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 application of next-generation sequencing (NGS) technology. Although methods such as quantitative PCR and microarray analysis are useful for relative quantification of small RNAs, they suffer from two major drawbacks.
The first is that these methods are hybridization-based, which presents problems when trying to discriminate two small RNAs whose sequence differs by only a nucleotide or two. The second drawback is that both of these methods are only able to interrogate an a priori determined set of small RNAs, which both limits the scope of studies and prevents discovery of new small RNAs.
Both of these drawbacks of hybridization-based methods are addressed by using NGS for small RNA studies, as NGS can reliably distinguish small RNAs that differ by only a single base, and NGS is not limited to the study of a predetermined set of sequences. However, a major drawback of NGS methods for the study of small RNAs is the substantial bias that has been shown to exist in traditional library preparation protocols. This bias has been shown to be introduced during the two ligation steps, and the combined effect of the bias introduced in these steps results in some small RNAs being ligated to adapters much more efficiently than others.
Some of the studies that demonstrated the substantial bias introduced by RNA ligases showed that this bias resulted from the adapter sequence proximal to the ligation junction, and that adapters with 2–4 randomized bases at this junction could be used to substantially reduce ligation bias.
Jayaprakash et al. first demonstrated that NGS libraries prepared with this strategy showed little evidence of ligase bias and that data generated from these libraries correlated well with microarray and qPCR data.1 Bioo Scientific has obtained an exclusive license on this patent pending technology and has since developed a library preparation kit for high-throughput sequencing of small RNAs.
(read more…)
[box type=”shadow” align=”alignleft” ]Jayaprakash AD, et al. (2011)
Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res 39(21): e141. [
article][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]
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 application of next-generation sequencing (NGS) technology. Although methods such as quantitative PCR and microarray analysis are useful for relative quantification of small RNAs, they suffer from two major drawbacks.
The first is that these methods are hybridization-based, which presents problems when trying to discriminate two small RNAs whose sequence differs by only a nucleotide or two. The second drawback is that both of these methods are only able to interrogate an a priori determined set of small RNAs, which both limits the scope of studies and prevents discovery of new small RNAs.
Both of these drawbacks of hybridization-based methods are addressed by using NGS for small RNA studies, as NGS can reliably distinguish small RNAs that differ by only a single base, and NGS is not limited to the study of a predetermined set of sequences. However, a major drawback of NGS methods for the study of small RNAs is the substantial bias that has been shown to exist in traditional library preparation protocols. This bias has been shown to be introduced during the two ligation steps, and the combined effect of the bias introduced in these steps results in some small RNAs being ligated to adapters much more efficiently than others.
Some of the studies that demonstrated the substantial bias introduced by RNA ligases showed that this bias resulted from the adapter sequence proximal to the ligation junction, and that adapters with 2–4 randomized bases at this junction could be used to substantially reduce ligation bias.
Jayaprakash et al. first demonstrated that NGS libraries prepared with this strategy showed little evidence of ligase bias and that data generated from these libraries correlated well with microarray and qPCR data.1 Bioo Scientific has obtained an exclusive license on this patent pending technology and has since developed a library preparation kit for high-throughput sequencing of small RNAs.
(read more…)
[box type=”shadow” align=”alignleft” ]Jayaprakash AD, et al. (2011) Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res 39(21): e141. [article][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]Related Posts
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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 application of next-generation sequencing (NGS) technology. Although methods such as quantitative PCR and microarray analysis are useful for relative quantification of small RNAs, they suffer from two major drawbacks.
The first is that these methods are hybridization-based, which presents problems when trying to discriminate two small RNAs whose sequence differs by only a nucleotide or two. The second drawback is that both of these methods are only able to interrogate an a priori determined set of small RNAs, which both limits the scope of studies and prevents discovery of new small RNAs.
Both of these drawbacks of hybridization-based methods are addressed by using NGS for small RNA studies, as NGS can reliably distinguish small RNAs that differ by only a single base, and NGS is not limited to the study of a predetermined set of sequences. However, a major drawback of NGS methods for the study of small RNAs is the substantial bias that has been shown to exist in traditional library preparation protocols. This bias has been shown to be introduced during the two ligation steps, and the combined effect of the bias introduced in these steps results in some small RNAs being ligated to adapters much more efficiently than others.
Some of the studies that demonstrated the substantial bias introduced by RNA ligases showed that this bias resulted from the adapter sequence proximal to the ligation junction, and that adapters with 2–4 randomized bases at this junction could be used to substantially reduce ligation bias.
Jayaprakash et al. first demonstrated that NGS libraries prepared with this strategy showed little evidence of ligase bias and that data generated from these libraries correlated well with microarray and qPCR data.1 Bioo Scientific has obtained an exclusive license on this patent pending technology and has since developed a library preparation kit for high-throughput sequencing of small RNAs.
(read more…)
[box type=”shadow” align=”alignleft” ]Jayaprakash AD, et al. (2011) Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res 39(21): e141. [article][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]Related Posts
RNA Sequencing identifies new tick-borne virus that causes flu-like illness
Worm’s radical transformation shows metamorphosis can change the functions of cells
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Atlas of the brain’s striatum could guide researchers to new drug treatments
Immune cells offer insights on billion-dollar virus
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Unlocking the past – new method helps gain insights into old tissue
Novel AI model trained on RNA-Seq data accurately detects key gene mutations and predicts biomarkers across 32 cancer types
Transcriptomic aging clock reveals age-related molecular patterns in opioid dependence
RNA sequencing helps predict stem cell transplant benefit in pediatric AML
Protein ‘switch’ determines whether liposarcoma cells will become aggressive
Precursor tRNAs sense temperature changes: heat stress-induced capped pre-tRNAs suppress protein synthesis
Ketamine increases neuroplasticity in female mice but not in males
Somatic mutations linked to vascular damage in progeria
Scientists map dormant cancer cells’ hideouts, opening new targets for treatment
Soluble signals released by neighboring cells direct how the human kidney is built
Genetics influence how cancer arises – and how it evolves
RNA-based testing uncovers extraordinary diversity in mutations driving lung cancer
Study offers new insights into why ex-smokers remain at elevated risk of lung disease
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