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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Related Experiment Video

Updated: Feb 11, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
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BCseq: accurate single cell RNA-seq quantification with bias correction.

Liang Chen1, Sika Zheng2

  • 1Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.

Nucleic Acids Research
|May 3, 2018
PubMed
Summary
This summary is machine-generated.

BCseq is a new bias-corrected sequencing analysis method that accurately quantifies gene expression from single-cell RNA sequencing data. This robust model improves the detection of cell subtypes and differentially expressed genes.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables cell subtype detection and developmental/disease trajectory analysis.
  • The utility of scRNA-seq is limited by the robustness of data analysis methods.
  • Technical noise and biases can obscure true biological signals in scRNA-seq data.

Purpose of the Study:

  • To develop a robust computational model for accurate gene expression quantification from scRNA-seq data.
  • To address inherent biases and technical noise in scRNA-seq datasets.
  • To provide a reliable method for identifying cell subtypes and differentially expressed genes.

Main Methods:

  • BCseq (bias-corrected sequencing analysis) model was developed.
  • The model corrects scRNA-seq bias in a data-adaptive manner.
  • It removes technical noise and rescues dropouts using weighted similar cell consideration.
  • Gene expression quality scores are assigned for downstream analysis.

Main Results:

  • BCseq accurately quantifies gene expression from scRNA-seq data.
  • The method effectively corrects for bias and removes technical noise.
  • BCseq demonstrates increased robustness in detecting differentially expressed genes.
  • Improved cell subtype classification accuracy was observed compared to existing methods.

Conclusions:

  • BCseq offers a robust and accurate approach for scRNA-seq data analysis.
  • The model enhances the discovery potential of scRNA-seq by improving data quality and analytical reliability.
  • BCseq provides objective quality scores to guide gene selection for downstream applications.