Multiplexed Illumina sequencing data was demultiplexed using Illumina bcl2fastq converter (version v2.17.1.14). Raw reads in fastq format were processed to get rid of low quality bases and possible adapter contamination using Trimmomatic (version 0.33) (Bolger et al., 2014 (link)) (settings: ILLUMINACLIP:TruSeq.fa:2:25:6 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36). The filtered reads were aligned to the C.elegans genome (ce10 genome build - WormBase WS220 released in October, 2010) using the splice-aware short read aligner STAR (version 2.4.2a) with the default settings (Dobin et al., 2013 (link)) except for setting “—outFilterMultimapNmax” argument to 1. The expression level of each gene was quantified using R/Bioconductor package quasR (Gaidatzis et al., 2015 (link)) using genome annotation data in GTF file format from the Ensembl database (version 70) (Yates et al., 2016 (link)) Differential expression analysis of the quantified expression levels of genes between different samples was done using the R/Bioconductor package DESeq2 (Love et al., 2014 (link)) Up/down regulated genes are detected based on the differential expression criteria of adjusted p-value of at least 0.1 and at least two-fold increase/decrease in expression levels in relation to the control samples.
Bcl2fastq converter
Manufactured by Illumina
Sourced in United States
The Bcl2fastq converter is a software tool designed to convert Illumina's binary base call (BCL) data files into the commonly used FASTQ format. This conversion process enables the downstream analysis of sequencing data using various bioinformatics tools and pipelines.
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Lab products found in correlation
Novaseq platform, by Illumina (2 mentions)
Phix library, by Illumina (1 mentions)
Nextseq 500 550 high output kit v2, by Illumina (1 mentions)
Nextseq 500, by Illumina (1 mentions)
Bioanalyzer 2100 system, by Agilent Technologies (1 mentions)
Bioruptor plus, by Diagenode (1 mentions)
Countess 2 automated cell counter, by Countstar (1 mentions)
14 protocols using bcl2fastq converter
1
Multiplexed RNA-seq Data Analysis
2
Robust RNA-seq Data Processing Pipeline
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For human samples, FASTQ files were generated with the Illumina bcl2fastq converter (version 2.17). Nextera TruSight adapters were trimmed, and degenerate bases at the 3’- end, as well as highly degenerate reads were removed using skewer (version 0.2.2), which by default removes reads with a remaining length of less than 18 nt. For the mouse samples, basecalling, barcode demultiplexing and adaptor trimming was performed using the Ion Torrent Suite software, and reads less than 30 nt in length were discarded. The quality of the reads was assessed using FastQC (version 0.11.5). The preprocessed sequence reads were mapped to the genome of the respective species (canonical chromosomes of release hg 19 for human and mm10 for mouse, respectively) using STAR (Dobin et al., 2013 (link)). Reads mapping uniquely to each gene of the Ensembl 75 annotation of the respective species were quantified with the featureCounts program from the subread software package (version 1.5.2, using parameters -s 0 -O–fracOverlap 0.5 (Liao et al., 2019 (link)).
3
NovaSeq Paired-end Sequencing Protocol
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The libraries were sequenced on the NovaSeq platform (Illumia) to generate 150 bp paired-end Reads, according to the manufacturer’s instructions. Raw data (Raw Reads) of fastq files were assembled from the Raw BCL files using Illumina’s bcl2fastq converter. Raw data firstly were processed through primary quality control. The monitored quality assessment parameters were, (i) contain N more than 3; (ii) the proportion of base with quality value below 5 is more than 20%; (iii) adapter sequence. All the downstream analyses were based on the clean data with high quality.
4
Robust RNA-seq Data Processing Pipeline
5
Comprehensive SFMC and PBMC Profiling
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Raw data were assembled using Illumina’s bcl2fastq converter. After initial quality control, adaptor sequences, sequences with over three uncertain nucleotides (designated as N) and low-quality reads were removed. Only clean data for high-quality reads were aligned to the human reference genome with the 10x Genomics Cell Ranger pipeline using default parameters. Unique molecule identifiers (UMIs) for each gene and each cell barcode were counted and added to gene expression matrices. Doublets detected by Scrublet. Doublet clusters expressing markers of more than two major cell lineages, and cells meeting the following criteria were removed: (1) number of genes detected per cell <200 or >5000; (2) UMIs detected per cell over 200; (3) counts of mitochondrial genes constituting over 12% of all gene counts; and (4) counts of red blood cell genes constituting over 0.3% of all gene counts. Finally, we obtained 40,482 SFMCs and 64,819 PBMCs for downstream analyses.
6
Multiplexed Illumina Sequencing Data Analysis
7
Chromium Single Cell 5' RNA-seq Library Construction
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ScRNA-seq library construction was performed using the Chromium Single Cell 5′ Library & Gel Bead Kit (10× Genomics) for all samples according to the manufacturer’s instructions. Briefly, a single-cell suspension (containing 10000 cells), barcoded gel beads and partitioning oil were loaded onto Chromium Chip A to generate a single-cell gel bead-in-emulsion (GEM). Cells were captured in GEMs during targeted cell recovery. After the reverse transcription step, barcoded cDNA was purified with Dynabeads, and PCR amplification was performed as follows. A 5′ gene expression library was constructed on the basis of amplified cDNA. For gene expression library construction, 50 ng of amplified cDNA was fragmented and end-repaired, 150-bp paired-end reads and raw BCL files were produced after the cDNA was double size-selected with SPRIselect beads and sequenced on a NovaSeq platform (Illumina). Raw data in FASTQ format were assembled from the raw BCL files using Illumina’s bcl2fastq converter.
8
Multiplexed Illumina Sequencing Data Analysis
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Multiplexed Illumina sequencing data was demultiplexed using Illumina bcl2fastq converter (version v2.17.1.14). The read quality of all the libraries were assessed using fastQC. The 3’ adaptor was trimmed from raw reads using Cutadapt (option -a TGGAATTCTCGGGTGCCAAGG). Only reads with at least 24 nt after trimming (but including 5' and 3' randomized tetramers) were kept (option -m 24). Reads in which the adaptor was not found were also kept (option --untrimmed-output). 5’ and 3’ end tetramers were removed using Cutadapt (options -u 4 and -u -4). The reads were aligned on C. elegans genome (ce11, C. elegans Sequencing Consortium WBcel235) using Bowtie2 with the default parameters. After the alignment on C. elegans genome, read counting with featureCounts and differential expression analysis with DESeq2 were performed the same way as for RNA-seq data, with the same criteria to detect up-regulated and down-regulated genes.
9
Single-Cell Transcriptome Analysis Pipeline
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The FASTQ files were assembled from the raw BCL files using Illumina’s bcl2fastq converter and run through the FASTQC codes (Babraham bioinformatics; https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) to check for consistency in library qualities. The monitored quality assessment parameters monitored were (i) per-base sequence quality (especially for the read 2 of the gene), (ii) per-base N content, (iii) per-base sequence content, and (iv) over-represented sequences. The FASTQ files were then merged and converted into binaries using PICARD’s FastqToSam algorithm. The sequencing reads were converted into a digital gene expression matrix using the Drop-seq bioinformatics pipeline37 .
10
RNA-seq analysis pipeline for human genome
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Multiplexed Illumina sequencing data were demultiplexed using Illumina bcl2fastq converter (version 2.20.0.422). Reads were aligned on the Homo sapiens genome (Build version GRCh38, NCBI) using Hisat2 (Kim et al, 2015 (link)) (version 2.2.1) with the default settings. After alignment, reads mapping to annotated protein‐coding genes were counted using featureCounts (version 2.0.1). Annotations were obtained from the Ensembl release 100. Counted reads for protein‐coding genes were used for differential expression analysis using the R/Bioconductor package DESeq2 (Love et al, 2014 (link)) (version 1.26.0).
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