CCR Collaborative Bioinformatics Resource

Bioinformatics support for CCR research

Request a Project

Start a new collaboration by sharing your project goals, data types, and timelines through the CCBR Project Request Form.

Open project request form →

View Office Hours

See the current Bethesda and Frederick consultation schedule, plus information for arranging virtual meetings.

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Contact Us

Connect with the CCBR team, explore organizational groups, and find the right contact for your collaboration needs.

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Welcome to CCBR

The CCR Collaborative Bioinformatics Resource (CCBR) is a central resource that helps CCR researchers obtain a broad range of bioinformatics support. CCBR is part of the Center for Cancer Research at the National Cancer Institute (NCI), National Institutes of Health (NIH).

Our team collaborates with researchers across the CCR community, providing expertise in variant analysis, bulk and single cell transcriptomics and epigenomics, multimodal data integration, and spatial profiling. We work with researchers throughout the research life cycle of a project, including supporting manuscripts & site-visits, advising on experimental design, analyzing data from raw data to figure generation and publication, and assisting with data management. CCBR's support is provided at no fee to CCR investigators.

NIDAP

NIDAP is NIH's cloud-based collaborative analysis environment for user-friendly bioinformatics workflows, component analysis tools, and data visualization. The updated NIDAP page outlines the original platform, the NIDAP 2.0 direction, and current workflow-development efforts.

Featured Projects

About CCBR

About the CCR Collaborative Bioinformatics Resource

About CCBR

The CCR Collaborative Bioinformatics Resource (CCBR) is a centralized bioinformatics group embedded within the Center for Cancer Research (CCR) at NCI. CCBR was established to provide CCR investigators with access to sophisticated bioinformatics expertise they may not have within their own laboratory groups.

CCBR is staffed by a multidisciplinary team of bioinformaticians, computational biologists, and data scientists with expertise in next-generation sequencing data analysis, statistical genomics, and software development.

Organizational Structure

CCBR is part of the Office of Science and Technology Resources (OSTR) within CCR and coordinates with:

CCR Sequencing Facility: Generates raw sequencing data for many CCBR-supported projects and coordinates sample submission and library preparation.
NIH Biowulf HPC: High-performance computing cluster where CCBR pipelines are executed at scale.
NIDAP: Cloud-based platform hosting CCBR workflows, interactive visualizations, and collaborative analysis tools.
BTEP: Bioinformatics Training and Education Program providing training resources to CCR scientists.

CCBR is provided guidance and oversight by the Advisory Committee for Informatics and Computational Resources (ACICR). This committee advises the CCR Collaborative Bioinformatics Resource (CCBR) operations, including the vetting of projects and ensuring equitable access to the resource. Current committee roster and charter can be found here.

Collaborative Support Model

CCBR provides bioinformatics support at no cost through a collaborative, effort-based model. CCBR bioinformaticians are assigned to projects as scientific collaborators:

CCBR staff contribute intellectual input and are eligible for co-authorship on resulting publications
Projects are accepted based on scientific fit, feasibility, and available CCBR capacity
Investigators are expected to engage early and provide the information needed to enable efficient completion
Support is available to all CCR principal investigators working on high-throughput sequencing projects

Scientific & Computational Expertise

CCBR provides collaborative expertise across a broad range of genomics, transcriptomics, epigenomics, and computational analysis areas. Our support spans both domain-specific analysis and the computational methods needed to generate interpretable, reproducible results.

Scientific Expertise

DNA Sequencing & Variant Analysis

WGS, WES, targeted DNA sequencing, and somatic or germline variant analysis
Structural variant and copy-number analysis

Transcriptomics & Gene Expression

Bulk RNA-seq, small RNA-seq, circRNA-seq, EV-seq, microarray, and allele-specific expression
QC, alignment, quantification, differential expression, pathway analysis, and visualization

Single-Cell, Spatial & Multiome

scRNA-seq, scATAC-seq, integrated multiome, and spatial transcriptomics analysis
Clustering, annotation, trajectory analysis, accessibility analysis, and tissue-context interpretation

Epigenomics, Chromatin & 3D Genome

ChIP-seq, ATAC-seq, CUT&RUN, DNA methylation analysis, and chromatin-state profiling
Hi-C and related 3D genome assays

Immunogenomics & Functional Genomics

TCR/BCR repertoire analysis and broader immunogenomic interpretation
CRISPR screen analysis and perturbation-based functional genomics

Microbiome, Public Data & Specialized Analyses

Metagenomics and microbiome profiling
Public data mining, cross-study integration, and external dataset reanalysis
Proteomics, metabolomics, survival analysis, and other specialized collaborative analyses

Long-Read Sequencing

PacBio and Oxford Nanopore workflows for genome and transcriptome profiling

Collaborative Research Support

Grant bioinformatics plans, methods writing, figure preparation, and reviewer-response support

Computational Expertise

Statistical & Analytical Methods

Statistical modeling and experimental design consultation
Differential analysis, pathway interpretation, and quantitative result synthesis

Data Integration and ML

Integrative multi-omics analysis across modalities
Machine learning for genomic data
Cross-platform integration and biological interpretation

Workflow Engineering & Reproducibility

Pipeline development and automation using Snakemake and Nextflow
Containerized, reproducible execution on NIH Biowulf

Visualization & Reporting

Interactive data visualization using Shiny, Plotly, and custom tools
Publication-ready figures, reporting, and result communication

Day	Time	Format	Location / Access
Thursday	3:00 – 5:00 PM	Virtual / In-person	Bethesda: Bldg 37, Rm 3041 Join via Microsoft Teams
Tuesday	11:00 AM – 1:00 PM	In-Person	Frederick: Bldg 1043, Rm 13

Authorship Guidelines

Authorship should be based on scientific contribution to the project.

Best practice: Discuss and agree on authorship criteria with your CCBR collaborator early in the project to avoid ambiguity later.

Authorship is generally appropriate when CCBR staff make substantive intellectual contributions such as:

Experimental design or analytical strategy development
Custom or novel bioinformatics analyses developed for the project
Data analysis together with biological interpretation of results
Generation of figures or results central to the manuscript
Substantive manuscript writing or critical revision of scientific content

Acknowledgments

All manuscript submissions and presentations that used CCBR support should acknowledge CCBR, even when a CCBR scientist is not included as a co-author.

Required acknowledgment text

Required Acknowledgment Text — Copy Exactly

"We acknowledge support from the CCR Collaborative Bioinformatics Resource (CCBR), National Cancer Institute, NIH."

Citing NIDAP for Bulk RNA-seq Analysis

Copy into your Methods section

Bulk RNA-seq reads were trimmed using Trim Galore (v0.6.7) and aligned to the [human/mouse] reference genome ([GRCh38/GRCm39]) using STAR (v2.7.10a). Gene-level read counts were quantified using RSEM (v1.3.3). Differential expression analysis was performed using DESeq2 (v1.36.0) in R (v4.2.0). Analyses were performed using CCBR pipelines on the NCI Integrated Data Analysis Platform (NIDAP).

Recommended Citation for NIDAP

Analyses were performed using the CCR Collaborative Bioinformatics Resource (CCBR) pipelines available on the NCI Integrated Data Analysis Platform (NIDAP; National Cancer Institute, NIH).

Questions about authorship or acknowledgment requirements should be discussed with your CCBR collaborator early in the project, or directed to nciccbr@mail.nih.gov.

Resources

CCBR Resources

Pipelines, software, and experimental design guidance for CCR researchers.

Pipelines & Software

CCBR develops and maintains a suite of standardized, reproducible bioinformatics pipelines for NGS data. All pipelines are version-controlled, containerized, and designed to run on NIH Biowulf using Snakemake or Nextflow workflow managers.

View CCBR on GitHub →

More information about Pipeliner can be found at https://ccbr.github.io/Pipeliner/.

For the broader scientific software catalog maintained by BTEP, see the Training page's Scientific Software Catalog resource.

Available Pipelines on Biowulf

#	Data Type	Pipeline	Full Name
1	Bulk RNA-seq	RENEE	Rna sEquencing aNalysis pipElinE
2	WES	XAVIER	eXome Analysis and Variant explorER
3	ATAC-seq	ASPEN	Atac Seq PipEliNe
4	ChIP-seq	CHAMPAGNE	CHromAtin iMmuno PrecipitAtion sequencinG aNalysis pipEline
5	CRISPR-seq	CRISPIN	CRISPr screen sequencing analysis pipelINe
6	CUT&RUN	CARLISLE	Cut And Run anaLysIS pipeLinE
7	circRNA-seq	CHARLIE	Circrnas in Host And viRuses anaLysis pIpEline
8	scRNA-seq	SINCLAIR	SINgle CelL AnalysIs Resource
9	WGS	LOGAN	whoLe genOme-sequencinG Analysis pipeliNe
10	EV-seq	ESCAPE	Extracellular veSiCles rnAseq PipelinE

RENEE starts with raw FASTQ files and ends with a counts matrix. Downstream DEG support will be added at a later date. In the meantime, NIDAP or iDEP can be used for DEG analysis.
XAVIER (eXome Analysis and Variant explorER) will be soon available on Biowulf.
ASPEN has limited support for differential ATAC-seq signal analysis. CCBR has other pipelines for footprinting analysis such as TOBIAS. Please reach out for details.
CHAMPAGNE — CCBR plans to completely revamp ChIP-seq analysis. In the interim, we recommend using the ENCODE pipeline on Biowulf for ChIP-seq analysis.
CRISPIN (previously called CRUISE) performs CRISPRSeq analysis with MAGeCK, drugZ, and BAGEL2.
CARLISLE supports human and mouse samples with (recommended) or without spike-ins.
CHARLIE finds known and novel circRNAs in human/mouse + virus genomes. Differential circRNA analysis is planned for a future release.
SINCLAIR addresses various single-cell modalities — e.g. single-cell expression, CITESeq, TCR-seq, and more.
LOGAN will soon be CCBR's newest whole-genome sequencing offering.
ESCAPE is designed for extracellular vesicle RNA-seq data.

For any other data type or pipeline not listed here, email us 📫 directly to get the conversation started!

Experimental Design Best Practices

Talk to us early. We often cannot save a poorly designed experiment, but we can help you design a successful one. We are committed to helping you determine the optimal experimental design given your budget — discussing sample size, number of replicates, sources of batch effects, sequencing depth, and overall impressions of the experiment from a bioinformatics perspective.

General Principles

Minimum 3 biological replicates per condition
Plan for confounders: batch effects, sex, passage number, time of collection
Include appropriate controls: input, IgG, vehicle-treated, matched normal
Discuss sample size and statistical power with CCBR before proceeding
Plan metadata collection early so study design, sample annotations, processed outputs, and raw files are ready for downstream analysis and GEO submission

Bulk RNA-seq

≥3 biological replicates per condition (≥5–6 for primary patient tumors)
≥30–50M reads/sample; ≥100M for fusions or rare transcripts
Poly-A for RIN ≥7; ribo-depletion for degraded RNA
Never pool biological replicates
Discuss batch layout if samples span multiple sequencing runs

ChIP-seq

Matched input controls for each sample/condition
≥30–50M reads for TFs; ≥50–100M for broad marks
Spike-in normalization for absolute cross-condition comparisons
Validate antibody efficiency by qPCR before committing to sequencing
≥2 biological replicates for IDR reproducibility

Whole-Exome Sequencing

Include matched normal sample for somatic calling
≥100× mean on-target for somatic; ≥30× for germline
Document exome capture kit and target BED file
Avoid FFPE when possible; use FFPE-specific kits if required

Whole-Genome Sequencing

≥30× for germline; ≥60–100× for somatic with matched normal
PCR-free library prep preferred
Always include matched normal for somatic analyses

Single-cell RNA-seq

Spatial Transcriptomics

Select the appropriate Visium workflow for your sample type. Fresh frozen tissue is commonly used for poly(A)-capture Visium workflows, while FFPE tissue can be used with probe-based Visium assays, particularly for archived clinical specimens. In all cases, minimize ischemia time, avoid repeated freeze-thaw cycles, and process samples consistently across the study.
Perform a pilot study for new tissue types. Pilot experiments are strongly recommended when working with a new tissue or specimen type. A small-scale test can help optimize sectioning, staining, tissue handling, and sequencing depth before scaling up.
Optimize section thickness and tissue placement. Section thickness should be optimized for the tissue type and assay workflow. As a general starting point, approximately 10 µm is common for fresh frozen samples, while approximately 5 µm is common for FFPE samples. Tissue should be positioned carefully within the capture area to maximize usable spots. Avoid regions with extensive necrosis, folding, damage, or empty space.
Include adequate biological replication. At least 2 biological replicates per condition may be sufficient for exploratory studies, but 3 or more are recommended for stronger statistical comparisons. Replicates should be independent biological samples, not serial sections from the same specimen.
Balance samples across slides and batches. When comparing multiple conditions, distribute samples across slides, processing days, and library preparation batches rather than processing one condition at a time. If batching cannot be avoided, include samples from each condition in every batch to help identify and account for batch effects during analysis.
Plan sequencing depth appropriately. Sequencing depth should be based on assay type, tissue complexity, and the number of spots covered by tissue. In general, fresh frozen Visium experiments require deeper sequencing than FFPE workflows. Sequencing should be budgeted based on tissue-covered spots rather than the total number of spots on the slide.
Sample comparable anatomical regions across conditions. When comparing groups such as disease and control, select similar anatomical regions whenever possible. Spatial differences in tissue composition can otherwise confound biological interpretation.
Use consistent processing and library preparation methods. Standardize tissue handling, staining, imaging, library preparation, and sequencing across all samples to reduce technical variability and improve comparability.
Consider matched reference data for downstream analysis. If cell type annotation, deconvolution, or label transfer will be part of the analysis, matched scRNA-seq or snRNA-seq data from the same or a similar tissue can improve interpretation.
Record experimental metadata carefully. Document key variables such as tissue source, ischemia time, fixation or freezing conditions, section thickness, staining parameters, slide position, imaging settings, and batch information. These factors may explain technical variation and should be considered during downstream analysis.
Include tissue quality control early in study planning. Sample quality is a major determinant of success. For FFPE specimens especially, fixation quality, embedding conditions, and RNA quality should be assessed before committing to a larger study.

For further assistance in planning your NGS experiment, please contact nciccbr@mail.nih.gov or visit our Bethesda office hours on Thursdays from 3:00 to 5:00 PM. For cost information and assistance with experiment setup, please visit the Frederick Sequencing and Genomics Core (FSGC) website or contact Bao Tran at bao.tran@nih.gov.

GEO Submission Guidelines

This page summarizes how investigators can prepare CCBR-supported NGS datasets for submission to NCBI GEO. It is intended as a practical checklist for assembling the study description, sample metadata, processed data, and raw sequencing files before formal upload. For authoritative requirements, always review the official GEO submission page and the GEO guidance for high-throughput sequencing studies.

Downloadable helper prompt: Users who want LLM assistance organizing GEO metadata can download this starter prompt: GEO submission LLM prompt (Markdown).

Before You Start

Confirm that the study is appropriate for GEO and that the submission will be made using a My NCBI account associated with a GEO Profile
If the project includes multiple assay types, plan to submit each assay type separately unless GEO treats them as one integrated multi-omics submission
Assemble the final study title, summary, overall design, contributor list, and key protocol details before starting the spreadsheet
Create a clean submission folder that contains only the raw files, processed files, and support documents that will be referenced in GEO

Required GEO Submission Components

Metadata spreadsheet: GEO requires a completed spreadsheet for each submitted data type
Processed data files: GEO expects quantitative processed outputs that support the study findings
Raw data files: GEO requires the original raw sequencing files, and GEO will transfer those raw files to SRA on the submitter's behalf

Metadata Spreadsheet

Complete all required study-level and sample-level fields in the GEO spreadsheet
Provide enough detail for a reader to understand the study, samples, biological groups, and protocols without relying on internal lab notes
Keep sample identifiers consistent across the spreadsheet, processed files, raw files, and manuscript draft
Spell out acronyms and structure sample characteristics clearly, preferably as `key: value` pairs
Include the reference assembly, assay details, and protocol information needed to interpret the processed results
Optional MD5 checksums may be included in the spreadsheet to help troubleshoot file corruption, but they should not be submitted as separate files

Processed Data Files

Submit quantitative processed data that support the conclusions of the study, such as count matrices, normalized abundance tables, peak files with quantitative values, or signal tracks
Do not treat intermediary alignment files such as BAM or SAM as the main processed-data deliverable for GEO
Use unique file names throughout the submission package
If you provide a matrix covering all samples, make sure the column headers match the sample or library names used in the metadata spreadsheet
Processed data should be complete rather than restricted to only significant results; for example, submit a full feature-by-sample matrix rather than only a differential hit list

Raw Data Files

Provide the original raw sequencing files generated by the instrument in accepted formats such as FASTQ
All raw file names must be unique and safe for public release, using only letters, numbers, hyphens, and underscores
Do not place sensitive information in raw file names
Paired-end runs must include complete matching file sets, and the files for a run must contain the same number of reads
If compressed files exceed GEO size limits, split them before submission, and do not use ZIP archives for upload

Special Notes for Single-cell and Multi-omics Studies

Single-cell studies require both raw and processed data, and GEO expects processed outputs at the cell level
For common 10x-style studies, multiplexed raw FASTQ files are typically preferred, while each GEO sample should still map to a unique set of raw files
Accepted processed outputs may include MEX files, H5 or HDF5 files, RDS objects, and VDJ-related outputs where relevant
If Cell Ranger aggregation or 10x feature-reference files were used, include those supporting files when applicable
For multi-omics studies, ensure each library type is labeled clearly and consistently in the metadata spreadsheet

How an LLM Can Help

An LLM can serve as an organizational assistant while you prepare the GEO package, especially when the project folder contains many pipeline outputs and the manuscript draft is still being finalized.

Point the LLM to the project folder so it can help inventory candidate raw files, processed outputs, count matrices, peak files, and support documents
Point the LLM to the manuscript draft so it can help extract the study title, summary, overall design, organism, assay type, and experimental groups for the GEO spreadsheet
Ask the LLM to cross-check whether sample IDs, replicate labels, filenames, and group names are used consistently across the manuscript, data files, and spreadsheet draft
Use the LLM to draft a checklist of missing metadata, such as protocol text, reference assembly, contributor names, processed-file descriptions, or assay-specific notes
When the manuscript will eventually cite the GEO accession, the LLM can help identify the sections that should be updated once the accession number is assigned

Recommended workflow: Organize a final GEO-prep folder first, then use the downloadable prompt to have an LLM draft the Series and Sample metadata from the project files and manuscript draft. Review every field manually before submission, since the investigator remains responsible for the accuracy and completeness of the GEO package.

If the study has unusual submission needs, such as prior SRA deposition, very large files, or non-standard processed outputs, review the official GEO guidance pages above and contact nciccbr@mail.nih.gov if you would like help organizing the submission package.

NIDAP: NIH Integrated Data Analysis Platform

NIDAP is an innovative, cloud-based, collaborative data aggregation and analysis platform that hosts user-friendly bioinformatics workflows and component analysis and visualization tools developed by the NCI developer community based on open source tools and makes them immediately available to biologist end-users across NIH.

NIDAP 1.0 was a cloud-based analysis platform hosted by Palantir at NCI, providing a secure collaborative environment for running CCBR workflows, sharing data, and exploring results interactively. The Palantir-hosted NIDAP 1.0 platform retired September 28, 2025.

NIDAP 2.0 is being developed to restore and improve standardized bioinformatics workflows for CCR researchers, with an emphasis on reproducibility, modular execution, and user-friendly scientific analysis. This next-generation approach is intended to preserve what researchers valued most in NIDAP: graphical access to sophisticated analyses, sharable workflows, and results that can be reproduced with confidence over time.

What can you do on NIDAP?

Run pre-built CCBR workflows such as bulk RNA-seq and scRNA-seq through a point-and-click interface
Securely share results and processed data with collaborators inside and outside NCI
Explore interactive visualizations such as heatmaps, volcano plots, and UMAP embeddings
Access supported CCBR workflows in a collaborative cloud environment

What to Expect

Standardized workflows for bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and related analyses
Self-contained execution environments designed to improve reproducibility over time
User-focused workflow design for review, sharing, and reuse of results
Lower technical barriers for researchers while improving the stability, transparency, and reusability of computational analyses

Why it matters: NIDAP 2.0 aims to lower technical barriers for researchers while improving the stability, transparency, and reusability of computational analyses.

Workflow Directions Under Evaluation

Galaxy

Galaxy is being evaluated as a framework for standardized bioinformatics pipelines in NIDAP 2.0. The current prototype emphasizes structured, multi-step workflows for count processing, normalization, differential analysis, and visualization of bulk RNA-seq data.

Code Ocean

Code Ocean is being explored for modular, self-contained workflows that support reproducible scientific analysis and more flexible user interaction. This direction is intended to support result review today and future interactive workflow building.

Galaxy workflow prototype showing a structured multi-step workflow for count processing, normalization, differential analysis, and visualization of bulk RNA-seq data — Galaxy workflow prototype. Example of a structured multi-step workflow for count processing, normalization, differential analysis, and visualization of bulk RNA-seq data.

Training & Tutorials

Training Resources for CCR Scientists

BTEP workshops, NIDAP tutorials, the scientific software catalog, and the NIH bioinformatics events calendar.

Bioinformatics Training and Education Program (BTEP)

BTEP is the training arm of CCBR, dedicated to helping CCR scientists understand and perform informed bioinformatics analyses. The mission is to build conceptual foundations that enable effective collaboration and informed interpretation of results.

Workshops & Courses

Hands-on training in Bulk RNA-seq, R/Bioconductor, Python, single-cell analysis, and more — offered throughout the year on campus and virtually.

Seminars & Lectures

Invited speaker seminars, journal clubs, and topical lectures. All events listed on the NIH Bioinformatics Calendar.

Online Learning Resources

Self-paced tutorials on NIDAP and the BTEP website — recorded workshops, slide decks, and code notebooks.

Scientific Software Catalog

Curated catalog of bioinformatics tools — commercial and open-source — with descriptions, use cases, and documentation links.

Visit the BTEP Site →

Team & Contacts

Meet the CCBR Team

Office Location

CCBR is composed of members across different organizational groups. Through scientific and operational integration across core and embedded teams, we bridge silos of expertise and collectively advance standardization, reproducibility, and provenance across CCR-wide bioinformatics support services.

CCR Collaborative Bioinformatics Resource (CCBR)
Building 37, Room 3041 · National Cancer Institute, NIH
9000 Rockville Pike, Bethesda, MD 20892 · nciccbr@mail.nih.gov

Office of Science and Technology Resources (OSTR, CCR)

Name	Title	Email
Maggie Cam, Ph.D.	Head	maggie.cam@nih.gov
Richard Finney, B.A.	Staff Scientist	finneyr@mail.nih.gov
Huaitian Liu, Ph.D.	Staff Scientist	huaitian.liu@nih.gov

Frederick National Laboratory for Cancer Research (FNLCR)

Name	Title	Email
Parthav Jailwala, M.Sc.	Bioinformatics Manager	parthav.jailwala@nih.gov
Vishal Koparde, Ph.D.	Technical Lead	vishal.koparde@nih.gov
Jing Bian, B.S.	Bioinformatics Analyst	jing.bian@nih.gov
Alexei Lobanov, Ph.D.	Bioinformatics Analyst	alexei.lobanov@nih.gov
Samarth Mathur, Ph.D.	Bioinformatics Analyst	samarth.mathur@nih.gov
Nathan Wong, Ph.D.	Bioinformatics Analyst	nathan.wong@nih.gov
Philip Homan, Ph.D.	Bioinformatics Analyst	philip.homan@nih.gov
Thomas Joshua Meyer, Ph.D.	Bioinformatics Analyst	thomas.meyer@nih.gov
Kelly Sovacool-Caruthers, Ph.D.	Bioinformatics Analyst	kelly.sovacool@nih.gov
Katherine Goldfarbmuren, Ph.D.	Bioinformatics Analyst	kate.goldfarbmuren@nih.gov
Yasha Butt, Ph.D.	Bioinformatics Analyst	yasha.butt@nih.gov

Embedded Team Members

RNA Biology Laboratory

Wilfried Guiblet, Ph.D.

RNA Biology Laboratory

wilfried.guiblet@nih.gov

Genetics Branch: OMICS Facility

Erica Pehrsson, Ph.D.	Genetics Branch: OMICS Facility	erica.pehrsson@nih.gov
Shaoli Das, Ph.D.	Genetics Branch: OMICS Facility	shaoli.das@nih.gov

Genetics Branch: OncoGenomics

Vineela Gangalapudi, M.Sc.	Genetics Branch: OncoGenomics	vineela.gangalapudi@nih.gov
Xinyu Wen, M.Sc.	Genetics Branch: OncoGenomics	xinyu.wen@nih.gov

Lab of Pathology: NCI-COMPASS

Kristin Valdez, M.Sc.	Lab of Pathology: NCI-COMPASS	kristin.valdez@nih.gov
Robert Schultz, Ph.D.	Lab of Pathology: NCI-COMPASS	rob.schultz@nih.gov

Pediatric Oncology Branch

Vidhur Daulatabad, Ph.D.	Pediatric Oncology Branch: MyPART/POB	vidhur.daulatabad@nih.gov
Ying Wu, Ph.D.	Pediatric Oncology Branch: CCDI/TEGS	ying.wu@nih.gov

SpITR: Spatial Imaging Technology Resource

Edmund Cauley, M.Sc.

SpITR: Spatial Imaging Technology Resource

ned.cauley@nih.gov

General Contact

Contact Type	Details
General CCBR inquiries	nciccbr@mail.nih.gov
BTEP Training inquiries	NCI-BTEP@mail.nih.gov
Office hours	See Consultation Hours & Directions
Project requests	CCBR Project Request Form

Project Submission

Request a Project

Learn about the collaborative process and request a new bioinformatics project from CCBR.

Collaborative Process

CCBR services are available to all CCR Principal Investigators and their laboratory groups at NCI. Investigators must be affiliated with CCR (the intramural research program of NCI). CCBR does not currently support extramural researchers or other NIH Institutes/Centers outside of specific inter-institute agreements. All new collaborations begin with a project request through the CCBR Project Request Form. Following submission, requests are reviewed by CCBR leads, and a decision is taken to accept, decline, or redirect the project. Early consultation — ideally before experiments are performed — leads to the best outcomes. CCBR can help with experimental design and sample size planning.

What happens after submission

Project Request

The investigator initiates a project request through the CCBR Project Request Form, outlining the scientific goals, assay type, timeline, and key project details.

CCBR Review and Assignment

CCBR leadership reviews the request for scientific fit, feasibility, and staffing, then assigns the project to the most appropriate bioinformatician or team.

Data Transfer & Quality Control

Once a project is accepted, raw data and metadata are transferred to the analysis environment, and CCBR performs initial quality control checks before downstream analysis begins.

Data Analysis and Interpretation

CCBR carries out the agreed analytical workflow, works with the investigator to interpret the results, and refines analyses as needed through iterative discussion.

Results Delivery & Publication Support

Final outputs, figures, and documentation are shared with the research team, with follow-on support available for manuscript preparation, revisions, and publication.

Publications

CCBR Publications & Featured Projects

Explore representative CCBR collaborations through featured projects, or browse the broader list of CCBR-supported publications. CCBR co-authors are shown in bold.

Featured Projects

These examples highlight how CCBR collaborates with CCR investigators from study design through analysis, interpretation, figure development, and publication. Each project combines scientific context with representative bioinformatics outputs that contributed to the final manuscript.

Cancer Immunology Vaccine Biology

High levels of endogenous omega-3 fatty acids promote dendritic cell antigen presentation and improve dendritic cell-based cancer vaccine efficacy in mice

Tiwary S, Hsu KS, Goldfarbmuren KC, Xia Z, Berzofsky JA. Published October 1, 2025.

View on PubMed →

Scientific Context

This study investigated how endogenous omega-3 fatty acids influence dendritic cell function and whether that biology can improve dendritic cell-based cancer vaccine responses. The analysis connected immunologic phenotypes with transcriptomic signatures that helped explain how lipid-state changes shape antigen presentation and anti-tumor activity.

CCBR Contribution

RNA-seq processing and differential-expression analysis of wild-type and FAT-1 dendritic cells across immature and mature states.
Stage-specific gene-expression summaries, heatmaps, and pathway/network interpretation related to antigen presentation.
Figure preparation linking transcriptomic changes with vaccine efficacy outcomes.

Figure 6 from the omega-3 dendritic cell vaccine efficacy manuscript — **Representative Figure:** Figure 6 summarizes stage-specific and shared differential-expression patterns between FAT-1 and wild-type dendritic cell conditions, including the heatmap and pathway-network views that support the study's mechanistic interpretation.

Tumor Immunology Pan-cancer Transcriptomics

A pan-cancer comparative analysis of the cancer genome atlas transcriptomic TIL-immune signatures

Hitscherich KJ, Nousome D, Dinerman AJ, Dulemba V, Lowery FJ, Nilubol N. Published August 1, 2025.

View on PubMed →

Scientific Context

This study performed a broad comparison of tumor-infiltrating lymphocyte immune signatures across cancer types using transcriptomic data from TCGA. The goal was to identify how immune-infiltration patterns vary across diseases and to provide a more systematic framework for interpreting TIL-related biology in cancer cohorts.

CCBR Contribution

Large-scale processing of TCGA recount3 bulk RNA-seq data across 33 cancer types.
GSVA-based scoring of curated TIL immune signatures with comparative analyses by tumor type, germ-cell origin, and signature cluster.
Summary visualizations of signature overlap, prognostic comparisons, and pan-cancer clustering patterns.

Figure 1 from the pan-cancer TIL immune signature manuscript — **Figure 1:** This overview figure introduces the TIL-immune signature library construction and illustrates the high degree of shared genes across compiled signatures.

Figure 5 from the pan-cancer TIL immune signature manuscript — **Figure 5:** This cluster-analysis summary highlights groups of similar signatures and shows how cluster-level patterns support the pan-cancer prognostic comparison.

Neuroblastoma Transcriptional Dependency

Cohesin loading factor NIPBL is essential for MYCN expression and MYCN-driven oncogenic transcription in neuroblastoma

Kang J-Y, Tremble KA, Homan P, Thiele CJ. Published August 9, 2025.

View on PubMed →

Scientific Context

This project examined how the cohesin loading factor NIPBL supports MYCN expression and downstream oncogenic transcriptional programs in neuroblastoma. The analysis focused on defining transcriptional dependencies and clarifying how perturbation of this regulatory axis alters the gene-expression landscape in a MYCN-driven cancer context.

CCBR Contribution

RNA-seq analysis of NIPBL-depleted neuroblastoma cells to quantify MYCN-associated transcriptional changes.
ChIP-seq integration, peak-overlap analysis, motif enrichment, GREAT annotation, and GSEA of neuronal differentiation programs.
Integrated genomic figure preparation linking enhancer co-occupancy with transcriptional regulation.

Figure 4 from the neuroblastoma NIPBL and MYCN manuscript — **Representative Figure:** Figure 4 shows NIPBL and MYCN co-occupancy at enhancers in neuroblastoma, including overlap analysis, genome-browser tracks, motif enrichment, and GO summaries.

Suggested next step: replace the placeholder figure panels above with 1-2 actual manuscript figures per project, ideally those most directly generated or finalized through the CCBR analysis effort.

Request a Project

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Contact Us

Welcome to CCBR

NIDAP

Featured Projects

About the CCR Collaborative Bioinformatics Resource

About CCBR

Organizational Structure

Collaborative Support Model

Scientific & Computational Expertise

Scientific Expertise

DNA Sequencing & Variant Analysis

Transcriptomics & Gene Expression

Single-Cell, Spatial & Multiome

Epigenomics, Chromatin & 3D Genome

Immunogenomics & Functional Genomics

Microbiome, Public Data & Specialized Analyses

Long-Read Sequencing

Collaborative Research Support

Computational Expertise

Statistical & Analytical Methods

Data Integration and ML

Workflow Engineering & Reproducibility

Visualization & Reporting

Data Handling

Sequencing Data Flow

CCR Sequencing Facility (FSGC)

Globus Transfer

NIH Biowulf HPC

Analysis & Quality Control

Results Delivery

Data Security & Compliance

NIDAP Integration

Consultation Hours & Directions

In-Person Consultations

Virtual Consultations

Authorship Guidelines

Authorship is generally appropriate when CCBR staff make substantive intellectual contributions such as:

Acknowledgments

Required acknowledgment text

Citing NIDAP for Bulk RNA-seq Analysis

Recommended Citation for NIDAP

CCBR Resources

Pipelines & Software

Available Pipelines on Biowulf

Experimental Design Best Practices

General Principles

Bulk RNA-seq

ChIP-seq

Whole-Exome Sequencing

Whole-Genome Sequencing

Single-cell RNA-seq

Spatial Transcriptomics

GEO Submission Guidelines

Before You Start

Required GEO Submission Components

Metadata Spreadsheet

Processed Data Files

Raw Data Files

Special Notes for Single-cell and Multi-omics Studies

How an LLM Can Help

NIDAP: NIH Integrated Data Analysis Platform

What can you do on NIDAP?

What to Expect

Workflow Directions Under Evaluation

Galaxy

Code Ocean

Training Resources for CCR Scientists

Bioinformatics Training and Education Program (BTEP)

Workshops & Courses

Seminars & Lectures

Online Learning Resources

Scientific Software Catalog

NIDAP Training

Training topics

NIH Bioinformatics Calendar

What is listed on the Calendar

Meet the CCBR Team

Office Location