Hiseq 6000
The HiSeq 6000 is a high-throughput sequencing system designed for large-scale genomic projects. It utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data. The HiSeq 6000 is capable of producing up to 6 billion reads per run, making it a powerful tool for researchers and organizations working on large-scale genomic analysis projects.
Lab products found in correlation
55 protocols using hiseq 6000
RNA-Seq Analysis of Plant Stalk Transcriptomes
Comprehensive Transcriptome Analysis of RNA-seq
Ancient DNA Sequencing Protocol
Adapter sequences were removed and paired-end reads were merged with ClipAndMerge v1.7.7. [61 (link)]. Shotgun sequence data was mapped to the human genome (build hg19) using BWA v0.7.12 [62 (link)] with a reduced mapping stringency parameter ‘-n 0.01’ to account for mismatches in aDNA. Duplicated reads were removed with DeDup v0.12.2 [61 (link)].
To confirm the ancient origin of the sequences, terminal damage of the reads (C to T substitutions) was assessed with DamageProfiler [63 (link)]. After the validation, the first two positions from the 5′ and 3′-ends of the reads were trimmed. Furthermore, X-chromosome and mitochondrial DNA contamination were assessed with ANGSD and Schmutzi, respectively [64 (link),65 (link)].
RNA Isolation and RNA-seq Library Preparation
RNA-seq Analysis Pipeline for Differential Gene Expression
Transcriptome Analysis of Plant Samples
All raw sequencing data were submitted to the Genome Sequence Archive (GSA) database under the BioProject accession number CRA007535 at the BIG Sub website (
Transcriptomic Analysis of Leaf RNA Responses
The Expectation Maximization (RSEM) was used to map the transcripts to reference unigenes via RNA-seq. The read count numbers were calculated and translated to FPKM (fragments per kilobase of transcript per million mapped reads) gene values [51 (link)]. DESeq was used for analysing differentially expressed genes (DEGs) [52 (link)]. We used FDR ≤ 0.05 and |log2Fold > Change| ≥ 1 as threshold for screening DEGs. All identified DEGs were mapped to gene ontology (GO) and Tokyo Encyclopedia of Genes and Genomes (KEGG) databases. The significantly enriched biochemical pathways were obtained using KOBAS with corrected P-value ≤0.05.
RNA-seq Analysis of Zebrafish Embryos
Transcriptome Analysis via QuantSeq 3' RNA-Seq
Sample Preparation for RNA-seq of Fungus
For read mapping, the clean reads were separately aligned to the reference genome (
To identify DEGs (differential expression genes) between two different samples, differential expression analysis was performed using DESeq2 with p-adjust < 0.05 and |log2FC| ≥ 1. GO and KEGG were performed by DAVID (
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