Protein Motif Recognition

Current biosequence analysis programs must be able to recognize protein-level homology or pattern in DNA sequences containing spurious insertions and deletions (indels) of nucleotides. We present a method for nding simple protein motifs in such sequences, building upon the standard dynamic programming sequence alignment algorithm. The commonly used frameshift penalty is replaced by a probabilistic model of the indel rate, providing both a means of controlling selectivity and a guide for evaluation. Furthermore, given a threshold on edit distance, the algorithm avoids computation of cells of the dynamic programming matrix that could not possibly be part of an acceptable alignment path. The method is practical for scanning large DNA sequence databases for protein motifs

Benefits of developing graduate medical education programs in community health systems

The creation of new CMS-funded Graduate Medical Education (GME) cap positions by the Consolidated Appropriations Act 2021 offers a unique opportunity for systems in community and rural settings to develop and expand their training programs. This article provides a review of the evidence behind the value proposition for system administrators to foster the growth of GME in community health systems. The infrastructure needed to accredit GME programs may reduce the cost of care for both the patients and the system through improved patient outcomes and facilitation of system efforts to recognize and mitigate social determinants of health. Residents, fellows and medical students expand the capacity of the current healthcare workforce of a system by providing coverage during healthcare emergencies and staffing services in difficult-to-recruit specialties. Those trainees are the nucleus of succession planning for the current medical staff, can facilitate the creation and expansion of service lines, and may elevate the profile of the system through scholarly work and equity and quality improvement activities. While creating GME programs in a community health system may, at first glance, be perceived as cost-prohibitive, there are robust advantages to a system for their creation

ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions.

Summary: Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organism\u27s genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy. Availability and implementation: Our tools are available as a web service with no installation needed (ProbAnnoWeb) at probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy) at github.com/PriceLab/probannopy. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Optimal Walker Height and Baseline Positioning for Functional Mobility: A Pilot Study

Background: Millions of adults use walkers for functional mobility. Inappropriate walker use is associated with incorrect height, forward-leaning posture, and increased energy expenditure. Few studies have investigated the impact of walker positions and their influence on triceps activity. Aims: The present study assessed walker height and baseline positioning for individuals with a 50% weight bearing restriction of the lower extremity, and implications for energy conservation. Methods: A total of 38 young adults participated in our study with a two-wheeled walker. Participants executed five walker positions while maintaining 50% weight bearing of the lower extremity to determine the effect on triceps electromyography (EMG) activity. Findings: Triceps EMG activity did significantly change across the five walker positions assessed, X2 (df = 37, p \u3c .001). Our study showed that the median EMG activity for positions B and A was significantly less than the median EMG activity for position E (p \u3c .001). The effect sizes for comparisons of positions B and E (r = 0.49) as well as A and E were the largest (r = 0.53). Conclusions: These findings suggest an elbow angle of 26 to 35 degrees (position A) to be superior in minimizing exertion of the triceps, followed by an elbow angle of 15 to 25 degrees (position B) when the walker is in line with heels of the client

Trans-Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets

Electron transfer dissociation (ETD) is an alternative fragmentation technique to CID that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline to process ETD data. Specifically, we have added support for fragment ion spectra from high-charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing data sets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced Trans-Proteomic Pipeline can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100\% over native output from a single sequence search engine

Reproducible Quantification of Cancer-Associated Proteins in Body Fluids Using Targeted Proteomics

The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies