Difference between revisions of "Literature Studies"
(Thee papers found at bioRxiv) |
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=== Omics Workflows === | === Omics Workflows === | ||
+ | ''' 2015 '''</br> | ||
+ | * [[ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines]] | ||
''' 2017 '''</br> | ''' 2017 '''</br> | ||
* [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | * [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | ||
+ | * [[A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies]] | ||
+ | ''' 2019 '''</br> | ||
+ | * [[A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines]] | ||
+ | * [[Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays]] | ||
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''' 2014 '''</br> | ''' 2014 '''</br> | ||
* [[Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets]] | * [[Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets]] | ||
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Revision as of 11:42, 22 May 2019
Page summary |
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Here outcomes of benchmarking studies from the literature are collected. The focus is on computational methods for analyzing experimental data (instead of comparing experimental techniques or platforms). Please extend this list by creating a new page and adding a link below. |
Contents
1 Results from Literature
1.1 Classification
2003
2005
2016
1.2 Selection of Differential Features and Regions
1.2.1 Identifying differential features
2006
2010
2017
- Identification of differentially expressed peptides in high-throughput proteomics data
- In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
- Strategies for analyzing bisulfite sequencing data
2018
1.2.2 Identifying differential regions (e.g. DMRs)
2015
- De novo identification of differentially methylated regions in the human genome
- MethylAction: detecting differentially methylated regions that distinguish biological subtypes
- metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data
2016
- seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data
- Statistical methods for detecting differentially methylated regions based on MethylCap-seq data
2017
2018
- Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus
- DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts
- MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)
1.2.3 Identifying sets of features (e.g. gene set analyses)
1.2.4 Dimension reduction
2008
2015
1.3 Imputation methods for missing values
2001
2015
- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
- Multiple imputation and analysis for high-dimensional incomplete proteomics data
2018
1.4 ODE-based Modelling
2001
2008
2013
- Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
- ODE parameter inference using adaptive gradient matching with Gaussian processes
2018
1.5 Omics Workflows
2015
2017
- A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
- A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies
2019
- A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines
- Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays
1.6 Preprocessing high-throughput data
2003
2005
- Comparison of Affymetrix GeneChip Expression Measures
- Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays
2006
2008
2009
2010
- Consistency of predictive signature genes and classifiers generated using different microarray platforms
- Detecting and correcting systematic variation in large-scale RNA sequencing data
- Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
- Normalization of RNA-seq data using factor analysis of control genes or samples
2011
2012
2014