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Computational Methods for Proteome Analysis

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Epigenetics and Proteomics of Leukemia

Abstract

The importance of two-dimensional electrophoresis (2DE) in the life sciences, more precisely protein studies, is large. Although the 2DE technology has shortcomings, however, due to significant 2DE possibilities, this technology still is irreplaceable. Thus we focus on the analysis of 2DE gels (2DEG) and its automation capabilities. In Chapter 6 are formulated the efficiency and reliability of 2DEG analysis evaluation criteria and based on them proposed automatic 2DEG image analysis strategy. The strategy consists of two essential steps: image matching and protein expression analysis. In order to eliminate geometric distortions in gel images, new 2DEG image matching solutions are proposed and investigated. The most problematic protein expression analysis part—2DEG image segmentation, is approached presenting novel image segmentation into meaningful areas algorithms and protein spot modeling studies involving protein spots reconstruction and parameterization.

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References

  • Ahmad N, Zhang J, Brown PJ, James D, Birch JR, Racher AJ, Smales CM (2006) On the statistical analysis of the GS-NS0 cell proteome: imputation, clustering and variability testing. Biochim. Biophys. Acta, Proteins Proteomics 1764(7):1179–1187

    Article  CAS  Google Scholar 

  • Aittokallio T, Salmi J, Nyman TA, Nevalainen OS (2005) Geometrical distortions in two-dimensional gels: applicable correction methods. J Chromatogr B Anal Technol Biomed Life Sci 815(1–2):25–37

    Article  CAS  Google Scholar 

  • Alterovitz G, Liu J, Chow J, Ramoni MF (2006) Automation, parallelism, and robotics for proteomics. Proteomics 6(14):4016–4022

    Article  CAS  PubMed  Google Scholar 

  • Anderson NL, Taylor J, Scandora AE, Coulter BP, Anderson NG (1981) The TYCHO system for computer-analysis of two-dimensional gel-electrophoresis patterns. Clin Chem 27(11):1807–1820

    Article  CAS  PubMed  Google Scholar 

  • Becher B, Knofel AK, Peters J (2006) Time-based analysis of silver-stained proteins in acrylamide gels. Electrophoresis 27(10):1867–1873

    Article  CAS  PubMed  Google Scholar 

  • Bettens E, Scheunders P, Vandyck D, Moens L, Vanosta P (1997) Computer analysis of two-dimensional electrophoresis gels: a new segmentation and modeling algorithm. Electrophoresis 18(5):792–798

    Article  CAS  PubMed  Google Scholar 

  • Biron DG, Brun C, Lefevre T, Lebarbenchon C, Loxdale HD, Chevenet F, Brizard JP, Thomas F (2006) The pitfalls of proteomics experiments without the correct use of bioinformatics tools. Proteomics 6(20):5577–5596

    Article  CAS  PubMed  Google Scholar 

  • Bookstein FL (1989) Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6):567–585

    Article  Google Scholar 

  • Brauner JM, Groemer TW, Stroebel A, Grosse-Holz S, Oberstein T, Wiltfang J, Kornhuber J, Maler JM (2014) Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm. BMC Bioinf 15(1):181

    Article  CAS  Google Scholar 

  • Canny J (1986) A computational approach to edge-detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  CAS  PubMed  Google Scholar 

  • Coleman TF, Li Y (1994) On the convergence of reflective newton methods for large-scale nonlinear minimization subject to bounds. Math Program 67(2):189–224

    Article  Google Scholar 

  • Coleman TF, Li Y (1996) An interior trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6(2):418–445

    Article  Google Scholar 

  • Corzett TH, Fodor IK, Choi MW, Walsworth VL, Chromy BA, Turteltaub KW, Mccutchen-Maloney SL (2006) Statistical analysis of the experimental variation in the proteomic characterization of human plasma by two-dimensional difference gel electrophoresis. J Proteome Res 5(10):2611–2619

    Article  CAS  PubMed  Google Scholar 

  • de Jesus JR, Guimarães IC, Arruda MAZ (2019) Quantifying proteins at microgram levels integrating gel electrophoresis and smartphone technology. J Proteomics 198:45–49

    Article  PubMed  CAS  Google Scholar 

  • Dowsey AW, Yang GZ (2008) The future of large-scale collaborative proteomics. Proc IEEE 96(8):1292–1309

    Article  CAS  Google Scholar 

  • Dowsey AW, Dunn MJ, Yang GZ (2008) Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline. Bioinformatics 24(7):950–957

    Article  CAS  PubMed  Google Scholar 

  • Dowsey AW, English JA, Lisacek F, Morris JS, Yang GZ, Dunn MJ (2010) Image analysis tools and emerging algorithms for expression proteomics. Proteomics 10(23):4226–4257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dzemyda G, Kurasova O, Žilinskas J (2008) Daugiamačiu̧ duomenu̧ vizualizavimo metodai. Matematikos ir informatikos institutas, Vilnius

    Google Scholar 

  • Eravci M, Fuxius S, Broedel O, Weist S, Eravci S, Mansmann U, Schluter H, Tiemann J, Baumgartner A (2007) Improved comparative proteome analysis based on two-dimensional gel electrophoresis. Proteomics 7(4):513–523

    Article  CAS  PubMed  Google Scholar 

  • Fernandez-Lozano C, Seoane JA, Gestal M, Gaunt TR, Dorado J, Pazos A, Campbell C (2016) Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Sci. Rep. 6:19256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fodor IK, Nelson DO, Alegria-Hartman M, Robbins K, Langlois RG, Turteltaub KW, Corzett TH, Mccutchen-Maloney SL (2005) Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using decyder (tm). Bioinformatics 21(19):3733–3740

    Article  CAS  PubMed  Google Scholar 

  • Garrels JI (1989) The quest system for quantative analysis of two-dimensional gels. J. Biol. Chem. 264(9):5269–5282

    Article  CAS  PubMed  Google Scholar 

  • Glasbey CA, Mardia KV (1998) A review of image-warping methods. J. Appl. Stat. 25(2):155–171

    Article  Google Scholar 

  • Goez MM, Torres-Madroñero MC, Röthlisberger S, Delgado-Trejos E (2018) Preprocessing of 2-dimensional gel electrophoresis images applied to proteomic analysis: a review. Genomics Proteomics Bioinf 16(1):63–72

    Article  Google Scholar 

  • Gonzalez RC, Woods RE, Eddins SL (2003) Digital image processing using MATLAB(R). Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Gustafsson JS, Blomberg A, Rudemo M (2002) Warping two-dimensional electrophoresis gel images to correct for geometric distortions of the spot pattern. Electrophoresis 23(11):1731–1744

    Article  CAS  PubMed  Google Scholar 

  • Hunsucker SW, Duncan MW (2006) Is protein overlap in two-dimensional gels a serious practical problem? Proteomics 6(5):1374–1375

    Article  CAS  PubMed  Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. In MATLAB curriculum series. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Jegelevicius D, Lukosevicius A, Paunksnis A, Barzdziukas V (2002) Application of data mining. technique for diagnosis of posterior uveal melanoma. Informatica 13(4):455–464

    Google Scholar 

  • Johansson B (2004) Low level operations and learning in computer vision. Phd. Linköping University, Linköping

    Google Scholar 

  • Kasperavičius P (1997) Patentologija. Baltic ECO, Vilnius

    Google Scholar 

  • Keller Y, Averbuch A (2006) Multisensor image registration via implicit similarity. IEEE Trans Pattern Anal Mach Intell 28(5):794–801

    Article  PubMed  Google Scholar 

  • Kim YI, Cho JY (2019) Gel-based proteomics in disease research: is it still valuable? Biochim Biophys Acta (BBA)-Proteins Proteomics 1867(1):9–16

    Article  CAS  Google Scholar 

  • Kirvaitis R (1999) Loginės schemos. Vilnius, Enciklopedija

    Google Scholar 

  • Kohlrausch J, Rohr K, Stiehl H (2005) A new class of elastic body splines for nonrigid registration of medical images. J Math Imaging Vision 23(3):253–280

    Article  Google Scholar 

  • Kostopoulou E, Zacharia E, Maroulis D (2014) An effective approach for detection and segmentation of protein spots on 2-D gel images. IEEE J Biomed Health Inf 18(1):67–76

    Article  Google Scholar 

  • Kostopoulou E, Katsigiannis S, Maroulis D (2015) 2d-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information. Comput Methods Programs Biomed 122(1):26–39

    Article  CAS  PubMed  Google Scholar 

  • Kovesi PD (2020) MATLAB and Octave functions for computer vision and image processing. http://www.peterkovesi.com/matlabfns/

  • Krogh M, Fernandez C, Teilum M, Bengtsson S, James P (2007) A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments. J Proteome Res 6(8):3335–3343

    Article  CAS  PubMed  Google Scholar 

  • Laptik R, Navakauskas D (2005) Application of artificial neural networks for the recognition of car number plates. Elektronika ir elektrotechnika 8(64):27–31. in Lithuanian

    Google Scholar 

  • Laptik R, Navakauskas D (2007) Application of Ant Colony Optimization for image segmentation. Elektronika ir elektrotechnika 8(80):13–18

    Google Scholar 

  • Laptik R, Navakauskas D (2009) MAX-MIN Ant System in image processing. Elektronika ir elektrotechnika 1(89):21–24

    Google Scholar 

  • Lo SL, You T, Lin Q, Joshi SB, Chung MCM, Hew CL (2006) SPLASH: systematic proteomics laboratory analysis and storage hub. Proteomics 6(6):1758–1769

    Article  CAS  PubMed  Google Scholar 

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110

    Article  Google Scholar 

  • Loy G, Zelinsky A (2003) Fast radial symmetry for detecting points of interest. IEEE Trans Pattern Anal Mach Intell 25(8):959–973

    Article  Google Scholar 

  • Luhn S, Berth M, Hecker M, Bernhardt J (2003) Using standard positions and image fusion to create proteome maps from collections of two-dimensional gel electrophoresis images. Proteomics 3(7):1117–1127

    Article  CAS  PubMed  Google Scholar 

  • Mahnke RC, Corzett TH, Mccutchen-Maloney SL, Chromy BA (2006) An integrated proteomic workflow for two-dimensional differential gel electrophoresis and robotic spot picking. J Proteome Res 5(9):2093–2097

    Article  CAS  PubMed  Google Scholar 

  • Marczyk M (2017) Mixture modeling of 2-d gel electrophoresis spots enhances the performance of spot detection. IEEE Trans. Nanobiosci. 16(2):91–99

    Article  Google Scholar 

  • Mateika D, Martavicius R (2008) Large image formation using harris-plessey corner detection algorithm. Elektronika ir elektrotechnika 5(85):21–24

    Google Scholar 

  • Matuzevičius D (2010a) Automatinė dvimatės elektroforezės geliu̧ su mažiausiais geometriniais iškraipymais atranka. Mokslas—Lietuvos Ateitis Elektronika ir Elektrotechnika 2(1):9–13

    Article  Google Scholar 

  • Matuzevičius D (2010b) Dvimatės elektroforezės geliu̧ vaizdu̧ analizė taikant intelektualiuosius metodus. Vilniaus Gedimino Technikos Universitetas, Daktaro Disertacija

    Google Scholar 

  • Matuzevičius D, Navakauskas D (2005) Investigation of segmentation methods for proteomics. Elektronika ir elektrotechnika 7(63):66–70. in Lithuanian

    Google Scholar 

  • Matuzevičius D, Navakauskas D (2008) Feature selection for segmentation of 2-D electrophoresis gel images. In: Proceedings of the 11th International Biennial Baltic Electronics Conference, BEC 2008, Tallinn, Estonia, pp 341–344

    Google Scholar 

  • Matuzevičius D, Navakauskas D (2010) Comparison of distance measures according to suitability for 2D electrophoresis image registration using synthetic image data and SOFN. In: Romaniuk RS, Kulpa KS (eds) Proceedings of SPIE, Photonics applications in astronomy, communications, industry, and high-energy physics experiments, vol 7745. SPIE, Bellingham, p CID 7745 16

    Google Scholar 

  • Matuzevičius D, Serackis A, Navakauskas D (2007) Mathematical models of oversaturated protein spots. Elektronika ir elektrotechnika 1(73):63–68

    Google Scholar 

  • Matuzevicius D, Zurauskas E, Navakauskiene R, Navakauskas D (2008) Improved proteomic characterization of human myocardium and heart conduction system by using computational methods. Biologija 4:283–289

    Article  CAS  Google Scholar 

  • Matuzevičius D, Serackis A, Navakauskas D (2010) Application of K-Means and MLP in the automation of matching of 2DE gel images. In: Lecture notes in computer science: proceedings of the 20th international conference on artificial neural networks, ICANN 2010, vol 1. Springer, Thessaloniki, pp 541–550

    Chapter  Google Scholar 

  • Miller MD, Acey RA, Lee LYT, Edwards AJ (2001) Digital imaging considerations for gel electrophoresis analysis systems. Electrophoresis 22(5):791–800

    Article  CAS  PubMed  Google Scholar 

  • Millioni R, Puricelli L, Sbrignadello S, Iori E, Murphy E, Tessari P (2012) Operator-and software-related post-experimental variability and source of error in 2-DE analysis. Amino Acids 42(5):1583–1590

    Article  CAS  PubMed  Google Scholar 

  • Miura K (2001) Imaging and detection technologies for image analysis in electrophoresis. Electrophoresis 22(5):801–813

    Article  CAS  PubMed  Google Scholar 

  • Miura K (2003) Imaging technologies for the detection of multiple stains in proteomics. Proteomics 3(7):1097–1108

    Article  CAS  PubMed  Google Scholar 

  • Moche M, Albrecht D, Maaß S, Hecker M, Westermeier R, Büttner K (2013) The new horizon in 2D electrophoresis: New technology to increase resolution and sensitivity. Electrophoresis 34(11):1510–1518

    Article  CAS  PubMed  Google Scholar 

  • Moritz B, Meyer HE (2003) Approaches for the quantification of protein concentration ratios. Proteomics 3(11):2208–2220

    Article  CAS  PubMed  Google Scholar 

  • Morris JS, Clark BN, Gutstein HB (2008) Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data. Bioinformatics 24(4):529–536

    Article  CAS  PubMed  Google Scholar 

  • Morris JS, Clark BN, Wei W, Gutstein HB (2009) Evaluating the performance of new approaches to spot quantification and differential expression in 2-dimensional gel electrophoresis studies. J Proteome Res 9(1):595–604

    Article  CAS  Google Scholar 

  • Natale M, Maresca B, Abrescia P, Bucci EM (2011) Image analysis workflow for 2-D electrophoresis gels based on ImageJ. Proteomics Insights 4:37–49

    Article  Google Scholar 

  • Navakauskas D (2005) Grid computing for proteomics. In: Notes of seminar at the lithuanian academy of sciences

    Google Scholar 

  • Navakauskiene R, Treigyte G, Borutinskaite VV, Matuzevicius D, Navakauskas D, Magnusson KE (2012) Alpha-dystrobrevin and its associated proteins in human promyelocytic leukemia cells induced to apoptosis. J Proteomics 75(11):3291–3303. https://doi.org/10.1016/j.jprot.2012.03.041

    Article  CAS  PubMed  Google Scholar 

  • Navakauskiene R, Borutinskaite VV, Treigyte G, Savickiene J, Matuzevicius D, Navakauskas D, Magnusson KE (2014) Epigenetic changes during hematopoietic cell granulocytic differentiation—comparative analysis of primary CD34+cells, KG1 myeloid cells and mature neutrophils. BMC Cell Biol 15:4. https://doi.org/10.1186/1471-2121-15-4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Nhek S, Tessema B, Indahl UG, Martens H, Mosleth EF (2015) 2d electrophoresis image segmentation within a pixel-based framework. Chemom Intell Lab Syst 141:33–46

    Article  CAS  Google Scholar 

  • Nickelson L, Asmontas S, Martavicius R, Engelson V (2006) Microwave pulse propagation inside a 3D heart model. Elektronika ir Elektrotechnika 8(72):5–10

    Google Scholar 

  • Oliveira BM, Coorssen JR, Martins-de Souza D (2014) 2DE: The phoenix of proteomics. J Proteomics 104:140–150

    Article  CAS  PubMed  Google Scholar 

  • Penney GP, Weese J, Little JA, Desmedt P, Hill DLG, Hawkes DJ (1998) A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Trans Med Imaging 17(4):586–595

    Article  CAS  PubMed  Google Scholar 

  • Pivoriūnas A, Surovas A, Borutinskaitė V, Matuzevičius D, Treigytė G, Savickienė J, Tunaitis V, Aldonytė R, Jarmalavičiūtė A, Suriakaitė K, Liutkevičius E, Venalis A, Navakauskas D, Navakauskienė R, Magnusson KE (2010) Proteomic analysis of stromal cells derived from the dental pulp of human exfoliated deciduous teeth. Stem Cells Dev 19(7):1081–1093

    Article  CAS  Google Scholar 

  • Pleisner KP, Hoffmann F, Kriegel K, Wenk C, Wegner S, Sahlstrom A, Oswald H, Alt H, Fleck E (1999) New algorithmic approaches to protein spot detection and pattern matching in two-dimensional electrophoresis gel databases. Electrophoresis 20(4–5):755–765

    Article  Google Scholar 

  • Pomastowski P, Buszewski B (2014) Two-dimensional gel electrophoresis in the light of new developments. TrAC, Trends Anal Chem 53:167–177

    Article  CAS  Google Scholar 

  • Quadroni M, James P (1999) Proteomics and automation. Electrophoresis 20(4–5):664–677

    Article  CAS  PubMed  Google Scholar 

  • Rabilloud T, Chevallet M, Luche S, Lelong C (2010) Two-dimensional gel electrophoresis in proteomics: past, present and future. J Proteomics 73(11):2064–2077

    Article  CAS  PubMed  Google Scholar 

  • Rashwan S, Sarhan A, Faheem MT, Youssef BA (2015) Fuzzy watershed segmentation algorithm: an enhanced algorithm for 2d gel electrophoresis image segmentation. Int J Data Min Bioinf 12(3):275–293

    Article  Google Scholar 

  • Rodriguez A, Fernandez-Lozano C, Dorado J, Rabuñal JR (2014) Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models. Anal Biochem 454:53–59

    Article  CAS  PubMed  Google Scholar 

  • Rogers M, Graham J, Tonge RP (2003) Statistical models of shape for the analysis of protein spots in two-dimensional electrophoresis gel images. Proteomics 3(6):887–896

    Article  CAS  PubMed  Google Scholar 

  • Savelonas MA, Mylona EA, Maroulis D (2012) Unsupervised 2d gel electrophoresis image segmentation based on active contours. Pattern Recognit 45(2):720–731

    Article  Google Scholar 

  • Sengar RS, Upadhyay AK, Singh M, Gadre VM (2016) Analysis of 2d-gel images for detection of protein spots using a novel non-separable wavelet based method. Biomed Signal Process Control 25:62–75

    Article  Google Scholar 

  • Serackis A (2008) Vaizdo rekonstravimo technologijos baltymu̧ pėdsakams parametrizuoti. daktaro disertacija. Vilniaus Gedimino Technikos Universitetas, Vilniaus

    Google Scholar 

  • Serackis A, Navakauskas D (2008) Reconstruction of overlapped protein spots using RBF networks. Elektronika ir Elektrotechnika 1(81):83–88

    Google Scholar 

  • Serackis A, Matuzevičius D, Navakauskas D (2006) Reconstruction of protein spots using DSP modules. In: Proceedings of 29th international conference on fundamentals of electrotechnics and circuit theory, IC-SPETO 2006, vol 2. Gliwice-Ustron, Poland, pp 573–576

    Google Scholar 

  • Serackis A, Matuzevičius D, Navakauskas D (2010) 2DE gel image preprocessing using self-organizing maps. In: Romaniuk RS, Kulpa KS (eds) Proceedings of SPIE Photonics applications in astronomy, communications, industry, and high-energy physics experiments. SPIE, Washington, vol 7745, p CID 7745 1N

    Google Scholar 

  • Simutis R (1997) Exploratory analysis of biochemical processes using hybrid modeling methods. In: Komorowski J, Zytkow J (eds) Principles of data mining and knowledge discovery. Lecture notes in artificial intelligence, vol 1263. Springer, Berlin, pp 200–210

    Chapter  Google Scholar 

  • Smilansky Z (2001) Automatic registration for images of two-dimensional protein gels. Electrophoresis 22(9):1616–1626

    Article  CAS  PubMed  Google Scholar 

  • Sonka M, Hlavac V, Boyle R (2007) Image processing, analysis, and machine vision. Thomson-Engineering, London

    Google Scholar 

  • Thornbury JR (1994) Clinical efficacy of diagnostic-imaging—love it or leave it. Am J Roentgenol 162(1):1–8

    Article  CAS  Google Scholar 

  • Treigys P, Saltenis V, Dzemyda G, Barzdziukas V, Paunksnis A (2008) Automated optic nerve disc parameterization. Informatica 19(3):403–420

    Article  Google Scholar 

  • Treigytė G, Zaikova I, Matuzevičius D, Čeksterytė V, Dabkevičienė G, Kurtinaitienė B, Navakauskienė R (2014) Comparative proteomic analysis of pollen of trifolium pratense, t. alexandrinum and t. repens. Zemdirbyste-Agriculture 101(4):453–460

    Article  Google Scholar 

  • Valledor L, Jorrín J (2011) Back to the basics: maximizing the information obtained by quantitative two dimensional gel electrophoresis analyses by an appropriate experimental design and statistical analyses. J Proteomics 74(1):1–18

    Article  CAS  PubMed  Google Scholar 

  • Vedaldi A, Fulkerson B (2008) VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org/

  • Veeser S, Dunn MJ, Yang GZ (2001) Multiresolution image registration for two-dimensional gel electrophoresis. Proteomics 1(7):856–870

    Article  CAS  PubMed  Google Scholar 

  • Vijayendran C, Burgerneister S, Friehs K, Niehaus K, Flaschel E (2007) 2DBase: 2D-PAGE database of escherichia coli. Biochem Biophys Res Commun 363:822–827

    Article  CAS  PubMed  Google Scholar 

  • Vlahou A (2008) Clinical proteomics: methods and protocols, vol 428. Springer, Berlin

    Book  Google Scholar 

  • Wiener N (1965) Cybernetics: or the control and communication in the animal and the machine, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  • Wu Y, Zhang L (2011) Comparison of two academic software packages for analyzing two-dimensional gel images. J Bioinf Comput Biol 9(06):775–794

    Article  Google Scholar 

  • Zagorchev L, Goshtasby A (2006) A comparative study of transformation functions for nonrigid image registration. IEEE Trans Image Process 15(3):529–538

    Article  PubMed  Google Scholar 

  • Zech H, Echtermeyer C, Wöhlbrand L, Blasius B, Rabus R (2011) Biological versus technical variability in 2-D DIGE experiments with environmental bacteria. Proteomics 11(16):3380–3389

    Article  CAS  PubMed  Google Scholar 

  • Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF (2006) Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res 5(11):2909–2918. https://doi.org/10.1021/pr0600273

    Article  CAS  PubMed  Google Scholar 

  • Zhang L, Wen Q, Mao HP, Luo N, Rong R, Fan JJ, Yu XQ (2013) Developing a reproducible method for the high-resolution separation of peritoneal dialysate proteins on 2-D gels. Protein Expression Purif 89(2):196–202

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Molecular Cell Biology Institute of Biochemistry Life Sciences Center, Vilnius University, Vilnius, Lithuania

    Rūta Navakauskienė & Veronika Borutinskaitė

  2. Department of Electronic Systems Faculty of Electronics, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Dalius Navakauskas & Dalius Matuzevičius

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  1. Rūta Navakauskienė
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  3. Veronika Borutinskaitė
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  4. Dalius Matuzevičius
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Navakauskienė, R., Navakauskas, D., Borutinskaitė, V., Matuzevičius, D. (2021). Computational Methods for Proteome Analysis. In: Epigenetics and Proteomics of Leukemia. Springer, Cham. https://doi.org/10.1007/978-3-030-68708-3_6

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