Tag Archives: Bias

Experimental procedures for preparing RNA-seq and single-cell (sc) RNA-seq libraries are based on assumptions regarding their underlying enzymatic reactions. Here, researchers at the University of Warwick show that the fairness of these assumptions varies within libraries: coverage by sequencing reads ...

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 ...

Changes in small non-coding RNAs such as micro RNAs (miRNAs) can serve as indicators of disease and can be measured using next-generation sequencing of RNA (RNA-seq). Here, University of Maryland rsearchers highlight the need for approaches that complement RNA-seq, discover ...

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. A team led by researchers at the University of Cambridge ...

Currently quantitative RNA-Seq methods are pushed to work with increasingly small starting amounts of RNA that require PCR amplification to generate libraries. However, it is unclear how much noise or bias amplification introduces and how this effects precision and accuracy ...

Tests for differential gene expression with RNA-seq data have a tendency to identify certain types of transcripts as significant, e.g. longer and highly-expressed transcripts. This tendency has been shown to bias gene set enrichment (GSE) testing, which is used to ...

Various biases affect high-throughput sequencing read counts. Contrary to the general assumption, researchers from Ludwig-Maximilians-University Munich show that bias does not always cancel out when fold changes are computed and that bias affects more than 20% of genes that are ...

High-throughput sequencing (HTS) has become a powerful tool for the detection of and sequence characterization of microRNAs (miRNA) and other small RNAs (sRNA). Unfortunately, the use of HTS data to determine the relative quantity of different miRNAs in a sample ...

from Genetic Engineering News by Adam R. Morris & Masoud M. Toloue Randomized Adapter Strategy for Library Preparation Reduces Ligation Bias and Increases Accuracy of Small RNA-Seq The study of small RNAs, including miRNAs, siRNAs, and pi-RNAs, is an ideal ...