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Nat. Biotechnol. | An accurate and robust computational method for single-cell multi-omics integration and regulatory inference

Nat. Biotechnol. | An accurate and robust computational method for single-cell multi-omics integration and regulatory inference Gene transcription is a key link in the central dogma of biology. Compared to the relative static genome, the transcriptome exhibits substantial changes across different tissues, organs and developmental stages, forming crucial biological basis for the physiological and pathological Read more about Nat. Biotechnol. | An accurate and robust computational method for single-cell multi-omics integration and regulatory inference[…]

RiboCalc: Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells

Gene transcription and protein translation are two key steps of the “central dogma”. Cells often response to disease and environment stress via transcriptional and translational control. Meanwhile, besides coding proteins, RNAs could function as noncoding molecules. Benefitting from bioinformatics and computational genomics methods, it is possible to quantitatively de-convolute factors contributing to translational control in Read more about RiboCalc: Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells[…]

REVA: Systematical curation and evalutation for human expression-modulating variants

Approximately 97% of the human genome is noncoding. While more than 90% of disease- and trait-associated variants are noncoding variants, their biological functions and mechanisms remain largely elusive.   Recently, Gao Lab from Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein Read more about REVA: Systematical curation and evalutation for human expression-modulating variants[…]

A novel convolutional layer with adaptive kernels for effective mining complex patterns in omics data.

On July 6th, 2021, Gao Lab from Biomedical Pioneering Innovation Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG) published a research article named “Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network” on Briefings in Bioinformatics, proposing a novel convolutional layer for deep neural networks.   Deep Read more about A novel convolutional layer with adaptive kernels for effective mining complex patterns in omics data.[…]

Cell BLAST for China’s top ten bioinformatics advances of 2020

The Cell BLAST study (Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST) was recently awarded by Genomics, Proteomics and Bioinformatics (GPB) as China’s top ten bioinformatics advances of 2020. As a powerful tool for studying cellular heterogeneity, single-cell transcriptomic sequencing has seen rapid development in recent years, with large amounts of data Read more about Cell BLAST for China’s top ten bioinformatics advances of 2020[…]