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Biology subjects

LI, M.

Publications and source records attributed to LI, M..

4 recordsLinked to original sources

Exploring Genetic Interactions with Rare Variants Reveals Gene Networks Susceptible to Complex Diseases

Genetic interactions play a crucial role in understanding the susceptibility and etiology of complex human diseases. However, existing methods for analyzing gene-gene interactions are often limited to common variants and may not capture the effects of rare variants. In this study, we propose a novel method called GGI-RUNNER that is specifically designed for analyzing gene-gene interactions of rare variants. GGI-RUNNER evaluates the enrichment of rare variant interaction burden in patients relative to baselines in the general population. The baselines are estimated for pairwise genes by a recursive truncated negative-binomial regression model on multiple genomic features available from public data. Intensive simulations demonstrate that GGI-RUNNER exhibits substantially higher statistical power than alternative epistasis tests and maintains reasonable type I error rates even in stratified populations. Applied to real data, GGI-RUNNER identified significant rare variant interactions associated with Alzheimers disease, Hirschsprungs disease, Ulcerative colitis and Crohns disease. Furthermore, we found that these identified genes for each trait can form interconnected networks, which may provide valuable insights into the underlying molecular mechanisms.

bioinformatics↗

CAMOIP: A Web Server for Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer

Immune checkpoint inhibitors (ICIs) have completely changed the therapeutic approach for tumor patients. Immunotherapy has also produced much needed data about mutation, expression, and prognosis, providing an unprecedented opportunity for discovering candidate drug targets and screening for immunotherapy-relevant biomarkers. Although existing web tools enable biologists to analyze the expression, mutation, and prognosis data on tumors, they are currently not able to carry out data mining and mechanism analyses related to immune checkpoint therapy. Thus, we developed our own web-based tool called Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer (CAMOIP), in which we can screen prognostic markers and analyze the mechanisms involved with markers and immunotherapy (more than 4000 patients). The analyses include survival analysis, expression analysis, drug sensitivity analysis, mutational landscape, immune checkpoint analysis, immune related signature analysis, immune cell analysis, immune gene analysis, immunogenicity analysis and gene sets enrichment analysis (GSEA). This comprehensive analysis of biomarkers for immunotherapy can be carried out by a click of CAMOIP, and the software should greatly encourage the further development of immunotherapy. CAMOIP fills the gap between the big data of cancer genomics based on immunotherapy and providing comprehensive information to users, helping to release the value of current ICI-treated data resources. CAMOIP can be found in https://www.camoip.net.

genomics↗

An awareness-dependent saliency map in the human visual system

The bottom-up contribution to the allocation of exogenous attention is a saliency map. However, how the saliency map is distributed when multiple salient stimuli are presented simultaneously and how this distribution interacts with awareness remain unclear. These questions were addressed here using visible and invisible stimuli that consisting of two salient foregrounds: the high one served as the target and the low one served as the distractor, which did or did not interfere the target saliency, indicating a gradient or winner-take-all manner, respectively. By combining psychophysics, fMRI, and effective connectivity analysis, we found that the saliency map was distributed as a gradient or winner-take-all manner with and without awareness, respectively. Crucially, we further revealed that the gradient manner was derived by feedback from pIPS, whereas the winner-take-all manner was constructed in V1. Together, our findings indicate an awareness-dependent saliency map and reconcile previous, seemingly contradictory findings on the saliency map.

neuroscience↗

Genome structure and evolution of Antirrhnum majus L.

Snapdragon (Antirrhinum majus L.), a member of Plantaginaceae, is an important model for plant genetics and molecular studies on plant growth and development, transposon biology and self-incompatibility. Here we report a high-quality genome assembly of A. majus cultivated JI7 (A. majus cv.JI7) of a 510 Mb with 37,714 annotated protein-coding genes. The scaffolds covering 97.12% of the assembled genome were anchored on 8 chromosomes. Comparative and evolutionary analyses revealed that Plantaginaceae and Solanaceae diverged from their most recent ancestor around 62 million years ago (MYA). We also revealed the genetic architectures associated with complex traits such as flower asymmetry and self-incompatibility including a unique TCP duplication around 46-49 MYA and a near complete{psi} S-locus of ca.2 Mb. The genome sequence obtained in this study not only provides the first genome sequenced from Plantaginaceae but also bring the popular plant model system of Antirrhinum into a genomic age.

genomics↗