OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing Deep Neural Networks and incorporating the SHapley Additive exPlanations (SHAP) algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas (TCGA) data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high Area Under Curve (AUC) scores - 0.

Dicom_wsi: A Python Implementation for Converting Whole-Slide Images to Digital Imaging and Communications in Medicine Compliant Files.

Background: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies.

LPCAT1-TERT fusions are uniquely recurrent in epithelioid trophoblastic tumors and positively regulate cell growth.

Gestational trophoblastic disease (GTD) is a heterogeneous group of lesions arising from placental tissue. Epithelioid trophoblastic tumor (ETT), derived from chorionic-type trophoblast, is the rarest form of GTD with only approximately 130 cases described in the literature. Due to its morphologic mimicry of epithelioid smooth muscle tumors and carcinoma, ETT can be misdiagnosed.

Image-to-image translation for automatic ink removal in whole slide images.

Deep learning models are showing promise in digital pathology to aid diagnoses. Training complex models requires a significant amount and diversity of well-annotated data, typically housed in institutional archives. These slides often contain clinically meaningful markings to indicate regions of interest.

Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration.

For 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.

Indel sensitive and comprehensive variant/mutation detection from RNA sequencing data for precision medicine.

Background: RNA-seq is the most commonly used sequencing application. Not only does it measure gene expression but it is also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels) or fusion transcripts. However, detection of these variants is challenging and complex from RNA-seq.

Author Correction: UClncR: Ultrafast and comprehensive long non-coding RNA detection from RNA-seq.

A 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.

Shiny FHIR: An Integrated Framework Leveraging Shiny R and HL7 FHIR to Empower Standards-Based Clinical Data Applications.

In this study, we describe our efforts in building a clinical statistics and analysis application platform using an emerging clinical data standard, HL7 FHIR, and an open source web application framework, Shiny. We designed two primary workflows that integrate a series of R packages to enable both patient-centered and cohort-based interactive analyses. We leveraged Shiny with R to develop interactive interfaces on FHIR-based data and used ovarian cancer study datasets as a use case to implement a prototype.

UClncR: Ultrafast and comprehensive long non-coding RNA detection from RNA-seq.

Long 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.

Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016 : Cadiz, KY, USA. 8-10 April 2016.

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J.

Molecular Modeling and Functional Analysis of Exome Sequencing-Derived Variants of Unknown Significance Identify a Novel, Constitutively Active FGFR2 Mutant in Cholangiocarcinoma.

Purpose: Genomic testing has increased the quantity of information available to oncologists. Unfortunately, many identified sequence alterations are variants of unknown significance (VUSs), which thus limit the clinician's ability to use these findings to inform treatment. We applied a combination of in silico prediction and molecular modeling tools and laboratory techniques to rapidly define actionable VUSs.

Indel detection from RNA-seq data: tool evaluation and strategies for accurate detection of actionable mutations.

Driver somatic mutations are a hallmark of a tumor that can be used for diagnosis and targeted therapy. Mutations are primarily detected from tumor DNA. As dynamic molecules of gene activities, transcriptome profiling by RNA sequence (RNA-seq) is becoming increasingly popular, which not only measures gene expression but also structural variations such as mutations and fusion transcripts.

Chromosome X genomic and epigenomic aberrations and clinical implications in breast cancer by base resolution profiling.

Aim: Abnormal inactivation or loss of inactivated X chromosome (Xi) is implicated in women's cancer. However, the underlying mechanisms and clinical relevance are little known.

Materials & Methods: High-throughput sequencing was conducted on breast cancer cell lines for copy number, RNA expression and 5'-methylcytosine in ChrX.

Circulating atrial natriuretic peptide genetic association study identifies a novel gene cluster associated with stroke in whites.

Background: The goal of this study was to identify genetic determinants of plasma N-terminal proatrial natriuretic peptide (NT-proANP) in the general community by performing a large-scale genetic association study and to assess its functional significance in in vitro cell studies and on disease susceptibility.

Methods And Results: Genotyping was performed across 16 000 genes in 893 randomly selected individuals, with replication in 891 subjects from the community. Plasma NT-proANP1-98 concentrations were determined using a radioimmunoassay.

Nuclear factor of activated T-cell activity is associated with metastatic capacity in colon cancer.

Metastatic recurrence is the leading cause of cancer-related deaths in patients with colorectal carcinoma. To capture the molecular underpinnings for metastasis and tumor progression, we performed integrative network analysis on 11 independent human colorectal cancer gene expression datasets and applied expression data from an immunocompetent mouse model of metastasis as an additional filter for this biologic process. In silico analysis of one metastasis-related coexpression module predicted nuclear factor of activated T-cell (NFAT) transcription factors as potential regulators for the module.

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample.

Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs.

Adverse Drug Event-based Stratification of Tumor Mutations: A Case Study of Breast Cancer Patients Receiving Aromatase Inhibitors.

Adverse drug events (ADEs) are a critical factor for selecting cancer therapy options. The underlying molecular mechanisms of ADEs associated with cancer therapy drugs may overlap with their antineoplastic mechanisms; an aspect of toxicity. In the present study, we develop a novel knowledge-driven approach that provides an ADE-based stratification (ADEStrata) of tumor mutations.

Relating protein adduction to gene expression changes: a systems approach.

Modification of proteins by reactive electrophiles such as the 4-hydroxy-2-nonenal (HNE) plays a critical role in oxidant-associated human diseases. However, little is known about protein adduction and the mechanism by which protein damage elicits adaptive effects and toxicity. We developed a systems approach for relating protein adduction to gene expression changes through the integration of protein adduction, gene expression, protein-DNA interaction, and protein-protein interaction data.