Analyzing clinical data in XML: Bridging the gaps
Joshua Hui, Sarah Knoop, et al.
IHI 2012
The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.
Joshua Hui, Sarah Knoop, et al.
IHI 2012
Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
John M. Prager, Jennifer J. Liang, et al.
AMIA Joint Summits on Translational Science 2017
Rick Kjeldsen
Disability and Rehabilitation: Assistive Technology