For data analysis, reference genome and gene annotation files were downloaded from GENECODE (hg38). Fastq files were pre-processed by Fastp (v0.12.1) (Chen et al. 2018 (link)) with default parameters. Cleaned data were then aligned to the reference genome via Hisat2 (v2.1.0) (Kim et al. 2015 (link)). FeatureCounts (v1.5.3) (Liao et al. 2014 (link)) was used to count the reads mapped to each gene.
Nebnext ultra 2 library prep kit
The NEBNext Ultra II Library Prep Kit is a library preparation kit designed for the efficient and high-quality construction of DNA libraries for next-generation sequencing applications. The kit includes the necessary reagents and enzymes to perform fragmentation, end-repair, adapter ligation, and library amplification, enabling the generation of high-quality sequencing libraries from a variety of DNA input sources.
Lab products found in correlation
10 protocols using nebnext ultra 2 library prep kit
mRNA-seq Library Preparation and Sequencing Protocol
For data analysis, reference genome and gene annotation files were downloaded from GENECODE (hg38). Fastq files were pre-processed by Fastp (v0.12.1) (Chen et al. 2018 (link)) with default parameters. Cleaned data were then aligned to the reference genome via Hisat2 (v2.1.0) (Kim et al. 2015 (link)). FeatureCounts (v1.5.3) (Liao et al. 2014 (link)) was used to count the reads mapped to each gene.
Transcriptome Sequencing Library Preparation
RNA-seq Analysis of HNRNPL Knockdown
Chromatin Profiling via MNase-seq
Transcriptome Analysis of E. coli Planktonic and Swarm Cells
Library Prep for MNase-seq and ChIP-seq
Chromatin Profiling via MNase-Seq
Transcriptome analysis of wdr-23 mutants
SARS-CoV-2 Genome Sequencing Workflow
High throughput sequencing was performed using the Illumina NovaSeq 6000 system with a read length of 250 bp, which produced a range of 1.3 to 3.3 million paired-end sequence reads per sample. Additionally, sequencing reads with low-quality (quality score <30) and short sequence (<50 nt) were removed using Trimmomatic [17 (link)], assembled using BWA-MEM [18 ], variant called using LoFreq [19 (link)], and consensus called using SAMtools [20 (link)], implemented in the HaVoC pipeline [21 (link)].
Sua7 ChIP-seq in Med14-AID background
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