The Georgia Cancer Center's Bioinformatics Shared Resource provides expertise in integrative computational-based analysis solutions to basic, clinical, and translational research applications.

Bioinformatics support ranges in scope from simple consultations to more in-depth collaborations. We require the participation of the investigator during the course of our data analysis because we believe that input into the biological parameters are tantamount to success of the analysis.

Campus users have access to several advanced computing servers owned by the Georgia Cancer Center, including a High Performance Computing Server (HPC) that has 544 total compute cores and an aggregated memory of 2.9TB.  The system is composed of 15 PowerEdge R430 1U systems (128 GB RAM each node), 1 PowerEdge R830 (high memory 1024 GB RAM node), and a high-speed 40GbE interconnect for intraserver communication.  The HPC also houses 652 TB RAW storage capacity known as Qumulo, allowing the functionalities of effective management and maintenance as well as highly efficient analysis of large data sets, and is committed to the Bioinformatics Shared Resource. Training or a knowledge of Linux is required to use the HPC server.

iLab

Booking of server time is done through ilab. iLab allows researchers, PIs, financial managers, and core managers to ensure that they are using valid payment information (Chart Field Combination or CFC) at each step of the request and billing process for core facilities. Financial managers assign CFCs to lab members who will order services from AU cores. Researchers can order services with valid CFC, and core managers can bill for these services knowing that they are using valid CFCs.


Contact Us

Bioinformatics Shared Resource

Health Sciences Campus

Georgia Cancer Center - M. Bert Storey Research Building

1410 Laney Walker Boulevard, Augusta, GA

Sam Chang, PhD
Director

CN-2152

(706) 446-5528

chchang@augusta.edu

 

Credentials

Sam Chang, PhDSam Chang, PhD, director of the Bioinformatics Shared Resource, is a computational genomic data scientist specializing in NEXT-GEN SEQUENCING analysis. He received his certifications in Biostatistics, Bioinformatics, and Statistical Machine Learning, as well as has received Computational Programming Course Certificates as follows:

  • R Programming and Command-Line Tools on Linux Clusters, University of California, Riverside
  • Bisulfite-Sequencing Data Analysis, University of California, Los Angeles
  • Statistical Machine Learning, University of Washington
  • Statistical Genetics and Programming, University of California, Los Angeles & Stanford University
  • Applied Languages: R, SAS, Python and Unix Command Lines

Services and Fees

Click Here for Fee Listing

Quality Assessment

Assess the quality of sequencing for various kinds of metrics

Differential Methylation

  • Perform statistical tests
  • Generate a table for methylation change, p-values, and q-values
  • Generate figures for differential methylation analysis
  • Annotate differentially methylated regions
  • Prepare tracks for the IGV genome browser

Read Mapping

  • Align sequence reads to reference sequences
  • Summarize mapping results
  • Generate BAM, and BigWig files for the IGV genome browser

Enrichment Identification

  • Identify enriched regions (peaks) using statistical models
  • Generate a table for enriched regions
  • Generate figures for quality control of peak calling
  • Generate tag density plots for genomic features
  • Prepare tracks for the IGV genome browser

Sequence Variants

  • Identify sequence variants
    Generate VCF files
    Annotate sequence variants
    Compare to known databases
    Select high quality of variants by user’s criteria
    Prepare tracks for the IGV genome browser

 

Differential Enrichment

  • Perform statistical tests
  • Generate a table for fold change, p-values, and q-values
  • Generate figures for differential enrichment analysis
  • Annotate differentially enriched regions
  • Prepare tracks for the UCSC genome browser

Structure Variants

  • Identify structure variants
  • Generate VCF files
  • Annotate structure variants
  • Compare to known databases
  • Select high quality of variants by user’s criteria
  • Prepare tracks for the IGV genome browser

Sequence Motif

  • Motifs from peaks

  • Search peaks for sequence motifs
  • Compare to known databases
  • Generate binding logos for sequence motifs

Gene Set Enrichment Analysis

  • Prepare input files for GSEA
  • Run GSEA
  • Summarize the results

Differential Expression

  • Perform statistical tests
  • Generate a table for fold change, p-values, and q-values
  • Generate figures for differential expression analysis

Functional Annotation

  • Prepare input files
  • Compare gene sets with GO terms and pathways

Alternative Splicing

  • Measure alternative splicing in each sample
  • Compare samples or groups for splicing changes
  • Summarize alternative splicing and splicing changes
  • Generate figures for alternative splicing and splicing change analysis

De Novo Genome Assembly

  • Assemble sequence reads
  • Assess assembly statistics
  • Validate an assembly
  • Run BLAST to a nucleotide database
  • Compare to the closest public genomes
  • Ab initio gene prediction

Gene Fusion

Generate a table and figure for gene fusion

De Novo Transciptome Assembly

  • Assemble sequence reads
  • Assess assembly statistics
  • Run BLAST to a protein database
  • Compare to the closest public transcriptomes
  • Find orthologs and paralogs

Methlaytion Profile

  • Generate a table for beta values
  • Generate figures for quality control
  • Assesses the variation between samples and replicates
  • Detect outliers
  • Prepare tracks for the IGV genome browser

Expression Profile

  • Generate a table for read counts and FPKMs
  • Generate figures for quality control analysis
  • Assess the variation between samples and replicates
  • Detect outliers

NCBI Deposit

  • Generate necessary files in appropriate formats
  • Help to fill the form in meta files
  • Upload files onto NCBI database

 

Publications and Abstracts

Publications

Ding J, Li T, Wang X, Zhao E, Choi J, Yang L, Zha Y, Dong Z, Huang S, Asara JM, Cui H, Ding H-F. The histone H3 methyltransferase G9A epigenetically activates the serine-glycine synthesis pathway to sustain cancer cell survival and proliferation. (2013). Cell Metab. 3;18(6):896-907.

Ren M, Qin H, Ren R, Cowell JK. (2013) Ponatinib suppresses the development of myeloid and lymphoid malignancies associated with FGFR1 abnormalities. 27(1):32-40.

Ren M, Qin H, Kitamura E, Cowell JK. (2013). Dysregulated signaling pathways in the development of CNTRL-FGFR1-induced myeloid and lymphoid malignancies associated with FGFR1 in human and mouse models. 8:122(6):1007-16.

Pathania R, Ramachandran S, Elangovan S, Padia R, Yang P, Cinghu S, Veeranan-Karmegam R, Arjunan P, Gnana-Prakasam JP, Sadanand F, Pei L, Chang CS, Choi JH, Shi H, Manicassamy S, Prasad PD, Sharma S, Ganapathy V, Jothi R, Thangaraju M. (2015). DNMT1 is essential for mammary and cancer stem cell maintenance and tumorigenesis. Nat Commun. 24;6:6910.

John K Cowell, Haiyang Qin, Chang-Sheng Chang, Eiko Kitamura, Mingqiang Ren. (2016). A model of BCR-FGFR1 driven human AML in immunocompromised mice. British Journal of Haematology. 175(3): 542-545

Drewry M, Helwa I, Allingham RR, Hauser MA, Liu Y. (2016). miRNA profile in three different normal human ocular tissues by miRNA-seq. Invest Ophthalmol vis Sci. 1;57(8):3731-9

Pathania R, Ramachandran S, Mariappan G, Thakur P, Shi H, Choi JH, Manicassamy S, Kolhe R, Prasad PD, Sharma S, Lokeshwar BL, Ganapathy V, Thangaraju M. (2016). Combined Inhibition of DNMT and HDAC blocks the tumorigenicity of cancer stem-like cells and attenuates mammary tumor growth. Cancer Res. 1;76(11):3224-35.

Zhu X, Hu T, Ho MH, Wang Y, Yu M, Patel N, Pi W, Choi JH, Xu H, Kutlar F, Ganapathy V, Kutlar A, Tuan D. (2017). Hydroxyurea differentially modulates activator and repressors of γ-globin gene in erythroblasts of responsive and non-responsive patients with sickle cell disease in correlation with Index of Hydroxyurea Responsiveness.102(12):1995-2004.

Hu T, Chong Y, Qin H, Kitamura E, Chang C-S, Silva J, Ren M, Cowell JK. (2018). The miR-17/92 cluster is involved in the molecular etiology of the SCLL syndrome driven by the BCR-FGFR1 chimeric kinase. Oncogene, 37(14), 1926-1938.

Hu T, Chong Y, Lu S, Wang R, Qin H, Silva J, Kitamura E, Chang C-S, Hawthorn L, Cowell JK. (2018). MicroRNA339 promotes development of Stem Cell Leukemia/Lymphoma syndrome through downregulation of the BCL2L11 and BAX pro-apoptotic genes. Cancer Research, 1;78(13):3522-3531

Chang C-S, Kitamura E, Johnson J, Bollag R, Hawthorn L. (2018). Genomic analysis of racial differences in triple negative breast cancer. Genomics S0888-7543(18)30045-4

Hu T, Chong Y, Qin H, Kitamura E, Chang C-S, Silva J, Ren M, Cowell JK. (2018). The miR-17/92 cluster is involvedin the molecular etiology of the SCLL syndrome driven by the BCR-FGFR1 chimeric kinase. Oncogene 78 (13)

Silva J, Chang C-S, Hu T, Qin H, Kitamura M, Hawthorn L, Ren M, Cowell JK. (2018). Distinct signaling programs associated with progression of FGFR1 driven leukemia in a mouse model of stem cell leukemia lymphoma syndrome. S0888-7543(18)30273-8.

Khaled ML, Bykhovskaya Y, Yablonski SER, Li H, Drewry MD, Aboobakar IF, Estes A, Gao XR, Stamer WD, Xu H, Allingham RR, Hauser MA, Rabinowitz YS, Liu Y. (2018). Differential Expression of Coding and Long Noncoding RNAs in Keratoconus-Affected Corneas. Invest Ophthalmol Vis Sci. 1;59(7):2717-2728.

Chong Y, Liu Y, Lu S, Cai B, Gin H, Chang CS, Ren M, Cowell JK, Hu T. (2019). Critical individual roles of the BCR and FGFR1 kinase domains in BCR-FGFR1-driven stem cell leukemia/lymphoma syndrome. Int J Cancer.PMID:31525277

Silva J, Chang C-S, Hu T, Qin H, Kitamura M, Hawthorn L, Ren M, Cowell JK. (2019). Distinct signaling programs associated with progression of FGFR1 driven leukemia in a mouse model of stem cell leukemia lymphoma syndrome. Genomics 111 (6) 1566

Yu M, Guo G, Huang L, Deng L, Chang C-S, Achyut BR, Canning M, Xu N, Arbab AS, Bollag RJ, Rodriguez PC, Mellor AL, Shi H, Munn DH, Cui Y, CD73 on cancer-associated fibroblasts enhanced by the A2B-mediated feedforward circuit enforces an immune checkpoint. Nat Commu 2020; 11: 515

Kitamura E, Cowell JK, Chang C-S, Hawthorn L (2020) Variant profiles of Genes Mapping to Chromosome 16q Loss in Willms Tumors Reveals Link to Cilia-Related Genes and Pathways. Genes & Cancer 2020 Dec. 31; 11(3-4): 137-153

Silva J, Hawthorn L, Cowell JK. (2020) Inactivation of Lgi1 in murine neuronal precursor cells leads to dysregulation of axon guidance pathways. Genomics. 112(2):1167-1172

Abstracts

Pathania R, Kolhe RB, Ramachandran S, Mariappan G, Thakur P, Prasad PD, Ganapathy V. (2016) Combination of DNMT and HDAC inhibitors reprogram cancer stem cell signaling to overcome drug resistance. AACR, Abstract#3325.

Drewry M, Helwa I, Allingham RR, Hauser MA, Liu Y. (2016). miRNA-seq in four different normal human ocular tissues. ARVO 

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