Dataset Information

Chloroplast-like transfer RNA genes expressed in wheat mitochondria.

ABSTRACT: In the course of a systematic survey of wheat mitochondrial tRNA genes, we have sequenced chloroplast-like serine (trnS-GGA), phenylalanine (trnF-GAA) and cysteine (trnC-GCA) tRNA genes and their flanking regions. These genes are remnants of 'promiscuous' chloroplast DNA that has been incorporated into wheat mtDNA in the course of its evolution. Each gene differs by one or a few nucleotides from the authentic chloroplast homolog previously characterized in wheat or other plants, and each could potentially encode a functional tRNA whose secondary structure shows no deviations from the generalized model. To determine whether these chloroplast-like tRNA genes are actually expressed, wheat mitochondrial tRNAs were resolved by a series of polyacrylamide gel electrophoreses, after being specifically end-labeled in vitro by 3'-CCA addition mediated by wheat tRNA nucleotidyltransferase. Subsequent direct RNA sequence analysis identified prominent tRNA species corresponding to the mitochondrial and not the chloroplast trnS, trnF and trnC genes. This analysis also revealed chloroplast-like elongator methionine, asparagine and tryptophan tRNAs. Our results suggest that at least some chloroplast-like tRNA genes in wheat mtDNA are transcribed, with transcripts undergoing processing, post-transcriptional modification and 3'-CCA addition, to produce mature tRNAs that may participate in mitochondrial protein synthesis.

SUBMITTER: Joyce PB

PROVIDER: S-EPMC318170 | biostudies-other | 1989 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Chloroplast-like transfer RNA genes expressed in wheat mitochondria.

Joyce P B PB Gray M W MW

Nucleic acids research 19890701 14

In the course of a systematic survey of wheat mitochondrial tRNA genes, we have sequenced chloroplast-like serine (trnS-GGA), phenylalanine (trnF-GAA) and cysteine (trnC-GCA) tRNA genes and their flanking regions. These genes are remnants of 'promiscuous' chloroplast DNA that has been incorporated into wheat mtDNA in the course of its evolution. Each gene differs by one or a few nucleotides from the authentic chloroplast homolog previously characterized in wheat or other plants, and each could p ...[more]

PMID: 2762145

Similar Datasets

Project description:BackgroundA limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets.ResultsWe implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer.ConclusionsDEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

Dataset Information

Chloroplast-like transfer RNA genes expressed in wheat mitochondria.

Publications

Chloroplast-like transfer RNA genes expressed in wheat mitochondria.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets