Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-22T09:19:46.190Z Has data issue: false hasContentIssue false

5 - No-Boundary Thinking for Transcriptomics and Proteomics Big Data

Published online by Cambridge University Press:  14 September 2023

By
Mariola J. Ferraro ,
Andy D. Perkins ,
Mahalingam Ramkumar  and
Bindu Nanduri
Edited by
Xiuzhen Huang ,
Jason H. Moore  and
Yu Zhang
Xiuzhen Huang
Affiliation:
Cedars-Sinai Medical Center, Los Angeles
Jason H. Moore
Affiliation:
Cedars-Sinai Medical Center, Los Angeles
Yu Zhang
Affiliation:
Trinity University, Texas
Get access

Summary

Ideal healthcare should provide prevention and treatment strategies in the context of individual variability. The promise of genomics and big data for understanding the complex disease etiology and development of treatment strategies for translating research findings in a laboratory setting to the bedside requires a paradigm shift in how we conduct biomedical research. The take-home message from the Human Genome Sequencing Project is the need for a bold vision, even in the absence of a clear path. The No-Boundary Thinking (NBT) approach that advocates a scientific dialogue among individuals with varying expertise in a “discipline-free” manner at the problem definition stage is a pragmatic approach to leverage big data for precision medicine. Genomics big data as it pertains to understanding the molecular function of genes and proteins is discussed in this chapter. We also discuss the challenges in the adoption of NBT to genomics research.

Keywords

proteomicsRNA-Seqbig dataNo-Boundary Thinkingbiomedical research
Type
Chapter
Information
Integrative Bioinformatics for Biomedical Big Data
A No-Boundary Thinking Approach
 , pp. 67 - 86
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aderem, A, Adkins, JN, Ansong, C, et al., 2011. A systems biology approach to infectious disease research: innovating the pathogen–host research paradigm. MBio, 2(1):e00325–00310.Google Scholar
Aebersold, R, Mann, M, 2016. Mass-spectrometric exploration of proteome structure and function. Nature, 537(7620):347–355.CrossRefGoogle ScholarPubMed
Aebersold, R, Burlingame, AL, Bradshaw, RA, 2013. Western blots versus selected reaction monitoring assays: time to turn the tables? Mol Cell Proteomics, 12(9):2381–2382.CrossRefGoogle ScholarPubMed
Anders, S, Huber, W, 2010. Differential expression analysis for sequence count data. Genome Biol, 11(10):R106.Google Scholar
Auton, A, Brooks, LD, Durbin, RM, et al., 2015. A global reference for human genetic variation. Nature, 526(7571):68–74.Google ScholarPubMed
Barrera, NP, Isaacson, SC, Zhou, M, et al., 2009. Mass spectrometry of membrane transporters reveals subunit stoichiometry and interactions. Nat Methods, 6(8):585–587.CrossRefGoogle ScholarPubMed
Bell, AW, Deutsch, EW, Au, CE, et al., 2009. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nat Methods, 6(6):423–430.CrossRefGoogle ScholarPubMed
Benoist, C, Lanier, L, Merad, M, et al., 2012. Consortium biology in immunology: the perspective from the Immunological Genome Project. Nat Rev Immunol, 12(10):734–740.CrossRefGoogle ScholarPubMed
Bereman, MS, Lyndon, MM, Dixon, RB, Muddiman, DC, 2008. Mass measurement accuracy comparisons between a double-focusing magnetic sector and a time-of-flight mass analyzer. Rapid Commun Mass Spectrom, 22(10):1563–1566.Google Scholar
Chen, R, Mias, GI, Li-Pook-Than, J, et al., 2012. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell, 148(6):1293–1307.CrossRefGoogle ScholarPubMed
Dang, X, Scotcher, J, Wu, S, et al., 2014. The first pilot project of the consortium for top-down proteomics: a status report. Proteomics, 14(10):1130–1140.CrossRefGoogle Scholar
Deutsch, EW, Csordas, A, Sun, Z, et al., 2017. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res, 45(D1):D1100–D1106.CrossRefGoogle ScholarPubMed
Ellis, MJ, Gillette, M, Carr, SA, et al., 2013. Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discov, 3(10):1108–1112.Google Scholar
ENCODE Consortium, 2012. An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414):57–74.CrossRefGoogle Scholar
Grabherr, MG, Haas, BJ, Yassour, M, et al., 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol, 29(7):644–652.CrossRefGoogle ScholarPubMed
Green, ED, Guyer, MS, National Human Genome Research Institute, 2011. Charting a course for genomic medicine from base pairs to bedside. Nature, 470(7333):204–213.CrossRefGoogle Scholar
Green, ED, Watson, JD, Collins, FS, 2015. Human Genome Project: twenty-five years of big biology. Nature, 526(7571):29–31.CrossRefGoogle ScholarPubMed
Hood, L, Friend, SH, 2011. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol, 8(3):184–187.CrossRefGoogle ScholarPubMed
Huang, X, Bruce, B, Buchan, A, et al., 2013. No-boundary thinking in bioinformatics research. BioData Min, 6(1):19.CrossRefGoogle ScholarPubMed
International HapMap, 2003. The International HapMap Project. Nature, 426(6968):789–796.Google Scholar
Jay, JJ, Eblen, JD, Zhang, Y, et al., 2012. A systematic comparison of genome-scale clustering algorithms. BMC Bioinformatics, 13(Suppl. 10):S7.CrossRefGoogle ScholarPubMed
Kim, MS, Pinto, SM, Getnet, D, et al., 2014. A draft map of the human proteome. Nature, 509(7502):575–581.CrossRefGoogle ScholarPubMed
Lander, ES, Linton, LM, Birren, B, et al., 2001. Initial sequencing and analysis of the human genome. Nature, 409(6822):860–921.Google ScholarPubMed
Laney, D, 2001. 3D data management: controlling data volume, velocity and variety. META Group Research Note 6.Google Scholar
Ledford, H, 2015. End of cancer-genome project prompts rethink. Nature, 517(7533):128–129.Google Scholar
Leinonen, R, Sugawara, H, Shumway, M, International Nucleotide Sequence Database Collaboration, 2011. The sequence read archive. Nucleic Acids Res, 39:D19–D21.CrossRefGoogle ScholarPubMed
Li, Z, Zhang, Z, Yan, P, et al., 2011. RNA-Seq improves annotation of protein-coding genes in the cucumber genome. BMC Genomics, 12:540.CrossRefGoogle ScholarPubMed
Lin, E, Lane, HY, 2017. Machine learning and systems genomics approaches for multi-omics data. Biomark Res, 5:2.CrossRefGoogle ScholarPubMed
Liu, C, Che, D, Liu Song, Y, 2013. Applications of machine learning in genomics and systems biology. Comput Math Methods Med, 2013:587492.Google Scholar
Lockhart, DJ, Dong, H, Byrne, MC, et al., 1996. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol, 14(13):1675–1680.CrossRefGoogle ScholarPubMed
Lopez-Maestre, H, Brinza, L, Marchet, C, et al., 2016. SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence. Nucleic Acids Res, 44(19):e148.Google Scholar
Martens, L, Vizcaino, JA, 2017. A golden age for working with public proteomics data. Trends Biochem Sci, 42(5):333–341.Google Scholar
Morris, KV, Mattick, JS, 2014. The rise of regulatory RNA. Nat Rev Genet, 15(6):423–437.CrossRefGoogle ScholarPubMed
Nesvizhskii, AI, 2014. Proteogenomics: concepts, applications and computational strategies. Nat Methods, 11(11):1114–1125.Google Scholar
Pavlou, MP, Diamandis, EP, Blasutig, IM, 2013. The long journey of cancer biomarkers from the bench to the clinic. Clin Chem, 59(1):147–157.Google Scholar
Pepe, MS, Etzioni, R, Feng, Z, et al., 2001. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst, 93(14):1054–1061.CrossRefGoogle ScholarPubMed
Perry, RH, Cooks, RG, Noll, RJ, 2008. Orbitrap mass spectrometry: instrumentation, ion motion and applications. Mass Spectrom Rev, 27(6):661–699.CrossRefGoogle ScholarPubMed
Picotti, P, Aebersold, R, 2012. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods, 9(6):555–566.CrossRefGoogle ScholarPubMed
Piskol, R, Ramaswami, G, Li, JB, 2013. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet, 93(4):641–651.CrossRefGoogle ScholarPubMed
Plebani, M, 2005. Proteomics: the next revolution in laboratory medicine? Clin Chim Acta, 357(2):113–122.CrossRefGoogle ScholarPubMed
Poste, G, 2011. Bring on the biomarkers. Nature, 469(7329):156–157.CrossRefGoogle ScholarPubMed
Roberts, A, Pimentel, H, Trapnell, C, Pachter, L, 2011. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics, 27(17):2325–2329.Google Scholar
Robinson, MD, McCarthy, DJ, Smyth, GK, 2010. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1):139–140.CrossRefGoogle ScholarPubMed
Seyednasrollah, F, Laiho, A, Elo, LL, 2015. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform, 16(1):59–70.CrossRefGoogle ScholarPubMed
Smith, TF, Waterman, MS, 1981. Identification of common molecular subsequences. J Mol Biol, 147(1):195–197.CrossRefGoogle ScholarPubMed
Sowe, SK, Zettsu, K, 2014. Curating big data made simple: perspectives from scientific communities. Big Data, 2(1):23–33.CrossRefGoogle ScholarPubMed
Tang, B, Wang, Y, Zhu, J, Zhao, W, 2015. Web resources for model organism studies. Genomics Proteomics Bioinformatics, 13(1):64–68.CrossRefGoogle ScholarPubMed
Thorisson, GA, Smith, AV, Krishnan, L, Stein, LD, 2005. The International HapMap Project web site. Genome Res, 15(11):1592–1593.CrossRefGoogle ScholarPubMed
Toby, TK, Fornelli, L, Kelleher, NL, 2016. Progress in top-down proteomics and the analysis of proteoforms. Annu Rev Anal Chem (Palo Alto Calif), 9(1):499–519.CrossRefGoogle ScholarPubMed
UniProt Consortium, 2017. UniProt: the universal protein knowledgebase. Nucleic Acids Res, 45(D1):D158–D169.CrossRefGoogle Scholar
Velculescu, VE, Zhang, L, Vogelstein, B, Kinzler, KW, 1995. Serial analysis of gene expression. Science, 270(5235):484–487.Google Scholar
Vizcaino, JA, Csordas, A, del-Toro, N, et al., 2016. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res, 44(D1):D447–D456.CrossRefGoogle ScholarPubMed
Vorontsov, EA, Rensen, E, Prangishvili, D, Krupovic, M, Chamot-Rooke, J, 2016. Abundant lysine methylation and N-terminal acetylation in sulfolobus islandicus revealed by bottom-up and top-down proteomics. Mol Cell Proteomics, 15(11):3388–3404.CrossRefGoogle ScholarPubMed
Wehling, M, 2021. Principles of Translational Science in Medicine: From Bench to Bedside.. Amsterdam: Elsevier.Google Scholar
Wilhelm, M, Schlegl, J, Hahne, H, et al., 2014. Mass-spectrometry-based draft of the human proteome. Nature, 509(7502):582–587.CrossRefGoogle ScholarPubMed
Wolfe, CJ, Kohane, IS, Butte, AJ, 2005. Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinformatics, 6:227.Google Scholar
Xie, Y, Wu, G, Tang, J, et al., 2014. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics, 30(12):1660–1666.Google Scholar
Zhang, Y, Fonslow, BR, Shan, B, Baek, MC, Yates, JR, 2013. Protein analysis by shotgun/bottom-up proteomics. Chem Rev, 113(4):2343–2394.CrossRefGoogle ScholarPubMed
Zubarev, RA, 2013. The challenge of the proteome dynamic range and its implications for in-depth proteomics. Proteomics, 13(5):723–726.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×