Comparison of software packages for detecting unannotated translated small open reading frames by Ribo-seq.

Accurate and comprehensive annotation of microprotein-coding small open reading frames (smORFs) is critical to our understanding of normal physiology and disease. Empirical identification of translated smORFs is carried out primarily using ribosome profiling (Ribo-seq). While effective, published Ribo-seq datasets can vary drastically in quality and different analysis tools are frequently employed. Here, we examine the impact of these factors on identifying translated smORFs. We compared five commonly used software tools that assess open reading frame translation from Ribo-seq (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH) and found surprisingly low agreement across all tools. Only ~2% of smORFs were called translated by all five tools, and ~15% by three or more tools when assessing the same high-resolution Ribo-seq dataset. For larger annotated genes, the same analysis showed ~74% agreement across all five tools. We also found that some tools are strongly biased against low-resolution Ribo-seq data, while others are more tolerant. Analyzing Ribo-seq coverage revealed that smORFs detected by more than one tool tend to have higher translation levels and higher fractions of in-frame reads, consistent with what was observed for annotated genes. Together these results support employing multiple tools to identify the most confident microprotein-coding smORFs and choosing the tools based on the quality of the dataset and the planned downstream characterization experiments of the predicted smORFs.

Integrated workflow for discovery of microprotein-coding small open reading frames.

Small open reading frame (smORF)-encoded microproteins, proteins containing less than 100-150 amino acids, are an emerging class of functional biomolecules. Here, we present a protocol for identifying translated smORFs in mammalian systems genome wide. We describe steps for generation of ribosome profiling (Ribo-seq) data, in silico translation of a transcriptome assembly to create an ORF database, and computational analysis of Ribo-seq to score individual smORFs for translation. Identification of translated smORFs is the first step to studying the functions of microproteins. For complete details on the use and execution of this protocol, please refer to Martinez et al.1.

Comparison of software packages for detecting unannotated translated small open reading frames by Ribo-seq.

Accurate and comprehensive annotation of microprotein-coding small open reading frames (smORFs) is critical to our understanding of normal physiology and disease. Empirical identification of translated smORFs is carried out primarily using ribosome profiling (Ribo-seq). While effective, published Ribo-seq datasets can vary drastically in quality and different analysis tools are frequently employed. Here, we examine the impact of these factors on identifying translated smORFs. We compared five commonly used software tools that assess ORF translation from Ribo-seq (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH), and found surprisingly low agreement across all tools. Only ~2% of smORFs were called translated by all five tools and ~15% by three or more tools when assessing the same high-resolution Ribo-seq dataset. For larger annotated genes, the same analysis showed ~72% agreement across all five tools. We also found that some tools are strongly biased against low-resolution Ribo-seq data, while others are more tolerant. Analyzing Ribo-seq coverage as a proxy for translation levels revealed that highly translated smORFs are more likely to be detected by more than one tool. Together these results support employing multiple tools to identify the most confident microprotein-coding smORFs, and choosing the tools based on the quality of the dataset and planned downstream characterization experiments of predicted smORFs.

Risk of contrast-induced nephropathy for patients receiving intravenous vs. intra-arterial iodixanol administration.

PURPOSE: To compare the incidence of contrast-induced nephropathy (CIN) for intravenous vs. intra-arterial administration of iodixanol, compared to non-administration. METHODS: We retrospectively identified 650 patients who had intravenous iodixanol-enhanced CT, 695 with intra-arterial iodixanol cardiac catheterization, 651 with unenhanced CT, and those who also had baseline and follow-up serum creatinine within 5 days of the exam. From the medical records, we recorded the gender, age, baseline and follow-up serum creatinine/eGFR; underlying renal injury risk factors; indication for imaging; contrast material administration volume, concentration, and route of administration; and use of pre-imaging prophylactic measures for CIN. Univariate and multivariate models were used to determine predictors of CIN. RESULTS: Baseline eGFR was lower for patients undergoing unenhanced CT than intravenous or intra-arterial patients (68 vs. 74.6 and 72.2, respectively, p < 0.01) and not different between intravenous and intra-arterial patients (p = 0.735). Simple logistic regression did not show a difference in the rate of CIN in patients who received intravenous vs. intra-arterial iodixanol (28 of 650, 4%, vs. 28 of 695, 4%, respectively, p = 0.798), nor a higher rate of CIN than seen with unenhanced CT (45 of 651, 7%, p = 0.99 and p = 0.98 by one-sided t test). Multivariate regression modeling showed that only elevated baseline creatinine or decreased eGFR and low hematocrit/hemoglobin were associated with CIN incidence (odds ratio 1.28 and 2.5; p < 0.023 and <0.006, respectively). CONCLUSIONS: Elevation in serum creatinine due to intravenous and intra-arterial iodixanol administration is infrequent and is not more common than after unenhanced CT scans.

Use of emergency department imaging in patients with minor trauma.

BACKGROUND: Advanced radiographic studies have detrimental risks, yet the prevalence of CT utilization in patients with minor trauma presenting to the emergency department (ED) has never been fully evaluated. Our objective was to evaluate the frequency of CT imaging in patients presenting to the ED for minor trauma. MATERIALS AND METHODS: A retrospective analysis of the California Office of Statewide Health Planning and Development Emergency Department and Ambulatory Surgery Data from 2005 to 2013 was performed. A total of 8,535,831 patients were identified using the following inclusion criteria: adult patients (age ≥18 y); with a traumatic ECODE diagnosis and injury severity score <9; and discharge to home. The primary study outcome measurement was the prevalence of CT imaging for each year in the study period. We performed univariate and multivariate analysis to evaluate clinical and hospital-level factors related to CT use in this population. We also performed a trend analysis using Poisson logistic regression to assess the trend of imaging scans over the study period. RESULTS: Of the study population, 5.9% received at least one CT study during their ED visit. The proportion of patients with at least one CT scan increased from 3.51% in 2005 to 7.17% in 2013 (P < 0.005). Adjusted predictors for CT included age 18-24 y or >45 y (P < 0.005), Medicare and self-pay patients (P < 0.005), fall injuries (P < 0.005), motor vehicle collision injuries (P < 0.005), and patients seen at level I/II trauma centers (P = 0.005). CONCLUSIONS: Even after clinical and demographic predictors were adjusted for, there was a 1.97-fold increase in CT among minor trauma patients from 2005-2013.

Synthetic MRI signal standardization: application to multi-atlas analysis.

From the image analysis perspective, a disadvantage of MRI is the lack of image intensity standardization. Differences in coil sensitivity, pulse sequence and acquisition parameters lead to very different mappings from tissue properties to image intensity levels. This presents challenges for image analysis techniques because the distribution of image intensities for different brain regions can change substantially from scan to scan. Though intensity correction can sometimes alleviate this problem, it fails in more difficult scenarios in which different types of tissue are mapped to similar gray levels in one scan but different intensities in another. Here, we propose using multi-spectral data to create synthetic MRI scans matched to the intensity distribution of a given dataset using a physical model of acquisition. If the multi-spectral data are manually annotated, the labels can be transfered to the synthetic scans to build a dataset-tailored gold standard. The approach was tested on a multi-atlas based hippocampus segmentation framework using a publicly available database, significantly improving the results obtained with other intensity correction methods.