Peng Jiang and X. Shirley Liu. 2015. “
Big data mining yields novel insights on cancer.” Nat Genet, 47, 2, Pp. 103-4.
AbstractRecent years have seen the rapid growth of large-scale biological data, but the effective mining and modeling of 'big data' for new biological discoveries remains a significant challenge. A new study reanalyzes expression profiles from the Gene Expression Omnibus to make novel discoveries about genes involved in DNA damage repair and genome instability in cancer.
views_Big_data.pdf Peng Jiang, Matthew L Freedman, Jun S Liu, and Xiaole Shirley Liu. 2015. “
Inference of transcriptional regulation in cancers.” Proc Natl Acad Sci U S A, 112, 25, Pp. 7731-6.
AbstractDespite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancer-associated TFs, we comprehensively integrated 686 ENCODE ChIP-seq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNA-binding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3'UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.
manuscript_RABIT.pdf Peng Jiang, Hongfang Wang, Wei Li, Chongzhi Zang, Bo Li, Yinling J Wong, Cliff Meyer, Jun S Liu, Jon C Aster, and X. Shirley Liu. 2015. “
Network analysis of gene essentiality in functional genomics experiments.” Genome Biol, 16, Pp. 239.
AbstractMany genomic techniques have been developed to study gene essentiality genome-wide, such as CRISPR and shRNA screens. Our analyses of public CRISPR screens suggest protein interaction networks, when integrated with gene expression or histone marks, are highly predictive of gene essentiality. Meanwhile, the quality of CRISPR and shRNA screen results can be significantly enhanced through network neighbor information. We also found network neighbor information to be very informative on prioritizing ChIP-seq target genes and survival indicator genes from tumor profiling. Thus, our study provides a general method for gene essentiality analysis in functional genomic experiments (
http://nest.dfci.harvard.edu ).
manuscript_NEST.pdf