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CNV (1) ELIXIR (1) Structural Biology (1) Structural Variants (1) Translational Bioinformatics (1) biomedical_research (3) cancer_genomics (1) comparative genomics (3) genomics (2) orthology (3) phylogenetics (4) transcriptomics (1)

	Tool	Description	Author	Operations	Long Description	Keywords	Metakeywords	id	visualizer
	GMI Outbreak Detection	Service to measure the performance of the GMI outbreak detection system using a phylogenomic pipeline.	Sara Monzón, Isabel Cuesta	Evaluate Tree	The Global Microbial Identifier pathogen tracking initiative (GMI) develops a global resource of sequence data for identification and diagnostics of pathogenic organisms and infectious diseases. A worldwide network of shared genomic information for bacterial, viral, and parasitic microorganisms can be used widely, from establishing a global disease outbreak detection system to personalised medicine.	human-microbiome, virus, phylogenetics	phylogenetics	GMI_OD
	TransBioNet WG1: CNVs benchmark 2020	TransBioNet's working group #1 (WG1) focuses in Standards and Benchmarking of different relevant aspects for translational bioinformatics. In this context, there is a concerted effort on benchmarking structural variants, especially, on copy number variations (CNVs)	Lorena de la Fuente	Evaluate CNV predictions		genomics, CNV, Structural Variants	genomics, CNV, Structural Variants, Translational Bioinformatics, biomedical_research, ELIXIR	TBN_CNV
	APAeval 2021 Benchmarking Event Quantification	Evaluation of alternative polyadenilation prediction methods. 2021 Benchmarking event	Alexander Kanitz	Evaluate Poly(A) predictor	A community effort to evaluate computational methods for the detection and quantification of poly(A) sites and the estimation of their differential usage across RNA-seq samples.	poly(A), RNA-seq	biomedical_research, genomics	APAEVAL_2021
	CAID1	Determine the state of the art in predicting intrinsically disordered regions in proteins and the subset of disordered residues involved in binding other molecules.	Damiano Piovesan, Mahta Mehdiabadi	Evaluate Proteins	Intrinsically disordered proteins defying the traditional protein structure-function paradigm represent a challenge to study experimentally. As a large part of our knowledge rests on computational predictions, it is crucial for their accuracy to be high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins and the subset of disordered residues involved in binding other molecules. A total of 43 methods, 32 for disorder and 11 for binding regions, were evaluated on a dataset of 646 manually curated proteins from DisProt.	Intrinsic protein Disorder, Bindings, Prokaryotes	Structural Biology	CAID1
	QfO 2018 Benchmark	Quest for Orthologs Consortium - Challenge 6. Evaluation of ortholgy prediction methods	Adrian Altenhoff	Evaluate Orthologs		proteomics, orthologs, phylogenetics	phylogenetics, orthology, comparative genomics	QFO_6
	QfO 2020 Benchmark	Quest for Orthologs Consortium - 2020 Benchmark. Evaluation of ortholgy prediction methods	Adrian Altenhoff	Evaluate Orthologs		proteomics, orthologs, phylogenetics	phylogenetics, orthology, comparative genomics	QfO_2020
	QfO 2020 Benchmark New Extended	Quest for Orthologs Consortium - 2020 Benchmark. Evaluation of ortholgy prediction methods	Adrian Altenhoff	Evaluate Orthologs		proteomics, orthologs, phylogenetics	phylogenetics, orthology, comparative genomics	QfO_2020_extended
	TCGA Cancer Drivers	Service to measure cancer drive genes predicted by individual tools and/or workflows. This is based on the efforts made by the TCGA working group	Javier Garrayo - Salvador Capella	Evaluate Genes	The Cancer Genome Atlas (TCGA) is a joint effort to characterize cancer driver genes in 33 different cancer types from nearly 10,000 exomes. In such an effort, several methods for predicting cancer genes are constantly being developed and improved. This benchmarking service evalute these methods.	driver genes, target_cancer, cancer genomics	cancer_genomics	TCGA_CD
	LRGASP challenge 3	The Long-read RNA-seq Genome Annotation Assessment Project challenge.	Tianyuan Liu	Evaluate Method	The LRGASP Consortium has organized a systematic evaluation of different methods for transcript computational identification and quantification using long-read sequencing technologies such as PacBio and Oxford Nanopore. We are interested in characterizing the strengths and potential remaining challenges in using these technologies to annotate and quantify the transcriptomes of both model and non-model organisms. The consortium has generated cDNA and direct RNA datasets using different platforms and protocols in human, mouse, and manatee samples. Participants are provided with the data to generate annotations of expressed genes and transcripts as well as measure their expression levels. Evaluators from different institutions will determine which pipelines have the highest accuracy for different aspects that include transcription detection, quantification and differential expression.	RNA-seq	transcriptomics, biomedical_research	lrgasp_challenge_3