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Sicherman, J.

Publications and source records attributed to Sicherman, J..

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Cataloging the potential functional diversity of Cacna1e splice variants using long-read sequencing

Voltage gated calcium channels (VGCCs) regulate the influx of calcium ions in many cell types, but our lack of knowledge about the plethora of VGCC splice variants remains a gap in our understanding of calcium channel function. A recent advance in profiling gene splice variation is to use long-read RNA-sequencing technology. We sequenced Cacna1e transcripts from the rat thalamus using Oxford Nanopore sequencing, yielding the full structure of 2,110 Cacna1e splice variants. However, we observed that only 154 Cacna1e splice variants were likely to encode for a functional VGCC based on predicted amino acid sequences. We then computationally prioritized these 154 splice variants using expression and evolutionary conservation and found that four splice variants are candidate functionally distinct splice isoforms. Our work not only provides long-read sequencing of Cacna1e for the first time, but also the first computational evaluation of which Cacna1e splice variants are the best candidates for future follow-up. SIGNIFICANCE STATEMENTVoltage gated calcium channels (Cacna1x genes) are implicated in many neurological disorders and their encoding genes are predicted to have complex patterns of alternative splicing. Previous approaches relied on short-read RNA-seq to characterize calcium channel splice variants. Here, we use long-read nanopore sequencing to establish a set of Cacna1e transcripts in the rat thalamus and use computational methods to prioritize four transcripts as functionally distinct splice isoforms. Our work to provide the field with prioritized transcripts will not only improve our understanding of Cacna1e function but its role in disease as well.

neuroscience↗

Curation of over 10,000 transcriptomic studies to enable data reuse

Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing is necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface, and web services. Here we present an update on Gemmas holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10,811 manually curated datasets (primarily human, mouse, and rat), over 395,000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA-sequencing). Dataset topics were represented with 10,215 distinct terms from 12 ontologies, for a total of 54,316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service, and an R package. Database URL: https://gemma.msl.ubc.ca/home.html

bioinformatics↗