So how can I possibly access the own's element in the view layout inside the fragment definition?
I am using a navigation view to switch between fragments and the main activity has a fragment that displays the selected item in the menu I have added the extension that allows me to reference the element directly but it is not exactly what I am looking for
Taken together, our results showed that QueryFuse is efficient and reliable for detecting gene-specific fusion events.I am trying to access an TextView element on a wrapped Relative layout by id but it doesnt shows up in the reference's Id, So how could I possibly get the element's view by id with the findViewById method? Finally, using QueryFuse, we identified a novel fusion event with a potential therapeutic implication in clinical samples. QueryFuse performed better or at comparable levels with two popular tools (deFuse and TopHatFusion) on both simulated and well-annotated cell-line datasets. By aligning reads to Query genes at the pre-processing step with a more sensitive, memory intensive local aligner, QueryFuse can reduce alignment time and improve detection sensitivity. It is designed to help biologists quickly find and/or computationally validate fusions of interest, together with visualization and detailed properties of supporting reads. Therefore, we have developed QueryFuse, a novel hypothesis-based algorithm that identifies gene-specific fusion from pre-aligned RNA-seq data. However, some transciptome realignment procedures for these methods are unnecessary, making this task computationally expensive and time consuming. RNA-seq is an ideal platform for detecting transcribed fusions, and computational methods have been developed to identify fusion transcripts from RNA-seq data. They can serve as valuable diagnostic and therapeutic targets. Recurrent chromosomal translocations, known as fusions, play important roles in carcinogenesis. Supplementary data are available at Bioinformatics online.
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The source code and documentation is available at. Due to the use of real RNA-seq datasets we believe that ArtiFuse provides a more realistic benchmark that can be used to develop more accurate fusion gene prediction tools for application in clinical settings.ĪrtiFuse is implemented in Python. As ArtiFuse affords total control over involved genes and breakpoint position, we also assessed performance with regard to gene-related properties, showing a drop in recall value for low expressed genes in high coverage samples and genes with co-expressed paralogues.Overall tool performance assessed from ArtiFusions is lower compared to previously reported estimates on simulated reads. We demonstrate our approach on eight RNA-seq datasets for three fusion gene prediction tools: Average recall values peak for all three tools between 0.4 and 0.56 for high quality and high coverage datasets. Here we present ArtiFuse, a novel approach that simulates fusion genes by sequence modification to the genomic reference, and therefore can be applied to any RNA-seq dataset without the need for any simulated reads. Often simulated reads are used, but these cannot account for all technical biases in RNA-seq data generated from real samples. However, real world performance of these tools is unclear due to the lack of known positive and negative events, especially with regard to fusion genes in individual samples.
Gene fusions are an important class of transcriptional variants that can influence cancer development and can be predicted from RNA sequencing (RNA-seq) data by multiple existing tools.
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