Author

Kristen Dang

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Biology Disciplines

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Bioinformatics· 1 Genomics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1

Frequent Co-Authors

A. Ercument Cicek· 1 shared Aaron Topol· 1 shared Adam A. Margolin· 1 shared Adam D. EwingiD· 1 shared Allan I. LeveyiD· 1 shared Andrew Browne· 1 shared Andrew Chess· 1 shared Anna Y. Lee· 1 shared

Activity by Year

2016-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperGenomics

A. Ercument Cicek

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3 papers

Recent·Most cited

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Benjamin A. Logsdon, Thanneer Malai Perumal, Vivek Swarup, Minghui Wang, Cory C. Funk, Chris Gaiteri, Mariet Allen, Xue Bing Wang, Eric B. Dammer, Gyan Prakash Srivastava, Sumit Mukherjee, Solveig K. Sieberts, Larsson Omberg, Kristen Dang, James A. Eddy, , Lara M. Mangravite

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.

bioRxiv · Jan 3, 2019 · PreprintSystems Biology

Combining accurate tumour genome simulation with crowd-sourcing to benchmark somatic structural variant detection

Anna Y. Lee, Adam D. Ewing, Kyle Ellrott, Yin Hu, Kathleen E. Houlahan, J Christopher Bare, Shadrielle M. G. Espiritu, Vincent Huang, Kristen Dang, Zechen Chong, Cristian Caloian, Takafumi N. Yamaguchi, ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants, Michael Kellen, Ken Chen, , Paul Christopher Boutros

The paper benchmarks somatic structural variant detection methods using synthetic tumors and crowd-sourced submissions, finding that ensembles do not always outperform individual methods.

Genome biology · Nov 6, 2018Peer-reviewedBioinformatics

Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia

Menachem Fromer, Panos Roussos, Solveig K Sieberts, Jessica S Johnson, David H Kavanagh, Thanneer M Perumal, Douglas M Ruderfer, Edwin C Oh, Aaron Topol, Hardik R Shah, Lambertus L Klei, Robin Kramer, Dalila Pinto, Zeynep H Gumus, A. Ercument Cicek, , Lara M Mangravit

The paper uses gene expression data from schizophrenia cases and controls to show that common variants at 20% of schizophrenia risk loci regulate brain gene expression, with functional impacts on neural progenitor cells and zebrafish neuroanatomy.

bioRxiv · May 9, 2016Genomics