Adam Siepel

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Adjunct Professor

Cornell University

United States

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About

Adam Siepel is an Adjunct Professor at Cornell University, USA. His research focuses on evolutionary genomics, transcription dynamics, and machine learning applications in bioinformatics. His recent work includes studies on the evolution of immunity-related genes in bats, the impact of adenylosuccinate lyase on human brain development, and probabilistic methods for analyzing transcription elongation rates.

Recent Grants

Grant: Close

Defining the cellular & molecular effects of aging and age of pregnancy on breast tissue homeostatis & cancer initiation

Open Date: 2020-09-01

Close Date: 2025-05-01

Grant: Close

Graphical Processing Units and a Large-Memory Compute Node for Applications in Genomics, Neuroscience, and Structural Biology

Open Date: 2020-07-01

Close Date: 2021-06-30

Grant: Close

Evolution of Chromatin Architecture and Transcriptional Regulation in Mammals

Open Date: 2019-05-01

Close Date: 2023-02-28

Grant: Close

RESEARCH-PGR: PanAnd - Harnessing convergence and constraint to predict adaptations to abiotic stress for maize and sorghum

Open Date: 2018-09-01

Close Date: 2022-08-31

Grant: Close

Evolutionary Human Genomics: Demography, Natural Selection, and Transcriptional Regulation

Open Date: 2018-03-01

Close Date: 2023-02-28

Articles (17)

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UNITED STATES

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Daniel Becker

Assistant Professor

University of Oklahoma

UNITED STATES

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Adam Siepel

Research Interests

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About

Recent Grants

Articles (17)

The activity and expression of adenylosuccinate lyase were reduced during modern human evolution, affecting brain and behavior

A humanized NOVA1 splicing factor alters mouse vocal communications

Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data

Teosinte Pollen Drive guides maize diversification and domestication by RNAi

Long-Read Sequencing Reveals Rapid Evolution of Immunity- and Cancer-Related Genes in Bats

Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

Extreme purifying selection against point mutations in the human genome

RNA Pol II pausing facilitates phased pluripotency transitions by buffering transcription

A multiple alignment workflow shows the effect of repeat masking and parameter tuning on alignment in plants

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph

Deconvolution of expression for nascent RNA-sequencing data (DENR) highlights pre-RNA isoform diversity in human cells

ACE: a probabilistic model for characterizing gene-level essentiality in CRISPR screens

An inferred fitness consequence map of the rice genome

Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps

Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph

From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection.

Collaborators (7)

Michael Schatz

Simon Groen

Edward Buckler

Melissa Hubisz

Yi-Fei Huang

Amy L Williams

Daniel Becker

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