About Us
- We are a Bioinformatics and Computational Genomics research group at Peking University, Beijing. Our research is focused on developing computational methods to decipher the “coded messages” in genome via mining biological “big data”. During past decades, we developed multiple bioinformatics tools to analyze, integrate and visualize omics data, with applications to profile and model the function and evolution of gene regulatory systems in human and various model organisms.
- Corresponding to the interdisciplinary nature of our research, members of our group have very diverse backgrounds. People from Biology, Medicine, Chemistry, as well as Mathematics and Computer Science have been working together closely in the lab, to advance the boundaries of knowledge.
- We are seeking for talented and motivated Students, PostDocs, Technicians as well as Research Scientists. Please do not hesitate to contact us if you are!
News
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[…]
Welcome ShiQI Yang to join the Gao lab
ShiQi Yang is the first staff join us via the changping lab. Warmly welcome!
Welcome XianYi Lian to join the Gao lab and congratulation her on winning the Peking University Boya Postdoctoral Fellowship
Welcome XianYi Lian to join the Gao lab. Congratulation her on winning the Peking University Boya Postdoctoral Fellowship!
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[…]
Congratulations to Fangyuan Shi for her Ph.D.!
Well done, Dr. Shi! Wish you more success and prosperity in days to come!
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[…]
Congratulations to Lan Ke for her Ph.D.!
Well done, Dr. Ke! From a seed to a sapling, and a tree in one day! Wish you more success and prosperity in days to come!
Congratulations to Feng Tian for his Ph.D.!
Well done, Dr. Tian! Stick to your way, step by step and you will arrive your destination finally. Wish you more success and prosperity in days to come!