scholarly journals Identification and prevention of a GC content bias in SAGE libraries

2001 ◽  
Vol 29 (12) ◽  
pp. 60e-60 ◽  
Author(s):  
E. H. Margulies
Keyword(s):  
Gc Content ◽  
Content Bias

scholarly journals Summarizing and correcting the GC content bias in high-throughput sequencing

2012 ◽  
Vol 40 (10) ◽  
pp. e72-e72 ◽  
Author(s):  
Yuval Benjamini ◽  
Terence P. Speed
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Gc Content ◽  
Content Bias

scholarly journals Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data

2016 ◽  
Author(s):  
Mingxiang Teng ◽  
Rafael A. Irizarry
Keyword(s):  
Binding Sites ◽  
False Positive ◽  
Statistical Approach ◽  
Gc Content ◽  
Batch Effects ◽  
Peak Calling ◽  
Protein Binding Sites ◽  
Content Bias ◽  
On Chip ◽  
Genomic Regions

AbstractThe main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics public catalogs are based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-Seq experiments and that this variability leads to false-positive peak calls. More concerning is that GC-effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell-line. However, accounting for GC-content in ChIP-Seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signal with the unwanted variability. To account for this challenge we introduce a statistical approach that accounts for GC-effects on both non-specific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false positive peaks as well as improved consistency across laboratories.

scholarly journals Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

2017 ◽  
Vol 27 (11) ◽  
pp. 1930-1938 ◽  
Author(s):  
Mingxiang Teng ◽  
Rafael A. Irizarry
Keyword(s):  
Gc Content ◽  
Systematic Errors ◽  
Batch Effects ◽  
Content Bias

scholarly journals Differential Gene Expression between Fungal Mating Types Is Associated with Sequence Degeneration

2020 ◽  
Vol 12 (4) ◽  
pp. 243-258 ◽  
Author(s):  
Wen-Juan Ma ◽  
Fantin Carpentier ◽  
Tatiana Giraud ◽  
Michael E Hood
Keyword(s):  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Mating Type ◽  
Sex Chromosomes ◽  
Sex Chromosome ◽  
Gc Content ◽  
Differentially Expressed ◽  
Mating Types ◽  
Sexual Antagonism ◽  
Recombination Suppression

Abstract Degenerative mutations in non-recombining regions, such as in sex chromosomes, may lead to differential expression between alleles if mutations occur stochastically in one or the other allele. Reduced allelic expression due to degeneration has indeed been suggested to occur in various sex-chromosome systems. However, whether an association occurs between specific signatures of degeneration and differential expression between alleles has not been extensively tested, and sexual antagonism can also cause differential expression on sex chromosomes. The anther-smut fungus Microbotryum lychnidis-dioicae is ideal for testing associations between specific degenerative signatures and differential expression because 1) there are multiple evolutionary strata on the mating-type chromosomes, reflecting successive recombination suppression linked to mating-type loci; 2) separate haploid cultures of opposite mating types help identify differential expression between alleles; and 3) there is no sexual antagonism as a confounding factor accounting for differential expression. We found that differentially expressed genes were enriched in the four oldest evolutionary strata compared with other genomic compartments, and that, within compartments, several signatures of sequence degeneration were greater for differentially expressed than non-differentially expressed genes. Two particular degenerative signatures were significantly associated with lower expression levels within differentially expressed allele pairs: upstream insertion of transposable elements and mutations truncating the protein length. Other degenerative mutations associated with differential expression included nonsynonymous substitutions and altered intron or GC content. The association between differential expression and allele degeneration is relevant for a broad range of taxa where mating compatibility or sex is determined by genes located in large regions where recombination is suppressed.

scholarly journals Large-Scale, High-Throughput Validation of Short Hairpin RNA Sequences for RNA Interference

2006 ◽  
Vol 11 (3) ◽  
pp. 236-246 ◽  
Author(s):  
Laurence H. Lamarcq ◽  
Bradley J. Scherer ◽  
Michael L. Phelan ◽  
Nikolai N. Kalnine ◽  
Yen H. Nguyen ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Strong Support ◽  
Gc Content ◽  
Rapid Identification ◽  
Hairpin Rna ◽  
Rna Sequences ◽  
Short Hairpin ◽  
Short Hairpin Rnas ◽  
Interfering Rna

A method for high-throughput cloning and analysis of short hairpin RNAs (shRNAs) is described. Using this approach, 464 shRNAs against 116 different genes were screened for knockdown efficacy, enabling rapid identification of effective shRNAs against 74 genes. Statistical analysis of the effects of various criteria on the activity of the shRNAs confirmed that some of the rules thought to govern small interfering RNA (siRNA) activity also apply to shRNAs. These include moderate GC content, absence of internal hairpins, and asymmetric thermal stability. However, the authors did not find strong support for positionspecific rules. In addition, analysis of the data suggests that not all genes are equally susceptible to RNAinterference (RNAi).

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