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Chandrashekar, P.

Publications and source records attributed to Chandrashekar, P..

3 recordsLinked to original sources

Visual Perception of 3D Space and Shape in Time - Part II: 3D Space Perception with Holographic Depth

Visual perception plays a critical role in navigating 3D space and extracting semantic information crucial to survival. Even though visual stimulation on the retina is fundamentally 2D, we seem to perceive the world around us in vivid 3D effortlessly. This reconstructed 3D space is allocentric and faithfully represents the external 3D world. How can we recreate stable 3D visual space so promptly and reliably? To solve this mystery, we have developed new concepts MePMoS (Memory-Prediction-Motion-Sensing) and NHT (Neural Holography Tomography). These models state that visual signal processing must be primarily top-down, starting from memory and prediction. Our brains predict and construct the expected 3D space holographically using traveling alpha brainwaves. Thus, 3D space is represented by the three time signals in three directions. To test this hypothesis, we designed reaction time (RT) experiments to observe predicted space-to-time conversion, especially as a function of distance. We placed LED strips on a horizontal plane to cover distances from close up to 2.5 m or 5 m, either using a 1D or a 2D lattice. Participants were instructed to promptly report observed LED patterns at various distances. As expected, stimulation at the fixation cue location always gave the fastest RT. Additional RT delays were proportional to the distance from the cue. Furthermore, both covert attention (without eye movements) and overt attention (with eye movements) created the same RT delays, and both binocular and monocular views resulted in the same RTs. These findings strongly support our predictions, in which the observed RT-depth dependence is indicative of the spatiotemporal conversion required for constructing allocentric 3D space. After all, we perceive and measure 3D space by time as Einstein postulated a century ago.

neuroscience↗

TreeMap: A Structured Approach to Fine Mapping of eQTL Variants

MotivationExpression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals. ResultsWe developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression. AvailabilityTreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap.

bioinformatics↗

Contextual Classifications of Cancer Driver Genes

Functions of cancer driver genes depend on cellular contexts that vary substantially across tissues and organs. Distinguishing oncogenes (OGs) and tumor suppressor genes (TSGs) for each cancer type is critical to identifying clinically actionable targets. However, current resources for context-aware classifications of cancer drivers are limited. In this study, we show that the direction and magnitude of somatic selection of missense and truncating mutations of a gene are suggestive of its contextual activities. By integrating these features with ratiometric and conservation measures, we developed a computational method to categorize OGs and TSGs using exome sequencing data. This new method, named genes under selection in tumors (GUST) shows an overall accuracy of 0.94 when tested on manually curated benchmarks. Application of GUST to 10,172 tumor exomes of 33 cancer types identified 98 OGs and 179 TSGs, >70% of which promote tumorigenesis in only one cancer type. In broad-spectrum drivers shared across multiple cancer types, we found heterogeneous mutational hotspots modifying distinct functional domains, implicating the synchrony of convergent and divergent disease mechanisms. We further discovered two novel OGs and 28 novel TSGs with high confidence. The GUST program is available at https://github.com/liliulab/gust. A database with pre-computed classifications is available at https://liliulab.shinyapps.io/gust

bioinformatics↗