Dataset Information

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations.

ABSTRACT:

Background

Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training.

Results

We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools.

Conclusions

OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists.

SUBMITTER: Berres S

PROVIDER: S-EPMC10369718 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations.

Berres Sven S Gromoll Jörg J Wöste Marius M Sandmann Sarah S Laurentino Sandra S

BMC bioinformatics 20230726 1

<h4>Background</h4>Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and vis ...[more]

PMID: 37496002

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Project description:BackgroundRetroviruses transcribe messenger RNA for the overlapping Gag and Gag-Pol polyproteins, by using a programmed -1 ribosomal frameshift which requires a slippery sequence and an immediate downstream stem-loop secondary structure, together called frameshift stimulating signal (FSS). It follows that the molecular evolution of this genomic region of HIV-1 is highly constrained, since the retroviral genome must contain a slippery sequence (sequence constraint), code appropriate peptides in reading frames 0 and 1 (coding requirements), and form a thermodynamically stable stem-loop secondary structure (structure requirement).ResultsWe describe a unique computational tool, RNAsampleCDS, designed to compute the number of RNA sequences that code two (or more) peptides p,q in overlapping reading frames, that are identical (or have BLOSUM/PAM similarity that exceeds a user-specified value) to the input peptides p,q. RNAsampleCDS then samples a user-specified number of messenger RNAs that code such peptides; alternatively, RNAsampleCDS can exactly compute the position-specific scoring matrix and codon usage bias for all such RNA sequences. Our software allows the user to stipulate overlapping coding requirements for all 6 possible reading frames simultaneously, even allowing IUPAC constraints on RNA sequences and fixing GC-content. We generalize the notion of codon preference index (CPI) to overlapping reading frames, and use RNAsampleCDS to generate control sequences required in the computation of CPI. Moreover, by applying RNAsampleCDS, we are able to quantify the extent to which the overlapping coding requirement in HIV-1 [resp. HCV] contribute to the formation of the stem-loop [resp. double stem-loop] secondary structure known as the frameshift stimulating signal. Using our software, we confirm that certain experimentally determined deleterious HCV mutations occur in positions for which our software RNAsampleCDS and RNAiFold both indicate a single possible nucleotide. We generalize the notion of codon preference index (CPI) to overlapping coding regions, and use RNAsampleCDS to generate control sequences required in the computation of CPI for the Gag-Pol overlapping coding region of HIV-1. These applications show that RNAsampleCDS constitutes a unique tool in the software arsenal now available to evolutionary biologists.ConclusionSource code for the programs and additional data are available at http://bioinformatics.bc.edu/clotelab/RNAsampleCDS/ .

Dataset Information

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations.

Background

Results

Conclusions

Publications

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations.

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