Reproductomics: Exploring the Applications and Advancements of Computational Tools

. 2024 Nov 12 ; 73 (5) : 687-702.

Jazyk angličtina Země Česko Médium print

Typ dokumentu časopisecké články, přehledy

Perzistentní odkaz   https://www.medvik.cz/link/pmid39530905

Over recent decades, advancements in omics technologies, such as proteomics, genomics, epigenomics, metabolomics, transcriptomics, and microbiomics, have significantly enhanced our understanding of the molecular mechanisms underlying various physiological and pathological processes. Nonetheless, the analysis and interpretation of vast omics data concerning reproductive diseases are complicated by the cyclic regulation of hormones and multiple other factors, which, in conjunction with a genetic makeup of an individual, lead to diverse biological responses. Reproductomics investigates the interplay between a hormonal regulation of an individual, environmental factors, genetic predisposition (DNA composition and epigenome), health effects, and resulting biological outcomes. It is a rapidly emerging field that utilizes computational tools to analyze and interpret reproductive data, with the aim of improving reproductive health outcomes. It is time to explore the applications of reproductomics in understanding the molecular mechanisms underlying infertility, identification of potential biomarkers for diagnosis and treatment, and in improving assisted reproductive technologies (ARTs). Reproductomics tools include machine learning algorithms for predicting fertility outcomes, gene editing technologies for correcting genetic abnormalities, and single cell sequencing techniques for analyzing gene expression patterns at the individual cell level. However, there are several challenges, limitations and ethical issues involved with the use of reproductomics, such as the applications of gene editing technologies and their potential impact on future generations are discussed. The review comprehensively covers the applications and advancements of reproductomics, highlighting its potential to improve reproductive health outcomes and deepen our understanding of reproductive molecular mechanisms.

Zobrazit více v PubMed

Dai X, Shen L. Advances and trends in omics technology development. Front Med (Lausanne) 2022;9:911861. doi: 10.3389/fmed.2022.911861. PubMed DOI PMC

Miroslava R, Katarína Š, Ivana Š, Peter U. Omics applications in reproductive medicine. In: BARH D, editor. Biotechnology in Healthcare Volume 1. Academic Press; Elsevier, London: 2022. pp. 103–123. DOI

Chicco D. Gene expression from GEO: An R package to facilitate data reading from Gene Expression Omnibus (GEO) Methods Mol Biol. 2022;2401:187–194. doi: 10.1007/978-1-0716-1839-4_12. PubMed DOI

Baker M. Gene data to hit milestone. Nature. 2012;487:282–283. doi: 10.1038/487282a. PubMed DOI

Tapia A, Vilos C, Marín JC, Croxatto HB, Devoto L. Bioinformatic detection of E47, E2F1 and SREBP1 transcription factors as potential regulators of genes associated to acquisition of endometrial receptivity. Reprod Biol Endocrinol. 2011;9:1–14. doi: 10.1186/1477-7827-9-14. PubMed DOI PMC

Zhang D, Sun C, Ma C, Dai H, Zhang W. Data mining of spatial-temporal expression of genes in the human endometrium during the window of implantation. Reprod Sci. 2012;19:1085–1098. doi: 10.1177/1933719112442248. PubMed DOI

Bhagwat SR, Chandrashekar DS, Kakar R, Davuluri S, Bajpai AK, Nayak S, Bhutada S, Acharya K, Sachdeva G. Endometrial receptivity: a revisit to functional genomics studies on human endometrium and creation of HGEx-ERdb. PLoS One. 2013;8:e58419. doi: 10.1371/journal.pone.0058419. PubMed DOI PMC

Talbi S, Hamilton A, Vo K, Tulac S, Overgaard MT, Dosiou C, Le Shay N, Nezhat CN, Kempson R, Lessey BA, Nayak NR, Giudice LC. Molecular phenotyping of human endometrium distinguishes menstrual cycle phases and underlying biological processes in normo-ovulatory women. Endocrinol. 2006;147:1097–1121. doi: 10.1210/en.2005-1076. PubMed DOI

Burney RO, Talbi S, Hamilton AE, Vo KC, Nyegaard M, Nezhat CR, Lessey BA, Giudice LC. Gene expression analysis of endometrium reveals progesterone resistance and candidate susceptibility genes in women with endometriosis. Endocrinol. 2007;148:3814–3826. doi: 10.1210/en.2006-1692. PubMed DOI

Hever A, Roth RB, Hevezi P, Marin ME, Acosta JA, Acosta H, Rojas J, et al. Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator. Proc Natl Acad Sci U S A. 2007;104:12451–12456. doi: 10.1073/pnas.0703451104. PubMed DOI PMC

Altmäe S, Esteban FJ, Stavreus-Evers A, Simon C, Giudice L, Lessey BA, Horcajadas JA, et al. Guidelines for the design, analysis and interpretation of ‘omics’ data: focus on human endometrium. Hum Reprod Update. 2014;20:12–28. doi: 10.1093/humupd/dmt048. PubMed DOI PMC

Wang Y-F, Chang M-Y, Chiang R-D, Hwang L-J, Lee C-M, Wang Y-H. Mining medical data: a case study of endometriosis. J Med Sys. 2013;37:1–7. doi: 10.1007/s10916-012-9899-y. PubMed DOI

Mathew D, Drury J, Valentijn A, Vasieva O, Hapangama D. In silico, in vitro and in vivo analysis identifies a potential role for steroid hormone regulation of FOXD3 in endometriosis-associated genes. Hum Reprod. 2016;31:345–354. doi: 10.1093/humrep/dev307. PubMed DOI

Liu J-L, Zhao M. A PubMed-wide study of endometriosis. Genomics. 2016;108:151–157. doi: 10.1016/j.ygeno.2016.10.003. PubMed DOI

Vincent ZL, Farquhar CM, Mitchell MD, Ponnampalam AP.Expression and regulation of DNA methyltransferases in human endometrium Fertil Steril 2011951522–5..e110.1016/j.fertnstert.2010.09.030 PubMed DOI

Caplakova V, Babusikova E, Blahovcova E, Balharek T, Zelieskova M, Hatok J. DNA methylation machinery in the endometrium and endometrial cancer. Anticancer Res. 2016;36:4407–4420. doi: 10.21873/anticanres.10984. PubMed DOI

Houshdaran S, Zelenko Z, Irwin JC, Giudice LC. Human endometrial DNA methylome is cycle-dependent and is associated with gene expression regulation. Mol Endocrinol. 2014;28:1118–1135. doi: 10.1210/me.2013-1340. PubMed DOI PMC

Saare M, Modhukur V, Suhorutshenko M, Rajashekar B, Rekker K, Sõritsa D, Karro H, et al. The influence of menstrual cycle and endometriosis on endometrial methylome. Clin Epigenetics. 2016;8:2. doi: 10.1186/s13148-015-0168-z. PubMed DOI PMC

Kukushkina V, Modhukur V, Suhorutšenko M, Peters M, Mägi R, Rahmioglu N, Velthut-Meikas A, Altmäe S, Esteban FJ, Vilo J, Zondervan K, Salumets A, Laisk-Podar T. DNA methylation changes in endometrium and correlation with gene expression during the transition from pre-receptive to receptive phase. Sci Rep. 2017;7:3916. doi: 10.1038/s41598-017-03682-0. PubMed DOI PMC

Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15:R37. doi: 10.1186/gb-2014-15-2-r37. PubMed DOI PMC

Wan J, Oliver VF, Wang G, Zhu H, Zack DJ, Merbs SL, Qian J. Characterization of tissue-specific differential DNA methylation suggests distinct modes of positive and negative gene expression regulation. BMC Genomics. 2015;16:49. doi: 10.1186/s12864-015-1271-4. PubMed DOI PMC

Bunkar N, Pathak N, Lohiya NK, Mishra PK. Epigenetics: A key paradigm in reproductive health. Clin Exp Reprod Med. 2016;43:59–81. doi: 10.5653/cerm.2016.43.2.59. PubMed DOI PMC

Walker E, Hernandez AV, Kattan MW. Meta-analysis: Its strengths and limitations. Cleveland Clin J Med. 2008;75:431–439. doi: 10.3949/ccjm.75.6.431. PubMed DOI

Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 2012;28:573–580. doi: 10.1093/bioinformatics/btr709. PubMed DOI PMC

Altmäe S, Koel M, Võsa U, Adler P, Suhorutšenko M, Laisk-Podar T, Kukushkina V, et al. Meta-signature of human endometrial receptivity: a meta-analysis and validation study of transcriptomic biomarkers. Sci Rep. 2017;7:10077. doi: 10.1038/s41598-017-10098-3. PubMed DOI PMC

Horcajadas J, Pellicer A, Simon C. Wide genomic analysis of human endometrial receptivity: new times, new opportunities. Hum Reprod Update. 2007;13:77–86. doi: 10.1093/humupd/dml046. PubMed DOI

Tseng L-H, Chen I, Chen M-Y, Yan H, Wang C-N, Lee C-L. Genome-based expression profiling as a single standardized microarray platform for the diagnosis of endometrial disorder: an array of 126-gene model. Fertil Steril. 2010;94:114–119. doi: 10.1016/j.fertnstert.2009.01.130. PubMed DOI

Rahmioglu N, Nyholt DR, Morris AP, Missmer SA, Montgomery GW, Zondervan KT. Genetic variants underlying risk of endometriosis: insights from meta-analysis of eight genome-wide association and replication datasets. Hum Reprod Update. 2014;20:702–716. doi: 10.1093/humupd/dmu015. PubMed DOI PMC

Bagheri M, Khansarinejad B, Mondanizadeh M, Azimi M, Alavi S. miRNAs related in signaling pathways of women’s reproductive diseases: an overview. Mol Biol Rep. 2024;51:414. doi: 10.1007/s11033-024-09357-0. PubMed DOI

Zhou Y, Li Q, You S, Jiang H, Jiang L, He F, Hu L. Efficacy of mesenchymal stem cell-derived extracellular vesicles in the animal model of female reproductive diseases: A meta-analysis. Stem Cell Rev Rep. 2023;19:2299–2310. doi: 10.1007/s12015-023-10576-4. PubMed DOI

Robinson SW, Fernandes M, Husi H. Current advances in systems and integrative biology. Comput Struct Biotechnol J. 2014;11:35–46. doi: 10.1016/j.csbj.2014.08.007. PubMed DOI PMC

Chervitz SA, Deutsch EW, Field D, Parkinson H, Quackenbush J, Rocca-Serra P, Sansone SA, et al. Data standards for omics data: the basis of data sharing and reuse. Methods Mol Biol. 2011;719:31–69. doi: 10.1007/978-1-61779-027-0_2. PubMed DOI PMC

Altmäe S, Reimand J, Hovatta O, Zhang P, Kere J, Laisk T, Saare M, et al. Research resource: interactome of human embryo implantation: identification of gene expression pathways, regulation, and integrated regulatory networks. Mol Endocrinol. 2012;26:203–217. doi: 10.1210/me.2011-1196. PubMed DOI PMC

Gracie S, Pennell C, Ekman-Ordeberg G, Lye S, McManaman J, Williams S, Palmer L, et al. An integrated systems biology approach to the study of preterm birth using”-omic” technology-a guideline for research. BMC Pregnancy Childbirth. 2011;11:71. doi: 10.1186/1471-2393-11-71. PubMed DOI PMC

Mayhew T. Morphomics: An integral part of systems biology of the human placenta. Placenta. 2015;36:329–340. doi: 10.1016/j.placenta.2015.01.001. PubMed DOI

Jumeau F, Chalmel F, Fernandez-Gomez F-J, Carpentier C, Obriot H, Tardivel M, Caillet-Boudin ML, et al. Defining the human sperm microtubulome: an integrated genomics approach. Biol Reprod. 2017;96:93–106. doi: 10.1095/biolreprod.116.143479. PubMed DOI

Ghosh D, Sengupta J.A systems biology approach to elucidate the process of blastocyst implantation Indian J Physiol Pharmacol 20105441–50.. PubMed

Diaz-Beltran L, Cano C, Wall DP, Esteban FJ. Systems biology as a comparative approach to understand complex gene expression in neurological diseases. Behav Sci. 2013;3:253–272. doi: 10.3390/bs3020253. PubMed DOI PMC

Kyrgiou M, Pouliakis A, Panayiotides JG, Margari N, Bountris P, Valasoulis G, Paraskevaidi M, et al. Personalised management of women with cervical abnormalities using a clinical decision support scoring system. Gynecol Oncol. 2016;141:29–35. doi: 10.1016/j.ygyno.2015.12.032. PubMed DOI

Seli E, Robert C, Sirard M-A. OMICS in assisted reproduction: possibilities and pitfalls. Mol Hum Reprod. 2010;16:513–530. doi: 10.1093/molehr/gaq041. PubMed DOI

Haouzi D, Dechaud H, Assou S, Monzo C, De Vos J, Hamamah S. Transcriptome analysis reveals dialogues between human trophectoderm and endometrial cells during the implantation period. Hum Reprod. 2011;26:1440–1449. doi: 10.1093/humrep/der075. PubMed DOI

Aghajanova L, Shen S, Rojas AM, Fisher SJ, Irwin JC, Giudice LC. Comparative transcriptome analysis of human trophectoderm and embryonic stem cell-derived trophoblasts reveal key participants in early implantation. Biol Reprod. 2012;86:1–21. doi: 10.1095/biolreprod.111.092775. PubMed DOI

Leung A, Bader GD, Reimand J. HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery. Bioinformatics. 2014;30:2230–2232. doi: 10.1093/bioinformatics/btu172. PubMed DOI PMC

Silva JV, Yoon S, Domingues S, Guimarães S, Goltsev AV, da Cruz E, Silva EF, Mendes JF, et al. Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction. BMC Bioinformatics. 2015;16:12. doi: 10.1186/s12859-014-0432-9. PubMed DOI PMC

Li MJ, Wang P, Liu X, Lim EL, Wang Z, Yeager M, Wong MP, et al. GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2012;40:D1047–D1054. doi: 10.1093/nar/gkr1182. PubMed DOI PMC

Beck T, Hastings RK, Gollapudi S, Free RC, Brookes AJ. GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. European J Hum Genet. 2014;22:949–952. doi: 10.1038/ejhg.2013.274. PubMed DOI PMC

Song Q, Decato B, Hong EE, Zhou M, Fang F, Qu J, Garvin T, et al. A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLoS One. 2013;8:e81148. doi: 10.1371/journal.pone.0081148. PubMed DOI PMC

Xiong Y, Wei Y, Gu Y, Zhang S, Lyu J, Zhang B, Chen C, et al. DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database. Nucleic Acids Res. 2017;45:D888–D895. doi: 10.1093/nar/gkw1123. PubMed DOI PMC

Griffiths-Jones S, Saini HK, Van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2007;36:D154–D158. doi: 10.1093/nar/gkm952. PubMed DOI PMC

Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008;36:D149–D153. doi: 10.1093/nar/gkm995. PubMed DOI PMC

Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–210. doi: 10.1093/nar/30.1.207. PubMed DOI PMC

Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, Holloway E, et al. ArrayExpress-a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 2007;35:D747–D750. doi: 10.1093/nar/gkl995. PubMed DOI PMC

Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, Chen S, et al. The PeptideAtlas project. Nucleic Acids Res. 2006;34:D655–D658. doi: 10.1093/nar/gkj040. PubMed DOI PMC

Vizcaíno JA, Csordas A, Del-Toro N, Dianes JA, Griss J, Lavidas I, Mayer G, et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 2015;44:D447–D456. doi: 10.1093/nar/gkv1145. PubMed DOI PMC

Nanjappa V, Thomas JK, Marimuthu A, Muthusamy B, Radhakrishnan A, Sharma R, Ahmad Khan A, et al. Plasma Proteome Database as a resource for proteomics research: 2014 update. Nucleic Acids Res. 2014;42:D959–D965. doi: 10.1093/nar/gkt1251. PubMed DOI PMC

Kim M-S, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, Madugundu AK, et al. A draft map of the human proteome. Nature. 2014;509:575–581. doi: 10.1038/nature13302. PubMed DOI PMC

Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, Ziegler E, et al. Mass-spectrometry-based draft of the human proteome. Nature. 2014;509:582–587. doi: 10.1038/nature13319. PubMed DOI

Yu W, Clyne M, Khoury MJ, Gwinn M. Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics. 2010;26:145–146. doi: 10.1093/bioinformatics/btp618. PubMed DOI PMC

Tarín JJ, García-Pérez MA, Hamatani T, Cano A. Infertility etiologies are genetically and clinically linked with other diseases in single meta-diseases. Reprod Biol Endocrinol. 2015;13:31. doi: 10.1186/s12958-015-0029-9. PubMed DOI PMC

Henry VJ, Bandrowski AE, Pepin A-S, Gonzalez BJ, Desfeux A. OMICtools: an informative directory for multi-omic data analysis. Database (Oxford) 2014;2014:bau069. doi: 10.1093/database/bau069. PubMed DOI PMC

Hua J, Xu B, Yang Y, Ban R, Iqbal F, Cooke HJ, Zhang Y, Shi Q. Follicle Online: an integrated database of follicle assembly, development and ovulation. Database (Oxford) 2015;2015:bav036. doi: 10.1093/database/bav036. PubMed DOI PMC

Zhang Y, Zhong L, Xu B, Yang Y, Ban R, Zhu J, Cooke HJ, et al. SpermatogenesisOnline 1.0: a resource for spermatogenesis based on manual literature curation and genome-wide data mining. Nucleic Acids Res. 2013;41:D1055–D1062. doi: 10.1093/nar/gks1186. PubMed DOI PMC

Luk AC-S, Gao H, Xiao S, Liao J, Wang D, Tu J, Rennert OM, et al. GermlncRNA: a unique catalogue of long non-coding RNAs and associated regulations in male germ cell development. Database (Oxford) 2015;2015:bav044. doi: 10.1093/database/bav044. PubMed DOI PMC

Darde TA, Sallou O, Becker E, Evrard B, Monjeaud C, Le Bras Y, Jégou B, et al. The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community. Nucleic Acids Res. 2015;43:W109–W116. doi: 10.1093/nar/gkv345. PubMed DOI PMC

Bai W, Yang W, Wang W, Wang Y, Liu C, Jiang Q, Hua J, Liao M. GED: a manually curated comprehensive resource for epigenetic modification of gametogenesis. Brief Bioinform. 2017;18:98–104. doi: 10.1093/bib/bbw007. PubMed DOI

Leo CP, Vitt UA, Hsueh AJ. The Ovarian Kaleidoscope database: an online resource for the ovarian research community. Endocrinology. 2000;141:3052–3054. doi: 10.1210/endo.141.9.7679. PubMed DOI

Ben-Shlomo I, Vitt UA, Hsueh AJ. Perspective: the Ovarian Kaleidoscope database-II. Functional genomic analysis of an organ-specific database. Endocrinol. 2002;143:2041–2044. doi: 10.1210/endo.143.6.8851. PubMed DOI

Hsueh AJ, Rauch R. Ovarian Kaleidoscope database: ten years and beyond. Biol Reprod. 2012;86:192. doi: 10.1095/biolreprod.112.099127. PubMed DOI PMC

Kim M, Cooper BA, Venkat R, Phillips JB, Eidem HR, Hirbo J, Nutakki S, et al. GEneSTATION 1.0: a synthetic resource of diverse evolutionary and functional genomic data for studying the evolution of pregnancy-associated tissues and phenotypes. Nucleic Acids Res. 2016;44:D908–D916. doi: 10.1093/nar/gkv1137. PubMed DOI PMC

Uzun A, Triche EW, Schuster J, Dewan AT, Padbury JF. dbPEC: a comprehensive literature-based database for preeclampsia related genes and phenotypes. Database (Oxford) 2016;2016:baw006. doi: 10.1093/database/baw006. PubMed DOI PMC

Uzun A, Laliberte A, Parker J, Andrew C, Winterrowd E, Sharma S, Istrail S, Padbury JF. dbPTB: a database for preterm birth. Database (Oxford) 2012;2012:bar069. doi: 10.1093/database/bar069. PubMed DOI PMC

Saeed AI, Bhagabati NK, Braisted JC, Liang W, Sharov V, Howe EA, Li J, et al. TM4 microarray software suite. Methods Enzymol. 2006;411:134–193. doi: 10.1016/S0076-6879(06)11009-5. PubMed DOI

Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M, Scheinin I, Koski M, et al. Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics. 2011;12:507. doi: 10.1186/1471-2164-12-507. PubMed DOI PMC

Hilker R, Stadermann KB, Doppmeier D, Kalinowski J, Stoye J, Straube J, Winnebald J, Goesmann A. ReadXplorer-visualization and analysis of mapped sequences. Bioinformatics. 2014;30:2247–2254. doi: 10.1093/bioinformatics/btu205. PubMed DOI PMC

Reimers M, Carey VJ. Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol. 2006;411:119–134. doi: 10.1016/S0076-6879(06)11008-3. PubMed DOI

Zhang Y, Szustakowski J, Schinke M. Bioinformatics analysis of microarray data. Methods Mol Biol. 2009;573:259–284. doi: 10.1007/978-1-60761-247-6_15. PubMed DOI

Ekmekci B, McAnany CE, Mura C. An introduction to programming for bioscientists: a Python-based primer. PLoS Comput Biol. 2016;12:e1004867. doi: 10.1371/journal.pcbi.1004867. PubMed DOI PMC

Rausch TK, Schillert A, Ziegler A, Lüking A, Zucht H-D, Schulz-Knappe P. Comparison of pre-processing methods for multiplex bead-based immunoassays. BMC Genomics. 2016;17:601. doi: 10.1186/s12864-016-2888-7. PubMed DOI PMC

Esteban F, Cano C, De la Haza I, Cano-Ortiz A, Vélez de Mendizábal N, Goñi J, Horcajadas JA.Análisis bioinformático de datos: aplicación en microarrays CMR 20081487–96..

Sui Y, Zhao X, Speed TP, Wu Z. Background adjustment for DNA microarrays using a database of microarray experiments. J Comput Biol. 2009;16:1501–1515. doi: 10.1089/cmb.2009.0063. PubMed DOI PMC

Mirroshandel SA, Ghasemian F, Monji-Azad S. Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment. Comput Methods Programs Biomed. 2016;137:215–229. doi: 10.1016/j.cmpb.2016.09.013. PubMed DOI

Goncalves A, Tikhonov A, Brazma A, Kapushesky M. A pipeline for RNA-seq data processing and quality assessment. Bioinformatics. 2011;27:867–869. doi: 10.1093/bioinformatics/btr012. PubMed DOI PMC

Law CW, Alhamdoosh M, Su S, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1–2-3 with limma, Glimma and edgeR. F1000Res. 2016;5:ISCB Comm J-1408. doi: 10.12688/f1000research.9005.2. PubMed DOI PMC

McCarthy DJ, Campbell KR, Lun AT, Wills QF. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017;33:1179–1186. doi: 10.1093/bioinformatics/btw777. PubMed DOI PMC

Cazaly E, Thomson R, Marthick JR, Holloway AF, Charlesworth J, Dickinson JL. Comparison of pre-processing methodologies for Illumina 450k methylation array data in familial analyses. Clin Epigenetics. 2016;8:75. doi: 10.1186/s13148-016-0241-2. PubMed DOI PMC

Cruz-Marcelo A, Guerra R, Vannucci M, Li Y, Lau CC, Man T-K. Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics. 2008;24:2129–2136. doi: 10.1093/bioinformatics/btn398. PubMed DOI PMC

Egea RR, Puchalt NG, Escrivá MM, Varghese AC. OMICS: current and future perspectives in reproductive medicine and technology. J Hum Reprod Sci. 2014;7:73–92. doi: 10.4103/0974-1208.138857. PubMed DOI PMC

Silvestri E, Lombardi A, de Lange P, Glinni D, Senese R, Cioffi F, Lanni A, et al. Studies of complex biological systems with applications to molecular medicine: the need to integrate transcriptomic and proteomic approaches. J Biomed Biotechnol. 2011:810242. doi: 10.1155/2011/810242. PubMed DOI PMC

Li P, Piao Y, Shon HS, Ryu KH. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data. BMC Bioinformatics. 2015;16:347. doi: 10.1186/s12859-015-0778-7. PubMed DOI PMC

Yang S, Mercante DE, Zhang K, Fang Z. An integrated approach for RNA-seq data normalization. Cancer Inform. 2016;15:129–141. doi: 10.4137/CIN.S39781. PubMed DOI PMC

Borgaonkar SP, Hocker H, Shin H, Markey MK. Comparison of normalization methods for the identification of biomarkers using MALDI-TOF and SELDI-TOF mass spectra. OMICS. 2010;14:115–126. doi: 10.1089/omi.2009.0082. PubMed DOI

Chawade A, Alexandersson E, Levander F. Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. J Proteome Res. 2014;13:3114–3120. doi: 10.1021/pr401264n. PubMed DOI PMC

Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, Liquet B, Vermeulen RC. Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers. Environ Mol Mutagen. 2013;54:542–557. doi: 10.1002/em.21797. PubMed DOI

Wagner F. GO-PCA: An unsupervised method to explore gene expression data using prior knowledge. PLoS One. 2015;10:e0143196. doi: 10.1371/journal.pone.0143196. PubMed DOI PMC

Jiang D, Tang C, Zhang A. Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng. 2004;16:1370–1386. doi: 10.1109/TKDE.2004.68. DOI

Altmäe S, Tamm-Rosenstein K, Esteban FJ, Simm J, Kolberg L, Peterson H, Metsis M, et al. Endometrial transcriptome analysis indicates superiority of natural over artificial cycles in recurrent implantation failure patients undergoing frozen embryo transfer. Reprod Biomed Online. 2016;32:597–613. doi: 10.1016/j.rbmo.2016.03.004. PubMed DOI

Hatfield GW, Hung Sp, Baldi P. Differential analysis of DNA microarray gene expression data. Mol Microbiol. 2003;47:871–877. doi: 10.1046/j.1365-2958.2003.03298.x. PubMed DOI

Guo Y, Graber A, McBurney RN, Balasubramanian R. Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms. BMC Bioinformatics. 2010;11:447. doi: 10.1186/1471-2105-11-447. PubMed DOI PMC

Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics. 2015;8:33. doi: 10.1186/s12920-015-0108-y. PubMed DOI PMC

Franco D, Bonet F, Hernandez-Torres F, Lozano-Velasco E, Esteban FJ, Aranega AE. Analysis of microRNA microarrays in cardiogenesis. Methods Mol Biol. 2016;1375:207–221. doi: 10.1007/7651_2015_247. PubMed DOI

Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Wadi L, et al. Pathway enrichment analysis and visualization of omics data using g: Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc. 2019;14:482–517. doi: 10.1038/s41596-018-0103-9. PubMed DOI PMC

Paczkowska M, Barenboim J, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, et al. Integrative pathway enrichment analysis of multivariate omics data. Nat Commun. 2020;11:735. doi: 10.1038/s41467-019-13983-9. PubMed DOI PMC

Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. PubMed DOI

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–29. doi: 10.1038/75556. PubMed DOI PMC

Reimand J, Arak T, Vilo J. g: Profiler-a web server for functional interpretation of gene lists (2011 update) Nucleic Acids Res. 2011;39:W307–W315. doi: 10.1093/nar/gkr378. PubMed DOI PMC

Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma’ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128. doi: 10.1186/1471-2105-14-128. PubMed DOI PMC

Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6:e21800. doi: 10.1371/journal.pone.0021800. PubMed DOI PMC

Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio-the analysis and navigation of molecular networks. Bioinformatics. 2003;19:2155–2157. doi: 10.1093/bioinformatics/btg290. PubMed DOI

Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11. doi: 10.1186/1752-0509-8-S4-S11. PubMed DOI PMC

Santiago JA, Bottero V, Potashkin JA. Dissecting the molecular mechanisms of neurodegenerative diseases through network biology. Frontiers Aging Neurosci. 2017;9:166. doi: 10.3389/fnagi.2017.00166. PubMed DOI PMC

Gehlenborg N, O’donoghue SI, Baliga NS, Goesmann A, Hibbs MA, Kitano H, Kohlbacher O, et al. Visualization of omics data for systems biology. Nat Methods. 2010;7(3 Suppl):S56–S68. doi: 10.1038/nmeth.1436. PubMed DOI

Paquette J, Tokuyasu T. EGAN: exploratory gene association networks. Bioinformatics. 2010;26:285–286. doi: 10.1093/bioinformatics/btp656. PubMed DOI PMC

Hayrabedyan S, Todorova K, Jabeen A, Metodieva G, Toshkov S, Metodiev MV, Mincheff M, Fernández N. Sertoli cells have a functional NALP3 inflammasome that can modulate autophagy and cytokine production. Sci Rep. 2016;6:18896. doi: 10.1038/srep18896. PubMed DOI PMC

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–D452. doi: 10.1093/nar/gku1003. PubMed DOI PMC

Horcajadas JA, Mínguez P, Dopazo J, Esteban FJ, Domínguez F, Giudice LC, Pellicer A, Simón C. Controlled ovarian stimulation induces a functional genomic delay of the endometrium with potential clinical implications. J Clin Endocrinol Metab. 2008;93:4500–4510. doi: 10.1210/jc.2008-0588. PubMed DOI

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–2504. doi: 10.1101/gr.1239303. PubMed DOI PMC

Liu H, Zeng L, Yang K, Zhang G. A network pharmacology approach to explore the pharmacological mechanism of xiaoyao powder on anovulatory infertility. Evid Based Complement Alternat Med. 2016;2016:2960372. doi: 10.1155/2016/2960372. PubMed DOI PMC

Sabetian S, Shamsir MS. Systematic analysis of protein interaction network associated with azoospermia. Int J Mol Sci. 2016;17:1857. doi: 10.3390/ijms17111857. PubMed DOI PMC

Biswas N, Chakrabarti S. Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. Front Oncol. 2020;10:588221. doi: 10.3389/fonc.2020.588221. PubMed DOI PMC

Frank E, Hall MA, Witten IH. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques. Fourth Edition. Morgan Kaufmann, The University of Waikato; New Zealand: 2016. The WEKA Workbench; p. 128.

Hung F-H, Chiu H-W. Cancer subtype prediction from a pathway-level perspective by using a support vector machine based on integrated gene expression and protein network. Comput Methods Programs Biomed. 2017;141:27–34. doi: 10.1016/j.cmpb.2017.01.006. PubMed DOI

Liu Q, Gan M, Jiang R. A sequence-based method to predict the impact of regulatory variants using random forest. BMC Syst Biol. 2017;11:7. doi: 10.1186/s12918-017-0389-1. PubMed DOI PMC

Way GP, Allaway RJ, Bouley SJ, Fadul CE, Sanchez Y, Greene CS. A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma. BMC Genomics. 2017;18:127. doi: 10.1186/s12864-017-3519-7. PubMed DOI PMC

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...