2篇
您的检索式：作者名="Collin Tokheim"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | PrimerSeq: Design and Visualization of RT-PCR Primers for Alternative Splicing Using RNA-seq Data显示文摘The vast majority of multi-exon genes in higher eukaryotes are alternatively spliced and changes in alternative splicing(AS) can impact gene function or cause disease. High-throughput RNA sequencing(RNA-seq) has become a powerful technology for transcriptome-wide analysis of AS, but RT-PCR still remains the gold-standard approach for quantifying and validating exon splicing levels. We have developed PrimerSeq, a user-friendly software for systematic design and visualization of RT-PCR primers using RNA-seq data. PrimerSeq incorporates user-provided transcriptome profiles(i.e., RNA-seq data) in the design process, and is particularly useful for largescale quantitative analysis of AS events discovered from RNA-seq experiments. PrimerSeq features a graphical user interface(GUI) that displays the RNA-seq data juxtaposed with the expected RT-PCR results. To enable primer design and visualization on user-provided RNA-seq data and transcript annotations, we have developed PrimerSeq as a stand-alone software that runs on local computers. PrimerSeq is freely available for Windows and Mac OS X along with source code at http://gffzzb60816251e064f15hbc69vobxxv6c6pcx.ffgz.tsg.suse.edu.cn/. With the growing popularity of RNA-seq for transcriptome studies, we expect PrimerSeq to help bridge the gap between high-throughput RNA-seq discovery of AS events and molecular analysis of candidate events by RT-PCR. | Collin Tokheim Juw Won Park Yi Xing | 2014 | Genomics, Proteomics & Bioinformatics2014，12，2： | 3 |
| 2 | Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation显示文摘Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery.However,the susceptibility of proteins for targeting by TPD approaches,termed“degradability”,is largely unknown.Here,we developed a machine learning model,model-free analysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein targets.MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision–recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to independent non-kinase proteins.We found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most predictive.By structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase degradability.Finally,we extended MAPD predictions to the entire proteome to find964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development. | Wubing Zhang Shourya S.Roy Burman Jiaye Chen Katherine A.Donovan Yang Cao Chelsea Shu Boning Zhang Zexian Zeng Shengqing Gu Yi Zhang Dian Li Eric S.Fischer Collin Tokheim X.Shirley Liu | 2022 | Genomics, Proteomics & Bioinformatics2022，20，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员