AIMC Topic: Open Reading Frames

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A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

BMC genomics Oct 18, 2017
BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed whe...
Support Vector Machine Zebrafish Molecular Sequence Annotation RNA, Untranslated Open Reading Frames Mice Computational Biology RNA, Messenger Humans Animals
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Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.

BMC bioinformatics Sep 30, 2017
BACKGROUND: Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms...
DNA, Satellite User-Computer Interface Open Reading Frames Machine Learning ROC Curve Geminiviridae Area Under Curve Internet Plants
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Geminivirus data warehouse: a database enriched with machine learning approaches.

BMC bioinformatics May 5, 2017
BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic los...
Machine Learning Algorithms Phylogeny Computational Biology DNA, Viral Open Reading Frames Geminiviridae Databases, Genetic Plants DNA, Single-Stranded
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Model-enabled gene search (MEGS) allows fast and direct discovery of enzymatic and transport gene functions in the marine bacterium .

The Journal of biological chemistry Apr 26, 2017
Whereas genomes can be rapidly sequenced, the functions of many genes are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence databases. To address this weakness, we describe here...
Genetic Complementation Test Gene Deletion Open Reading Frames Genomic Library Aliivibrio fischeri Molecular Sequence Annotation Pacific Ocean Escherichia coli Computer Simulation Expert Systems Aquaculture Reproducibility of Results Genomics Hawaii Bacterial Proteins Animals Aquatic Organisms Recombinant Proteins Species Specificity High-Throughput Nucleotide Sequencing Models, Genetic Decapodiformes
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LncRNA-ID: Long non-coding RNA IDentification using balanced random forests.

Bioinformatics (Oxford, England) Aug 26, 2015
MOTIVATION: Long non-coding RNAs (lncRNAs), which are non-coding RNAs of length above 200 nucleotides, play important biological functions such as gene expression regulation. To fully reveal the functions of lncRNAs, a fundamental step is to annotate...
Proteins Animals Humans Open Reading Frames Mice Machine Learning Ribosomes RNA, Long Noncoding Software
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Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Nucleic acids research Apr 10, 2025
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human tran...
Humans Neural Networks, Computer Transcriptome Deep Learning Protein Biosynthesis Peptide Chain Initiation, Translational RNA, Messenger Open Reading Frames Codon Usage RNA, Long Noncoding
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Discovering misannotated lncRNAs using deep learning training dynamics.

Bioinformatics (Oxford, England) Jan 1, 2023
MOTIVATION: Recent experimental evidence has shown that some long non-coding RNAs (lncRNAs) contain small open reading frames (sORFs) that are translated into functional micropeptides, suggesting that these lncRNAs are misannotated as non-coding. Cur...
Amino Acid Sequence Open Reading Frames Micropeptides Deep Learning Proteome RNA, Long Noncoding
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Feature extraction approaches for biological sequences: a comparative study of mathematical features.

Briefings in bioinformatics Sep 2, 2021
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems,...
Humans Deep Learning Fourier Analysis Open Reading Frames RNA, Circular Computational Biology RNA, Messenger Models, Theoretical RNA, Long Noncoding Base Sequence Entropy
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DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.

Briefings in bioinformatics Mar 22, 2021
The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods per...
Humans Neural Networks, Computer RNA, Long Noncoding Open Reading Frames Animals Deep Learning RNA, Messenger
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OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.

Database : the journal of biological databases and curation Jan 1, 2020
Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However,...
Open Reading Frames Algorithms Proteins Genome, Bacterial Machine Learning
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