Gregory W. Schwartz

Table of Contents


I am a postdoctoral researcher in the Faryabi Lab at the University of Pennsylvania.


Life is a complex system of interconnecting networks: from the transcriptome to the proteome, from a single cell to clonal populations, each layer of information can be interpreted as a network where each molecule, cell, or tissue in some way affects the overall system. Systems biology attempts to take into account as many of these levels as possible in order to answer key biology questions pertaining to not only the organization of life but also finding the signal in the noise to discover key components of disease or adverse clinical outcomes. While the sampling technologies give partial insight to these issues, the other half of the solution, namely mathematical and computational analyses of biological data, are needed more than ever in this high-throughput era.

The need for such analyses drives my research. In my efforts to elucidate the relationships between genotype and phenotype, clonal diversity and selection, and integration of multiple levels of information (transcriptome, proteome, phosphoproteome, etc.), I have developed algorithms, programs, and mathematical measures.

More specifically, I am interested in clonal behavior and evolution through next-generation sequencing (8, 4, 6, 2), pattern recognition of the interactome through integration (3), network structure similarity, data visualization (6), and everything in-between.


  • Petrovic J, Zhou Y, Fasolino M, Goldman N, Schwartz GW, Mumbach MR, Nguyen SC, Rome KS, Sela Y, Zapataro Z, Blacklow SC, Kruhlak MJ, Shi J, Aster JC, Joyce EF, Little SC, Vahedi G, Pear WS, Faryabi RB. Oncogenic Notch Promotes Long-Range Regulatory Interactions within Hyperconnected 3D Cliques. Mol. Cell. 73 1174-1190.e12. (2019). doi:10.1016/j.molcel.2019.01.006
  • Schwartz GW, Manning BS, Zhou Y, Velu PD, Bigdeli A, Astles R, Lehman AW, Morrissette JJD, Perl AE, Li M, Carroll M, and Faryabi RB. Classes of ITD predict outcomes in AML patients treated with FLT3 inhibitors. Clin. Cancer Res. (2019). doi:10.1158/1078-0432.CCR-18-0655
  • Schwartz GW, Petrovic J, Zhou Y, and Faryabi RB. Differential integration of transcriptome and proteome identifies pan-cancer prognostic biomarkers. Front. Genet. 9, 205 (2018). doi:10.3389/fgene.2018.00205
  • Meng W*, Zhang B*, Schwartz GW, Rosenfeld AM, Ren D, Thome J JC, Carpenter DJ, Matsuoka N, Lerner H, Friedman AL, Granot T, Farber DL, Shlomchik MJ, Hershberg U and Luning Prak ET. An atlas of B cell clonal distribution in the human body. Nat. Biotechnol. 35, 879-884 (2017). doi:10.1038/nbt.3942
  • Antell GC, Dampier W, Aiamkitsumrit B, Nonnemacher MR, Jacobson JM, Pirrone V, Zhong W, Kercher K, and Passic S, Williams JW, Schwartz G, Hershberg U, Krebs FC, and Wigdahl B. Utilization of HIV-1 envelope V3 to identify X4- and R5-specific Tat and LTR sequence signatures. Retrovirology 13, 32 (2016). doi:10.1186/s12977-016-0266-9
  • Schwartz GW, Shokoufandeh A, Ontañón S, and Hershberg U. Using a novel clumpiness measure to unite data with metadata: finding common sequence patterns in immune receptor germline V genes. Pattern Recogn. Lett. 74, 24-29 (2016). doi:10.1016/j.patrec.2016.01.011
  • Meng W, Jayaraman S, Zhang B, Schwartz GW, Daber RD, Hershberg U, Garfall AL, Carlson CS and Luning Prak ET. Trials and tribulations with VH replacement. Front. Immunol. 5, 10 (2014). doi:10.3389/fimmu.2014.00010
  • Schwartz GW, Hershberg U. Germline Amino Acid Diversity in B Cell Receptors is a Good Predictor of Somatic Selection Pressures. Front. Immunol. 4, 357 (2013). doi:10.3389/fimmu.2013.00357
  • Schwartz GW, Hershberg U. Conserved variation: identifying patterns of stability and variability in BCR and TCR V genes with different diversity and richness metrics. Phys. Biol. 10, 035005 (2013). doi:10.1088/1478-3975/10/3/035005

Partial list of programs and libraries

Program Github Hackage Stackage Docker
birch-beer \(\bullet\) \(\bullet\)   \(\bullet\)
clumpiness (6, 4) \(\bullet\) \(\bullet\) \(\bullet\)  
convert-annotation \(\bullet\) \(\bullet\) \(\bullet\)  
differential \(\bullet\) \(\bullet\)    
diversity (9,8) \(\bullet\) \(\bullet\) \(\bullet\)  
fasta \(\bullet\) \(\bullet\) \(\bullet\)  
find-clumpiness (6, 4) \(\bullet\) \(\bullet\) \(\bullet\)  
heatitup (2) \(\bullet\) \(\bullet\)    
heatitup-complete (2) \(\bullet\) \(\bullet\)    
hierarchical-spectral-clustering \(\bullet\) \(\bullet\)    
integreat (3) \(\bullet\) \(\bullet\)    
modify-fasta \(\bullet\) \(\bullet\) \(\bullet\)  
modularity \(\bullet\) \(\bullet\)    
random-tree (6) \(\bullet\) \(\bullet\) \(\bullet\)  
rank-product \(\bullet\) \(\bullet\) \(\bullet\)  
spectral-clustering \(\bullet\) \(\bullet\)    
too-many-cells \(\bullet\) \(\bullet\)   \(\bullet\)
tree-fun (6) \(\bullet\) \(\bullet\) \(\bullet\)  



Author: Gregory W. Schwartz