https:\/\/github.com\/gerstung-lab\/delphi<\/a><\/p>\n\n\n\n Delphi-2M is an AI model that learns disease trajectories from healthcare data and can provide individualised predictions. Delphi-2M is trained on 400K patient health trajectories from the UK Biobank data, and in this repository<\/a> we provide more details about the implementation and training of the model. See our paper<\/a> for more details.<\/p>\n\n\n\n <\/p>\n\n\n\n The CNest workflow runs the CNest pipeline which has been specifically developed for large scale analysis of copy number from NGS data (https:\/\/doi.org\/10.1101\/2021.08.19.456963<\/a>). It primarily uses read depth (coverage) information to generate robust copy number estimates for individual samples and is most approprate for use in very large cohorts (minimum of 1000 samples). The main objective of the CNest pipeline is the calculation of copy number estimate that are robust enough to allow genome wide association analysis for CNVs from NGS datasets (CNV-GWAS).<\/p>\n\n\n\n <\/p>\n\n\n\n Software to reconstruct the 3D structure of DNA inside the cell using super resolution microscopy data. Publication can be found here https:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1006002<\/a><\/p>\n\n\n\n https:\/\/gitlab.com\/Chromotrace_utils\/chromotrace<\/a><\/p>\n\n\n\n A new approach to reporting and sharing Methods<\/a><\/p>\n\n\n\n The ENCODE consortium have published an integrated analysis of ENCODE genome-wide data<\/a>. Each analysis depends upon specific software processing that has a series of source data files. These are transformed into output files relating to specific statements and figures in the paper and their corresponding analysis. <\/p>\n\n\n\n We have established a virtual machine<\/a> instance of this software, using the code bundles from ftp.ebi.ac.uk\/pub\/databases\/ensembl\/encode\/supplementary\/<\/a>, where each analysis program has been tested and run.<\/p>\n\n\n\n Where possible, the virtual machine enables complete reproduction of the analysis as it was performed, and generates figures, tables or other information. In cases where the analysis involved highly parallelised processing within a specialised multiprocessor environment, a partial example has been implemented, leaving it to the reader to decide whether and how to scale to a full analysis.<\/p>\n\n\n\n We hope that this structure provides the opportunity to run the same analyses in the wild.<\/p>\n\n\n\n You can “instantiate” an Amazon EC2 instance to examine figures from the ENCODE analysis in the cloud. To get started, follow the instructions at http:\/\/scofield.bx.psu.edu\/~dannon\/encodevm\/#<\/a><\/p>\n\n\n\n Velvet is a sequence assembler for very short reads.<\/p>\n\n\n\n DELPHI: Learning the natural history of human disease with generative transformers https:\/\/github.com\/gerstung-lab\/delphi Delphi-2M is an AI model that learns disease trajectories from healthcare data and can provide individualised predictions. Delphi-2M is trained on 400K patient health…<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-title-left-aligned.php","meta":{"_acf_changed":false,"footnotes":""},"embl_taxonomy":[],"class_list":["post-83","page","type-page","status-publish","hentry"],"acf":[],"embl_taxonomy_terms":[],"_links":{"self":[{"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/pages\/83","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/comments?post=83"}],"version-history":[{"count":7,"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/pages\/83\/revisions"}],"predecessor-version":[{"id":35357,"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/pages\/83\/revisions\/35357"}],"wp:attachment":[{"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/media?parent=83"}],"wp:term":[{"taxonomy":"embl_taxonomy","embeddable":true,"href":"https:\/\/www.ebi.ac.uk\/research\/birney\/wp-json\/wp\/v2\/embl_taxonomy?post=83"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}CNest: Copy Number Estimation for Large NGS Cohorts<\/h2>\n\n\n\n
ChromoTrace: Computational reconstruction of 3D chromosome configurations for super-resolution microscopy<\/h2>\n\n\n\n
ENCODE virtual machine<\/h2>\n\n\n\n
ENCODE cloud instance<\/h2>\n\n\n\n
Velvet<\/h2>\n\n\n\n
\n