26 research outputs found
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.
Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples
Multiplex sequencing of paired-end ditags (MS-PET): a strategy for the ultra-high-throughput analysis of transcriptomes and genomes
The paired-end ditagging (PET) technique has been shown to be efficient and accurate for large-scale transcriptome and genome analysis. However, as with other DNA tag-based sequencing strategies, it is constrained by the current efficiency of Sanger technology. A recently developed multiplex sequencing method (454-sequencing™) using picolitre-scale reactions has achieved a remarkable advance in efficiency, but suffers from short-read lengths, and a lack of paired-end information. To further enhance the efficiency of PET analysis and at the same time overcome the drawbacks of the new sequencing method, we coupled multiplex sequencing with paired-end ditagging (MS-PET) using modified PET procedures to simultaneously sequence 200 000 to 300 000 dimerized PET (diPET) templates, with an output of nearly half-a-million PET sequences in a single 4 h machine run. We demonstrate the utility and robustness of MS-PET by analyzing the transcriptome of human breast carcinoma cells, and by mapping p53 binding sites in the genome of human colorectal carcinoma cells. This combined sequencing strategy achieved an approximate 100-fold efficiency increase over the current standard for PET analysis, and furthermore enables the short-read-length multiplex sequencing procedure to acquire paired-end information from large DNA fragments
ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing
ChIA-PET Tool can be used to process long-range chromatin interaction data. Results are visualized on a user-friendly genome browser
The use of multiple displacement amplification to amplify complex DNA libraries
Complex libraries for genomic DNA and cDNA sequencing analyses are typically amplified using bacterial propagation. To reduce biases, large numbers of colonies are plated and scraped from solid-surface agar. This process is time consuming, tedious and limits scaling up. At the same time, multiple displacement amplification (MDA) has been recently developed as a method for in vitro amplification of DNA. However, MDA has no selection function for the removal of ligation multimers. We developed a novel method of briefly introducing ligation reactions into bacteria to select single insert DNA clones followed by MDA to amplify. We applied these methods to a Gene Identification Signatures with Paired-End diTags (GIS-PET) library, which is a complex transcriptome library created by pairing short tags from the 5′ and 3′ ends of cDNA fragments together, and demonstrated that this selection and amplification strategy is unbiased and efficient
Next-Generation Sequencing of Apoptotic DNA Breakpoints Reveals Association with Actively Transcribed Genes and Gene Translocations
DNA fragmentation is a well-recognized hallmark of apoptosis. However, the precise DNA sequences cleaved during apoptosis triggered by distinct mechanisms remain unclear. We used next-generation sequencing of DNA fragments generated in Actinomycin D-treated human HL-60 leukemic cells to generate a high-throughput, global map of apoptotic DNA breakpoints. These data highlighted that DNA breaks are non-random and show a significant association with active genes and open chromatin regions. We noted that transcription factor binding sites were also enriched within a fraction of the apoptotic breakpoints. Interestingly, extensive apoptotic cleavage was noted within genes that are frequently translocated in human cancers. We speculate that the non-random fragmentation of DNA during apoptosis may contribute to gene translocations and the development of human cancers
Extensive Promoter-Centered Chromatin Interactions Provide a Topological Basis for Transcription Regulation
Higher-order chromosomal organization for transcription
regulation is poorly understood in eukaryotes.
Using genome-wide Chromatin Interaction
Analysis with Paired-End-Tag sequencing (ChIAPET),
we mapped long-range chromatin interactions
associated with RNA polymerase II in human cells
and uncovered widespread promoter-centered intragenic,
extragenic, and intergenic interactions. These
interactions further aggregated into higher-order
clusters, wherein proximal and distal genes were
engaged through promoter-promoter interactions.
Most genes with promoter-promoter interactions
were active and transcribed cooperatively, and
some interacting promoters could influence each
other implying combinatorial complexity of transcriptional
controls. Comparative analyses of
different cell lines showed that cell-specific chromatin
interactions could provide structural frameworks
for cell-specific transcription, and suggested
significant enrichment of enhancer-promoter interactions
for cell-specific functions. Furthermore,
genetically-identified disease-associated noncoding
elements were found to be spatially engaged with
corresponding genes through long-range interactions.
Overall, our study provides insights into transcription
regulation by three-dimensional chromatin
interactions for both housekeeping and cell-specific
genes in human cells
Defining Essential Enhancers for Pluripotent Stem Cells Using a Features-Oriented CRISPR-Cas9 Screen
10.1016/j.celrep.2020.108309Cell Reports33410830