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Tan Z, Zhu J, Stemmer PM, Sun L, Yang Z, Schultz K, Gaffrey MJ, Cesnik AJ, Yi X, Hao X, Shortreed MR, Shi T, Lubman DM. Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line. J Proteome Res 2020; 19:1635-1646. [PMID: 32058723 DOI: 10.1021/acs.jproteome.9b00840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.
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Affiliation(s)
- Zhijing Tan
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Jianhui Zhu
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Stemmer
- Institute of Environmental Health Sciences, Wayne State University, Detroit, Michigan 48202, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zhichang Yang
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kendall Schultz
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew J Gaffrey
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Anthony J Cesnik
- Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Xiaohu Hao
- Shanghai Institutes for Biological Science, Chinese Academy of Science, Shanghai 200031, China
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Tujin Shi
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - David M Lubman
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
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2
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Tan Z, Nie S, McDermott SP, Wicha MS, Lubman DM. Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line. J Proteome Res 2017; 16:842-851. [PMID: 28076950 DOI: 10.1021/acs.jproteome.6b00824] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Cancers are initiated and developed from a small population of stem-like cells termed cancer stem cells (CSCs). There is heterogeneity among this CSC population that leads to multiple subpopulations with their own distinct biological features and protein expression. The protein expression and function may be impacted by amino acid variants that can occur largely due to single nucleotide changes. We have thus performed proteomic analysis of breast CSC subpopulations by mass spectrometry to study the presence of single amino acid variants (SAAVs) and their relation to breast cancer. We have used CSC markers to isolate pure breast CSC subpopulation fractions (ALDH+ and CD44+/CD24- cell populations) and the mature luminal cells (CD49f-EpCAM+) from the MCF-7 breast cancer cell line. By searching the Swiss-CanSAAVs database, 374 unique SAAVs were identified in total, where 27 are cancer-related SAAVs. 135 unique SAAVs were found in the CSC population compared with the mature luminal cells. The distribution of SAAVs detected in MCF-7 cells was compared with those predicted from the Swiss-CanSAAVs database, where we found distinct differences in the numbers of SAAVs detected relative to that expected from the Swiss-CanSAAVs database for several of the amino acids.
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Affiliation(s)
- Zhijing Tan
- Department of Surgery, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Song Nie
- Department of Surgery, University of Michigan , Ann Arbor, Michigan 48109, United States.,Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Sean P McDermott
- Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan , Ann Arbor, Michigan 48109, United States.,Comprehensive Cancer Center, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Max S Wicha
- Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan , Ann Arbor, Michigan 48109, United States.,Comprehensive Cancer Center, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - David M Lubman
- Department of Surgery, University of Michigan , Ann Arbor, Michigan 48109, United States
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Impact of germline and somatic missense variations on drug binding sites. THE PHARMACOGENOMICS JOURNAL 2016; 17:128-136. [PMID: 26810135 PMCID: PMC5380835 DOI: 10.1038/tpj.2015.97] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 11/02/2015] [Accepted: 11/13/2015] [Indexed: 11/10/2022]
Abstract
Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.
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Krasnov GS, Dmitriev AA, Melnikova NV, Zaretsky AR, Nasedkina TV, Zasedatelev AS, Senchenko VN, Kudryavtseva AV. CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms. Nucleic Acids Res 2016; 44:e62. [PMID: 26773058 PMCID: PMC4838350 DOI: 10.1093/nar/gkv1478] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 12/05/2015] [Indexed: 02/07/2023] Open
Abstract
The contribution of different mechanisms to the regulation of gene expression varies for different tissues and tumors. Complementation of predicted mRNA–miRNA and gene–transcription factor (TF) relationships with the results of expression correlation analyses derived for specific tumor types outlines the interactions with functional impact in the current biomaterial. We developed CrossHub software, which enables two-way identification of most possible TF–gene interactions: on the basis of ENCODE ChIP-Seq binding evidence or Jaspar prediction and co-expression according to the data of The Cancer Genome Atlas (TCGA) project, the largest cancer omics resource. Similarly, CrossHub identifies mRNA–miRNA pairs with predicted or validated binding sites (TargetScan, mirSVR, PicTar, DIANA microT, miRTarBase) and strong negative expression correlations. We observed partial consistency between ChIP-Seq or miRNA target predictions and gene–TF/miRNA co-expression, demonstrating a link between these indicators. Additionally, CrossHub expression-methylation correlation analysis can be used to identify hypermethylated CpG sites or regions with the greatest potential impact on gene expression. Thus, CrossHub is capable of outlining molecular portraits of a specific gene and determining the three most common sources of expression regulation: promoter/enhancer methylation, miRNA interference and TF-mediated activation or repression. CrossHub generates formatted Excel workbooks with the detailed results. CrossHub is freely available at https://sourceforge.net/projects/crosshub/.
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Affiliation(s)
- George S Krasnov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow 119121, Russia
| | - Alexey A Dmitriev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Nataliya V Melnikova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Andrew R Zaretsky
- M.M. Shemyakin-Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia
| | - Tatiana V Nasedkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia
| | - Alexander S Zasedatelev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia
| | - Vera N Senchenko
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Anna V Kudryavtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia
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Big Data and Cancer Research. BIG DATA ANALYTICS 2016. [DOI: 10.1007/978-81-322-3628-3_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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K.M. Ip C, Yin J, K.S. Ng P, Lin SY, B. Mills G. Genomic-Glycosylation Aberrations in Tumor Initiation, Progression and Management. AIMS MEDICAL SCIENCE 2016. [DOI: 10.3934/medsci.2016.4.386] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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7
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Nonsynonymous Single-Nucleotide Variations on Some Posttranslational Modifications of Human Proteins and the Association with Diseases. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:124630. [PMID: 26495027 PMCID: PMC4606098 DOI: 10.1155/2015/124630] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 05/12/2015] [Indexed: 01/17/2023]
Abstract
Protein posttranslational modifications (PTMs) play key roles in a variety of protein activities and cellular processes. Different PTMs show distinct impacts on protein functions, and normal protein activities are consequences of all kinds of PTMs working together. With the development of high throughput technologies such as tandem mass spectrometry (MS/MS) and next generation sequencing, more and more nonsynonymous single-nucleotide variations (nsSNVs) that cause variation of amino acids have been identified, some of which result in the damage of PTMs. The damaged PTMs could be the reason of the development of some human diseases. In this study, we elucidated the proteome wide relationship of eight damaged PTMs to human inherited diseases and cancers. Some human inherited diseases or cancers may be the consequences of the interactions of damaged PTMs, rather than the result of single damaged PTM site.
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Wan Q, Dingerdissen H, Fan Y, Gulzar N, Pan Y, Wu TJ, Yan C, Zhang H, Mazumder R. BioXpress: an integrated RNA-seq-derived gene expression database for pan-cancer analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav019. [PMID: 25819073 PMCID: PMC4377087 DOI: 10.1093/database/bav019] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BioXpress is a gene expression and cancer association database in which the expression levels are mapped to genes using RNA-seq data obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, Expression Atlas and publications. The BioXpress database includes expression data from 64 cancer types, 6361 patients and 17 469 genes with 9513 of the genes displaying differential expression between tumor and normal samples. In addition to data directly retrieved from RNA-seq data repositories, manual biocuration of publications supplements the available cancer association annotations in the database. All cancer types are mapped to Disease Ontology terms to facilitate a uniform pan-cancer analysis. The BioXpress database is easily searched using HUGO Gene Nomenclature Committee gene symbol, UniProtKB/RefSeq accession or, alternatively, can be queried by cancer type with specified significance filters. This interface along with availability of pre-computed downloadable files containing differentially expressed genes in multiple cancers enables straightforward retrieval and display of a broad set of cancer-related genes. Database URL:http://hive.biochemistry.gwu.edu/tools/bioxpress
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Affiliation(s)
- Quan Wan
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Hayley Dingerdissen
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Yu Fan
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Naila Gulzar
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Yang Pan
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Tsung-Jung Wu
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Cheng Yan
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Haichen Zhang
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA Department of Biochemistry and Molecular Medicine and McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, USA
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Simonyan V, Mazumder R. High-Performance Integrated Virtual Environment (HIVE) Tools and Applications for Big Data Analysis. Genes (Basel) 2014; 5:957-81. [PMID: 25271953 PMCID: PMC4276921 DOI: 10.3390/genes5040957] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Revised: 09/22/2014] [Accepted: 09/22/2014] [Indexed: 12/30/2022] Open
Abstract
The High-performance Integrated Virtual Environment (HIVE) is a high-throughput cloud-based infrastructure developed for the storage and analysis of genomic and associated biological data. HIVE consists of a web-accessible interface for authorized users to deposit, retrieve, share, annotate, compute and visualize Next-generation Sequencing (NGS) data in a scalable and highly efficient fashion. The platform contains a distributed storage library and a distributed computational powerhouse linked seamlessly. Resources available through the interface include algorithms, tools and applications developed exclusively for the HIVE platform, as well as commonly used external tools adapted to operate within the parallel architecture of the system. HIVE is composed of a flexible infrastructure, which allows for simple implementation of new algorithms and tools. Currently, available HIVE tools include sequence alignment and nucleotide variation profiling tools, metagenomic analyzers, phylogenetic tree-building tools using NGS data, clone discovery algorithms, and recombination analysis algorithms. In addition to tools, HIVE also provides knowledgebases that can be used in conjunction with the tools for NGS sequence and metadata analysis.
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Affiliation(s)
- Vahan Simonyan
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA.
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA.
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Pan Y, Karagiannis K, Zhang H, Dingerdissen H, Shamsaddini A, Wan Q, Simonyan V, Mazumder R. Human germline and pan-cancer variomes and their distinct functional profiles. Nucleic Acids Res 2014; 42:11570-88. [PMID: 25232094 PMCID: PMC4191387 DOI: 10.1093/nar/gku772] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.
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Affiliation(s)
- Yang Pan
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Konstantinos Karagiannis
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Haichen Zhang
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Hayley Dingerdissen
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Amirhossein Shamsaddini
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Quan Wan
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Vahan Simonyan
- Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA
| | - Raja Mazumder
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
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Faison WJ, Rostovtsev A, Castro-Nallar E, Crandall KA, Chumakov K, Simonyan V, Mazumder R. Whole genome single-nucleotide variation profile-based phylogenetic tree building methods for analysis of viral, bacterial and human genomes. Genomics 2014; 104:1-7. [PMID: 24930720 DOI: 10.1016/j.ygeno.2014.06.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Accepted: 06/04/2014] [Indexed: 10/25/2022]
Abstract
UNLABELLED Next-generation sequencing data can be mapped to a reference genome to identify single-nucleotide polymorphisms/variations (SNPs/SNVs; called SNPs hereafter). In theory, SNPs can be compared across several samples and the differences can be used to create phylogenetic trees depicting relatedness among the samples. However, in practice this is difficult because currently there is no stand-alone tool that takes SNP data directly as input and produces phylogenetic trees. In response to this need, PhyloSNP application was created with two analysis methods 1) a quantitative method that creates the presence/absence matrix which can be directly used to generate phylogenetic trees or creates a tree from a shrunk genome alignment (includes additional bases surrounding the SNP position) and 2) a qualitative method that clusters samples based on the frequency of different bases found at a particular position. The algorithms were used to generate trees from Poliovirus, Burkholderia and human cancer genomics NGS datasets. AVAILABILITY PhyloSNP is freely available for download at http://hive.biochemistry.gwu.edu/dna.cgi?cmd=phylosnp.
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Affiliation(s)
- William J Faison
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA.
| | - Alexandre Rostovtsev
- Center for Biologics Evaluation and Research, US Food and Drug Administration, 1451 Rockville Pike, Rockville, MD 20852, USA.
| | - Eduardo Castro-Nallar
- Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA.
| | - Keith A Crandall
- Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA.
| | - Konstantin Chumakov
- Center for Biologics Evaluation and Research, US Food and Drug Administration, 1451 Rockville Pike, Rockville, MD 20852, USA.
| | - Vahan Simonyan
- Center for Biologics Evaluation and Research, US Food and Drug Administration, 1451 Rockville Pike, Rockville, MD 20852, USA.
| | - Raja Mazumder
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA; McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
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