The value of RNA-seq data
RNA-seq data analysis pipeline
... typical RNA-seq data analyis ...general RNA-seq data analysis pipeline using specific tools; completed an example pipeline by using data from a public database and ...
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GC-Content Normalization for RNA-Seq Data
... There are two different types of GC-content effects. The first effect is to act as a proxy for sample size, in a similar manner as length, and relates to power: as GC-content increases, read counts first increase then ...
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Computational analysis of bacterial RNA-Seq data
... in data from two conditions, we perform a statistical test for the null hypothesis, which is that the expression of the gene in the two conditions is the ...from RNA-seq data ...
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A Novel Assembly Algorithm That Optimizes For RNA-Seq Data
... first value is called “ori-cut,” which is the number of k-mers shared between two short ...ori-cut value, the two short reads will not be thought to have any relationship by the ...second value is ...
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A survey of statistical software for analysing RNA-seq data
... Each read is sampled independently and uni- formly from every possible nucleotide in the sample. The number of tags coming from a gene follows a binomial distribution, which can be approximated by a Poisson distri- ...
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A survey of best practices for RNA seq data analysis
... There are, however, some aligners (such as PatMaN [99] and MicroRazerS [100]) that have been designed to map short sequences with preset parameter value ranges suited for optimal alignment of short reads. The map- ...
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A survey of best practices for RNA-seq data analysis
... the data to be previously normalized to remove all possible ...include data transformation methods that take into account the sampling variance of small read counts and create discrete gene expression ...
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Statistical methods for the analysis and interpretation of RNA-Seq data
... Bottomly data (Bottomly et ...pre-processed RNA-Seq data comparing ten C57BL/6J (B6) and eleven DBA/2J (D2) mouse striatum was downloaded from the ReCount project (Frazee et ...All data ...
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A comparison of methods for differential expression analysis of RNA-seq data.
... control was worse when all genes were regulated in the same direction. The high false discovery rate seen for ShrinkSeq can possibly be reduced by setting a non-zero value for the fold change threshold defining ...
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Computational Methods for the Analysis of Single-Cell RNA-Seq Data
... TF-IDF graph-based Greedy clustering with Euclidean, Pearson, cosine and Jaccard distances (TF-IDF Bin Greedy E, TF-IDF Bin Greedy P, TF-IDF Bin Greedy C, TF-IDF Bin Greedy J). In these methods we begin by building an ...
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Outlier Detection for Mixed Model with Application to RNA-Seq Data
... We randomly generate truncated quadratic functions in R 2 with varying degrees of complexity. Specifically, given a quadratic function with a positive definite Hessian matrix in R 2 truncated at zero, the truncation ...
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Sample size calculations and normalization methods for RNA-seq data.
... the RNA-seq data have not yet been ...microarray data, an RNA-seq dataset contains thousands of genes to be tested simultaneously and independently for differential expression ...
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Improving the value of public RNA-seq expression data by phenotype prediction.
... genomic data are a valuable re- source for studying normal human variation and dis- ease, but these data are often not well labeled or an- ...genomic data severely limits their utility for ad- ...
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Bioinformatics for RNA‐Seq Data Analysis
... degraded RNA. After quality and quantity of the RNA samples have been addressed, one must then choose whether to profile the total RNA space or the mRNA space ...most RNA‐seq library ...
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SCRABBLE: single cell RNA seq imputation constrained by bulk RNA seq data
... bulk RNA-seq data to im- pute dropout data in order to reduce unwanted bias during ...experimental data suggests that SCRABBLE achieves significant improvement in terms of recovering ...
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Statistical challenges in RNA-Seq data analysis
... Sensitive to the presence of majority genes Less effective stabilization of distributions Ineffective (similar to RawCount). FQ[r] ...
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NGS Data Analysis: An Intro to RNA-Seq
... depth. RNA-Seq Depends on complexity of the transcriptional profile you’re working on and if you need to capture rare events Rule of thumb is that more replicates are ...
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Re-using public RNA-Seq data
... kogumas. RNA-Seq on NGS tehnika, mis võimaldab geeniekspressioo- ni tasemete ...avaldamiseks. RNA-Seq toorandmed on mahult üsna suured ja üksikute eksperimentide analüüs üsnagi ...like ...
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Challenges Analyzing RNA Seq Gene Expression Data
... quencing) data is very ...Transform RNA- sequencing count data to a continuous variable or continue to work with count ...each data type, analysis tools have been developed and seem ...
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FastqPuri: high-performance preprocessing of RNA-seq data
... Discussion RNA-seq is currently widely used to assess transcript and gene expression lev- ...sequence data quality control and preprocessing the most time demanding steps in data ...and ...
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