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Interpreting function of non-coding sequences with synthetic biology and machine learning

Award Number: F31HG011431
ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 07/01/2020
PERIOD OF PERFORMANCE END DATE: 06/04/2023

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Interpreting function of non-coding sequences with synthetic biology and machine learning - PROJECT SUMMARY/ABSTRACT Most disease-associated variants lie in non-coding regions of the genome and exert their influence through effects on gene expression. However, we lack a predictive framework to interpret such non-coding variants, limiting how genomic data is used in precision medicine. We may be able to interpret non-coding variants with new machine learning algorithms, but so far the practical applications of machine learning in functional genomics have been limited because of two major challenges. First, the size and diversity of training data sets in functional genomics are orders of magnitude smaller than in applications where machine learning has been successful, such as image recognition and product recommendation. A second challenge is that if training data are not collected in an appropriate in vitro cellular model, then the resulting machine learning models may not generalize to relevant in vivo cell types. To improve the application of machine learning to non-coding variants, I propose to address both the limited size of training data sets and the efficacy of cell culture models. A core principle of machine learning is that model performance improves with more data. In Aim 1, I propose to increase the size and diversity of training data by performing iterative cycles of machine learning and experimental validation with Massively Parallel Reporter Assays (MPRAs). The key aspect of my approach is to algorithmically design each successive MPRA library to contain sequences that are most likely to improve the next round of modeling. I recently trained my first model on data that I collected from MPRA experiments of cis-regulatory sequences that function in mammalian photoreceptors. To avoid any issues with cell lines, I performed these experiments in ex vivo developing retinas, which retain the appropriate tissue architecture. However, unlike photoreceptors, most cell types are not experimentally tractable in their native physiological context. Thus, it will be important to determine how well in vitro cell lines recapitulate in vivo cis-regulation. In Aim 2, I propose to determine whether a tractable cell culture model can recapitulate results from ex vivo retinas. I will use existing MPRA data from ex vivo retinas as a standard to compare against data collected in cell lines engineered to express combinations of photoreceptor transcription factors. I aim to address whether engineering tractable cell lines to express tissue-specific transcription factors might be a general approach for collecting data to train machine learning models that generalize to in vivo systems. Successful completion of these aims will produce a general approach to increase the size and diversity of functional genomic training data, and may result in a general method for producing experimentally tractable systems for machine learning applications, ultimately helping us better apply genomic data to precision medicine.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = -$1,950 )
2024	2022	WASHINGTON UNIVERSITY, THE	1 BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	002	3	4/24/2024	NON-COMPETING CONTINUATION	-$47
2024	2022	WASHINGTON UNIVERSITY, THE	ONE BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	001	3	10/27/2023	NON-COMPETING CONTINUATION	$318
2024	2022	WASHINGTON UNIVERSITY, THE	ONE BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	000	3	10/25/2023	NON-COMPETING CONTINUATION	-$2,221
														Subtotal = -$1,950

Issue Date FY: 2022 ( Subtotal = $32,686 )
2022	2022	WASHINGTON UNIVERSITY, THE	ONE BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	000	3	6/29/2022	NON-COMPETING CONTINUATION	$32,686
														Subtotal = $32,686

Issue Date FY: 2021 ( Subtotal = $31,970 )
2021	2021	WASHINGTON UNIVERSITY, THE	ONE BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	000	2	6/3/2021	NON-COMPETING CONTINUATION	$31,970
														Subtotal = $31,970

Issue Date FY: 2020 ( Subtotal = $31,454 )
2020	2020	WASHINGTON UNIVERSITY, THE	ONE BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	001	1	5/28/2020	NEW	$0
2020	2020	WASHINGTON UNIVERSITY, THE	ONE BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Human Genome Research	000	1	5/28/2020	NEW	$31,454
														Subtotal = $31,454

Grand Total All Awards = $94,160

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Interpreting function of non-coding sequences with synthetic biology and machine learning

Award Number: F31HG011431

ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 07/01/2020

PERIOD OF PERFORMANCE END DATE: 06/04/2023

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