İnsanda Uzun Kodlanmayan RNA’ların Doku İfade Örüntüleri ve Varsayımsal İşlevlerinin Benzeşme Yayılması Algoritması ile İncelenmesi

Year 2017, Volume: 7 Issue: 2, 249 - 257, 30.06.2017

Gökhan Karakülah

Abstract

Kümeleme yöntemleri, yüksek hacimli gen ifadesi örüntülerinden biyolojik olarak anlamlı bilginin elde
edilmesinde yaygın olarak kullanılmaktadır. Benzeşme yayılması algoritması, veri noktaları arasından örnekler
adı verilen küme merkezlerinin belirlendiği ve bunların etrafında kümelerin oluşturulduğu yeni bir yaklaşımdır.
Bu çalışmada, hastalık, gelişim ve farklılaşma gibi farklı hücresel olaylarda düzenleyici transkriptler olan uzun
kodlanmayan RNA’ların, benzeşme yayılması algoritması ile 16 farklı sağlıklı insan dokusundaki ifade örüntüleri
incelendi. Bununla beraber uzun kodlanmayan RNAların varsayımsal işlevleri, kümeleme yaklaşımı ile bilgisayar
tabanlı olarak tahminlendi ve kapsamlı bir ifade örüntüsü – işlev kataloğu hazırlandı

Keywords

Benzeşme yayılması algoritması, biyoenformatik, kümeleme, uzun kodlanmayan RNA

References

• Andrews S, 2010. FastQC: a quality control tool for high throughput sequence data. from http://www.bioinformatics.babraham.ac.uk/projects/fastqc. (Erişim tarihi: 1 Ağustos, 2016)
• Ashburner M, Ball C A, Blake J A, Botstein D, Butler H, Cherry J M, Davis A P, Dolinski K, Dwight S S, Eppig J T, Harris M A, Hill D P, Issel-Tarver L, Kasarskis A, Lewis S, Matese J C, Richardson J E, Ringwald M, Rubin G M Sherlock G, 2000. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25(1): 25-29.
• Baş Ç, Ikizler-Cinbis N, 2013. Comparison of clustering methods for pose based video summarization. In Signal Processing and Communications Applications Conference (SIU), pp. 1-4. IEEE.
• Bodenhofer U, Kothmeier A, Hochreiter S, 2011. APCluster: an R package for affinity propagation clustering. Bioinformatics, 27(17): 2463-2464.
• Bolger A M, Lohse M, Usadel B, 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15): 2114-2120.
• Brenner S, Johnson M, Bridgham J, Golda G, Lloyd D H, Johnson D, Luo S, McCurdy S, Foy M, Ewan M, Roth R, George D, Eletr S, Albrecht G, Vermaas E, Williams S R, Moon K, Burcham T, Pallas M, DuBridge R B, Kirchner J, Fearon K, Mao J Corcoran K, 2000. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol, 18(6): 630-634.
• Cao H, Amendt B A, 2016. pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data. Biochim Biophys Acta.
• Caso G, de Nardis L, di Benedetto M G, 2015. A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning. Sensors, 15(11): 27692-27720.
• Chaitankar V, Karakülah G, Ratnapriya R, Giuste F O, Brooks M J Swaroop A, 2016. Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research. Prog Retin Eye Res.
• Childs K L, Davidson R M, Buell C R, 2011. Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes. PLoS One, 6(7).
• D'Haeseleer P, 2005. How does gene expression clustering work? Nat Biotechnol, 23(12): 1499-1501.
• Dobin A, Davis C A, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M Gingeras T R, 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1): 15-21.
• Fatica A, Bozzoni I, 2014. Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet, 15(1): 7-21.
• Frey B J, Dueck D, 2007. Clustering by passing messages between data points. Science, 315(5814): 972-976.
• Geisler S, Coller J, 2013. RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol, 14(11): 699-712.
• Gloss B S, Dinger M E, 2016. The specificity of long noncoding RNA expression. Biochim Biophys Acta, 1859(1): 16-22.
• Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T, Durbin R, Eyras E, Gilbert J, Hammond M, Huminiecki L, Kasprzyk A, Lehvaslaiho H, Lijnzaad P, Melsopp C, Mongin E, Pettett R, Pocock M, Potter S, Rust A, Schmidt E, Searle S, Slater G, Smith J, Spooner W, Stabenau A, Stalker J, Stupka E, Ureta-Vidal A, Vastrik I Clamp M, 2002. The Ensembl genome database project. Nucleic Acids Res, 30(1): 38-41.
• Jiang J, Huang J, Wang X R Quan Y H, 2016. Investigating key genes associated with ovarian cancer by integrating affinity propagation clustering and mutual information network analysis. Eur Rev Med Pharmacol Sci, 20(12): 2532-2540.
• Kerr G, Ruskin H J, Crane M Doolan P, 2008. Techniques for clustering gene expression data. Comput Biol Med, 38(3): 283-293.
• Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database C, 2011. The sequence read archive. Nucleic Acids Res, 39(Database issue): D19-21.
• Li K M, Guo L, Li G, Nie J X, Faraco C, Zhao Q, Miller L S Liu T M, 2010. Cortical Surface Based Identification of Brain Networks Using High Spatial Resolution Resting State Fmri Data. 2010 7th Ieee International Symposium on Biomedical Imaging: From Nano to Macro: 656-659.
• Li X, Wang, H X, 2015. Identification of functional networks in resting state fMRI data using adaptive sparse representation and affinity propagation clustering. Frontiers in Neuroscience, 9.
• Meng J, Hao H, Luan Y, 2016. Classifier ensemble selection based on affinity propagation clustering. J Biomed Inform, 60: 234-242.
• Reimand J, Kolde R, Arak M T R, Curl I, 2016. Package ‘gProfileR’.
• Ren Y, Cui Y, Li X, Wang B, Na L, Shi J, Wang L, Qiu L, Zhang K, Liu G Xu Y, 2015. A co-expression network analysis reveals lncRNA abnormalities in peripheral blood in early-onset schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry, 63: 1-5.
• Robinson M D, McCarthy D J, Smyth G K, 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1): 139-140.
• Schena M, Shalon D, Davis R W, Brown P O, 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270(5235): 467-470.
• Sha Y, Phan J H, Wang M D, 2015. Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data. Conf Proc IEEE Eng Med Biol Soc, 2015: 6461-6464.
• Sun C X, Huo H W, Yu Q, Guo H T, Sun Z G, 2015. An Affinity Propagation-Based DNA Motif Discovery Algorithm. Biomed Research International.
• Team R C (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013, ISBN 3-900051-07-0.
• Wong D C J, Sweetman C, Ford C M, 2014. Annotation of gene function in citrus using gene expression information and co-expression networks. Bmc Plant Biology, 14.
• Zhang J, Tuo X G, Yuan Z, Liao W, Chen H F, 2011. Analysis of fMRI Data Using an Integrated Principal Component Analysis and Supervised Affinity Propagation Clustering Approach. Ieee Transactions on Biomedical Engineering, 58(11): 3184-3196.
• Zhang T, Wu R B, 2015. Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking. Sensors, 15(9): 22646-22659.
• Zhao X L, Xu W X, 2015. An Extended Affinity Propagation Clustering Method Based on Different Data Density Types. Computational Intelligence and Neuroscience.