs. Nucleic Acids Res 2009, 37:D89-D92.
• [50]Bu D, Yu K, Sun S, Xie C, Skogerbo G, Miao R, Xiao R, Liao Q, Luo H, Zhao G, Zhao H, Liu Z, Liu C, Chen R, Zhao Y: NONCODE v3.0: integrative annotation of long noncoding RNAs. Nucleic Acids Res 2012, 40:D210-D215.
• [51]Cingolani P, Platts A, Wang Ie L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 2012, 6:80-92.