Bioinformatics support for Omics data analysis
We are experienced in analyzing genomic, transcriptomic, proteomic, epigenetic and metagenomic data. Our previous and current projects include studies using various high-throughput technologies, such as whole genome sequencing (WGS), whole exome sequencing (WES), targeted region sequencing, RNA-Seq, ChIP-Seq, Meth-Seq, gene expression microarray, genotyping microarray and protein microarray. ASU Bioinformatics Core scientists can help investigators with omics data analysis using available software and can also develop customized software tools.

Project design and data analysis consultation
Consultation services are available upon request or during office hours. Coordinated project design consultation and data analysis support are available with other ASU core facilities, including genomics, proteomics, microarray and imaging facilities.

Software, database and website development
Bioinformatics support is available for both basic research and clinical research. ASU Bioinformatics Core supports bioinformatics analysis software and develops analysis pipelines for diverse data types. The facility also designs and hosts Laboratory Information Management Systems (LIMS) research databases. Examples of such activities include: (1) designing database systems for specific research problems; (2) developing interfaces for data access, storage and analysis; and (3) deploying these applications on the core’s computing resources. ASU Bioinformatics Core scientists also can help researchers integrate diverse data sets, such as genomics, proteomics, metabolomics and imaging information. Databases can be designed to support basic research projects and clinical research studies.

Service Activities:

  • Consultation on sequencing, bioinformatics & experimental design
  • Targeted Sequencing
  • RNA-Sequencing: standard, novel transcripts/isoforms identification
  • Dual RNAseq for both host and pathogen
  • Denovo Assembly and Annotation
  • Metagenomics
  • Whole Genome Re-sequencing
  • Whole-Exon-Sequencing
  • Machine Learning for personalized medicine (e.g. biomarker discovery, clinical trial research, electronic health records)
  • Power Analysis
  • Small RNA and miRNA profiling and discovery
  • Gene fusion, CNV and structural variants detection
  • Cell-free DNA sequencing analysis