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Awesome-Bioinformatics-Benchmarks
A curated list of bioinformatics bench-marking papers and resources.
https://github.com/j-andrews7/Awesome-Bioinformatics-Benchmarks
Last synced: 6 days ago
JSON representation
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Format & Organization
- Essential guidelines for computational method benchmarking
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
- Systematic benchmarking of omics computational tools
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DNase, ATAC, and ChIP-seq
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Peak Callers
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Normalization Methods
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RNA-seq
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Alignment/Quantification Methods
- Alignment and mapping methodology influence transcript abundance estimation
- A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification
- Selective Alignment - bio.sourceforge.net/bowtie2/index.shtml) was considerably slower than all three of these approaches. However, in terms of accuracy, SA yielded the best results, followed by alignment to the genome (with subsequent transcriptomic projection) using STAR and SA (using carefully selected decoy sequences). Bowtie2 generally performed similarly to SA, but without the benefit of decoy sequences, seemed to admit more spurious mappings. Finally, lightweight mapping of sequencing reads to the transcriptome showed the lowest overall consistency with quantifications derived from the oracle alignments. Note: Both Selective Alignment and quasi-mapping are part of the [salmon](https://combine-lab.github.io/salmon/) codebase.
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Normalisation Methods
- Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions
- Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment
- A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
- Github
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Differential Gene Expression
- How well do RNA-Seq differential gene expression tools perform in a complex eukaryote? A case study in Arabidopsis thaliana.
- edgeR
- How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
- Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
- Github
- DESeq2
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Differential Splicing
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_de novo_ Assembly and Quantification
- Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data
- De novo transcriptome assembly: A comprehensive cross-species comparison of short-read RNA-Seq assemblers
- electronic supplement website
- Cufflinks
- Trinity - ABySS](https://github.com/bcgsc/transabyss) were typically among the best. For assembly evaluation, the authors recommend a hybrid approach combining both biological-based (BUSCO, # of full length transcripts) and reference-free metric (e.g. `TransRate`, `DETONATE`).
- Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data
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Cell-Type Deconvolution
- Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology
- Comprehensive benchmarking of computational deconvolution of transcriptomics data
- EPIC
- R package called immunedeconv - lab/immune_deconvolution_benchmark) available so that others can reproduce/extend it to test their own tools/methods.
- on Github
- Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology
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Gene Set Enrichment Analysis
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Single Cell
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scRNA Transformations/Normalization
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Gene Signature Scoring
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scRNA Sequencing Protocols
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- python package
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- here
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
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scRNA Analysis Pipelines
- Comparison of visualization tools for single-cell RNAseq data
- Comparison of high-throughput single-cell RNA sequencing data processing pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- GitHub
- A systematic evaluation of single cell RNA-seq analysis pipelines
- Github - seq-pipelines) to reproduce their analyses.
- Comparison of visualization tools for single-cell RNAseq data
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- Comparison of visualization tools for single-cell RNAseq data
- Comparison of high-throughput single-cell RNA sequencing data processing pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- Comparison of visualization tools for single-cell RNAseq data
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
- A systematic evaluation of single cell RNA-seq analysis pipelines
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scRNA Imputation Methods
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scRNA Differential Gene Expression
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- conquer
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
- Bias, robustness and scalability in single-cell differential expression analysis
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Trajectory Inference
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Gene Regulatory Network Inference
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- SCENIC
- Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- BEELINE - to-use and uniform interface to each method in the form of a Docker image.
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
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Integration/Batch Correction
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Dimensionality Reduction
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Cell Annotation/Inference
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Variant Calling
- Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data
- SAMtools - identity regions), [Strelka2](https://github.com/Illumina/strelka) (good performance when read depth >5), [FreeBayes](https://github.com/ekg/freebayes) (good specificity/sensitivity in cases with high variant allele frequencies), and CTAT (no alignment step necessary) were top performers.
- on Github
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ATAC-seq
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CRISPR Screens
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Hit/Dependency Identification
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DNA Methylation
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Platforms and Library Prep Methods
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Comparison and imputation-aided integration of five commercial platforms for targeted DNA methylome analysis
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Github
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
- Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing
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CpG Methylation from Nanopore Data
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Variant Callers
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Germline SNP/Indel Callers
- Benchmarking variant callers in next-generation and third-generation sequencing analysis
- Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers
- Comparison of three variant callers for human whole genome sequencing
- A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference
- Novoalign - UnifiedGenotyper](https://github.com/broadinstitute/gatk) exhibited the highest sensitivity while producing few false positives.
- Benchmarking variant callers in next-generation and third-generation sequencing analysis
- DeepVariant
- Strelka2 - scores for both SNP and indel calling in addition to being the most computationally performant.
- BWA-mem - UnifiedGenotyper` performed well across the top aligners (BWA, bowtie2, and Novoalign).
- Benchmarking variant callers in next-generation and third-generation sequencing analysis
-
Somatic SNV/Indel callers
-
CNV Callers
-
SV callers
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing
- Evaluating nanopore sequencing data processing pipelines for structural variation identification
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- GRIDSS
- minimap2
- all code used in the study - installed programs and all seven pipeline.
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software
-
-
Statistics
-
False Discovery Rates
- A practical guide to methods controlling false discoveries in computational biology
- available on GitHub - shiny). The source code, ExperimentHub package, and Shiny application are all made available under the MIT license.
-
-
Microbiome
-
Diversity analysis
- Evaluating Bioinformatic Pipeline Performance for Forensic Microbiome Analysis
- mothur v1.39.5 - RAST v4.0.3](https://mg-rast.org). For postmortem data, MG-RAST had a much smaller effect size than mothur and QIIME2 due to the twofold reduction in samples. QIIME2 and Mothur returned similar results, with Mothur showing inflated richness due to unclassified taxa. Adjusting minimum library size had significant effects on microbial community structure, sample size less so except for low abundant taxa.
- GitHub
-
-
Hi-C/Hi-ChIP
-
TAD Calling
-
Normalization Methods
-
Categories
Sub Categories
scRNA Sequencing Protocols
104
Platforms and Library Prep Methods
59
scRNA Analysis Pipelines
58
SV callers
56
Gene Regulatory Network Inference
53
scRNA Differential Gene Expression
51
Normalization Methods
14
Germline SNP/Indel Callers
10
Cell-Type Deconvolution
6
Differential Gene Expression
6
Peak Callers
6
CNV Callers
6
_de novo_ Assembly and Quantification
6
Dimensionality Reduction
5
Somatic SNV/Indel callers
4
Trajectory Inference
4
Normalisation Methods
4
Integration/Batch Correction
3
ATAC-seq
3
Variant Calling
3
Diversity analysis
3
Alignment/Quantification Methods
3
Gene Set Enrichment Analysis
3
Cell Annotation/Inference
2
CpG Methylation from Nanopore Data
2
Differential Splicing
2
scRNA Imputation Methods
2
False Discovery Rates
2
scRNA Transformations/Normalization
2
Hit/Dependency Identification
2
Gene Signature Scoring
2
TAD Calling
1
Keywords
bioinformatics
5
r
4
genomics
3
gene-expression
3
rna-seq
3
sequencing
2
sequence-alignment
2
single-cell
2
scrna-seq
2
single-cell-analysis
2
atac-seq
2
genome
1
dna
1
shell
1
deepvariant
1
deep-neural-network
1
deep-learning
1
cell-type
1
cancer-cells
1
unix
1
bulk-data
1
transcriptome
1
single-cell-rna-seq
1
10x
1
selective-alignment
1
c-plus-plus
1
salmon
1
sailfish
1
rnaseq
1
quantification
1
rna-seq-quantification
1
quasi-mapping
1
false-discovery-rate
1
benchmarking
1
singlecell
1
reproducible
1
python
1
jupyternotebook
1
benchmark
1
bash
1
single-cell-atac-seq
1
machine-learning-algorithms
1
epigenetics
1
bioinformatics-pipeline
1
dimensionality-reduction
1
data-integration
1
algorithm
1
simulation-framework
1
simulation
1
power-analysis
1