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Commercial ChIP-Seq library preparation kits performed differently for different classes of protein targets

ABSTRACT: Commercial ChIP-Seq library preparation kits performed differently for different classes of protein targets

PROVIDER: PRJNA804722 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR17955900.fastq.gz	Fastqsanger.gz
	SRR17955901.fastq.gz	Fastqsanger.gz
	SRR17955902.fastq.gz	Fastqsanger.gz
	SRR17955903.fastq.gz	Fastqsanger.gz
	SRR17955804.fastq.gz	Fastqsanger.gz

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Commercial ChIP-Seq library preparation kits performed differently for different classes of protein targets

Project description:ChIP-Seq is a powerful method commonly used to study global protein-DNA interactions including both transcription factors and histone modifications. We have found that the choice of ChIP-Seq library preparation protocol plays an important role in overall ChIP-Seq data quality. However, very few studies have compared ChIP-Seq libraries prepared by different protocols using multiple targets and a broad range of input DNA levels. In this study, we evaluated the performance of four ChIP-Seq library preparation protocols [NEB NEBNext Ultra II, Roche KAPA HyperPrep, Diagenode MicroPlex, and Bioo (now PerkinElmer) NEXTflex] on three target proteins, chosen to represent the three typical signal enrichment patterns in ChIP-Seq experiments: sharp peaks (H3K4me3), broad domains (H3K27me3) and punctate peaks with a protein binding motif (CTCF). We also tested a broad range of different input DNA levels from 0.10 to 10 ng for H3K4me3 and H3K27me3 experiments. Our results suggest that the NEB protocol may be better for preparing H3K4me3 (and potentially other histone modifications with sharp peak enrichment) libraries; the Bioo protocol may be better for preparing H3K27me3 (and potentially other histone modifications with broad domain enrichment) libraries, and the Diagenode protocol may be better for preparing CTCF (and potentially other transcription factors with well-defined binding motifs) libraries. For ChIP-Seq experiments using novel targets without a known signal enrichment pattern, the NEB protocol might be the best choice as it performed well for each of the three targets we tested across a wide array of input DNA levels.

2023-06-23 | GSE196402 | GEO

Commercial ChIP-Seq Library Preparation Kits Performed Differently for Different Classes of Protein Targets.

Project description:BackgroundChromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) is a powerful method commonly used to study global protein-DNA interactions including both transcription factors and histone modifications. We have found that the choice of ChIP-Seq library preparation protocol plays an important role in overall ChIP-Seq data quality. However, very few studies have compared ChIP-Seq libraries prepared by different protocols using multiple targets and a broad range of input DNA levels.ResultsIn this study, we evaluated the performance of 4 ChIP-Seq library preparation protocols (New England Biolabs [NEB] NEBNext Ultra II, Roche KAPA HyperPrep, Diagenode MicroPlex, and Bioo [now PerkinElmer] NEXTflex) on 3 target proteins, chosen to represent the 3 typical signal enrichment patterns in ChIP-Seq experiments: sharp peaks (H3K4me3), broad domains (H3K27me3), and punctate peaks with a protein binding motif (CTCF). We also tested a broad range of different input DNA levels from 0.10 to 10 ng for H3K4me3 and H3K27me3 experiments.ConclusionsOur results suggest that the NEB protocol may be better for preparing H3K4me3 (and potentially other histone modifications with sharp peak enrichment) libraries; the Bioo protocol may be better for preparing H3K27me3 (and potentially other histone modifications with broad domain enrichment) libraries, and the Diagenode protocol may be better for preparing CTCF (and potentially other transcription factors with well-defined binding motifs) libraries. For ChIP-Seq experiments using novel targets without a known signal enrichment pattern, the NEB protocol might be the best choice, as it performed well for each of the 3 targets we tested across a wide array of input DNA levels.

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Evaluation of three small RNA library preparation kits on serum-derived RNA

Project description:We evaluated the effect of the small RNA library preparation method on 5' tRNA-halves and miRNA abundance in libraries prepared from serum RNA using three commercially available small RNA library preparation kits (TruSeq small RNA library preparation kit v2 (Illumina), TailorMix miRNA sample preparation kit v2 (Seqmatic) and the NEBNext Multiplex Small RNA library prep kit (New England Biolabs)). RNA isolated from 100 µl of serum collected from healthy mice was used as input for the preparation of a small RNA library in duplicate and libraries were single end sequenced.

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RNA-seq sample preparation kits strongly affect transcriptome profiles of a gas-fermenting bacterium

Project description:Transcriptome analysis via RNA sequencing (RNA-seq) has become a standard technique employed across a variety of biological fields of study. This rapid adoption of the RNA-seq approach has been mediated, in part, by the development of various commercial RNA-seq library preparation kits compatible with common next-generation sequencing (NGS) platforms. Generally, the essential steps of library preparation such as rRNA depletion and first-strand cDNA synthesis are tailored to a certain group of organisms (e.g. eukaryotes versus prokaryotes) or genomic GC content. Therefore, selection of appropriate commercial products is of crucial importance to capture the transcriptome of interest as closely to the native state as possible without introduction of technical bias. However, researchers rarely have the resources and time to test various commercial RNA-seq kits for their samples. In this work, we report a side-by-side comparison of RNA-seq data from the same Clostridium autoethanogenum input samples obtained using three commercial rRNA removal and strand-specific library construction products by NuGEN Technologies, Qiagen, and Zymo Research, and compare their performance with published data. While all three vendors advertise their products as suitable for prokaryotes, we found significant differences in their performance in terms of rRNA removal, strand-specificity, and, most importantly, transcript abundance distribution profiles. Notably, RNA-seq data obtained with Qiagen products were most similar to published data and delivered the best results in terms of library strandedness and transcript abundance distribution range. Our results highlight the importance of finding appropriate organism-specific workflows and library preparation products for RNA-seq studies.

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