Abstract:Fungal mitochondrial genomes encode for genes involved in crucial cellular processes, such as oxidative phosphorylation and mitochondrial translation, and these genes have been used as molecular markers for population genetics studies. Coccidioides immitis and C. posadasii are endemic fungal pathogens that cause coccidioidomycosis in arid regions across both American continents. To date, almost one hundred Coccidioides strains have been sequenced. The focus of these studies has been exclusively to infer patter… Show more
“…As a result, mapping RNAseq reads or splice-aligning assembled transcripts to the genome sequence often generates conflicting information that interferes with or misleads gene modelling. In the example shown in Figure 1 [data from (de Melo Teixeira et al, 2021)], expert inference of the gene structure was only possible by sequence comparison with intron-less gene homologs in related species.…”
Section: Rna Mapping Evidence Is Of Limited Value In Mitochondrial Ge...mentioning
confidence: 99%
“…The output of MFannot lists gene coordinates either in a format that can be directly loaded into NCBI sequence submission tools or in 'masterfile' format (a computer-parsable and Mapping of RNA-seq data to the mitochondrial genome of Coccidioides posadasii. The figure depicts a 2,579 nt window over the coordinates of exons 4 (6172-6736) and 5 (8458-8504) of the mitochondrial gene encoding the large subunit rRNA (rnl) of C. posadasii (see (de Melo Teixeira et al, 2021) for details on genome sequencing) as determined by rRNA sequence conservation and structural modelling of exons 4 and 5 plus adjacent introns. The read-mapping coverage distribution (grey) is shown below the annotated exons.…”
Section: The Mfannot Annotation Proceduresmentioning
Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.
“…As a result, mapping RNAseq reads or splice-aligning assembled transcripts to the genome sequence often generates conflicting information that interferes with or misleads gene modelling. In the example shown in Figure 1 [data from (de Melo Teixeira et al, 2021)], expert inference of the gene structure was only possible by sequence comparison with intron-less gene homologs in related species.…”
Section: Rna Mapping Evidence Is Of Limited Value In Mitochondrial Ge...mentioning
confidence: 99%
“…The output of MFannot lists gene coordinates either in a format that can be directly loaded into NCBI sequence submission tools or in 'masterfile' format (a computer-parsable and Mapping of RNA-seq data to the mitochondrial genome of Coccidioides posadasii. The figure depicts a 2,579 nt window over the coordinates of exons 4 (6172-6736) and 5 (8458-8504) of the mitochondrial gene encoding the large subunit rRNA (rnl) of C. posadasii (see (de Melo Teixeira et al, 2021) for details on genome sequencing) as determined by rRNA sequence conservation and structural modelling of exons 4 and 5 plus adjacent introns. The read-mapping coverage distribution (grey) is shown below the annotated exons.…”
Section: The Mfannot Annotation Proceduresmentioning
Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.
Coccidioidomycosis, or Valley fever, is caused by two species of dimorphic fungi. Based on molecular phylogenetic evidence, the genus Coccidioides contains two reciprocally monophyletic species: C. immitis and C. posadasii. However, phenotypic variation between species has not been deeply investigated. We therefore explored differences in growth rate under various conditions. A collection of 39 C. posadasii and 46 C. immitis isolates, representing the full geographical range of the two species, was screened for mycelial growth rate at 37 °C and 28 °C on solid media. The radial growth rate was measured for 16 days on yeast extract agar. A linear mixed effect model was used to compare the growth rate of C. posadasii and C. immitis at 37 °C and 28 °C, respectively. C. posadasii grew significantly faster at 37 °C, when compared to C. immitis; whereas both species had similar growth rates at 28 °C. These results indicate thermotolerance differs between these two species. As the ecological niche has not been well-described for Coccidioides spp., and disease variability between species has not been shown, the evolutionary pressure underlying the adaptation is unclear. However, this research reveals the first significant phenotypic difference between the two species that directly applies to ecological research.
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