Jiawei Wang

Marie Curie Fellow
EMBO Non-Stipendiary Fellow

European Bioinformatics Institute (EMBL-EBI)

Biography

I am a postdoc fellow at EMBL-EBI and the University of Cambridge, mainly working with Dr. John Marioni on single cell omics. I am a recipient of Marie Skłodowska-Curie Postdoctoral Fellowship, MSCA-cofunded EIPOD Fellowship and EMBO Non-Stipendiary Fellowship. 🇬🇧
Previously, I spent 4 years of PhD and postdoc at Monash University, Australia, mainly working with Prof. Trevor Lithgow in machine learning based sequence analysis. 🇦🇺
Previously, I was trained in Computer Science (e.g. programming and computer networking) at Peking University and Tongji University, China. 🇨🇳

Interests

Computational Biology
Computer Science
Machine Learning
Single Cell Omics

Education

PhD in Computational Biology, 2019

Monash University
MSc in Computer Science, 2015

Peking University
B.Eng in Software Engineering, 2010

Tongji University

Academic Positions

Postdoc Fellow

EMBL-EBI & The University of Cambridge

Jun 2021 – Present Cambridge, UK

I mainly work with Dr. John Marioni on single cell omics and multi-omics integration.

Affiliated Researcher

Monash University

Jun 2021 – Present Melbourne, Australia

I hold an affiliation with Monash Biomedicine Discovery Institute, for co-supervising students with Prof. Trevor Lithgow.

Postdoc Fellow

Monash University

Oct 2019 – May 2021 Melbourne, Australia

I was based at Monash Biomedicine Discovery Institute (with Prof. Trevor Lithgow), working on application of machine learning & deep learning into sequence modelling & analysis, machine learning feature generation and data platform construction.

Projects

Integrative Platform

Integrative platforms to provide all-in-one service for protein sequence analysis and prediction.

Contributed Projects

Projects that I got involved in and contributed to.

Feature Generation

Machine learning feature generation based on biological sequences.

Sequence Analysis

In silico methods for sequence-based prediction of protein identities, attributes and functions.

Forwarding Strategy in NDN

Research on intelligent forwarding strategies under Named Data Networking.

Recent & Upcoming Talks

Machine learning based prediction and analysis of anti-CRISPR proteins

A talk to introduce our work in machine learning based phage protein analysis.

May 1, 2021 1:00 PM — 1:30 PM School of Mathematics and Statistics, University of Sydney (Online)

Machine learning based prediction and analysis of anti-CRISPR proteins

Featured Publications

Jiawei Wang, Wei Dai, Jiahui Li, Qi Li, Ruopeng Xie, Yanju Zhang, Christopher Stubenrauch, Trevor Lithgow

January 2021 Nucleic Acids Research

AcrHub: an integrative hub for investigating, predicting and mapping anti-CRISPR proteins

Jiawei Wang, Jiahui Li, Yi Hou, Wei Dai, Ruopeng Xie, Tatiana T Marquez-Lago, André Leier, Tieli Zhou, Von Torres, Iain Hay, Christopher Stubenrauch, Yanju Zhang, Jiangning Song, Trevor Lithgow

January 2021 Nucleic Acids Research

BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria

Jiawei Wang, Wei Dai, Jiahui Li, Ruopeng Xie, Rhys A Dunstan, Christopher Stubenrauch, Yanju Zhang, Trevor Lithgow

January 2020 Nucleic acids research

PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins

A server for predicting and visualizing anti-CRISPR proteins. Predicted anti-CRISPRs can be further explored by interactive data visualizations on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations.