Hello, I am Chichau, a bioinformaticion in Cambridge. I am now working as research fellow in EMBL-EBI and visiting scientist in Sanger Institute.

My research includes:

  • computational structural biology

    • RNA-Puzzles, a community wide effort in RNA 3D structure prediction.

      structure prediction/folding is one out of 100 most significant scientific problem, also is the first problem in the 10 most wanted problems in bioinformatics. Critical Assessment of Protein Structure PredictionCASP is a biannual prediction competition started from 1994. Since 2011, Prof. Eric Westhof initiated RNA-Puzzles, which is a CASP-like assessment of RNA 3D structure prediction. I started to organize RNA-Puzzles since 2012 and organized the 1st RNA-Puzzles meeting in Oct 2016. I am now collaborating with Rfam/RNAcentral in predicting unknown RNA structures that are difficult to be solved by experiments, which is named as “Unknown Rfam Puzzle Project”. I am also collaborating with PDBe in structure recruitment.

    • Protein-RNA interaction prediction

      发表了RNA结合位点预测算法RBscore,提出RNA/DNA与蛋白的相互作用的原理类似,提出了RNA-蛋白质结合的“funnel”模型。本人现师从蛋白质-RNA相互作用专家Matthias Hentze教授(欧洲、德国科学院院士)。

    • Protein side-chain prediction and protein design

      In 2010, I developed RApid protein Side-chain PredictorRASP, which is 10 fold faster than existing methods while keeping the same accuracy. I was invited by Prof. Daisuke Kihara in editing the 3rd ed. of book. With the help of this program, I further established some international collaborations.

  • single cell RNA-seq

    • batch effect correction

      Batch effect widely exists in single cell RNA-seq, because of the variances in time, sample, buffer, etc. With batch effect, it is difficult to understand if the cell difference is due to batch effect or real biological essence. Computational correction of batch effect is a key problem. I am now collaborating with Prof. Fabian Theis, in investigating such computational methods.

    • Human Cell Atlas/Fetal Cell Atlas

      The Human Cell Atlas is an ambitious global initiative to create a description of every cell in the human body as a reference map to accelerate progress in biomedical science, which is funded by chan zuckerberg initiative. It aims to sequence millions of cells transcriptomes and analyze the gene expression patterns. It uses 10X and Drop-seq to briefly describe the cell types and subpopulations, and uses Smart-Seq2 to decipher the detailed transcriptome information. Fetal Cell Atlas is part of the Human Cell Atlas oriented in Fetus. It will analyze the gene expression during cell development, which has both biological and medical significance.