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
Consultations 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. The 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 designing database systems for specific research problems; developing interfaces for data access, storage and analysis; and deploying these applications on the core’s computing resources. 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 and analysis plans; bioinformatics; and results interpretation.
- Custom bioinformatics tool development (e.g., software, pipeline automation).
- Powerful HPC (high-performance computing) hardware.
- Support letter to secure research funding.
- Figures and written materials (e.g., 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 and electronic health records).
- Power analysis.
- Survival analysis.
- Cell-free DNA sequencing analysis.
- Data return via BIOFTP with a two- to three-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 Bioinformatics Facility can participate in your projects via research-oriented collaborations. We apply state-of-the-art computational approaches and develop novel methods to accelerate biomedical discoveries. Our expertise lies in molecular evolution, machine learning, and big data analytics.
We offer project-based collaborations funded through either joint research grants or fee-for-services. The facility scientists currently participate in multiple research collaborations. We welcome new collaborations and can work with you to apply for joint funding. The facility provides collaborative support for large scale multidisciplinary research projects in close coordination with other ASU core facilities including genomics, proteomics, microarray, imaging, and microbiome facilities. Our domain expertise includes:
Translational bioinformatics to assist precision medicine: Apply state-of-the-art bioinformatics tools and design novel methods to translate “Omics” data into discoveries to improve patient care. For example, discover clinical and molecular markers for early diagnosis or treatment optimization; identify potential drug targets for rational drug design; discover immunosignatures to monitor disease progression; and etc.
Machine learning in biomedical studies: Develop advanced machine-learning algorithms for biomedical research. For example, sparse-learning methods to identify clinical and molecular markers associated with a phenotype; deep-learning methods to model complicated biological data; structure-guided learning to incorporate relationships among biomarkers, and etc.
Evolutionary informatics in cancers and complex diseases: Apply theories and methods in phylogenetics and population genetics to study disease etiology. For example, infer subclonal evolution in cancers; investigate genetic patterns of ethnic disparity to address major health concerns for under-represented populations; and etc.
Functional assessment of genetic variations: Computational prediction of functional impact of genetic variations, including protein-coding variants, non-coding variants, and structural variants. We are especially interested in discovery of novel regulatory elements and functional assessment of noncoding variants.