Mass Spectrometry: An Ideal Method For Rna Modification Analysis

Currently there is no good way to measure and find the exact location of multiple RNA modifications. Existing technology can effectively find single varieties of modifications, but cannot identify co-occurrence. As the field of proteomics has shown, mass spectrometry is a powerful and versatile technique assessing broad ranges of chemical modifications in the context of the cellular environment. In this project I used our expertise in proteomics to build a mass spectrometry based platform for identifying RNA modifications. I have since set up both software and analytical platforms querying RNA modifications, and used this platform to survey human tRNA samples and identify what modifications there are, and where they occur

A qualitative study of urban hospital transitional care

This study is part of a mixed methods evaluation of a large urban medical center transitional care practice (NMG-TC). The NMG-TC provides integrated physical and behavioral health care for high need patients referred from the hospital emergency department or inpatient units and who lack a usual source of primary care. The study was designed for internal quality improvement and sought to evaluate staff perceptions of successful transitions for their medically and socially complex patients, and alternatively, the obstacles most likely to negatively impact patient outcomes. All 16 NMG-TC patient care staff were interviewed in a collaborative effort to produce empowered testimony that might go beyond expected clinical narratives. The interview schedule included questions on risk stratification, integrated mental health care, provider to provider handoffs, and how staff deal with key social determinates of patients’ health. The constant comparative method was used to deductively derive themes reflecting key domains of transitional care practice. Seven themes emerged: i) the need to quickly assess patient complexity; ii) emphasizing caring for major mental health and substance use issues; iii) obstacles to care for uninsured, often undocumented patients; iv) the intractability of homelessness; v) expertise in advancing patients’ health literacy, engagement and activation; vi) fragmented handoffs from hospital care and vii) to primary care in the community. Respondent stories emphasized methods of nurturing patients’ self-efficacy in a very challenging urban health environment. Findings will be used to conceptualize pragmatic, potentially high-impact transitional care quality improvement initiatives capable of better addressing frequent hospital use

A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

Abstract: The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. Existing software solutions lack the raw performance and statistical grounding to efficiently handle the numerous modifications found on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in four original datasets of varying complexity. In human tRNA, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification

A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

Abstract: The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. Existing software solutions lack the raw performance and statistical grounding to efficiently handle the numerous modifications found on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in four original datasets of varying complexity. In human tRNA, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification

Navigating the Patient Experience: A Qualitative Study on Patient Satisfaction in the Rush University Emergency Department

Mentor:Peter Benson From the Washington University Undergraduate Research Digest: WUURD, Volume 9, Issue 1, Fall 2013. Published by the Office of Undergraduate Research. Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences

A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry.

The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. Existing software solutions lack the raw performance and statistical grounding to efficiently handle the numerous modifications found on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in four original datasets of varying complexity. In human tRNA, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification

The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics

Many high-dimensional statistical inference problems are believed to possess inherent computational hardness. Various frameworks have been proposed to give rigorous evidence for such hardness, including lower bounds against restricted models of computation (such as low-degree functions), as well as methods rooted in statistical physics that are based on free energy landscapes. This paper aims to make a rigorous connection between the seemingly different low-degree and free-energy based approaches. We define a free-energy based criterion for hardness and formally connect it to the well-established notion of low-degree hardness for a broad class of statistical problems, namely all Gaussian additive models and certain models with a sparse planted signal. By leveraging these rigorous connections we are able to: establish that for Gaussian additive models the "algebraic" notion of low-degree hardness implies failure of "geometric" local MCMC algorithms, and provide new low-degree lower bounds for sparse linear regression which seem difficult to prove directly. These results provide both conceptual insights into the connections between different notions of hardness, as well as concrete technical tools such as new methods for proving low-degree lower bounds