Jianxing Feng

Jianxing Feng
Tongji University · College of Life Science and Technology

PhD

About

32
Publications
6,373
Reads
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2,374
Citations
Citations since 2016
13 Research Items
1824 Citations
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
Introduction
Skills and Expertise
Additional affiliations
August 2010 - present
Tongji University
Position
  • Lecturer
August 2008 - February 2010
University of California, Riverside
Position
  • Visting Schoolar

Publications

Publications (32)
Article
Full-text available
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the av...
Data
The specific TF-cell line list for each subset of Table 3. (CSV)
Data
The specific TF-cell line list for each subset of Table 1. (CSV)
Data
(A) The AUC of TFImpute at different random partitions of Base based on Table 2. Run0 corresponds to the result in Fig 3A. (B) The recall rates of TFImpute, gkm-SVM and PIQ at FDR 0.01 and 0.1 on union DHS regions. (C) The recall rates of the three methods at FDR 0.5, 0.9, 0.95, 0.99. (TIFF)
Data
The motif IDs for running PIQ on TestSet3. (TXT)
Data
Selected 19 TF ChIP-seq datasets from Cistrome Data Browser database [34]. (TXT)
Data
Predicted binding affinity change between two alleles of SNP rs4953223 (C/T). The color in each cell represents the predicted binding affinity of allele T minus that of allele C for the corresponding TF and cell line. (TIFF)
Data
Predicted binding differences for the major and minor alleles of SNPs. (XLSX)
Data
Hierarchical clustering of TFs based on their learned embedding. (TIF)
Data
Spearman rank correlation between the enhancer sequence and the predicted value. (TIFF)
Data
Hierarchical clustering of cell lines based on their learned embedding. (TIFF)
Data
AUC and recall rate comparison of TFImpute and gkm-SVM on datasets using GC matched negative instances. The predictions were grouped by TFs. (TIFF)
Data
Predicted binding affinity change between two alleles of SNP rs4784227 (C/T). The color in each cell represents the predicted binding affinity of allele T minus that of allele C for the corresponding TF and cell line. (TIFF)
Article
Recurrent mutations in histone-modifying enzymes imply key roles in tumorigenesis, yet their functional relevance is largely unknown. Here, we show that JARID1B, encoding a histone H3 lysine 4 (H3K4) demethylase, is frequently amplified and overexpressed in luminal breast tumors and a somatic mutation in a basal-like breast cancer results in the ga...
Article
Full-text available
The organization of nucleosomes influences transcriptional activity by controlling accessibility of DNA binding proteins to the genome. Genome-wide nucleosome binding profiles have identified a canonical nucleosome organization at gene promoters, where arrays of well-positioned nucleosomes emanate from nucleosome-depleted regions. The mechanisms of...
Article
What is an algorithm? An algorithm is a sequence of unambiguous instructions for solving a problem, i.e., for obtaining a required output for any legitimate input in a finite amount of time. Figure 5.1 gives illustrative description of the relation between problem, algorithm and, the input and output of an algorithm. © 2013 Tsinghua University Pres...
Article
Full-text available
Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate...
Article
Full-text available
Motivation: RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq da...
Article
Full-text available
With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method...
Article
Full-text available
Various aspects of genome organization have been explored based on data from distinct technologies, including histone modification ChIP-Seq, 3C, and its derivatives. Recently developed Hi-C techniques enable the genome wide mapping of DNA interactomes, thereby providing the opportunity to study genome organization in detail, but these methods also...
Data
Full-text available
Figures S1-S12 and Table S1. This file contains supplementary figures S1-S12 and Table S1 corresponding to a summary of the results of Markov Clustering.
Article
Full-text available
Chromatin dynamics across cellular differentiation states is an emerging perspective from which the mechanism of global gene expression regulation may be better understood. While the roles of some histone marks have been partially interpreted in terms of their association with gene transcription, the dynamics of histone marks from a loci-specific p...
Article
Full-text available
The new second generation sequencing technology revolutionizes many biology-related research fields and poses various computational biology challenges. One of them is transcriptome assembly based on RNA-Seq data, which aims at reconstructing all full-length mRNA transcripts simultaneously from millions of short reads. In this article, we consider t...
Article
Model-based Analysis of ChIP-Seq (MACS) is a command-line tool designed by X. Shirley Liu and colleagues to analyze data generated by ChIP-Seq experiments in eukaryotes, especially mammals. MACS can be used to identify transcription factor binding sites and histone modification-enriched regions if the ChIP-Seq data, with or without control samples,...
Article
Full-text available
The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA) f...
Article
Full-text available
Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages...
Conference Paper
The second generation sequencing technology revolutionizes many biology related research fields, and posts various computational biology challenges. One of them is transcriptome assembly based on RNA-Seq data, which aims at reconstructing all full-length mRNA transcripts (i.e., isoforms) simultaneously from millions of short reads. We propose three...
Conference Paper
Full-text available
The new second generation sequencing technology revolutionizes many biology related research fields, and posts various computational biology challenges. One of them is transcriptome assembly based on RNA-Seq data, which aims at reconstructing all full-length mRNA transcripts simultaneously from millions of short reads. In this paper, we consider th...
Conference Paper
Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages...
Article
Full-text available
In this article, we present a new data structure, called the permutation tree, to improve the running time of sorting permutation by transpositions and sorting permutation by block interchanges. The existing 1.5-approximation algorithm for sorting permutation by transpositions has time complexity O(n3/2 &sqrt;logn). By means of the permutation tree...
Conference Paper
In this paper, we present a new data structure–permutation tree to improve the running time of sorting permutation by transpositions and sorting permutation by block-interchanges. The 1.5-approximation algorithm for sorting permutation by transpositions has time complexity O(n\frac32 Ö{log n})O(n^{\frac{3}{2}} \sqrt{log n}). By the permutation tr...

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