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Tool | Description | Author | Operations | Long Description | Keywords | Metakeywords | id | visualizer | |
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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 | |||
QfO 2022 Benchmark | Quest for Orthologs Consortium - 2022 Benchmark. Evaluation of ortholgy prediction methods | Adrian Altenhoff | Evaluate Orthologs | proteomics, orthologs, phylogenetics | phylogenetics, orthology, comparative genomics | QfO_2022 | |||
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 1 | Identify which sequencing platform, library prep, and computational tool(s) combination give the highest sensitivity and precision for transcript detection. | Enrique Sapena Ventura | Evaluate Participant | 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_1 | ||
LRGASP challenge 1 Human | Identify which sequencing platform, library prep, and computational tool(s) combination give the highest sensitivity and precision for transcript detection. | Enrique Sapena Ventura | Evaluate Participant | 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_1_human | ||
LRGASP challenge 3 Mouse | Long-read RNA-seq Genome Annotation Assessment Project challenge for Mouse ES cell line | 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 |