Computational Tools for Parsimony Phylogenetic Analysis of Omics Data

OMICS A Journal of Integrative Biology

Volumn 19, Issue 8, 2015, Pages 471-477

  Salazar, Jose ^a   Amri, Hakima ^b   Noursi, David ^a   Abu Asab, Mones ^a  

^a NATIONAL EYE INSTITUTE   (United States)

^b GEORGETOWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ARTICLE; BIOINFORMATICS; DATA ANALYSIS SOFTWARE; GENE EXPRESSION; GENOMICS; MASS SPECTROMETRY; METABOLOMICS; NEXT GENERATION SEQUENCING; PARSIMONY ANALYSIS; PHYLOGENY; PRIORITY JOURNAL; PROTEOMICS; ALGORITHM; CLASSIFICATION; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; INFORMATION DISSEMINATION; INFORMATION PROCESSING; INFORMATION RETRIEVAL; MALE; METABOLISM; PATHOLOGY; PROCEDURES; PROSTATE; PROSTATIC NEOPLASMS; SOFTWARE;

ALGORITHMS; DATASETS AS TOPIC; GENE EXPRESSION PROFILING; GENOMICS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; INFORMATION DISSEMINATION; INFORMATION STORAGE AND RETRIEVAL; MALE; METABOLOMICS; PROSTATE; PROSTATIC NEOPLASMS; SOFTWARE;

EID: 84938360711     PISSN: 15362310     EISSN: 15578100     Source Type: Journal    
DOI: 10.1089/omi.2015.0018     Document Type: Article

References (24)

1. Abu-Asab M, Chaouchi M, and Amri H. (2008a). Evolutionary medicine: A meaningful connection between omics, disease, and treatment. Proteomics Clin Appl 2, 122-134.
2. Abu-Asab M, Koithan M, Shaver J, and Amri H. (2012). Analyzing heterogeneous complexity in complementary and alternative medicine research: A systems biology solution via parsimony phylogenetics. Forsch Komplementmed 19 Suppl 1, 42-48.
3. Abu-Asab MS, Abu-Asab N, Loffredo CA, Clarke R, and Amri H. (2013a). Identifying early events of gene expression in breast cancer with systems biology phylogenetics. Cytogenet Genome Res 139, 206-214.
4. Abu-Asab MS, Chaouchi M, Alesci S, et al. (2011). Biomarkers in the age of omics: Time for a systems biology approach. Omics 15, 105-112.
5. Abu-Asab MS, Chaouchi M, and Amri H. (2008b). Phylogenetic modeling of heterogeneous gene-expression microarray data from cancerous specimens. Omics 12, 183-199.
6. Abu-Asab MS, and Deleo J. (2014). Phylogenetic cladograms: Tools for analyzing biomedical data. In: Kasabov, N. (Ed.), Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg, pp. 247-263.
7. Abu-Asab MS, Salazar J, Tuo J, and Chan CC. 2013b. Systems biology profiling of AMD on the basis of gene expression. J Ophthalmol 2013, 453934.
8. Amir El AD, Davis KL, Tadmor MD, et al. (2013). viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31, 545-552.
9. Braun R. (2014). Systems analysis of high-throughput data. Adv Exp Med Biol 844, 153-187.
10. Brim H, Abu-Asab MS, Nouraie M, et al. (2014). An integrative CGH, MSI and candidate genes methylation analysis of colorectal tumors. PloS One 9, e82185.
11. Brim H, Lee E, Abu-Asab MS, et al. (2012). Genomic aberrations in an African American colorectal cancer cohort reveals a MSI-specific profile and chromosome X amplification in male patients. PloS One 7, e40392.
12. Felsenstein J. (1989). PHYLIP: Phylogeny Inference Package (Version 3.2). Cladistics 5, 164-166.
13. Ferreiro E, Baldeiras I, Ferreira IL, et al. (2012). Mitochondrialand endoplasmic reticulum-Associated oxidative stress in Alzheimer's disease: From pathogenesis to biomarkers. Int J Cell Biol 2012, 735206.
14. Fletcher J. (2007). What is heterogeneity and is it important? BMJ 334, 94-96.
15. Glorikian H. (2014). Early detection: The real way to beat cancer. Clin OMICs 15, 14-16.
16. Goloboff PA. (1999). Analyzing large data sets in reasonable times: Solutions for composite optima. Cladistics 15, 415-428.
17. Goloboff PA, and Pol D. (2007). On divide-And-conquer strategies for parsimony analysis of large data sets: Rec-I-DCM3 versus TNT. Syst Biol 56, 485-495.
18. Gorlov IP, Yang JY, Byun J, Logothetis C, Gorlova OY, Do KA, and Amos C. (2014). How to get the most from microarray data: Advice from reverse genomics. BMC Genomics 15, 223.
19. Hiley CT, and Swanton C. (2014). Spatial and temporal cancer evolution: Causes and consequences of tumour diversity. Clin Med 14, s33-37.
20. Joshi A, and Gottgens B. (2011). Maximum parsimony analysis of gene expression profiles permits the reconstruction of developmental cell lineage trees. Dev Biol 353, 440-447.
21. Kolaczkowski B, and Thornton JW. (2004). Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous. Nature 431, 980-984.
22. Rice K, and Warnow T. (1997). Parsimony is hard to beat! In: Jiang T, Lee DT (Eds), Computing and Combinatorics. Springer Berlin Heidelberg, pp. 124-133.
23. West SL, Gartlehner G, Mansfield AJ, et al. (2010). Comparative Effectiveness Review Methods: Clinical Heterogeneity [Internet]. (Agency for Healthcare Research and Quality (US); Available from: http://www.ncbi.nlm.nih.gov/books/NBK53310/toc/, Rockville (MD)). Last access: 5/21/15.
24. Yu YP, Landsittel D, Jing L, et al. (2004). Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. J Clin Oncol 22, 2790-2799.