The Ensembl REST API: Ensembl Data for Any Language

Andrew Yates,Kathryn Beal,Stephen Keenan,William McLaren,Miguel Pignatelli,Graham R. S. Ritchie,Magali Ruffier,Kieron Taylor,Alessandro Vullo,Paul Flicek

Bioinformatics（2014）CCF BSCI 3区SCI 2区

European Bioinformat Inst

Cited 183|Views104

Abstract

Motivation: We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
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We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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