Difference between revisions of "Literature Studies"

(Omics Workflows)
(Omics Workflows)
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=== Omics Workflows ===
 
=== Omics Workflows ===
''' 2015 '''</br>
 
* [[ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines]]
 
''' 2017 '''</br>
 
* [[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]]
 
 
 
{| class="wikitable sortable"
 
{| class="wikitable sortable"
 
|-
 
|-
 
! Year || First Author || Title
 
! Year || First Author || Title
 
|-
 
|-
| 2003 || || [[A comparison of normalization methods for high density oligonucleotide array data based on variance and bias ]]
+
| 2015 || || [[ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines]]
 
+
|-
 +
| 2017 || ||  [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]]
 +
|-
 +
| 2017 || ||  [[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]]
 +
|-
 +
| 2019 || ||  [[Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays]]
 
|}
 
|}
  

Revision as of 12:22, 25 February 2020

Page summary
Here outcomes of benchmarking studies from the literature are collected. The primary aim is a comprehensive overview about neutral benchmark studies, i.e. assessments which were performed independenty on publication of a new approach. Studies which are not neutral are put in brackets.

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.
Use the guidelines described here.

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

2018

1.2.2 Identifying differential regions (e.g. DMRs)

2015

2016

2017

2018

1.2.3 Identifying sets of features (e.g. gene set analyses)

2009

2018

1.2.4 Dimension reduction

2008

2015

1.3 Imputation methods for missing values

1996

1999

2001

2002

2003

2005

2007

2008

2011

2012

2013

2014

2015

2016

2018

1.4 ODE-based Modelling

2001

2008

2011

2013

2017

2018

2019


2020

1.5 Omics Workflows

Year First Author Title
2015 ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines
2017 A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
2017 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
2019 Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays

1.6 Preprocessing high-throughput data

Year First Author Title
2003 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
2005 Comparison of Affymetrix GeneChip Expression Measures
2005 Meleth S The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins
2005 Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays
2006 Using RNA sample titrations to assess microarray platform performance and normalization techniques
2006 Wang P Normalization regarding non-random missing values in high-throughput mass spectrometry data
2007 Carvalho B Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data
2007 Cannataro M MS‐Analyzer: preprocessing and data mining services for proteomics applications on the Grid
2008 Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix
2009 Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations
2010 Consistency of predictive signature genes and classifiers generated using different microarray platforms
2010 Detecting and correcting systematic variation in large-scale RNA sequencing data
2010 Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
2010 Normalization of RNA-seq data using factor analysis of control genes or samples
2011 Affymetrix GeneChip microarray preprocessing for multivariate analyses
2012 A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
2014 Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets
2014 Zhou X Prevention, diagnosis and treatment of high-throughput sequencing data pathologies
2015 Caraus I Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions
2015 Tam S Optimization of miRNA-seq data preprocessing
2016 Yi L Chemometric methods in data processing of mass spectrometry-based metabolomics: A review
2018 Mazoure B Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening