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Biology subjects

Stovner, E. B.

Publications and source records attributed to Stovner, E. B..

3 recordsLinked to original sources

Pyranges v1: a Python framework for ultrafast sequence interval operations

Sequence interval algebra is key to modern bioinformatics. Pyranges v1 offers a Python Pandas-based interface to a comprehensive palette of Rust-powered operations (e.g., overlap, count, slice intervals), enabling the intuitive development of efficient pipelines for diverse sequence data, including gene annotations, mapped reads, and protein domains. Pyranges is faster, consumes less memory, and offers more functionalities than alternative tools including BEDTools, emerging as an innovative one-stop shop for omics analysis.

bioinformatics↗

Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation

Proper regulation of the cell cycle is necessary for normal growth and development of all organisms. Conversely, altered cell cycle regulation often underlies proliferative diseases such as cancer. Long non-coding RNAs (lncRNAs) are recognized as important regulators of gene expression and are often found dysregulated in diseases, including cancers. However, identifying lncRNAs with cell cycle functions is challenging due to their often low and cell-type specific expression. We present a highly effective method that analyses changes in promoter activity, transcription, and RNA levels for identifying genes enriched for cell cycle functions. Specifically, by combining RNA sequencing with ChIP sequencing through the cell cycle of synchronized human keratinocytes, we identified 1009 genes with cell cycle-dependent expression and correlated changes in RNA polymerase II occupancy or promoter activity as measured by histone 3 lysine 4 trimethylation (H3K4me3). These genes were highly enriched for genes with known cell cycle functions and included 59 lncRNAs. We selected four of these lncRNAs - AC005682.5, RP11-132A1.4, ZFAS1, and EPB41L4A-AS1 - for further experimental validation and found that knockdown of each of the four lncRNAs affected cell cycle phase distributions and reduced proliferation in multiple cell lines. These results show that many genes with cell cycle functions have concomitant cell-cycle dependent changes in promoter activity, transcription, and RNA levels and support that our multi-omics method is well suited for identifying lncRNAs involved in the cell cycle.

molecular biology↗

PyRanges: efficient comparison of genomic intervals in Python

SummaryComplex genomic analyses often use sequences of simple set operations like intersection, overlap, and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python. Run single-threaded on binary set operations, PyRanges is in median 2.3-9.6 times faster than the popular R GenomicRanges library and is equally memory efficient; run multi-threaded on 8 cores, our library is up to 123 times faster. PyRanges is therefore ideally suited both for individual analyses and as a foundation for future genomic libraries in Python.\n\nAvailabilityPyRanges is available open-source under the MIT license at https://github.com/biocore-NTNU/pyranges and documentation exists at https://biocore-NTNU.github.io/pyranges/\n\nContactendrebak85@gmail.com\n\nSupplementary informationSupplementary data are available.

genomics↗