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abstract_specification.md

@@ -12,7 +12,7 @@ The goal of SOMA (“stack of matrices, annotated”) is a flexible, extensible,
 - support access to persistent, cloud-resident datasets
 - enable use within popular data-science environments (e.g., R, Python), using the tools of that environment (e.g., Python Pandas integration)
 - enable "out-of-core" access to data aggregations much larger than single-host main memory
-- enable distributed computation over datasets, and
+- enable distributed computation over datasets
 - provide a building block for higher-level API that may embody domain-specific conventions or schema around annotated 2D matrices (e.g., a cell "atlas").
 
 The SOMA data model is centered on annotated 2-D matrices, conceptually similar to commonly used single-cell 'omics data structures including Seurat Assay, Bioconductor SingleCellExperiment, and Scanpy AnnData. Where possible, the SOMA API attempts to be general-purpose and agnostic to the specifics of any given environment, or to the specific conventions of the Single Cell scientific ecosystem.