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Confronta modelli biologici, motori di docking e flussi di calcolo affiancati. Filtra per modello di accesso, impronta di calcolo e categoria per ottenere una tabella decisionale attuabile in meno di un minuto.
| Attributo | ＋ Aggiungi strumento | |||
|---|---|---|---|---|
| Category | Genomics & Sequence Modeling | Genomics & Bioinformatics | Genomics & Bioinformatics | — |
| Subcategory | Long-Read Sequencing Correction | Regulatory Genomics | Conventional or Statistical Baselines | — |
| Company | Google Health & PacBio | Google DeepMind | Research / method authors | — |
| Access mode | Open Source | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | BSD-3-Clause | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Multi-core CPU or NVIDIA GPU (CUDA) | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | — |
| Biological application | De novo genome assembly, structural variant discovery, and pangenomics | Non registrato | Non registrato | — |
| Research workflow | Raw PacBio subreads -> DeepConsensus inference -> high-accuracy HiFi BAM/FASTQ | Non registrato | Non registrato | — |
| Evidence level | Peer-reviewed publication (Nature Biotechnology 2022) | Peer-reviewed primary method | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Open source release | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Source | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | Available on GitHub and Bioconda | Public research method/code | Method paper or official documentation | — |
| Features | Deep learning consensus calling across subreads to correct systematic sequencing errors, Increases the yield of Q30 (>99.9% accurate) and Q40 (>99.99% accurate) HiFi reads by up to 40%, Integrated into standard PacBio SMRT Link secondary analysis pipelines, Optimized with GPU acceleration for high-throughput sequencing facilities | Predicts genomic regulatory tracks and predicted functional effects of noncoding DNA variants., Genomic regulatory track, sequence-variant contrast | bcftools scientific method, Reference genome with aligned short-read input and correct ploidy assumptions, VCF/BCF variant calls and variants QC operations | — |
| Entry type | Software Tool | Research AI model / method | Classical/statistical scientific method | — |
| AI role | Signal & Sequence Error Correction | Published biomedical AI method | NON_AI_STATISTICAL | — |
| Input data | Non registrato | DNA sequence interval; alleles and tissue/assay ontology | Reference genome with aligned short-read input and correct ploidy assumptions | — |
| Output data | Non registrato | Genomic regulatory track; sequence-variant contrast | VCF/BCF variant calls and variants QC operations | — |
| Validation | Non registrato | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Neural sequencing consensus caller | Non registrato | Non registrato | — |
| Intended use | Non registrato | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |
| Manufacturer | Google LLC & PacBio | Non registrato | Non registrato | — |
| Integration evidence | https://github.com/google/deepconsensus | Non registrato | Non registrato | — |
| Laboratory handoff | Generates ready-to-assemble reads for clinical and population genomics | Non registrato | Non registrato | — |