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Mutations

Mutations

Mutations

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

From DNA to Protein

From DNA to Protein

The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...

Nonsense-mediated mRNA Decay

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

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Related Experiment Video

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Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

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GeneValidator: identify problems with protein-coding gene predictions.

Monica-Andreea Drăgan¹, Ismail Moghul², Anurag Priyam²

¹Department of Computer Science, ETH Zürich, Zürich, Switzerland.

Bioinformatics (Oxford, England)

|January 21, 2016

Summary

This summary is machine-generated.

GeneValidator (GV) automates the identification of errors in gene predictions for new genomes, significantly speeding up manual curation and improving accuracy for researchers.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome sequencing of emerging model organisms is rapidly advancing.
Accurate gene prediction is crucial but challenging, with current algorithms making errors.
Manual curation of gene predictions is time-consuming and labor-intensive.

Purpose of the Study:

To develop an automated tool, GeneValidator (GV), to identify problematic gene predictions.
To assist biocurators and researchers in manual curation of gene predictions.
To accelerate and enhance the accuracy of gene prediction analysis for newly sequenced genomes.

Main Methods:

GeneValidator (GV) performs multiple analyses by comparing gene sequences against large databases.
GV generates a report for each gene, highlighting problematic predictions.
The report includes detailed statistics and visualizations to guide manual curation.

Main Results:

GV effectively identifies problematic gene predictions, reducing manual effort.
The tool provides comprehensive data to support curation decisions.
Automated analysis accelerates the process of obtaining accurate gene sets.

Conclusions:

GeneValidator (GV) is a valuable tool for improving the efficiency and accuracy of gene prediction curation.
The software aids researchers in analyzing newly sequenced genomes.
GV enhances the reliability of genomic data for downstream analyses.