27 research outputs found
sj-pdf-1-vmj-10.1177_1358863X221094082 – Supplemental material for Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index
No full textSupplemental material, sj-pdf-1-vmj-10.1177_1358863X221094082 for Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index by Robert D McBane, Dennis H Murphree, David Liedl, Francisco Lopez-Jimenez, Itzhak Zachi Attia, Adelaide Arruda-Olson, Christopher G Scott, Naresh Prodduturi, Steve E Nowakowski, Thom W Rooke, Ana I Casanegra, Waldemar E Wysokinski, Keith E Swanson, Damon E Houghton, Haraldur Bjarnason and Paul W Wennberg in Vascular Medicine</p
Author Correction: UClncR: Ultrafast and comprehensive long non-coding RNA detection from RNA-seq
No full textA correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.</jats:p
Additional file 1: of Indel sensitive and comprehensive variant/mutation detection from RNA sequencing data for precision medicine
No full textTable S1. Parameter Settings used for alignment, data pre-processing and variant calling. Table S2. PanMutsRx Run time in each step of processing. (DOCX 18 kb
UClncR: Ultrafast and comprehensive long non-coding RNA detection from RNA-seq
No full textAbstractLong non-coding RNA (lncRNA) is a large class of gene transcripts with regulatory functions discovered in recent years. Many more are expected to be revealed with accumulation of RNA-seq data from diverse types of normal and diseased tissues. However, discovering novel lncRNAs and accurately quantifying known lncRNAs is not trivial from massive RNA-seq data. Herein we describe UClncR, an Ultrafast and Comprehensive lncRNA detection pipeline to tackle the challenge. UClncR takes standard RNA-seq alignment file, performs transcript assembly, predicts lncRNA candidates, quantifies and annotates both known and novel lncRNA candidates, and generates a convenient report for downstream analysis. The pipeline accommodates both un-stranded and stranded RNA-seq so that lncRNAs overlapping with other genes can be predicted and quantified. UClncR is fully parallelized in a cluster environment yet allows users to run samples sequentially without a cluster. The pipeline can process a typical RNA-seq sample in a matter of minutes and complete hundreds of samples in a matter of hours. Analysis of predicted lncRNAs from two test datasets demonstrated UClncR’s accuracy and their relevance to sample clinical phenotypes. UClncR would facilitate researchers’ novel lncRNA discovery significantly and is publically available at http://bioinformaticstools.mayo.edu/research/UClncR.</jats:p
Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration.
No full textFor many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs-particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments
An overview of our proposed method.
No full textThe first step is get raw registration result from top three whole slide image levels. The second step is adopting Kernel Density Estimation to weight the raw registration. The last step is using hierarchical linear regression to get the optimal co-registration for whole slide images.</p
Screen shot of WSI registration tool.
No full textAfter loading WSIs, the image on the right can be shifted and rotated by adjusting the x and y offset positions. A green cross is attached to mouse cursor, so that details of two images at the same location can be compared easily. Source code of this tool can be found at our GitHub: https://github.com/smujiang/Re-stained_WSIs_Registration.</p
