HIVE Lab - Recent changes [en]

Volunteership Fall 2026

Maria.kim — Mon, 31 Aug 2026 17:07:18 GMT

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	\|Urnisha and Rene		\|Urnisha and Rene
	\|GlyGen AI-Biocuration		\|GlyGen AI-Biocuration
			\|-
			\|Pratik Shetty<sup>†</sup>
			\|BiomarkerKB
			\|Cyrus, Maria, Jeet
			\|BiomarkerKB Biocuration
			\|-
			\|Pradnya Tendolkar<sup>†</sup>
			\|BiomarkerKB
			\|Cyrus, Maria, Jeet
			\|BiomarkerKB Biocuration
	\|}		\|}
	<nowiki>*</nowiki>Returning volunteer.		<nowiki>*</nowiki>Returning volunteer.

Volunteership Fall 2026

Urnisha.bhuiyan — Fri, 28 Aug 2026 20:36:36 GMT

Contact

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	\|PredictMod		\|PredictMod
	\|-		\|-
	\|		\|Taylor Dimenna<nowiki>*</nowiki>
	\|		\|GlyGen
	\|		\|Urnisha and Rene
	\|		\|GlyGen AI-Biocuration
	\|-		\|-
	\|		\|Daniel Auerbach*<sup>†</sup>
	\|		\|GlyGen
	\|		\|Urnisha and Rene
	\|		\|GlyGen Publication Analysis
	\|-		\|-
	\|		\|Shashank Kesineni
	\|		\| GlyGen
	\|		\| Urnisha and Rene
	\|		\| GlyGen AI-Biocuration
			\|-
			\|Jumana Mahfouz
			\|GlyGen
			\|Urnisha and Rene
			\|GlyGen Publication Analysis
			\|-
			\|Swapnaneel Chatterjee<nowiki>*</nowiki>
			\|GlyGen
			\|Sujeet and Rene
			\|GlyGen AI ChatBot
			\|-
			\|Luis Jimenez
			\|GlyGen
			\|Urnisha and Rene
			\|GlyGen AI-Biocuration
	\|}		\|}
	<nowiki>*</nowiki>Returning volunteer.		<nowiki>*</nowiki>Returning volunteer.

AI ML Bootcamp 2026

Mazumder — Fri, 21 Aug 2026 12:26:51 GMT

Organizing Committee

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	'''Sponsors''': Dept. of Biochemistry and Molecular Medicine (GW) \| McCormick Genomic and Proteomic Center (GW) \| Lenovo		'''Sponsors''': Dept. of Biochemistry and Molecular Medicine (GW) \| McCormick Genomic and Proteomic Center (GW) \| Lenovo

	== ~~Draft~~ Agenda ==		== Agenda ==
	All times are in Eastern Standard Time.		All times are in Eastern Standard Time.
	{\| class="wikitable"		{\| class="wikitable"
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	<sup>3</sup>Research Technology Services, GWIT		<sup>3</sup>Research Technology Services, GWIT

	~~Voulenteers~~: Ali Mutasim, Jennifer Oneill		Volunteers: Ali Mutasim, Jennifer Oneill, Miao Wang

Volunteership Fall 2026

Hivelabwikiadmin — Thu, 20 Aug 2026 13:58:19 GMT

Contact

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	!Project Interested		!Project Interested
	\|-		\|-
	\|		\|Rhea Charles
	\|		\|PredictMod
	\|		\|Pat McNeely
	\|		\|PredictMod
	\|-		\|-
	\|		\|Sri Piramanayagam
	\|		\|PredictMod
	\|		\|Pat McNeely
	\|		\|PredictMod
	\|-		\|-
	\|		\|Aryan Jagani
	\|		\|PredictMod
	\|		\|Pat McNeely
	\|		\|PredictMod
	\|-		\|-
	\|		\|

AI ML Bootcamp 2026

Mazumder — Tue, 18 Aug 2026 15:57:42 GMT

Draft Agenda

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	\|9:15 am		\|9:15 am
	\|5-Year Industry Roadmap for Advancements in AI; Introduction to GW IT AI/ML Computing Resources		\|5-Year Industry Roadmap for Advancements in AI; Introduction to GW IT AI/ML Computing Resources
	\|~~Isaac Williamson~~<sup>4</sup>, Glen MacLachlan<sup>5</sup>		\|Daniel Stevens<sup>4</sup>, Glen MacLachlan<sup>5</sup>
	\|-		\|-
	\|10:00 am		\|10:00 am
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	<sup>3</sup>Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW		<sup>3</sup>Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW

	<sup>4</sup>~~HPC~~ and ~~AI Sales~~, Lenovo		<sup>4</sup>Healthcare and Life Sciences Strategy and Product Development, Lenovo

	<sup>5</sup>Asst. Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW		<sup>5</sup>Asst. Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW

Volunteership Fall 2026

Hivelabwikiadmin — Tue, 18 Aug 2026 04:12:58 GMT

← Older revision		Revision as of 04:12, 18 August 2026
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	'''Volunteer Zoom Kick-Off Meeting'''		'''Volunteer Zoom Kick-Off Meeting'''

	Date: ~~TBD~~		Date: August 28th \| 4:00 PM - 5:00 PM ET

	'''Program Dates: 9/7/2026 - 12/6/2026'''		'''Program Dates: 9/7/2026 - 12/6/2026'''
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	<sup>‡</sup>Not directly involved in the semester curriculum; long-term volunteer.		<sup>‡</sup>Not directly involved in the semester curriculum; long-term volunteer.

	== ~~Summer~~ 2026 Symposium ==		== Fall 2026 Symposium ==
	The Summer symposium will be held virtually.		The Summer symposium will be held virtually.
	'''Date:''' TBD		'''Date:''' TBD

Volunteership Fall 2026

Mazumder — Mon, 17 Aug 2026 16:45:10 GMT

← Older revision		Revision as of 16:45, 17 August 2026
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	August 22th \| 12:00 PM ET		August 22th \| 12:00 PM ET

	~~Please 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'''		'''Volunteer Zoom Kick-Off Meeting'''
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	=== Volunteer Expectations ===		=== Volunteer Expectations ===

	# Minimum commitment of 10 hours per week.		# Minimum commitment of 10 hours per week. If you want to commit more hours please let us know.
	# Progress updates via Slack at least 3 days per week (scrum).		# Progress updates via Slack at least 3 days per week (scrum).
	# Volunteers should be responsive to email/slack communications.		# Volunteers should be responsive to email/slack communications.

User:ReneRanzinger

Mazumder — Sun, 16 Aug 2026 14:14:14 GMT

User account ReneRanzinger was created by Mazumder and password was sent by email

AI ML Bootcamp 2026

Mazumder — Sat, 15 Aug 2026 12:18:51 GMT

Organizing Committee

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	<u>'''Attendance at one mandatory Zoom/in-person information session is required for all in-person attendees. Participants must attend either the August 10 session from 11:00 AM-12:00 PM or the August 12 session from 4:00-5:00 PM. Zoom details will be sent two weeks before the session date. Because space is limited, participants who do not attend one of the required sessions may be removed from the in-person program and placed in the online group.'''</u>		<u>'''Attendance at one mandatory Zoom/in-person information session is required for all in-person attendees. Participants must attend either the August 10 session from 11:00 AM-12:00 PM or the August 12 session from 4:00-5:00 PM. Zoom details will be sent two weeks before the session date. Because space is limited, participants who do not attend one of the required sessions may be removed from the in-person program and placed in the online group.'''</u>

	'''Sponsors''': Dept. of Biochemistry and Molecular Medicine, GW		'''Sponsors''': Dept. of Biochemistry and Molecular Medicine (GW) \| McCormick Genomic and Proteomic Center (GW) \| Lenovo

	== Draft Agenda ==		== Draft Agenda ==
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	<sup>3</sup>Research Technology Services, GWIT		<sup>3</sup>Research Technology Services, GWIT

			Voulenteers: Ali Mutasim, Jennifer Oneill

User:Rene Ranzinger

Mazumder — Sat, 15 Aug 2026 11:49:49 GMT

User account Rene Ranzinger was created by Mazumder and password was sent by email

Metagenomics

Hivelabwikiadmin — Fri, 14 Aug 2026 03:39:32 GMT

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	'''CensuScope''' is designed to provide a fast and computationally efficient overview of the taxonomic composition of metagenomic sequencing data.		'''CensuScope''' is designed to provide a fast and computationally efficient overview of the taxonomic composition of metagenomic sequencing data.


	== CensuScope ==		== CensuScope ==
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	# Aggregate taxonomic assignments across all iterations to estimate the taxonomic composition of the sample.		# Aggregate taxonomic assignments across all iterations to estimate the taxonomic composition of the sample.
	# Generate taxonomic summary reports and abundance estimates.		# Generate taxonomic summary reports and abundance estimates.

			<gallery widths="500" heights="400">

			File:Screenshot 2026-08-13 at 11.33.38 PM.png\|Figure 1. CensuScope Algorithm.

			</gallery>

	== slimNT ==		== slimNT ==

File:Screenshot 2026-08-13 at 11.33.38 PM.png

Hivelabwikiadmin — Fri, 14 Aug 2026 03:34:48 GMT

Hivelabwikiadmin uploaded File:Screenshot 2026-08-13 at 11.33.38 PM.png

New page

Metagenomics

Hivelabwikiadmin — Fri, 14 Aug 2026 03:33:49 GMT

Created page with "= Metagenomics Analysis using CensuScope = == Overview == Metagenomics is the study of genetic material recovered directly from environmental, clinical, or mixed biological samples. Unlike targeted sequencing approaches that focus on a single organism or gene, metagenomic sequencing captures nucleic acids from all organisms present within a sample. This allows simultaneous detection and characterization of bacteria, viruses, fungi, parasites, and host sequences. Becau..."

New page

= Metagenomics Analysis using CensuScope =

== Overview ==

Metagenomics is the study of genetic material recovered directly from environmental, clinical, or mixed biological samples. Unlike targeted sequencing approaches that focus on a single organism or gene, metagenomic sequencing captures nucleic acids from all organisms present within a sample. This allows simultaneous detection and characterization of bacteria, viruses, fungi, parasites, and host sequences.

Because metagenomic datasets often contain millions of sequencing reads from many different organisms, rapid taxonomic profiling is an important first step before more computationally intensive downstream analyses.

'''CensuScope''' is designed to provide a fast and computationally efficient overview of the taxonomic composition of metagenomic sequencing data.

== CensuScope ==

CensuScope is a census-based metagenomic taxonomic profiling tool developed to rapidly estimate the taxonomic composition of next-generation sequencing (NGS) datasets. Instead of aligning every sequencing read against a comprehensive reference database, CensuScope applies a subsampling strategy that randomly selects a user-defined number of reads from the input dataset for analysis.

Each randomly selected read set is aligned against the NCBI nucleotide (nt) database using BLAST, and the resulting alignments are mapped to the NCBI Taxonomy database to determine their taxonomic classification. This process is repeated across multiple independent iterations, and the taxonomic results are aggregated to estimate the overall composition of the metagenomic sample. Taxa consistently observed across the majority of iterations are retained, while sporadic or low-confidence classifications can be filtered to improve result stability.

By analyzing only a representative subset of sequencing reads rather than the complete dataset, CensuScope substantially reduces computational requirements while maintaining an accurate estimate of the taxonomic distribution. This sampling-based strategy enables rapid characterization of large metagenomic datasets on both personal computers and high-performance computing environments.

== General Workflow ==

The CensuScope workflow consists of the following steps:
# Load sequencing reads in FASTA or FASTQ format.
# Randomly select a user-defined number of reads from the input dataset.
# Align the sampled reads against the reference nucleotide database using BLAST.
# Retrieve the corresponding taxonomic information from the NCBI Taxonomy database.
# Assign each read to the selected taxonomic level.
# Repeat the random sampling and classification process for multiple iterations.
# Aggregate taxonomic assignments across all iterations to estimate the taxonomic composition of the sample.
# Generate taxonomic summary reports and abundance estimates.

== slimNT ==

'''slimNT''' is a curated nucleotide reference database developed to improve the efficiency of metagenomic analysis while maintaining broad taxonomic coverage.

Although CensuScope can be used with the complete NCBI nucleotide (nt) database, the continuously growing size of nt makes routine metagenomic analysis increasingly computationally expensive. Building and maintaining the database requires substantial storage space and computational resources, and BLAST searches against the complete database can be time-consuming.

To address these challenges, slimNT was developed as a streamlined subset of the nt database. By retaining representative nucleotide sequences while reducing the overall database size, slimNT provides a practical balance between taxonomic coverage and computational efficiency. This enables faster database construction, reduced storage requirements, shorter BLAST search times, and improved performance for routine metagenomic analyses.

When combined with CensuScope's sampling-based workflow, slimNT provides a rapid and efficient solution for taxonomic profiling of metagenomic sequencing data.

== Why Use slimNT Instead of nt? ==

The complete NCBI nt database provides the broadest available nucleotide sequence coverage and is appropriate when maximum sensitivity is required. However, its large size also presents several practical challenges for routine analysis.

Compared with the complete nt database, slimNT offers several advantages:

* Significantly smaller database size
* Lower storage requirements
* Faster database construction
* Reduced memory usage during analysis
* Shorter BLAST search times
* Easier deployment on local workstations, servers, and cloud environments

While the complete nt database remains the most comprehensive reference database, many metagenomic applications do not require searching every available nucleotide sequence. For these routine analyses, slimNT provides an effective compromise by preserving broad taxonomic representation while substantially reducing computational cost.

==Reference==
Shamsaddini, A., Pan, Y., Johnson, W. E., Krampis, K., Shcheglovitova, M., Simonyan, V., Zanne, A., & Mazumder, R. (2014). Census-based rapid and accurate metagenome taxonomic profiling. BMC genomics, 15(1), 918. https://doi.org/10.1186/1471-2164-15-918

User:Vishal.bakshi

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User:Skim658

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User:Lorikrammer

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User:JewelDias

Mazumder — Thu, 13 Aug 2026 12:36:09 GMT

User account JewelDias was created by Mazumder and password was sent by email

Volunteership Fall 2026

Mazumder — Thu, 13 Aug 2026 12:29:00 GMT

Dates

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	Date: TBD		Date: TBD

	'''Program Dates: ~~TBD~~'''		'''Program Dates: 9/7/2026 - 12/6/2026'''

			If you are interested, please email mazumder_lab@gwu.edu your resume and a ranked list of the [[Volunteership Fall 2026#Potential Projects\|projects]] that interest you most. You can also indicate if you want to focus on specific areas that are of interest to you.

	Remote \| Hybrid for GW employees and students (Ross Hall 5th floor)		Remote \| Hybrid for GW employees and students (Ross Hall 5th floor)
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	=== Volunteer Expectations ===		=== Volunteer Expectations ===

	# Minimum commitment of 20 hours per week.		# Minimum commitment of 10 hours per week.
	# Progress updates via Slack at least 3 days per week (scrum).		# Progress updates via Slack at least 3 days per week (scrum).
	# Volunteers should be responsive to email/slack communications.		# Volunteers should be responsive to email/slack communications.

AI ML Bootcamp 2026

Mazumder — Wed, 12 Aug 2026 11:59:22 GMT

Program Information

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	Date: Friday, August 21		Date: Friday, August 21

	Time: 9:00 AM – 5:00 PM		Time: 8:30 AM – 5:00 PM

	'''Location:''' Ross Hall, Himmelfarb Library - Rooms 102 -103B		'''Location:''' Ross Hall, Himmelfarb Library - Rooms 102 -103B
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	Anyone with a valid GW ID can register. You need to be logged into your browser with your GW credentials to access the form.		Anyone with a valid GW ID can register. You need to be logged into your browser with your GW credentials to access the form.

	<u>'''Attendance at one mandatory Zoom/in-person information session is required. Participants must attend either the August 10 session from 11:00 AM-12:00 PM or the August 12 session from 4:00-5:00 PM. Zoom details will be sent two weeks before the session date. Because space is limited, participants who do not attend one of the required sessions may be removed from the program.'''</u>		<u>'''Attendance at one mandatory Zoom/in-person information session is required for all in-person attendees. Participants must attend either the August 10 session from 11:00 AM-12:00 PM or the August 12 session from 4:00-5:00 PM. Zoom details will be sent two weeks before the session date. Because space is limited, participants who do not attend one of the required sessions may be removed from the in-person program and placed in the online group.'''</u>

			'''Sponsors''': Dept. of Biochemistry and Molecular Medicine, GW

	== Draft Agenda ==		== Draft Agenda ==
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	\|8:30 am		\|8:30 am
	\|Registration desk open		\|Registration desk open
	\|Jewel Dias		\|Jewel Dias<sup>1</sup>, Raja Mazumder<sup>2</sup>
			\|-
			\|8:45 am
			\|Logistics and welcome
			\|Jewel Dias<sup>1</sup>, Raja Mazumder<sup>2</sup>
	\|-		\|-
	\|9:00 am		\|9:00 am
	\|Introduction ~~and welcome~~		\|Introduction: Need of AI/ML in biomedical research
	\|~~Raja Mazumder,~~ Anelia Horvath		\|Anelia Horvath<sup>3</sup>
	\|-		\|-
	\|9:15 am		\|9:15 am
	\|5-Year Industry Roadmap for Advancements in AI; Introduction to GW IT AI/ML Computing Resources		\|5-Year Industry Roadmap for Advancements in AI; Introduction to GW IT AI/ML Computing Resources
	\|Isaac Williamson, Glen MacLachlan		\|Isaac Williamson<sup>4</sup>, Glen MacLachlan<sup>5</sup>
	\|-		\|-
	\|10:00 am		\|10:00 am
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	\|10:15 am		\|10:15 am
	\|AI/ML in biomedical sciences lecture		\|AI/ML in biomedical sciences lecture
	\|Patrick McNeely		\|Patrick McNeely<sup>6</sup>
	\|-		\|-
	\|1:40 am		\|11:40 am
	\|Hands-on model generation		\|Hands-on model generation
	\|Patrick McNeely		\|Patrick McNeely
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	\|1:30 pm		\|1:30 pm
	\|AI/ML lecture and hands-on		\|AI/ML lecture and hands-on
	\|Ryan Watkins		\|Ryan Watkins<sup>7</sup>
	\|-		\|-
	\|2:00 pm		\|2:00 pm
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	\|Pat McNeely and Ryan Watkins		\|Pat McNeely and Ryan Watkins
	\|}		\|}
			<sup>1</sup>Research Assoc. Dept. of Biochemistry and Molecular Medicine, SMHS, GW

			<sup>2</sup>Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW

			<sup>3</sup>Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW

			<sup>4</sup>HPC and AI Sales, Lenovo

			<sup>5</sup>Asst. Professor, Dept. of Biochemistry and Molecular Medicine, SMHS, GW

			<sup>6</sup>HPC Systems Architect \| AI Infrastructure, Research Technology Services, GWIT

			<sup>7</sup>Professor, Graduate School of Education and Human Development; Assoc. Director, GW Trustworthy AI

	== Organizing Committee ==		== Organizing Committee ==
	Raja Mazumder, Patrick McNeely, Ryan Watkins, Glen MacLachlan, Rubeel Iqbal, Anelia Horvath, Jewel Dias		Raja Mazumder<sup>1</sup>, Patrick McNeely<sup>1</sup>, Ryan Watkins<sup>2</sup>, Glen MacLachlan<sup>3</sup>, Rubeel Iqbal<sup>3</sup>, Anelia Horvath<sup>1</sup>, Jewel Dias<sup>1</sup>

			<sup>1</sup>Dept. of Biochemistry and Molecular Medicine, SMHS, GW

			<sup>2</sup>Graduate School of Education and Human Development

			<sup>3</sup>Research Technology Services, GWIT

Volunteership Fall 2026

Hivelabwikiadmin — Mon, 10 Aug 2026 16:00:32 GMT

6. BioCompute Objects User Research Project

← Older revision		Revision as of 16:00, 10 August 2026
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	==== 5. PredictMod Machine Learning (ML) Modeling Project ====		==== 5. PredictMod Machine Learning (ML) Modeling Project ====
	POC: ~~Lori Krammer,~~ Pat McNeely		POC: Pat McNeely

	Volunteers will conduct ML modeling using publicly-available -omics datasets that were previously identified (see our [[Recommended Publications for Intervention Outcome Prediction Models\|Recommended Publications for IOPMs]] page). This volunteership will involve data harmonization, model training, and pipeline documentation.		Volunteers will conduct ML modeling using publicly-available -omics datasets that were previously identified (see our [[Recommended Publications for Intervention Outcome Prediction Models\|Recommended Publications for IOPMs]] page). This volunteership will involve data harmonization, model training, and pipeline documentation.
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	# Volunteership documentation (final report, progress updates, symposium presentation)		# Volunteership documentation (final report, progress updates, symposium presentation)

	Interested individuals should reach out to ~~lorikrammer~~@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.		Interested individuals should reach out to pmcneely@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.

	==== 6~~. BioCompute Objects User Research Project ====~~		==== 6. BiomarkerKB Biocuration Project ====
	~~POC: Lori Krammer, Pat McNeely~~

	Volunteers will conduct individual audits and user researcher to improve the human readability of BioCompute Objects (BCOs) and the project documentation. This volunteership will involve user research, prototyping, and documentation.

	~~Tasks associated with the project include:~~

	# Reviewing existing documentation to gain a comprehensive understanding of BioCompute Objects, their relevance to bioinformatics, and key user personas. The volunteer will identify and report gaps in the current documentation.
	~~# Conducting user research to understand pain points and desired outcomes. The volunteer will develop user stories based on interviews with BCO users.~~
	~~# Prototyping improvements to the BCO documentation and/or portal based on user stories. This could involve visual diagrams, wiki restructuring, or decision logs.~~

	~~Deliverables will include:~~

	~~# User research report with user story maps~~
	~~# BCO documentation improvement plan~~
	~~# Volunteership documentation (final report, progress updates, symposium presentation)~~

	~~Interested individuals should reach out to lorikrammer@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.~~

	~~==== 7~~. BiomarkerKB Biocuration Project ====
	POC: Jeet Vora (primary), Maria Kim, Cyrus Au-Yeung		POC: Jeet Vora (primary), Maria Kim, Cyrus Au-Yeung

Volunteership Fall 2026

Urnisha.bhuiyan — Fri, 07 Aug 2026 18:18:35 GMT

← Older revision		Revision as of 18:18, 7 August 2026
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	The goal of this project is to develop a production-ready search and discovery platform for glycoscience databases and software tools. The volunteer will enhance an existing prototype that provides a modern, user-friendly alternative to traditional resource catalogs. The platform will enable researchers to efficiently discover relevant databases, software tools, and analytical resources based on their scientific needs.		The goal of this project is to develop a production-ready search and discovery platform for glycoscience databases and software tools. The volunteer will enhance an existing prototype that provides a modern, user-friendly alternative to traditional resource catalogs. The platform will enable researchers to efficiently discover relevant databases, software tools, and analytical resources based on their scientific needs.

	A major component of the project will be expanding the underlying resource catalog to include not only databases but also software tools and analysis platforms. The volunteer will improve the platform's filtering and search capabilities and investigate how LLMs can be incorporated to support natural language queries.		A major component of the project will be expanding the underlying resource catalog to include not only databases but also software tools and analysis platforms. The volunteer will improve the platform's filtering and search capabilities and investigate how LLMs can be incorporated to support natural language queries

	~~More Details Linked Here~~		[[:File:GlyGen Volunteership (Fall 2026).pdf\|Further information can be found here]]

	==== 2. GlyGen AI-Assisted Biocuration Project: Species, Tissue, and Disease Annotation ====		==== 2. GlyGen AI-Assisted Biocuration Project: Species, Tissue, and Disease Annotation ====
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	The resulting workflows will support GlyGen's ongoing efforts to harmonize metadata from publications, databases, and legacy resources, ultimately improving data quality and interoperability across the glycoscience ecosystem.		The resulting workflows will support GlyGen's ongoing efforts to harmonize metadata from publications, databases, and legacy resources, ultimately improving data quality and interoperability across the glycoscience ecosystem.

			[[:File:GlyGen Volunteership (Fall 2026).pdf\|Further information can be found here]]

	==== 3. GlyGen Publication Analysis Project ====		==== 3. GlyGen Publication Analysis Project ====
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	Ultimately, the analysis may be integrated with GlyGen usage metrics, such as Google Analytics data, to identify regions and research hotspots where glycobiology research is active, but GlyGen adoption appears limited. Such information can help guide future outreach, training, and community engagement activities.		Ultimately, the analysis may be integrated with GlyGen usage metrics, such as Google Analytics data, to identify regions and research hotspots where glycobiology research is active, but GlyGen adoption appears limited. Such information can help guide future outreach, training, and community engagement activities.

			[[:File:GlyGen Volunteership (Fall 2026).pdf\|Further information can be found here]]

	'''4. Glycoscience Educational Chatbot'''		'''4. Glycoscience Educational Chatbot'''
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	educators, providing valuable feedback for future development and broader deployment.		educators, providing valuable feedback for future development and broader deployment.

			[[:File:GlyGen Volunteership (Fall 2026).pdf\|Further information can be found here]]

	==== 5. PredictMod Machine Learning (ML) Modeling Project ====		==== 5. PredictMod Machine Learning (ML) Modeling Project ====

File:GlyGen Volunteership (Fall 2026).pdf

Urnisha.bhuiyan — Fri, 07 Aug 2026 18:12:59 GMT

Urnisha.bhuiyan uploaded File:GlyGen Volunteership (Fall 2026).pdf

New page

GlyGen Volunteership Details

Volunteership Fall 2026

Urnisha.bhuiyan — Fri, 07 Aug 2026 18:11:18 GMT

1. Glycoscience Resource Discovery and Search Platform

← Older revision		Revision as of 18:11, 7 August 2026
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	----		----

	==== 1. ~~GlycoSiteMiner Curation Project~~ ====		==== 1. Glycoscience Resource Discovery and Search Platform ====
	POC: ~~Kate Warner~~		POC: Rene Ranzinger

	~~GlycoSiteMiner (PMID: [https://pubmed.ncbi.nlm.nih.gov/40401984/ 40401984])~~ is ~~a large language model (LLM)-based tool developed by the GlyGen team~~ to ~~automate~~ a ~~literature~~-~~mining pipeline~~ that ~~extracts experimentally validated~~, ~~protein sequence–specific glycosylation sites from PubMed abstracts~~. ~~By leveraging natural language processing~~, ~~GlycoSiteMiner accelerates the identification of glycosylation evidence that would otherwise require extensive manual review~~.		The goal of this project is to develop a production-ready search and discovery platform for glycoscience databases and software tools. The volunteer will enhance an existing prototype that provides a modern, user-friendly alternative to traditional resource catalogs. The platform will enable researchers to efficiently discover relevant databases, software tools, and analytical resources based on their scientific needs.

	~~The objective~~ of ~~this~~ project is to ~~validate these text-mined entries~~ and ~~curate them into structured datasets using GlyTableMaker (https://glygen~~.~~ccrc.uga.edu/tablemaker), a companion tool designed to support~~ the ~~deposition of glycans~~ and ~~glycoproteins, assignment of standardized metadata,~~ and ~~generation of high-quality Excel/CSV tables. This process ensures that extracted information is accurate, consistent, and suitable for integration into GlyGen’s knowledgebase~~.		A major component of the project will be expanding the underlying resource catalog to include not only databases but also software tools and analysis platforms. The volunteer will improve the platform's filtering and search capabilities and investigate how LLMs can be incorporated to support natural language queries.

	This opportunity provides hands-on experience in biocuration workflows, including data validation, standardization, and quality control. Participants will deepen their understanding of glycobiology concepts, gain practical experience working with biological databases, and develop skills in evaluating and refining LLM-generated outputs for scientific applications.		More Details Linked Here

	==== 2. GlyGen Biocuration Project ====		==== 2. GlyGen AI-Assisted Biocuration Project: Species, Tissue, and Disease Annotation ====
	POC: Rene Ranzinger and Urnisha Bhuiyan		POC: Rene Ranzinger and Urnisha Bhuiyan

	~~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 contained within them remains highly valuable to~~ the ~~research community~~. ~~Integrating these legacy datasets into modern databases or knowledgebases~~, ~~such as GlyGen, presents a significant challenge because much of~~ the ~~associated metadata (e.g., species~~, tissue, disease~~, cell line) is recorded as free-text that does not conform to the standardized dictionaries and ontologies used by current resources~~.		The goal of this project is to improve and expand GlyGen's AI-assisted biocuration workflows for metadata normalization and ontology mapping. The volunteer will review the existing species annotation pipeline, evaluate its performance, and refine the species-specific system prompt used by the LLM. Based on lessons learned from species mapping, the project will then extend the methodology to additional biomedical concepts, particularly tissue and disease annotations.

	~~To address this challenge, this~~ project will ~~leverage large language models (LLMs) to automate the mapping of free-text metadata from legacy databases, specifically CarbBank and CFG~~, ~~to standardized accessions in authoritative resources such as NCBI Taxonomy, Disease Ontology~~, and ~~Cellosaurus~~. The LLM-~~based workflow will identify and normalize synonyms,~~ abbreviations, ~~and spelling variants (e.g.~~, ~~“human~~,” “man,” or “h. sapiens” mapped to Homo sapiens), enabling scalable and reproducible metadata harmonization that would otherwise require extensive manual curation. The LLM tasks will be performed using OpenAI resources integrated into the GlyGen curation pipeline. The project involves the development of Python scripts to read and write data, invoke the OpenAI API and compare results with manual curated data. Another aspect of the work is the development and finetunning of a prompt for ChatGPT to ensure reliable and ~~accurate mapping is produced~~.		A major focus of the project will be prompt engineering, performance evaluation, and quality assessment. The student will investigate how well the LLM can identify the correct ontology terms when presented with real-world biomedical metadata containing abbreviations, synonyms, misspellings, and incomplete descriptions.

	~~While the mapping process will be largely automated, manual validation~~ will ~~be incorporated as a quality-control step~~ to ~~assess model performance~~, ~~verify correctness~~, and ~~identify edge cases requiring refinement. This hybrid approach significantly reduces curator burden while ensuring high-~~quality~~, ontology-aligned annotations~~.		The resulting workflows will support GlyGen's ongoing efforts to harmonize metadata from publications, databases, and legacy resources, ultimately improving data quality and interoperability across the glycoscience ecosystem.

	~~The goal of this effort is to migrate and modernize datasets from CarbBank and CFG, making them interoperable with~~ GlyGen and ~~other contemporary glycoinformatics resources through a scalable, AI-assisted curation strategy.~~		==== 3. GlyGen Publication Analysis Project ====
			POC: Rene Ranzinger and Urnisha Bhuiyan

	~~Two projects:~~		The goal of this project is to further develop and expand an existing publication analysis and visualization framework that can be used to characterize and understand scientific research communities. Rather than focusing on a single predefined research domain, the analysis framework is designed to support dynamic community discovery through keyword-based literature searches. Publications identified through these searches will then be included in downstream analyses and graphically represented in form of charts, diagrams or graphs.

	~~# Taking predicted sites and curating them using table maker~~		The resulting analyses will help answer questions such as:
	~~# Website testing (all volunteers)~~

	~~For any questions, please contact Rene Ranzinger (rene@ccrc.uga.edu) or Kate Warner (k.warner1@email.gwu.edu).~~		* How large is a particular research community?
			* Where are its researchers geographically located?
			* Which institutions and investigators are most active?
			* Which organizations appear to be central contributors to the field?
			* Which research groups overlap with GlyGen's current user and collaborator communities?

	~~==== 3.~~ GlyGen ~~Publication Analysis Project ====~~		* Which potentially important communities or research groups are currently underrepresented in GlyGen outreach efforts?
	~~POC: Rene Ranzinger~~ and ~~Urnisha Bhuiyan~~
			Ultimately, the analysis may be integrated with GlyGen usage metrics, such as Google Analytics data, to identify regions and research hotspots where glycobiology research is active, but GlyGen adoption appears limited. Such information can help guide future outreach, training, and community engagement activities.

	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.		'''4. Glycoscience Educational Chatbot'''

	~~The project involves~~:		POC: Sujeet Kulkarni

	~~# Using~~ the ~~PubMed~~ web ~~API to filter publications based~~ on ~~keywords.~~		The goal of this project is to develop a beta version of the Glycoscience Educational Chatbot that can be deployed on a web server and evaluated by a pilot group of users. The student will analyze the strengths and limitations of the current alpha version and implement improvements that enhance usability, reliability, performance, and safety. Particular emphasis will be placed on improving the system&#39;s guardrails to ensure that responses remain focused on educational content, minimize hallucinations, and appropriately handle questions outside the scope of the knowledge base. The completed beta version will support formal user testing by researchers and
	# ~~Analyzing paper abstracts~~ to ~~identify research institutions~~ and ~~groups that form~~ the ~~community~~.
	~~# Filtering the community list to exclude unrelated co-authors~~

	~~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.		educators, providing valuable feedback for future development and broader deployment.

	==== 4. PredictMod Machine Learning (ML) Modeling Project ====		==== 5. PredictMod Machine Learning (ML) Modeling Project ====
	POC: Lori Krammer, Pat McNeely		POC: Lori Krammer, Pat McNeely

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	Interested individuals should reach out to lorikrammer@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.		Interested individuals should reach out to lorikrammer@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.

	==== 5. BioCompute Objects User Research Project ====		==== 6. BioCompute Objects User Research Project ====
	POC: Lori Krammer, Pat McNeely		POC: Lori Krammer, Pat McNeely

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	Interested individuals should reach out to lorikrammer@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.		Interested individuals should reach out to lorikrammer@gwu.edu. Please note that this project requires attendance at biweekly meetings and a final presentation of your work.

	==== 6. BiomarkerKB Biocuration Project ====		==== 7. BiomarkerKB Biocuration Project ====
	POC: Jeet Vora (primary), Maria Kim, Cyrus Au-Yeung		POC: Jeet Vora (primary), Maria Kim, Cyrus Au-Yeung