Implications for correcting bias in RNA-seq †
Improving RNA Seq expression estimates by correcting for fragment bias
... of RNA secondary structure on certain procedures ...of bias indir- ectly by inferring it from the data (fragment alignments) in an ...estimating bias, as Figure 2 ...without correcting for ...
14
IVT seq reveals extreme bias in RNA sequencing
... understand bias introduced by ...this RNA was mixed with complex mouse total RNA at various concentrations, and sequenced using the two most common RNA-seq protocols, polyA-seq ...
15
Gene ontology analysis for RNA seq: accounting for selection bias
... Unlike GO analysis for microarray data, the null proba- bility distribution does not conform to a standard distri- bution, precluding an analytical solution for determining the probability of a category being ...
12
Gene set analysis controlling for length bias in RNA-seq experiments
... the bias, we use the similarity of gene length as the sampling weight instead, so that the randomized sets will be more likely to consist of genes with similar ...the bias and yields more accurate null ...
18
Transcript length bias in RNA-seq data confounds systems biology
... the total number of reads for a given transcript is propor- tional to the expression level of the transcript multiplied by the length of the transcript. In other words a long tran- script will have more reads mapping to ...
10
iMapSplice: Alleviating Reference Bias Through Personalized RNA-seq Alignment
... of RNA-seq reads from that same ...of bias can be introduced if RNA sequencing data are aligned to a standard reference ...introduce bias against a special category of reads that ...
16
Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information
... binding-affinity bias on information ...binding-affinity bias with the given toy example of a ChIP-seq experiment for six binding sites in four ...affinity bias is equal in all species, ...
10
SCRABBLE: single cell RNA seq imputation constrained by bulk RNA seq data
... Abstract Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the ...
12
Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers
... S1), correcting or not correcting errors in the UMI sequences has little absolute effect on PCR dupli- cate ...or RNA, have become increasingly common [ 46 ], and they are more severely affected by ...
15
Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data
... positive gene-sets. Such false positives cannot be removed even by the sample-permutation procedure of GSEA. Then, the same simulation datasets were analyzed using the preranked GSEA which only makes use of the gene ...
11
Bioinformatics for RNA‐Seq Data Analysis
... expressed genes. Finally, pathway or network level analyses are performed to gain biological insight through systems biology approaches. 4.1. Quality check and preprocessing of raw reads Poor‐quality read data can arise ...
27
CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification
... Surprisingly, in assessing the performance of our classifier using cross-validation (Figures 4D and S4C), we found that for both the two- and the eight-cell stage sisters, predictions can be made with an average 80%–90% ...
8
An econometric method of correcting for unit nonresponse bias in surveys
... To further investigate the sensitivity of our correction method to the exact choice of specification, we report estimation results for a number of other specifications, for which the AIC in Table 4 suggests that they ...
39
Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome
... systematic bias in measures of relative ASE favoring the reference ...first, RNA-seq reads are aligned separately to mater- nal and paternal ...
13
Assessment of transcript reconstruction methods for RNA-seq
... tial bias imparted by the choice of alignment program, we calculated sensitivity and precision metrics for expressed genes using several different spliced aligners (GSNAP, STAR and TopHat2), with no substantial ...
10
Normalization of RNA-Seq
... EDASeq implements four within-lane normalization methods, namely: loess robust local regression of read counts (log) on a gene feature such as GC-content ( loess ), global-scaling betwee[r] ...
11
RNA-seq analysis in R
... Rank the genes by statisical significance - you will need to create a new ranking value using -log10({p value}) * sign({Fold Change}).. Run fgsea using the new ranked genes and the H pat[r] ...
14
VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis
... In RNA-seq, the reads after alignment are quantified on a per gene or per transcript basis to discern information regarding the level of gene expression in a population of ...
15
Correcting Length Bias in Neural Machine Translation
... An optimized word reward score always leads to improvements in METEOR scores over any of the best baselines. Across all language pairs, reward and norm have close METEOR scores, though the reward method wins out ...
12
Correcting the probate inventory record for wealth bias
... on correcting the probate record for age bias, assuming that such a correction will also remove wealth ...By correcting for age bias, that is, by reconstructing the society of the living from ...
25