Biology subjects

Amin, V.

Publications and source records attributed to Amin, V..

3 recordsLinked to original sources

bioRxiv2021

TReNCo: Topologically associating domain (TAD) aware regulatory network construction

There has long been a desire to understand, describe, and model gene regulatory networks controlling numerous biologically meaningful processes like differentiation. Despite many notable improvements to models over the years, many models do not accurately capture subtle biological and chemical characteristics of the cell such as high-order chromatin domains of the chromosomes. Topologically Associated Domains (TAD) are one of these genomic regions that are enriched for contacts within themselves. Here we present TAD-aware Regulatory Network Construction or TReNCo, a memory-lean method utilizing epigenetic marks of enhancer and promoter activity, and gene expression to create context-specific transcription factor-gene regulatory networks. TReNCo utilizes common assays, ChIP-seq, RNA-seq, and TAD boundaries as a hard cutoff, instead of distance based, to efficiently create context-specific TF-gene regulatory networks. We used TReNCo to define the enhancer landscape and identify transcription factors (TFs) that drive the cardiac development of the mouse. Our results show that we are able to build specialized adjacency regulatory network graphs containing biologically relevant connections and time dependent dynamics.

bioinformatics↗

bioRxiv2019

Mental Health, Schooling Attainment and Polygenic Scores: Are There Significant Gene-Environment Associations?

It is well-established that (1) there is a large genetic component to mental health, and (2) higher schooling attainment is associated with better mental health. Given these two observations, we test the hypothesis that schooling may attenuate the genetic predisposition to poor mental health. Specifically, we estimate associations between a polygenic score (PGS) for depressive symptoms, schooling attainment and gene-environment (GxE) interactions with mental health (depressive symptoms and depression), in two distinct United States datasets at different adult ages-29 years old in the National Longitudinal Study of Adolescent Health (Add Health) and 54 years old in the Wisconsin Longitudinal Study (WLS). OLS results indicate that the association of the PGS with mental health is similar in Add Health and the WLS, but the association of schooling attainment is much larger in Add Health than in the WLS. There is some suggestive evidence that the association of the PGS with mental health is lower for more-schooled older individuals in the WLS, but there is no evidence of any significant GxE associations in Add Health. Quantile regression estimates also show that in the WLS the GxE associations are statistically significant only in the upper parts of the conditional depressive symptoms score distribution. We assess the robustness of the OLS results to omitted variable bias by using the siblings samples in both datasets to estimate sibling fixed-effect regressions. The sibling fixed-effect results must be qualified, in part due to low statistical power. However, the sibling fixed-effect estimates show that college education is associated with fewer depressive symptoms in both datasets.

genomics↗

bioRxiv2018

Accurate differential analysis of transcription factor activity from gene expression

We present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)

bioinformatics↗