The genome of the basidiomycetous yeast and human pathogen Cryptococcus neoformans.
ABSTRACT Cryptococcus neoformans is a basidiomycetous yeast ubiquitous in the environment, a model for fungal pathogenesis, and an opportunistic human pathogen of global importance. We have sequenced its approximately 20-megabase genome, which contains approximately 6500 intron-rich gene structures and encodes a transcriptome abundant in alternatively spliced and antisense messages. The genome is rich in transposons, many of which cluster at candidate centromeric regions. The presence of these transposons may drive karyotype instability and phenotypic variation. C. neoformans encodes unique genes that may contribute to its unusual virulence properties, and comparison of two phenotypically distinct strains reveals variation in gene content in addition to sequence polymorphisms between the genomes.
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ABSTRACT: Background Alternative splicing is crucial for proteome diversity and functional complexity in higher organisms. However, the alternative splicing landscape in fungi is still elusive.ResultsThe transcriptome of the filamentous fungus Trichoderma longibrachiatum was deep sequenced using Illumina Solexa technology. A total of 14305 splice junctions were discovered. Analyses of alternative splicing events revealed that the number of all alternative splicing events (10034), intron retentions (IR, 9369), alternative 5¿ splice sites (A5SS, 167), and alternative 3¿ splice sites (A3SS, 302) is 7.3, 7.4, 5.1, and 5.9-fold higher, respectively, than those observed in the fungus Aspergillus oryzae using Illumina Solexa technology. This unexpectedly high ratio of alternative splicing suggests that alternative splicing is important to the transcriptome diversity of T. longibrachiatum. Alternatively spliced introns had longer lengths, higher GC contents, and lower splice site scores than constitutive introns. Further analysis demonstrated that the isoform relative frequencies were correlated with the splice site scores of the isoforms. Moreover, comparative transcriptomics determined that most enzymes related to glycolysis and the citrate cycle and glyoxylate cycle as well as a few carbohydrate-active enzymes are transcriptionally regulated.Conclusions This study, consisting of a comprehensive analysis of the alternative splicing landscape in the filamentous fungus T. longibrachiatum, revealed an unexpectedly high ratio of alternative splicing events and provided new insights into transcriptome diversity in fungi.BMC Genomics 02/2015; 16(1):54. DOI:10.1186/s12864-015-1251-8 · 4.04 Impact Factor
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ABSTRACT: The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available ( https://sourceforge.net/projects/codingquarry/ ), and suitable for incorporation into genome annotation pipelines.BMC Genomics 12/2015; 16(1):1344. DOI:10.1186/s12864-015-1344-4 · 4.04 Impact Factor
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ABSTRACT: Cryptococcus neoformans, a basidiomycetous human pathogenic yeast, has been widely used in research fields in medical mycology as well as basic biology. Gene cloning or identification of the gene responsible for a mutation of interest is a key step for functional analysis of a particular gene. The availability therefore, of the multiple methods for cloning is desirable. In this study, we proposed a method for a mapping-based gene identification/cloning (positional cloning) method in C. neoformans. To this end, we constructed a series of tester strains, one of whose chromosomes was labeled with the URA5 gene. A heterozygous diploid constructed by crossing one of the tester strains to a mutant strain of interest loses a chromosome(s) spontaneously, which is the basis for assigning a recessive mutant gene to a particular chromosome in the mitotic mapping method. Once the gene of interest is mapped to one of the 14 chromosomes, classical genetic crosses can then be performed to determine its more precise location. The positional information thus obtained can then be used to significantly narrow down candidate genes by referring to the Cryptococcus genome database. Each candidate gene is then examined whether it would complement the mutation. We successfully applied this method to identify CNA07390 encoding methylenetetrahydrofolate reductase as the gene responsible for a methionie-requiring mutant in our mutant collection. Copyright © 2015. Published by Elsevier Inc.Fungal Genetics and Biology 02/2015; 76. DOI:10.1016/j.fgb.2015.02.007 · 3.26 Impact Factor