Adapter-sequences of FastQ format RNA-seq reads were removed and the reads were trimmed of low quality ends (phred score = 20) by the use of Trim Galore! (version 0.4.2) (“Babraham Bioinformatics - Trim Galore!” 2017). The reads were aligned to the hg38 reference genome (Genbank: GCA_000001405.15) by using grape-nf (version 433e7621f6) (18 ), which combines STAR (version 2.4.0j) (19 (link)) for the alignment and RSEM (version 1.2.21) (20 (link)) for the read assignment.
Trim galore
Manufactured by Babraham Bioinformatics
Sourced in United Kingdom
Trim Galore is a tool to perform quality control on RNA or DNA sequencing data. It provides automated adapter trimming, quality trimming, and filtering of reads.
Automatically generated - may contain errors
Lab products found in correlation
Fastqc, by Babraham Bioinformatics (25 mentions)
Hiseq 4000, by Illumina (6 mentions)
Ovation ultralow library system, by Tecan (5 mentions)
Ovation rna seq system v2, by Tecan (5 mentions)
Rneasy plus micro kit, by Qiagen (5 mentions)
Bismark, by Babraham Bioinformatics (4 mentions)
Truseq rna library prep kit, by Illumina (4 mentions)
Novaseq 6000, by Illumina (3 mentions)
Bioanalyzer, by Agilent Technologies (3 mentions)
Nextseq 500 system, by Illumina (3 mentions)
Rneasy micro kit, by Qiagen (2 mentions)
Basespace, by Illumina (2 mentions)
Rneasy kit, by Qiagen (2 mentions)
Bioanalyser, by Agilent Technologies (2 mentions)
Smarter stranded total rna seq kit pico input mammalian, by Takara Bio (2 mentions)
Hiseq 2500, by Illumina (2 mentions)
Bismark v0, by Babraham Bioinformatics (2 mentions)
Purelink rna mini kit, by Thermo Fisher Scientific (2 mentions)
Ez dna methylation kit, by Zymo Research (1 mentions)
Pyromark pcr kit, by Qiagen (1 mentions)
88 protocols using trim galore
1
RNA-seq Data Processing Pipeline
2
Illumina Paired-End Sequencing Data Preprocessing
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Illumina paired end sequencing data were exported in FASTQ file format. The reads were trimmed using Trim Galore (Babraham Bioinformatics, https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ , accessed on 19 November 2019) and cutadapt [64 (link)] to remove bases where the PHRED quality value was less than 20. Potential 3’ adapter and poly(A)-tail fragments were also removed.
3
Transcriptomic Analysis of Redox, Stress, and Physiological Processes in Atlantic Salmon
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The read quality checks, alignment, and differentially expressed genes (DEGs) were conducted as described by Yin et al. [25 (link)]. In brief, raw RNA-seq reads were trimmed and cleaned by TrimGalore! (Babraham Bioinformatics—Trim Galore!, version 0.6.5) and aligned to the reference genome of Atlantic salmon (ICSASG_v2; https://www.ncbi.nlm.nih.gov/assembly/GCF_000233375.1 , accessed on 19 August 2022) by STAR [40 (link)] and quantified by featureCount [41 (link)]. The mRNA expression of genes chosen based on their importance within the redox system [21 (link),22 (link),23 (link),25 (link),42 (link)], oxidative stress response [43 (link),44 (link),45 (link)], and physiological processes including the GH–insulin-like growth factor (IGF) axis signaling [46 (link),47 (link)], and cell cycle regulation [18 (link),19 (link)] was studied in the liver, muscle, and brain (Supplementary Data S1 ). DEGs was defined by 10 pair-wise comparisons: (i) Apr vs. Mar, (ii) Jun vs. Mar, (iii) Aug vs. Mar, (iv) Sep vs. Mar, (v) Jun vs. Apr, (vi) Aug vs. Apr, (vii) Sep vs. Apr, (viii) Aug vs. June, (ix) Sep vs. Jun, and (x) Sep vs. Aug, by using the DESeq2 [48 ]. Genes were identified as DEGs when the adjusted p values were less than 0.05, and the absolute log fold changes (LFCs) were less than 1.2.
4
Genome-wide DNA methylation analysis
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The sequenced 2 × 100 bp paired-end libraries were assessed for sufficient sequencing quality and potential adapter contamination using the programs FastQC (Babraham Bioinformatics, FastQC/">https://www.bioinformatics.babraham.ac.uk/projects/FastQC/ ), trim_galore (version 0.6.5; Babraham Bioinformatics, trim_galore/">https://www.bioinformatics.ac.uk/projects/trim_galore/ ) and cutadapt70 (link). Quality-controlled libraries have been mapped against the mouse reference genome (assembly GRCm38) using the bisulfite short read mapping software BSMAP71 (link). Only uniquely, properly paired reads (methratio.py parameters: --unique, --paired, --remove-duplicate) were used to detect CpG methylation levels and coverage. CpG motifs with a minimum coverage of five mapped reads in at least two replicates of one condition served as input for methylation level smoothing and detection of DMRs using the Bioconductor package bsseq (version 1.16)72 (link). Regions were classified as differentially methylated between two condictions if they (1) contain at least three CpG motifs with (2) a maximal distance of 300 bases, (3) a mean methylation difference of at least 0.25, and (4) all CpGs in the region have an associated t-statistic (bsseq function Bsmooth.tstat) beyond a [low,high] cutoff with low = 0.01 and high = 0.99 (parameter q = (low,high) of bsseq function dmrFinder).
5
Robust RRBS Protocol for Methylome Analysis
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The raw sequence reads were evaluated and processed using software and methods as outlined previously [82 ]. Briefly, the reads were first assessed for quality using FastQC (version 0.11.5, Babraham Bioinformatics, UK), and then low-quality reads were trimmed off using Trim Galore (version 0.4.2, Babraham Bioinformatics, UK). Only the bases with a Phred > 30 were kept for downstream analysis and Illumina adaptors were also removed. A special setting designed for trimming RRBS data (–rrbs) in Trim Galore was also used. Specifically, Trim Galore trimmed the first two bases from the 3′ end of the reads so the cytosine (C) base closest to the second enzyme-cut site was not included in methylation calling, since the RRBS approach introduces artificial CpG sites at the end. Trimmed reads were then aligned and mapped with Bismark version 0.16.3 [83 ]/Bowtie2 version 2.2.9 [84 ] to an in silico bisulfite converted human reference genome (GRCh38). The unique alignment was then identified and used to make a methylation call after discarding reads with multiple mapping.
6
De-novo Transcriptome Assembly Using Trinity
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All fastq files were trimmed using Trimgalore! (Babraham Bioinformatics) with default parameters. Files were concatenated together into two fastq.gz files (one for each read -R1, and R2), which was then used to assemble transcripts fully de-novo (i.e. not genome-guided) using Trinity (Haas et al., 2013; (link)Grabherr et al., 2011) (link). Default parameters were used for Trinity with the exception of -SS lib type RF as all samples were sequenced as fr-second strand paired end.
7
Genome Comparison for Assembly-Free Analysis
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To mitigate problems arising from incompleteness and errors in de novo assembly of short sequence reads, we used a genome-comparison strategy based on aligning sequencing reads against a highquality reference genome sequence. For this assembly-free genomic comparison, we acquired shortread whole-genome Illumina sequence data as FastQ files (Cock et al. 2010) for Fusarium oxysporum f. sp. cubense from the SRA database (Kodama et al. 2012 ). The quality of the sequencing data was evaluated using FASTQC (Andrews n.d.) . Reads with low quality or containing adaptor sequences were trimmed using Trim Galore (Babraham Bioinformatics -Trim Galore! 2022) or Canu (Koren et al. 2017) as appropriate to the sequencing method that generated the data. The sequences were aligned against the reference genome of isolate UK0001 (GenBank:GCA_007994515) using the Burrows Wheeler Aligner (BWA) (Li and Durbin 2010, 2009) and Minimap2 (Li 2018) . The alignments were evaluated with Qualimap (García-Alcalde et al. 2012).
8
Genome Comparison for Assembly-Free Analysis
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To mitigate problems arising from incompleteness and errors in de novo assembly of short sequence reads, we used a genome-comparison strategy based on aligning sequencing reads against a highquality reference genome sequence. For this assembly-free genomic comparison, we acquired shortread whole-genome Illumina sequence data as FastQ files (Cock et al. 2010) for Fusarium oxysporum f. sp. cubense from the SRA database (Kodama et al. 2012 ). The quality of the sequencing data was evaluated using FASTQC (Andrews n.d.) . Reads with low quality or containing adaptor sequences were trimmed using Trim Galore (Babraham Bioinformatics -Trim Galore! 2022) or Canu (Koren et al. 2017) as appropriate to the sequencing method that generated the data. The sequences were aligned against the reference genome of isolate UK0001 (GenBank:GCA_007994515) using the Burrows Wheeler Aligner (BWA) (Li and Durbin 2010, 2009) and Minimap2 (Li 2018) . The alignments were evaluated with Qualimap (García-Alcalde et al. 2012).
9
RNA-seq Data Analysis Pipeline
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For all RNA-seq experiments, raw reads were processed with Trim Galore!
(version 0.4.1) (Babraham Bioinformatics;https://www.bioinformatics.babraham.ac.uk/projects/trim_galore )
to remove adapter sequences and aligned with STAR (version 2.4.2)48 (link) to the human genome (GRCh38,
primary assembly) using the GENCODE annotation (version 23). Differential
expression analysis was performed in R (version 3.2.1) (http://www.R-project.org/ ) using the DESeq2
package (version 1.10.0)49 (link).
Gene set enrichment analysis was performed with GSEA2 (version 2.2.0)
and gene sets from MSigDB (version 5.0)50 (link),51 (link). We used the
“preranked” algorithm to analyze gene lists ranked by the negative
decadic logarithm of adjusted p-values obtained from the differential-expression
analysis with DESeq2. To separate up- and down-regulated genes, we artificially
assigned a negative sign to values for downregulated genes (thus using the
decadic logarithm of adjusted p-values). We used the options –nperm 1000,
-set_max 1500, and –set_min 5.
(version 0.4.1) (Babraham Bioinformatics;
to remove adapter sequences and aligned with STAR (version 2.4.2)48 (link) to the human genome (GRCh38,
primary assembly) using the GENCODE annotation (version 23). Differential
expression analysis was performed in R (version 3.2.1) (
package (version 1.10.0)49 (link).
Gene set enrichment analysis was performed with GSEA2 (version 2.2.0)
and gene sets from MSigDB (version 5.0)50 (link),51 (link). We used the
“preranked” algorithm to analyze gene lists ranked by the negative
decadic logarithm of adjusted p-values obtained from the differential-expression
analysis with DESeq2. To separate up- and down-regulated genes, we artificially
assigned a negative sign to values for downregulated genes (thus using the
decadic logarithm of adjusted p-values). We used the options –nperm 1000,
-set_max 1500, and –set_min 5.
10
Bovine Genome Methylation Analysis
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The quality of the sequencing reads was evaluated using the software FastQC (v0.11.7, Babraham Bioinformatics, UK). Adaptor removal and trimming was performed when needed using the software Trim Galore (v0.4.4, Babraham Bioinformatics, UK). After quality control and processing, the resulting paired-end sequencing reads were aligned to ARS-UCD1.2 bovine reference genome using the software Bismark (v0.17.0, Babraham Bioinformatics, UK) [42 (link)]. The tool deduplicate_Bismark was used to remove duplicate read alignments. Methylation calls were performed using Bismark methylation extractor (v0.17.0, Babraham Bioinformatics) using the following parameters: --paired-end, −-comprehensive, −-bedGraph, and --cytosine_report [42 (link)].
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