Spatial transcriptomics technologies currently face a trade-off between spatial and molecular resolution. High-resolution single-cell methods such as MERFISH and Expansion Sequencing (ExSeq) resolve individual cells but typically profile limited gene panels, restricting the direct application of transcriptome-based cell typing strategies developed for single-cell RNA sequencing. Consequently, accurate cell-type identification in spatial single-cell data remains challenging when transcriptomic coverage per cell is sparse. Here, we introduce ModelistsGCN, a semi-supervised multimodal graph convolutional framework that integrates gene expression, spatial proximity, and quantitative cellular morphology for spatial single-cell cell typing. ModelistsGCN uses a small set of high-confidence representative cells, defined by marker-gene enrichment, to guide clustering while preserving flexibility in identifying cell types. By incorporating spatial neighborhood structure and interpretable morphological features alongside gene expression, the method compensates for limited molecular coverage and strengthens cell-type discrimination. Across a well-annotated ExSeq mouse visual cortex dataset and metastatic breast cancer tissues profiled by MERFISH and ExSeq, ModelistsGCN consistently demonstrated higher agreement with reference annotations, improved cluster separation, and stronger marker-gene coherence compared to existing spatial clustering approaches. ModelistsGCN improves cell-type inference in single-cell spatial transcriptomic datasets with limited gene coverage.

Bioinformatics (2009) doi:10.1093/bioinformatics/btp031 The following funding acknowledgement was omitted from the above paper: This research was supported in part by the Division of Intramural Research, NICHD, National Institutes of Health. The authors apologize for this error which has been corrected online.

Abstract Summary: DREAM: detecting RNA editing associated with microRNAs, is a webserver for the identification of mature microRNA editing events using deep sequencing data. Raw microRNA sequencing reads can be provided as input, the reads are aligned against the genome and custom scripts process the data, search for potential editing sites and assess the statistical significance of the findings. The output is a text file with the location and the statistical description of all the putative editing sites detected. Availability and implementation: DREAM is freely available on the web at http://www.cs.tau.ac.il/∼mirnaed/. Contact: [email protected]

MOTIVATION: The identification of functional cis-acting DNA regulatory elements is a crucial step towards understanding gene regulation. Ab initio motif detection algorithms have been extensively used in search of regulatory elements. Yet, their success in providing experimentally validated regulatory elements in vertebrates has been limited. RESULTS: Here we report in silico identification and in vivo validation of regulatory elements that determine enhanced gene expression in the pineal gland of zebrafish. Microarray data enabled detection of genes that exhibit high expression in the pineal gland. The promoter regions of these genes were computationally analyzed in order to identify overrepresented motifs. The highest ranking motif identified is a CRX/OTX binding site, known to govern expression in the pineal gland and retina. The second highest ranking motif was not reported before; we experimentally validated its function in vivo by mutational analysis. The methodology presented here may be applicable as a general scheme for finding regulatory elements that contribute to tissue-specific gene expression.