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.
- Bioinformatics Support for Omics/NGS data
- Consulting on experimental design, sequencing & analysis plans, bioinformatics, and results interpretation
- Custom bioinformatics tool development (software, pipeline automation)
- Powerful HPC (High-performance computing) hardware
- Support letter to secure research funding
- Figures and written materials (proposal and manuscripts) support
- Targeted Sequencing
- RNA-Sequencing: standard, novel transcripts/isoforms identification
- Dual RNAseq for both host and pathogen
- Denovo Assembly and Annotation
- Metagenomics, 16S / 18S rRNA
- Whole Genome Re-sequencing
- Small RNA and miRNA profiling and discovery
- Gene fusion, CNV and structural variants detection
- Machine Learning for personalized medicine (e.g. biomarker discovery, clinical trial research, electronic health records)
- Power Analysis
- Survival Analysis
- Cell-free DNA sequencing analysis
- Data return via BIOFTP with ~2-3 week turn-around time
Example RNA-seq project deliverables:
Transcript quantification involves QA/QC, read alignment, quantification and normalization. The output file contains information on the identity and quantity of each detected isoform of known transcript as annotated in the NCBI RefSeq or ENSEMBL database.
Differentially Expressed Gene (DEG) Analysis
DEG analysis is performed on each transcript with appropriate statistical test and correction for multiple comparisons.
Pathway analysis includes enrichment analysis that reports statistical significance for common pathways (e.g., KEGG pathways).
The MDS plot is used as a QC step to check the consistency of biological replicates by showing the euclidian distance between samples and sample grouping.
Table showing top differentially expressed genes.
Example of a gene expression heatmap visually showing the expression values of a gene subset by sample.