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Gordon, J. M.

Publications and source records attributed to Gordon, J. M..

2 recordsLinked to original sources

Efficient co-transcriptional splicing enforces rapid microexon definition and inclusion by SRRM4

Alternative splicing expands the coding potential of the genome. Typical human exons are 150 nucleotides long, encoding 50 amino acids. Microexons are only 3-27 nucleotides long; yet they are important regulators of cellular processes in neurons, muscle, and pancreas. In neurons, microexon inclusion is aided by binding of the neuronal splicing factor SRRM4 to flanking upstream 3 splice sites (3SSs). Whether this manner of exon definition can be achieved in the timeframe of co-transcriptional splicing is unknown. Here, we employed nascent RNA sequencing to analyze SRRM4-dependent microexons in neuronal cells and found that co-transcriptional microexon splicing is so efficient, the upstream intron is removed before the downstream intron is completely synthesized. This suggests a mechanism for microexon inclusion, whereby co-transcriptional removal of the upstream intron eliminates competition for the microexons non-canonical downstream 5SS. We found that strengthening this 5'SS promoted constitutive microexon inclusion independently of SRRM4, indicating that SRRM4 binding alone is a strong stimulator of microexon definition. Thus, SRRM4s role is to promote rapid splicing of the upstream intron, leaving the microexons non-canonical 5SS as the only option for further splicing. These physiologically significant splicing events thereby require co-transcriptionality to yield neuronal mRNA isoforms.

biochemistry↗

PlantCV v4: Image analysis software for high-throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEASO_LIPlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. C_LIO_LIPlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. C_LIO_LIThe software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science. C_LI

plant biology↗