Our Research 

Our lab aims to improve our understanding and treatment of human disease by the analysis of big data collected in relation to the diseases. In recent years our main focus was on the analysis of different types of genomic data, including genetics, methylation, RNA expression, single-cell and single-nucleus RNA, and microbiome data. Another major effort in the lab is the development of predictive methods for clinical outcomes using a combination of genomics data and clinical data available by the UCLA hospital and by other collaborators.

The methodology we apply and develop involves a combination of machine learning, optimization algorithms, combinatorial optimization, and Bayesian statistics. In particular, our lab is currently focusing on the development of methods in the following domains:

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• Methods for microbial source tracking. We are developing methods for the inference of the sources contributing to a microbiome sample across a large number of contexts.

• Methods for the analysis of time-series genomic data. We are developing statistical models that capture time-series data, particularly in microbiome. Such models can be useful to understand the dynamics of the genomic data under different scenarios.

• Data Mining of Medical Records. One of the current efforts of our lab is the prediction of health related outcomes (e.g., adverse surgery outcomes) using the large collection of medical records, genetic data, methylation data, clinical images, and physiological waveforms in the UCLA hospital, as well as other data provided through our collaborators. 

In addition to methods development, we work closely with groups around the world in order to study specific diseases. Particularly, we have been working on studies of non-Hodgkin lymphoma, leukemia, rheumatoid arthritis, asthma, coronary artery disease, myocardial infarction (heart attack), metabolic syndrome, and obesity. These studies shed important light on the biological mechanisms of these diseases, and they will hopefully pave the way for improved diagnosis and personalized treatment based on an individual's genomic data.

Our lab is currenly looking for data scientists. Please click here for more details.

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Software developed by

Software developed and maintained by our group.

• FEAST: Microbial source tracking using Expectation Maximization

• ReFACTor: Correcting cell-type heterogeneity in whole-genome methylation data.

• TCA: A deconvolution method for cell-type specific analysis of methylation data.

• Bisque: A method for the inference of cell-type composition in heterogeneous tissues using RNA data.  

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• CONFINED: A method for the detection of biological confounders (vs. technical) in methylation data.

• iphone小火箭下载安装: A user-friendly command-line tool for fast analysis of genome-wide DNA methylation data (EWAS). The package includes implementation of ReFACTor, EPIStructure, Linear Mixed Models association testing, reference-based cell type estimation.

• LAMP: Estimating Locus Specific Ancestry

• LAMP-LD: Leveraging LD in the estimation of Locus Specific Ancestry

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• GEVALT: Selecting the most predicting tag SNPs (maintained in Ron Shamir's group).

• RECYCLER: Detecting plasmids from de-novo assembly (maintained in Ron Shamir's group)
   

Retired Software

  The following are software packages that we developed in the past but no longer maintain:​​​

• SecureGenome: Maintaining your genomic privacy

• SEQEM: Assigning short reads to homologous genes in RNA-SEQ experiments. 

• iphone小火箭下载安装: Coalescent based Association Mapping

• WHAP: Weighted Haplotype Association Server. 

• 小火箭在线安装ios LD corrected spatial analysis 

• SPA: Spatial Ancestry Analysis in genetic data

• BARCODE: Compression of sequence data using Bloom filters 

296A Engineering VI

University of California, Los Angeles 90095-1600

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