Dataset Information

NommPred: Prediction of Mitochondrial and Mitochondrion-Related Organelle Proteins of Nonmodel Organisms.

ABSTRACT: To estimate the functions of mitochondria of diverse eukaryotic nonmodel organisms in which the mitochondrial proteomes are not available, it is necessary to predict the protein sequence features of the mitochondrial proteins computationally. Various prediction methods that are trained using the proteins of model organisms belonging particularly to animals, plants, and fungi exist. However, such methods may not be suitable for predicting the proteins derived from nonmodel organisms because the sequence features of the mitochondrial proteins of diversified nonmodel organisms can differ from those of model organisms that are present only in restricted parts of the tree of eukaryotes. Here, we proposed NommPred, which predicts the mitochondrial proteins of nonmodel organisms that are widely distributed over eukaryotes. We used a gradient boosting machine to develop 2 predictors-one for predicting the proteins of mitochondria and the other for predicting the proteins of mitochondrion-related organelles that are highly reduced mitochondria. The performance of both predictors was found to be better than that of the best method available.

SUBMITTER: Kume K

PROVIDER: S-EPMC6305954 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

NommPred: Prediction of Mitochondrial and Mitochondrion-Related Organelle Proteins of Nonmodel Organisms.

Kume Keitaro K Amagasa Toshiyuki T Hashimoto Tetsuo T Kitagawa Hiroyuki H

Evolutionary bioinformatics online 20181223

To estimate the functions of mitochondria of diverse eukaryotic nonmodel organisms in which the mitochondrial proteomes are not available, it is necessary to predict the protein sequence features of the mitochondrial proteins computationally. Various prediction methods that are trained using the proteins of model organisms belonging particularly to animals, plants, and fungi exist. However, such methods may not be suitable for predicting the proteins derived from nonmodel organisms because the s ...[more]

PMID: 30626996

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Project description:BackgroundSeveral recent studies have demonstrated the use of Roche 454 sequencing technology for de novo transcriptome analysis. Low error rates and high coverage also allow for effective SNP discovery and genetic diversity estimates. However, genetically diverse datasets, such as those sourced from natural populations, pose challenges for assembly programs and subsequent analysis. Further, estimating the effectiveness of transcript discovery using Roche 454 transcriptome data is still a difficult task.ResultsUsing the Roche 454 FLX Titanium platform, we sequenced and assembled larval transcriptomes for two butterfly species: the Propertius duskywing, Erynnis propertius (Lepidoptera: Hesperiidae) and the Anise swallowtail, Papilio zelicaon (Lepidoptera: Papilionidae). The Expressed Sequence Tags (ESTs) generated represent a diverse sample drawn from multiple populations, developmental stages, and stress treatments. Despite this diversity, > 95% of the ESTs assembled into long (> 714 bp on average) and highly covered (> 9.6x on average) contigs. To estimate the effectiveness of transcript discovery, we compared the number of bases in the hit region of unigenes (contigs and singletons) to the length of the best match silkworm (Bombyx mori) protein--this "ortholog hit ratio" gives a close estimate on the amount of the transcript discovered relative to a model lepidopteran genome. For each species, we tested two assembly programs and two parameter sets; although CAP3 is commonly used for such data, the assemblies produced by Celera Assembler with modified parameters were chosen over those produced by CAP3 based on contig and singleton counts as well as ortholog hit ratio analysis. In the final assemblies, 1,413 E. propertius and 1,940 P. zelicaon unigenes had a ratio > 0.8; 2,866 E. propertius and 4,015 P. zelicaon unigenes had a ratio > 0.5.ConclusionsUltimately, these assemblies and SNP data will be used to generate microarrays for ecoinformatics examining climate change tolerance of different natural populations. These studies will benefit from high quality assemblies with few singletons (less than 26% of bases for each assembled transcriptome are present in unassembled singleton ESTs) and effective transcript discovery (over 6,500 of our putative orthologs cover at least 50% of the corresponding model silkworm gene).

Dataset Information

NommPred: Prediction of Mitochondrial and Mitochondrion-Related Organelle Proteins of Nonmodel Organisms.

Publications

NommPred: Prediction of Mitochondrial and Mitochondrion-Related Organelle Proteins of Nonmodel Organisms.

Similar Datasets

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