Skip to main content
SpringerLink
Log in
Book cover

Bioinformatics and Drug Discovery pp 273–286Cite as

Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1939))

Abstract

The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. L1000 big datasets provide gene expression profiles induced by over 10,000 compounds, shRNAs, and kinase inhibitors using L1000 platform. We developed a systematic compound signature discovery pipeline named csNMF, which covers from raw L1000 data processing to drug screening and mechanism generation. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. In this way, the potential mechanisms of compounds’ efficacy are elucidated by our computational model.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Downey W, Liu C, Hartigan J (2010) Compound profiling: size impact on primary screening libraries. Drug Discovery World pp 81–86

    Google Scholar 

  2. Lehmann BD et al (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121(7):2750–2767

    Google Scholar 

  3. Duan Q et al (2014) LINCS canvas browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 42:W449–W460

    Google Scholar 

  4. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935

    Google Scholar 

  5. Peng HM, Zhao WL, Tan H, Ji ZW, Li JS, Li K, Zhou XB (2016) Prediction of treatment efficacy for prostate cancer using a mathematical model. Sci Rep 6:21599

    Google Scholar 

  6. Liu CL, Su J, Yang F, Wei K, Ma JW, Zhou XB (2015) Compound signature detection on LINCS L1000 big data. Mol BioSyst 11:714–722

    Google Scholar 

  7. Ji ZW, Wu D, Zhao WL, Peng HM, Zhao SJ, Huang DS, Zhou XB (2015) Systemic modeling myeloma-osteoclast interactions under normoxic/hypoxic condition using a novel computational approach. Sci Rep 5:13291

    Google Scholar 

  8. Subramanian A et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550

    Google Scholar 

  9. Lamb J et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795):1929–1935

    Google Scholar 

  10. Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems

    Google Scholar 

  11. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Google Scholar 

  12. Kim H, Park H (2007) Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23(12):1495–1502

    Google Scholar 

  13. Mering CV et al (2005) STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 33(suppl 1):D433–D437

    Google Scholar 

  14. You ZH, Li JQ, Gao X, He Z, Zhu L, Lei YK, Ji ZW (2015) Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines. Biomed Res Int 2015:867516

    Google Scholar 

  15. Lachmann A et al (2010) ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26(19):2438–2444

    Google Scholar 

  16. Shao HW, Peng T, Ji ZW, Su J, Zhou XB (2013) Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects. PLoS One 8(12):e80832

    Google Scholar 

  17. Ji ZW, Su J, Wu D, Peng HM, Zhao WL, Zhou XB (2017) Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model. Oncotarget 8:7647–7665

    Google Scholar 

  18. Gerl R, Vaux DL (2005) Apoptosis in the development and treatment of cancer. Carcinogenesis 26(2):263–270

    Google Scholar 

  19. Huang D et al (2007) The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8(9):R183

    Google Scholar 

  20. Siddiqa A et al (2008) Expression of HER-2 in MCF-7 breast cancer cells modulates anti-apoptotic proteins Survivin and Bcl-2 via the extracellular signal-related kinase (ERK) and phosphoinositide-3 kinase (PI3K) signalling pathways. BMC Cancer 8(1):129

    Google Scholar 

Download references

Acknowledgment

The work was supported by the grants of NIH U01HL111560-04 (Zhou) and NIH U01CA166886-03 (Zhou).

Author information

Authors and Affiliations

  1. Department of Radiology, Wake Forest University Medical School, Winston-Salem, NC, USA

    Wei Chen & Xiaobo Zhou

  2. School of Biomedical Informatics, The University of Texas, Health Science Center at Houston, Houston, TX, USA

    Xiaobo Zhou

Authors
  1. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaobo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaobo Zhou .

Editor information

Editors and Affiliations

  1. Department of Pathology, University of New Mexico, Albuquerque, NM, USA

    Richard S. Larson

  2. Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA

    Tudor I. Oprea

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Chen, W., Zhou, X. (2019). Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data. In: Larson, R., Oprea, T. (eds) Bioinformatics and Drug Discovery. Methods in Molecular Biology, vol 1939. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9089-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9089-4_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9088-7

  • Online ISBN: 978-1-4939-9089-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation