The Advanced Biomedical Computational Science (ABCS) group focuses on applications of bioinformatics, computational and data science, and artificial intelligence to support NCI researchers.
ABCS provides:
• Subject matter expertise in genomics, proteomics, and imaging.
• Machine learning/Artificial intelligence for image analysis, text mining/NLP.
• Expertise in protein and nucleotide modeling, cheminformatics and quantum chemistry.
• Statistical analysis, large scale data integration.
• Programming expertise in several languages and database skills.
Support is available at no additional cost to all NCI researchers and can be requested by submitting a project request at https://abcs-amp.nih.gov/project/request/ABCS/.
ABCS also provides training and outreach through 3 training series offered on Tuesdays at noon.
• Statistics for Lunch: explores statistics topics at an intuitive and high-level.
• FRCE and Computational Science: trainings on using large compute resources to solve various scientific challenges.
• Programmer’s Corner: series for programmers to discuss topics of interest.
For additional support, see the following links:
Website: https://frederick.cancer.gov/research/bioinformatics-and-computational-science/advanced-biomedical-computational-science
Project request: https://abcs-amp.nih.gov/project/request/ABCS/
Publications: https://bioinfo-abcc.ncifcrf.gov/bioinfo/public/publications
Training: https://bioinfo-abcc.ncifcrf.gov/training/
Scientific applications: https://bioinfo-abcc.ncifcrf.gov/
Code repositories: https://abcsfrederick.github.io/
The following outlines ABCS expertise by subject:
• Bioinformatics and next-generation sequencing (NGS)
o Gene expression analysis.
o SNPs/indels and large structural variant analysis.
o Genome assembly and annotation.
o Whole genome or targeted methylation analysis.
o Single cell analysis.
o Experimental design consultation.
o Result interpretation and visualization.
o Bioinformatics training.
• Biomedical image analysis and visualization
o Tumor segmentation and quantification.
o Custom analysis – cancer subtype identification, predict prognosis and survival.
o Whole animal image and tissue slide analysis.
o Spatial transcriptomics.
o Support tissue analysis core (TAC) and small animal imaging program (SAIP).
• Computational chemistry
o Elucidate biomolecular interactions.
o Predict fluorescence spectra.
• Protein modeling
o Drug target binding.
o Protein-Protein interactions.
o Molecular dynamics simulations.
• Statistics and mathematical analysis
• Biomedical data mining, annotations, and integration
o Automatic downloads and maintenance of 100s of annotations including genes, proteins, drugs, literature, and variants.
o Integrate annotations into existing or new applications.
o Multi-modal large scale data integrations with omics, clinical and imaging data.
• Scientific and high-performance computing (HPC), scientific web development, and scientific infrastructure.
o Scientific data sharing applications.
o Develop custom websites for hosting software developed in the labs.
o Integrate with services such as HPC, GridFTP, SQL, and NoSQL databases.
o Applications for managing sequencing and imaging efforts, and integrating with analysis workflows.
o Scientific catalogs, training resources.
o Web-based services.