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Next-generation sequencing of genomic DNA fragments bound to a transcription factor in vitro reveals its regulatory potential.

ABSTRACT: Several transcription factors (TFs) coordinate to regulate expression of specific genes at the transcriptional level. In Arabidopsis thaliana it is estimated that approximately 10% of all genes encode TFs or TF-like proteins. It is important to identify target genes that are directly regulated by TFs in order to understand the complete picture of a plant's transcriptome profile. Here, we investigate the role of the LONG HYPOCOTYL5 (HY5) transcription factor that acts as a regulator of photomorphogenesis. We used an in vitro genomic DNA binding assay coupled with immunoprecipitation and next-generation sequencing (gDB-seq) instead of the in vivo chromatin immunoprecipitation (ChIP)-based methods. The results demonstrate that the HY5-binding motif predicted here was similar to the motif reported previously and that in vitro HY5-binding loci largely overlapped with the HY5-targeted candidate genes identified in previous ChIP-chip analysis. By combining these results with microarray analysis, we identified hundreds of HY5-binding genes that were differentially expressed in hy5. We also observed delayed induction of some transcripts of HY5-binding genes in hy5 mutants in response to blue-light exposure after dark treatment. Thus, an in vitro gDNA-binding assay coupled with sequencing is a convenient and powerful method to bridge the gap between identifying TF binding potential and establishing function.

SUBMITTER: Kurihara Y

PROVIDER: S-EPMC4276929 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Next-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs. Although these technologies are frequently used in biological studies, NGS data analysis remains a challenging, time-consuming, and often irreproducible process. Therefore, there is a need for a comprehensive and flexible workflow platform that can accelerate data processing and analysis so more time can be spent on functional studies.We have developed an integrative, stand-alone workflow platform, named CIPHER, for the systematic analysis of several commonly used NGS datasets including ChIP-seq, RNA-seq, MNase-seq, DNase-seq, GRO-seq, and ATAC-seq data. CIPHER implements various open source software packages, in-house scripts, and Docker containers to analyze and process single-ended and pair-ended datasets. CIPHER's pipelines conduct extensive quality and contamination control checks, as well as comprehensive downstream analysis. A typical CIPHER workflow includes: (1) raw sequence evaluation, (2) read trimming and adapter removal, (3) read mapping and quality filtering, (4) visualization track generation, and (5) extensive quality control assessment. Furthermore, CIPHER conducts downstream analysis such as: narrow and broad peak calling, peak annotation, and motif identification for ChIP-seq, differential gene expression analysis for RNA-seq, nucleosome positioning for MNase-seq, DNase hypersensitive site mapping, site annotation and motif identification for DNase-seq, analysis of nascent transcription from Global-Run On (GRO-seq) data, and characterization of chromatin accessibility from ATAC-seq datasets. In addition, CIPHER contains an "analysis" mode that completes complex bioinformatics tasks such as enhancer discovery and provides functions to integrate various datasets together.Using public and simulated data, we demonstrate that CIPHER is an efficient and comprehensive workflow platform that can analyze several NGS datasets commonly used in genome biology studies. Additionally, CIPHER's integrative "analysis" mode allows researchers to elicit important biological information from the combined dataset analysis.

Project description:BackgroundIn recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.ResultsGAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools.ConclusionsThe difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.

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Next-generation sequencing of genomic DNA fragments bound to a transcription factor in vitro reveals its regulatory potential.

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