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2025-08-18T15:08:58Z

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== 2025 Volunteer Program Details ==

=== Dates ===
'''Application Deadline'''

August 22, 2025, Noon (email your updated resume and projects in order of preference). Acceptance letter/email will be sent to candidates latest the day after the kick-off meeting.

'''Volunteer Zoom Kick-Off Meeting'''

August 25, 2025 | 4:00 to 5:00 PM

'''Program Dates: September 1st, 2025 – November 30th, 2025''' (13 weeks)

Remote | Hybrid for GW employees and students (Ross Hall 5th floor)

[https://hivelab.biochemistry.gwu.edu/wiki/Volunteership_2025 Summer 2025 Volunteership] (Closed)
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=== Volunteer Expectations ===

# Minimum commitment of 10 hours per week.
# Progress updates via Slack at least 3 days per week (scrum).
# Regular Zoom meetings with the assigned project point of contact.
# Attend some lectures or seminars remotely (max 4-5).

'''''Important:''' If the scrum is not updated for 2 consecutive working days, the candidate will be automatically dropped from the program.''
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=== Potential Projects ===
We are excited to continue our bioinformatics volunteership program in Fall 2025. This program offers students the opportunity to work on bioinformatics projects supported by agencies such as the NIH, ARPA-H, and FDA. Participants will gain exposure to a variety of activities within a bioinformatics lab, including data analysis, computational biology, and genomics. If you are interested, please email mazumder_lab@gwu.edu your resume and a ranked list of the projects that interest you most. You can also indicate if you want to focus on specific areas that are of interest to you.
# BiomarkerKB (biomarkerkb.org) project: Biomarker curation project. Involves reading papers and collecting biomarkers.
# GlyGen (glygen.org) project: Review glycomics and glycoproteomics data and curate tissue, disease, and other related information.
# ARGOS (argosdb.org) project: Analyze genomics data using HIVE to identify reference genome assemblies.
# PredictMod (hivelab.biochemistry.gwu.edu/predictmod) project. Curating PMIDs for intervention outcome prediction dataset LLM recommendation training.

''Note: Individuals involved in the above projects with a background in programming and/or machine learning may also undertake additional tasks to support the development of ML models, which can be integrated into PredictMod or used to enhance AI/ML-ready datasets within GlyGen.''
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==== 1. BiomarkerKB Biocuration Project Ideas ====
POC: Daniall Masood, Maria Kim

# Curate biomarkers for a specific disease
## The student would be doing manual curation for about 4 weeks, with regular check-ins with me to ensure it is being done correctly.
## The next 4 weeks can be dedicated to developing an LLM or an automated process to extract biomarker details with data collected in the first 4 weeks as training data/example data.
# Top 50 biomarkers
## Curate the top 50 biomarkers for biomarkerkb.org.
## Define what constitutes a top 50 biomarker.
## Begin curating biomarkers from different sources and papers by collecting fields mentioned in the data model, as well as collecting cross-references.
# Biocuration of biomarkers from NLP/LLM work
## Use the biomarkers collected from NLP work.
## Curate biomarkers. Data provided was not provided in the biomarker data model.
## While curating the biomarkers, check if data collected from NLP is correct.
## After completion, the student can start using curated data to work on the NLP/LLM method.
# Curate biomarkers for a treatment
## See #1 above.
# Continue working on LLM methods started by volunteers over the summer.
## The data is available as well as some preliminary research and work done by previous volunteers in this area.

If the student has any other ideas, diseases, treatments, or methods they want to focus on, please reach out to daniallmasood@gwu.edu to discuss your idea and check if it will be feasible as a project for the summer.

==== 2. GlyGen Biocuration Project Ideas ====
POC: Rene Ranzinger, Urnisha Bhuiyan, Kate Warner

Over the last three decades, numerous glycomics database projects have been initiated to collect valuable information about glycans, proteins, and their interactions. Some of these databases have been discontinued due to the end of project funding. However, the data within these databases remains highly valuable to the community. Integrating these datasets into modern databases or knowledgebases, such as GlyGen, presents a challenge because much of the valuable metadata (e.g., species, tissue, disease, cell line) annotations are free-text terms that do not align with established standard dictionaries and ontologies used in modern resources. Automated matching of this information with dictionaries or ontologies is often not possible due to the use of synonyms, spelling errors, or abbreviations. For example, "human," "man," and "h. sapiens" all map to the scientific species name "Homo sapiens."

The GlyGen project aims to make datasets from two older databases (CarbBank, CFG) accessible by migrating the data and metadata into our database. For this project, we are seeking curators with a medical or biology background who are interested in helping map metadata terms from these old databases to standard dictionaries and ontologies.

The project involves:

# Using internet resources (e.g., Google, Wikipedia) to identify terms used in the old database.
# Mapping identified terms to corresponding dictionaries and ontologies using the webpages and search interfaces of these projects.
# Finding papers based on titles and author lists that may contain spelling errors.
# Interacting and discussing with other curators in case terms are mapped differently.

If you have any other ideas or methods you would like to focus on, please reach out to rene@ccrc.uga.edu to discuss them.

'''3. GlyGen Publication Analysis Project Ideas'''

POC: Rene Ranzinger and Urnisha Bhuiyan

One of the challenges for any bioinformatics project is understanding the size of its community, how well the project serves this community, and how widely its software/database is used. A potential solution is to analyze PubMed publication data. We are seeking applicants with programming skills (in Python or Java) to perform this analysis.

The project involves:

# Using the PubMed web API to filter publications based on keywords.
# Analyzing paper abstracts to identify research institutions and groups that form the community.
# Filtering the community list to exclude unrelated co-authors.
# Prioritize papers identified by GlycoSiteMiner for curation via TableMaker

A subproject will involve analyzing the full text of papers (when available) for keywords or resource and database names. The results of the analysis will be discussed with GlyGen project member who will suggest changes and improvements to the analysis and data presentation. Source code developed as part of this project will be documented and shared in a public GitHub repository. If you have any other ideas or methods you would like to explore, please reach out to rene@ccrc.uga.edu to discuss them.

==== 4. PredictMod Machine Learning Project Ideas ====
POC: Lori Krammer, Tianyi Wang, Pat McNeely (optional)

Identifying relevant and useful publicly-available datasets for machine learning is currently a resource-intensive task. This curation project aims to develop a corpus for training an AI model to recommend PMIDs with publicly-available datasets useful for intervention outcome prediction models. The corpus will include an annotation spreadsheet + annotated PDFs for PubMed articles relevant to prostate, lung, breast, and liver cancer, and focus on indicators such as condition, intervention, and response.

PMID curation involves:

# Identify potentially relevant PMIDs that may have publicly-available datasets for training intervention outcome prediction models.
# Curate indicators of useful ML publications that could be used to train an LLM to recommend relevant publications for cancer modeling.
# Review peer curations and resolve annotation conflicts.

Interested individuals should reach out to lorikrammer@gwu.edu.

'''5. FDA-ARGOS Computation and Pathogen Curation Project'''

POC: Christie Woodside, Jonathon Keeney

# Update data tables for more efficient computations
## Student would review and input additional data and IDs in the tables/sheets used to perform computations. This would be manual work (but super important), but would require high attention to detail.
## Additional Work: Requires Python/shell coding background. Student would run scripts that prepare and format data tables that are pushed to data.argosdb.org. Coding knowledge is needed in case of errors, bugs, or other mishaps in the code. Ongoing work as computations are performed.
# Curate and report on current pathogens to upload to ARGOS
## Student would work on manual curation of circulating pathogens to be added to data.argosdb.org. Regular check-ins and reports of what was found.
## Locate assembly IDs, reads, and metagenomic information for these pathogens to be used in computations and deposited into data.argosdb.org.
## Provide documentation on why they were curated, why they are important, how they were selected, and how data was collected.
# QC Analysis using HIVE
## Analyze the curated pathogens using our QC ARGOS one-click pipeline.
## The results will be added to our ARGOS database.

If the student has any other ideas or methods they want to focus on, please reach out to christie.woodside@email.gwu.edu to discuss your idea and check if it will be feasible as a project for the summer.
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=== Requirements for Completion ===
'''Note:''' The following are mandatory. Failure to complete any will result in an incomplete volunteer record.

==== Documentation ====
All volunteers must maintain adequate documentation of their work, including written protocols and scripts submitted to GitHub.

==== Written Report ====
Submit a 1–2 page summary of your tasks and accomplishments to the Admin during the final week of your program.

==== Presentation & Slide Submission ====
Present your work last week of the 13-week period.

Slides must be submitted to the POCs.
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=== Completion Certificate ===
A certificate of completion and a letter of recommendation will be provided to all participants who successfully complete the program.
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=== Contact ===
mazumder_lab@gwu.edu.
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=== Volunteers (TBD) ===
{| class="wikitable"
|+
!Name
!Project Assigned
!Projects Interested
|-
|Diya Kamalabharathy
|
|PredictMod; Glyco web development
|-
|Anika Sikka
|
|GlyGen
|-
|Akale Kinfe
|
|BiomarkerKB
|-
|Nahom Abel
|
|BiomarkerKB
|-
|Harivinay P. Gujjula
|
|GlyGen
|-
|Sparsh Gupta
|
|BiomarkerKB
|-
|Mathias Belay
|
|BiomarkerKB
|}

Projects

2024-12-03T14:47:23Z

Hivelabwikiadmin:

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<h3>[https://hive.biochemistry.gwu.edu/dna.cgi?cmd=main The High-performance Integrated Virtual Environment (HIVE) platform]</h3>
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HIVE is a cloud-based environment optimized for the storage and analysis of extra-large data, such as biomedical data, clinical data, next-generation sequencing (NGS) data, mass spectrometry files, confocal microscopy images, post-market surveillance data, medical recall data, and many others. HIVE provides secure web access for authorized users to deposit, retrieve, annotate and compute on Big Data, and analyze the outcomes using web user interfaces. [https://docs.google.com/document/d/1F5iq00uKkJfdSsbwanvKOy-nPnwijH56mwbwa_HhzfY/edit?tab=t.0#heading=h.7dlfmngwfzih More here].
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<h3>[https://www.biocomputeobject.org/ BioCompute Objects (BCO)]</h3>
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The BioCompute is FDA funded project to establish a framework for community-based development of standards for harmonization of High-throughput Sequencing (HTS), standardization of data formats, promotion of interoperability, and bioinformatics verification protocols. The BioCompute Object (BCO) was developed in the High-throughput Sequencing Computational Standards for Regulatory Sciences (HTS-CSRS) initiative in the BioCompute Objects Portal (BOP), a web portal to serve as a collaborative ground to encourage a dialogue to facilitate interoperability between different bioinformatic pipelines, industries, and developers. HIVE capabilities have been leveraged to support the development of the BCO. The BCO is versatile and adaptable to other common HTS analysis platforms. [https://docs.google.com/document/d/1WQFZm_PFiQXob4NyOKq6y-2ywnbmNoFHSS27fYf3l4Y/edit?tab=t.0#heading=h.bs8eki17tykx More here].
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<h3>[https://www.glygen.org/ GlyGen]</h3>
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GlyGen (gly-glycobiology; gen-information), is an advanced glycoinformatics resource developed to facilitate discovery in basic and translational glycobiology research along with enhancing the integration of multidisciplinary information from diverse resources. GlyGen includes knowledge about molecular, biophysical and functional properties of glycans, genes, and proteins organized in pathways and ontologies, plus a rapidly growing body of biological big data related to cancer mutation and expression. GlyGen adopts an innovative user-driven approach for implementing, prioritizing and knowledge disseminating tools to address the questions and needs of glycobiology community.
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<h3>[https://hivelab.biochemistry.gwu.edu/predictmod PredictMod]</h3>
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PredictMod is an application designed to predict the outcome of an intervention prior to a patient initiating treatment. Our goal is to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. The PredictMod platform utilizes machine learning tools and complex datasets based on electronic health records, gut microbiome, and -omics data to forecast patient outcomes, often in response to treatment for a particular condition. While our primary condition of interest is Prediabetes, the tool is designed to be used for a variety of conditions, interventions, and data types.
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<h3>[[GW-FEAST]]</h3>
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The GW Federated Ecosystems for Analytics and Standardized Technologies (GW-FEAST) project is part of the ARPA-H FEAST performer team initiative that includes academic and industry partners. The goal of the ARPA-H performer teams is “to create bridges across data silos to make health data more accessible and usable”.
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<h3>[https://hivelab.tst.biochemistry.gwu.edu/biomarker-partnership Biomarker Partnership]</h3>
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The Biomarker Partnership is a CFDE sponsored project to develop a knowledgebase that will organize and integrate biomarker data from different public sources. The data will be connected to contextual information to show a novel systems-level view of biomarkers. The motivation for this project is to improve the harmonization and organization of biomarker data. This will be done by mapping biomarkers from public sources to, and across, CF data elements. This mapping will bridge knowledge across multiple DCCs and biomedical disciplines.
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<h3>[https://hivelab.tst.biochemistry.gwu.edu/gfkb Gut Microbiome Analytic System (Microbiome)]</h3>
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The HIVE team received NSF funding to develop a Gut Microbiome Monitoring System (GutFeeling) as a tool which when used over time will allow users to rectify their dietary (such as consumption of probiotics and prebiotics) and other lifestyle habits and to help restore their normal microbiome. Rapid analysis of the large amount of metagenomic data, a major bottleneck, has been resolved by our group through the development of a novel algorithm and accompanying software called CensuScope. Through analysis of healthy gut microbiome data, we are actively developing a Knowledge Base (GutFeelingKB) to provide a clearer picture of not only an ideal personalized microbiome but also establish baseline characteristics for each customer. The Mazumder Lab is collaborating with the Milken School of Public Health and Kamtek Sequencing Facility to investigate the relationship between bacterial species commonly present in the digestive tract, diet, physical activity, lifestyle habits, and metabolic risk factors. [https://docs.google.com/document/d/18WyVTJrrf-FR0sHt634vO8Lwel-4OQxP9sNar7gYYro/edit?tab=t.0#heading=h.7qbm3f7lky31 More here].
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<h3>HIVE-EQAPOL Project on HIVE NGS Data Processing and Analysis</h3>
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For this project, our group works closely with the External Quality Assurance Program Oversight Laboratory (EQAPOL) team to conduct HIV NGS data analysis and collaborate in terms of analyzing, storing, and tracking HIV NGS Data. Reliable identification of strains is critical for developing new assays, validating assay platforms, assisting regulators to evaluate test kits, monitoring HIV drug resistance, and informing vaccine development. The HIVE tools and platform are used for virus identification, recombination analysis, and clone discovery.
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<h3>[https://www.oncomx.org/ OncoMX]</h3>
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The OncoMX mission is to create an integrated cancer mutation and expression resource for exploring cancer biomarkers. OncoMX is a collaboration between the George Washington University (GW), NASA's Jet Propulsion Laboratory (JPL), the Swiss Institute of Bioinformatics (SIB), and the University of Delaware (UD). The core knowledgebase of OncoMX is derived from BioMuta and BioXpress integrated cancer mutation and expression databases. Normal expression data from Bgee and custom text mining software augment the cancer data to improve functional interpretation of the reported variants and expression profiles. All data are wrapped into the OncoMX database and web portal, mapped to additional functional information from NCI Early Detection Research Network (EDRN) and Reactome. It is expected that the large-scale integration of cancer data and supporting information, provided by OncoMX with direct community feedback, will benefit cancer research by improving synthesis of information and may make earlier detection a reality.
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<h3>[https://hive.biochemistry.gwu.edu/dna.cgi?cmd=main Glycoproteomics Characterization Workflow and Data-Analysis Pipeline for Vaccines and Biosimilars]</h3>
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In this FDA funded project we are extending High-performance Integrated Virtual Environment (HIVE) capabilities through the development and integration of software tools and datasets for comparative analysis of glycoproteins. Glycomic analysis has many angles and has been extensively reviewed in recent literature. We propose to rely on the independent development of the glycomics field and incorporate these approaches in the HIVE pipeline as they mature while we develop a standardized glycoinformatics pipeline that will benefit investigators and regulators at the FDA.
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