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Basespace sequence hub

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BaseSpace Sequence Hub is a cloud-based software platform that provides secure storage, management, and analysis of next-generation sequencing data. It enables users to upload, process, and share sequencing data through a centralized, web-based interface.

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Lab products found in correlation

Nextseq 500 550 high output kit v2, by Illumina (7 mentions) Nanodrop, by Thermo Fisher Scientific (6 mentions) Bioanalyzer, by Agilent Technologies (5 mentions) Nextseq 500, by Illumina (5 mentions) Miseq platform, by Illumina (4 mentions) Trizol reagent, by Thermo Fisher Scientific (4 mentions) Agilent 2100 bioanalyzer, by Agilent Technologies (3 mentions) Mirneasy kit, by Qiagen (3 mentions) Truseq stranded mrna library prep kit, by Illumina (3 mentions) Bcl2fastq, by Illumina (3 mentions) Dragen covid lineage app, by Illumina (3 mentions) Qubit 3.0 fluorometer, by Thermo Fisher Scientific (3 mentions) Ampliseq library plus, by Illumina (2 mentions) Quick rna microprep kit, by Zymo Research (2 mentions) Kapa library quantification complete kit universal, by Roche (2 mentions) Rna 6000 nano kit, by Agilent Technologies (2 mentions) Covid lineage app, by Illumina (2 mentions) Miseq reagent kit v3, by Illumina (2 mentions) Nextera xt kit, by Illumina (2 mentions) Hs rna screentape, by Agilent Technologies (2 mentions)

76 protocols using basespace sequence hub

1

Targeted Sequencing of DLBCL Genes

Cited 3 times
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Targeted high-throughput sequencing was applied for somatic alterations. A total of 57 genes were selected in this study (listed in Table S2). Most genes were frequently altered in DLBCL, according to data from several previously published large-scale DLBCL group studies (32 (link)–34 (link)). Using genome build hg19/GRCh37 as a reference, a sequencing panel covering the coding sequences within five intronic base pairs around exons in 57 genes was designed online (Designstudio Sequencing, Illumina, San Diego, USA). Sequencing libraries were prepared with AmpliSeq™ Library PLUS for Illumina, using 20 ng of input genomic DNA per sample. Library sequencing was performed to 2000X coverage on a NextSeq™ 550 system using an Illumina NextSeq™ 500/550 High Output v2 Kit (Illumina, San Diego, USA). The alignment and variant calling were performed using a DNA Amplicon workflow with default parameters on BaseSpace Sequence Hub (Illumina). The generated variants were further annotated using ANNOVAR (35 (link)). Further details are described in the Supplementary Methods.
Wang J., Shen K., Mu W., Li W., Zhang M., Zhang W., Li Z., Ge T., Zhu Z., Zhang S., Chen C., Xing S., Zhu L., Chen L., Wang N., Huang L., Li D., Xiao M, & Zhou J. (2022). T Cell Defects: New Insights Into the Primary Resistance Factor to CD19/CD22 Cocktail CAR T-Cell Immunotherapy in Diffuse Large B-Cell Lymphoma. Frontiers in Immunology, 13, 873789.
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2

RNA-seq Analysis of POLG Macrophages

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Total cellular RNA from WT and POLG BMDMs and PerMacs was prepared using Quick-RNA microprep kit (Zymo Research) and used for the next-generation RNA-seq procedure at Texas A&M University Bioinformatics Core. RNA-seq data were analyzed using BaseSpace Sequence Hub (Illumina). Briefly, Spliced Transcripts Alignment to a Reference (STAR) algorithm of RNA-seq Alignment V2.0.0 software was used to align the results to reference genome Mus musculus/mm10 (RefSeq), and then RNA-seq Differential Expression V1.0.0 software was used to obtain raw gene expression files and determine statistically significant changes in gene expression in POLG macrophages relative to WT. Ingenuity Pathway Analysis software (QIAGEN) was used to identify gene families and putative upstream regulators in the datasets. Heatmaps were generated using GraphPad Prism. Raw and analyzed datasets are being deposited into the National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE171960.
Lei Y., Guerra Martinez C., Torres-Odio S., Bell S.L., Birdwell C.E., Bryant J.D., Tong C.W., Watson R.O., West L.C, & West A.P. (2021). Elevated type I interferon responses potentiate metabolic dysfunction, inflammation, and accelerated aging in mtDNA mutator mice. Science Advances, 7(22), eabe7548.
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3

Liver RNA Sequencing and Integrated Multiomics

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RNA was extracted from livers using the Qiagen miRNeasy kit (Qiagen, Hilden, Germany). RNA quality and quantity were determined using the RNA 6000 Nano Kit on the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA). Sequencing library preparation was done with the Illumina TruSeq Stranded Total RNA with RiboZero Gold kit (Illumina, San Diego, CA). Sequencing libraries were quantified using the KAPA Library Quantification Complete Kit Universal (KAPA Biosystems, Wilmington, MA). The pooled library (1.4pM) was loaded onto the NextSeq 500 instrument, using the NextSeq 500/550 High Output v2 Kit for 75 cycles (Illumina, San Diego, CA). RNA-Seq data have been securely transferred, stored, and analyzed in the Illumina BaseSpace Sequence Hub. All RNA-sequencing data have been uploaded to the NCBI Gene Expression Omnibus (Accession GSE217133). RNA Express software was used to assign aligned reads to genes and perform differential gene expression analysis. Cufflinks software was used to profile gene expression and to detect transcript isoforms. Leveraging KEGG, Ingenuity, and Panther GeneOntology databases, integrated analysis of individually matched metabolome and RNAseq results was carried out with MetaboAnalyst. Changes between genotypes at false discovery rate (FDR) p value < 0.05 were further evaluated by western blot.
Oaks Z., Patel A., Huang N., Choudhary G., Winans T., Faludi T., Krakko D., Duarte M., Lewis J., B.e.c.k.f.o.r.d., Blair S., Kelly R., Landas S., Middleton F., Asara J., Chung S., Fernandez D., Banki K, & Perl A. (2023). CYTOSOLIC ALDOSE METABOLISM CONTRIBUTES TO PROGRESSION FROM CIRRHOSIS TO HEPATOCARCINOGENESIS. Nature metabolism, 5(1), 41-60.
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4

Microbiome Analysis using QIIME2 and Decontamination

Cited 4 times
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Illumina paired-end reads were demultiplexed in the Illumina BaseSpace Sequence Hub [48 ]. The QIIME2 (v. 2021.4) software [49 (link)] was used to process reads, infer the taxonomic affiliation and perform diversity and abundance analysis. The removal of sequence errors, index and adapters was performed with the cutadapt plugin [50 (link)], whereas quality control, chimera removal and dereplication were performed with the DADA2 plugin [51 (link)]. Taxonomic affiliation of amplicon sequence variants (ASVs) was inferred using the Greengenes 13_8 99% classifier [52 (link),53 (link)]. Specifically for the reads assigned to the Acetobacteraceae family, we additionally used the BLASTN [54 (link)] and RDP classifier [55 (link)] for taxonomic identification. The dataset was submitted to a decontamination step using the microDecon package [56 (link)] in R (v. 4.1.1) to remove sequences considered to be cross-contamination (between biological samples) and contaminants from the laboratory environment, human manipulation [45 (link)], DNA extraction kit and PCR reagents (“kitome”) [57 (link)].
Baltar J.M., Pavan M.G., Corrêa-Antônio J., Couto-Lima D., Maciel-de-Freitas R, & David M.R. (2023). Gut Bacterial Diversity of Field and Laboratory-Reared Aedes albopictus Populations of Rio de Janeiro, Brazil. Viruses, 15(6), 1309.
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5

SARS-CoV-2 Lineage Detection Protocol

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The FASTQ files generated from the NextSeq Local Run manager were uploaded to Illumina BaseSpace Sequence Hub and analyzed with Illumina SARS-CoV-2 NGS Data Toolkit, DRAGEN COVID Lineage App. FASTQ files from both library preps were combined by uploading files under the same Biosample in BaseSpace. Consensus FASTA files generated against reference SARS-Cov-2 sequence (NC_045512) were uploaded for variant analysis and detection of mutation at Pangolin lineage (https://pangolin.cog-uk.io) and Nextclade (https://clades.nextstrain.org). For detailed sequence analysis, NCBI BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) was used. Mutation frequency of C29200T and C29203T was searched on the GISAID database using primer checker (https://www.epicov.org/epi3/frontend#407fad) with input of fwd TTACAAACATTG GCCGCAA, rev GCGCGACATTCCGAAGAC, and prb ACAATTTGCCCCCAGCGCTTCAG.
Choi H., Hwang M., Lukey J., Jinadatha C, & Navarathna D.H. (2022). Presumptive positive with the Cepheid Xpert Xpress SARS-CoV-2 assay due to N mutations in the Delta variant. Diagnostic Microbiology and Infectious Disease, 103(3), 115699.
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6

Immune Response Analysis from FFPE Samples

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Tumor tissue was carefully dissected from 3 to 5 undyed FFPE tissue sections (4 µm thickness) using a scalpel blade and deparaffinized in 640 µL deparaffinization solution (Qiagen, Hilden, Germany). Total RNA was refined using an AllPrep DNA/RNA FFPE Kit (Qiagen) according to the supplier’s instructions. The RNA integrity number and DV200 values were measured using a Bioanalyzer (Agilent Technologies, Santa Clara, California, USA) to evaluate the quality of the extracted RNA. RNA samples confirmed to be of sufficient quality were reverse-transcribed to cDNA using a SuperScript VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA) after assessing the density using a Qubit 4 Fluorometer (Thermo Fisher Scientific). cDNA samples were amplified and applied to the NGS using a PTC-100 thermal cycler (MJ Research, Watertown, Massachusetts, USA) and Ampliseq for the Illumina Immune Response Panel (Illumina, San Diego, California, USA). After quantification of the library using a Bioanalyzer, NGS analysis was performed using the MiniSeq System (Illumina). Data were uploaded and analyzed on the cloud-based software application BaseSpace Sequence Hub (Illumina). All data were uploaded to the national center for biotechnology information gene expression omnibus database (GSE154938).
Nakamura M., Nagase K., Yoshimitsu M., Magara T., Nojiri Y., Kato H., Kobayashi T., Teramoto Y., Yasuda M., Wada H., Ozawa T., Umemori Y., Ogata D, & Morita A. (2020). Glucose-6-phosphate dehydrogenase correlates with tumor immune activity and programmed death ligand-1 expression in Merkel cell carcinoma. Journal for Immunotherapy of Cancer, 8(2), e001679.
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7

RNA-Seq Analysis of Fibroblast Transcriptomes

Cited 1 time
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Total RNA from Patient, Control, and Unrelated Control fibroblasts (at passage 7) was isolated and quantified as described [35 (link)]. RNA-seq studies were conducted on total RNA (3 to 7 μg) at DNA Link USA, Inc. (San Diego, CA) using poly-A RNA enrichment and library preparation. RNA libraries were sequenced as 75 bp paired-end runs with at least 100 million reads per sample on an Illumina NextSeq 500 platform (Additional File 1: Table S1). The raw RNA-seq data (fastq) was stored and transferred using the BaseSpace Sequence Hub (Illumina, San Diego, CA).
Widyastuti H.P., Norden-Krichmar T.M., Grosberg A, & Zaragoza M.V. (2020). Gene expression profiling of fibroblasts in a family with LMNA-related cardiomyopathy reveals molecular pathways implicated in disease pathogenesis. BMC Medical Genetics, 21, 152.
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8

Illumina MiSeq Library Preparation

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The purified PCR product (1 ng) from each sample was indexed to make the sequencing library (5 μl) using the Nextera XT kit (Illumina, San Diego, CA). The normalized libraries were pooled and further quantified by quantitative PCR (qPCR) using the KAPA library quantification kit (Kapa Biosystems, Woburn, MA). The libraries were sequenced using the MiSeq reagent kit v3 (2 × 150 cycles) on an Illumina MiSeq. Raw sequence reads were filtered for Q-scores above 30. The filtered high-quality sequences from each sample were parsed based on the unique sequence tags using “BaseSpace Sequence Hub” (Illumina, San Diego, CA). The sequences from both ends for the same cluster were paired and exported as Fastq files for subsequent analyses.
Hora B., Gulzar N., Chen Y., Karagiannis K., Cai F., Su C., Smith K., Simonyan V., Shah S.A., Ahmed M., Sanchez A.M., Stone M., Cohen M.S., Denny T.N., Mazumder R, & Gao F. (2020). Streamlined Subpopulation, Subtype, and Recombination Analysis of HIV-1 Half-Genome Sequences Generated by High-Throughput Sequencing. mSphere, 5(5), e00551-20.
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9

Amplicon Sequencing Data Processing

Cited 2 times
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Paired-end reads were library demultiplexed and adapters were removed in the Illumina BaseSpace Sequence Hub. Each library was amplicon demultiplexed using cutadapt (v3.4) (Martin, 2011 (link)), generating two FASTQ files (with forward or reverse reads) for every library-amplicon combination. FASTQ files from the same amplicon were grouped in QIIME 2 artifacts and processed as independent datasets (hereinafter referred to as amplicon-specific datasets) using QIIME 2 (Bolyen et al., 2019 (link)).
Using DADA2 (Callahan et al., 2016 (link)) (q2-dada2 QIIME 2 plugin), reads were filtered based on default quality criteria, denoised and truncated (at the first instance of median quality score <30) to remove low quality bases at 3’ ends. Next, paired-end reads were merged using DADA2 to produce amplicon sequence variants (ASVs). Finally, chimeric ASVs were filtered using VSEARCH (Rognes et al., 2016 (link)) (q2-vsearch QIIME 2 plugin) and the SILVA database (v138) (Quast et al., 2013 (link)) as reference.
Heidrich V., Inoue L.T., Asprino P.F., Bettoni F., Mariotti A.C., Bastos D.A., Jardim D.L., Arap M.A, & Camargo A.A. (2022). Choice of 16S Ribosomal RNA Primers Impacts Male Urinary Microbiota Profiling. Frontiers in Cellular and Infection Microbiology, 12, 862338.
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10

SARS-CoV-2 Genomic Sequencing Workflow

Cited 5 times
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We demultiplexed raw sequence reads to fastq files based on index sequences using the BaseSpace Sequence Hub cloud service of Illumina. We then further demultiplexed individual libraries to fastq files for each sample using a custom Python script. Following this, we processed the samples using a Nextflow24 (link) (version 20.10.0) based analysis pipeline from nf-core25 (link) called viralrecon26 (version 1.1.0). In short, we trimmed the adapters from the fastq reads using fastp27 (link) (version 0.20.1) and aligned them to the SARS-CoV-2 reference genome (NC_045512.2) using bowtie 228 (link) (version 3.5.1). Following this, we sorted and indexed the reads using samtools29 (link) (version 1.9), we trimmed amplicon primer sequences using ivar30 (link) (version 1.2.2), called variants, and generated the subsequent consensus sequence also using ivar. To determine the percentage of reads that mapped to different organisms and common contaminants, we used FastQ-screen31 (link) (version 0.14.1). Briefly, 100,000 reads were sampled from the fastq files and aligned to 14 reference sequences using bowtie 228 (link) (version 3.5.1) (see Supplementary Table 2). We performed all subsequent analyses using custom R scripts.
Simonetti M., Zhang N., Harbers L., Milia M.G., Brossa S., Huong Nguyen T.T., Cerutti F., Berrino E., Sapino A., Bienko M., Sottile A., Ghisetti V, & Crosetto N. (2021). COVseq is a cost-effective workflow for mass-scale SARS-CoV-2 genomic surveillance. Nature Communications, 12, 3903.
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