Biology subjects

Gurinder S Atwal

Publications and source records attributed to Gurinder S Atwal.

3 recordsLinked to original sources

bioRxiv2015

Ancient trans-acting siRNAs Confer Robustness and Sensitivity onto the Auxin Response

Novel developmental programs often evolve via cooption of existing genetic networks. To understand this process, we explored cooption of the TAS3 tasiRNA pathway in the moss Physcomitrella patens. We find an ancestral function for this repeatedly redeployed pathway in the spatial regulation of a conserved set of Auxin Response Factors. In moss, this results in stochastic patterning of the filamentous protonemal tissue. Through modeling and experimentation, we demonstrate that tasiRNA regulation confers sensitivity and robustness onto the auxin response. Increased auxin sensitivity parallels increased developmental sensitivity to nitrogen, a key environmental signal. We propose that the properties lent to the auxin response network, along with the ability to stochastically modulate development in response to environmental cues, have contributed to the tasiRNA-ARF modules repeated cooption during evolution. The signaling properties of a genetic network, and not just its developmental output, are thus critical to understanding the evolution of multicellular forms.

Plant Biology↗

bioRxiv2015

Learning quantitative sequence-function relationships from high-throughput biological data

Understanding the transcriptional regulatory code, as well as other types of information encoded within biomolecular sequences, will require learning biophysical models of sequence-function relationships from high-throughput data. Controlling and characterizing the noise in such experiments, however, is notoriously difficult. The unpredictability of such noise creates problems for standard likelihood-based methods in statistical learning, which require that the quantitative form of experimental noise be known precisely. However, when this unpredictability is properly accounted for, important theoretical aspects of statistical learning which remain hidden in standard treatments are revealed. Specifically, one finds a close relationship between the standard inference method, based on likelihood, and an alternative inference method based on mutual information. Here we review and extend this relationship. We also describe its implications for learning sequence-function relationships from real biological data. Finally, we detail an idealized experiment in which these results can be demonstrated analytically.

Bioinformatics↗

bioRxiv2014

Interactive analysis and quality assessment of single-cell copy-number variations

We present an open-source visual-analytics web platform, Ginkgo (http://qb.cshl.edu/ginkgo), for the interactive analysis and quality assessment of single-cell copy-number alterations. Ginkgo automatically constructs copy-number profiles of individual cells from mapped reads, as well as constructing phylogenetic trees of related cells. We validate Ginkgo by reproducing the results of five major studies and examine the data characteristics of three commonly used single-cell amplification techniques to conclude DOP-PCR to be the most consistent for CNV analysis.

Bioinformatics↗