# SUPPORT AND DEVELOPMENT OF EMAN FOR ELECTRON MICROSCOPY IMAGE PROCESSING

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2021 · $344,862

## Abstract

EMAN is one of the most well-established and widely used scientific image processing suites
targeting the rapidly growing CryoEM/CryoET community worldwide. In turn, the CryoEM and
CryoET studies which it enables permit determination of the structures of interacting
macromolecules both in-vitro and in-vivo, and are being used to better understand the
biochemical processes taking place in cells, to better identify potential drug targets and develop
novel diagnostics. With the higher resolutions now possible in this field, direct drug interaction
structural studies are now possible, and being used to gain insight into the mode of action of drugs
within the cell. Unlike many newer tools in the field, such as Relion, CisTEM and CryoSparc,
which focus on specific refinement tasks, EMAN is a versatile, modular suite capable of
performing a variety of image processing tasks with hundreds of algorithms supporting virtually
all of the standard file formats and mathematical conventions used in the field, as well as other
related imaging fields. It provides an ideal platform for prototyping fundamental new algorithm
developments, while still able to achieve data-limited resolution in single particle reconstruction.
While high resolution single particle refinement has become routine in recent years, thanks largely
to the dramatic data quality improvements provided by new detector technology, there remain
significant opportunities for improvements in mitigating model bias, efficient use of data, and
analysis of complexes with compositional or conformational variability. Some of the most
important problems from a biological perspective involve the sort of compositional and
conformational variability which remain challenging problems. The field also remains susceptible
to problems of initial model bias, which are exacerbated in systems exhibiting structural variability,
and as a result many structures are still published with exaggerated resolution claims. The
standard protocols used by many in the field typically involve discarding a very large fraction of
the raw data (as much as 80-90% in some cases), often based on qualitative assessments, raising
questions related to rigor and reproducibility of structural results. In this proposal, we will develop
or adapt image processing techniques to help resolve these issues, based on developments or
unrealized concepts from mathematics and computer science.

## Key facts

- **NIH application ID:** 10242780
- **Project number:** 5R01GM080139-16
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** STEVEN J LUDTKE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $344,862
- **Award type:** 5
- **Project period:** 2006-06-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242780

## Citation

> US National Institutes of Health, RePORTER application 10242780, SUPPORT AND DEVELOPMENT OF EMAN FOR ELECTRON MICROSCOPY IMAGE PROCESSING (5R01GM080139-16). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10242780. Licensed CC0.

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