Tag Archives: G/C content

Technique detects technical biases that otherwise confound test results A new computational method can improve the accuracy of gene expression analyses, which are increasingly used to diagnose and monitor cancers and are a major tool for basic biological research. Researchers ...

Current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results. Researchers from the Dana-Farber Cancer Institute show that these systematic errors stem largely from a failure to model fragment GC content bias. Sample-specific ...

Current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results. Researchers from the Dana-Farber Cancer Institute show that these systematic errors stem largely from a failure to model fragment GC content bias. Sample-specific ...

Transcriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, several normalization approaches have emerged, differing both in the ...

Genome and transcriptome sequencing applications that rely on variation in sequence depth can be negatively affected if there are systematic biases in coverage. Researchers at Uppsala University, Sweden have investigated patterns of local variation in sequencing coverage by utilising ultra-deep ...