Cell ranger mkfastq

Manufactured by 10x Genomics

Cell Ranger mkfastq is a software tool that converts raw sequencing data from 10x Genomics single-cell RNA sequencing experiments into demultiplexed FASTQ files. It is a core component of the Cell Ranger analysis pipeline, responsible for the initial processing and organization of the sequencing data.

Automatically generated - may contain errors

Lab products found in correlation

Cell ranger count, by 10x Genomics (3 mentions) Novaseq 6000, by Illumina (3 mentions) Chromium next gem single cell 3 library gel bead kit v2, by 10x Genomics (2 mentions) V1 chemistry, by Illumina (2 mentions) Chromium next gem chip g single cell kit, by 10x Genomics (2 mentions) Nextseq 500, by Illumina (2 mentions) Cell ranger atac, by 10x Genomics (1 mentions) Totalseq antibody, by BioLegend (1 mentions) Novaseq, by Illumina (1 mentions) Cellranger multi, by 10x Genomics (1 mentions)

11 protocols using cell ranger mkfastq

Single-cell RNA-seq analysis of SARS-CoV-2 infection

Cited 3 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

The raw sequencing reads were demultiplexed using Cell Ranger mkfastq (10x Genomics). We trimmed the reads from the BIDMC liver samples for polyA tails and the template switching oligo 5’-AAGCAGTGGTATCAACGCAGAGTACATrGrGrG −3’ with cutadapt v.2.7⁷¹. The reads were aligned to generate the count matrix using Cell Ranger count (10x Genomics) on Terra with the cellranger_workflow in Cumulus^{72 (link)}. The reads were aligned to a custom-built Human GRCh38 and SARS-CoV-2 (“GRCh38_premrna_and_SARSCoV2”) RNA reference. The GRCh38 pre-mrna reference captures reads mapping to both exons or introns^{73 (link)}. The SARS-CoV-2 viral sequence (FASTA file) and accompanying gene annotation and structure (GTF file) are as previously described^{74 (link)}. The GTF file was edited to include only CDS regions, with added regions for the 5’ UTR (“SARSCoV2_5prime”), 3’ UTR (“SARSCoV2_3prime”), and anywhere within the Negative Strand (“SARSCoV2_NegStrand”) of SARS-CoV-2. Trailing A’s at the 3’ end of the virus were excluded from the SARSCoV2 fasta file^{6 (link)}. CellBender remove-background^{75 (link)} was run to remove ambient RNA and other technical artifacts from the count matrices. The workflow is available publicly as cellbender/remove-background (snapshot 11) and documented on the CellBender GitHub repository as v0.2.0: https://github.com/broadinstitute/CellBender.

Pita-Juarez Y., Karagkouni D., Kalavros N., Melms J.C., Niezen S., Delorey T.M., Essene A.L., Brook O.R., Pant D., Skelton-Badlani D., Naderi P., Huang P., Pan L., Hether T., Andrews T.S., Ziegler C.G., Reeves J., Myloserdnyy A., Chen R., Nam A., Phelan S., Liang Y., Amin A.D., Biermann J., Hibshoosh H., Veregge M., Kramer Z., Jacobs C., Yalcin Y., Phillips D., Slyper M., Subramanian A., Ashenberg O., Bloom-Ackermann Z., Tran V.M., Gomez J., Sturm A., Zhang S., Fleming S.J., Warren S., Beechem J., Hung D., Babadi M., Padera RF J.r., MacParland S.A., Bader G.D., Imad N., Solomon I.H., Miller E., Riedel S., Porter C.B., Villani A.C., Tsai L.T., Hide W., Szabo G., Hecht J., Rozenblatt-Rosen O., Shalek A.K., Izar B., Regev A., Popov Y., Jiang Z.G, & Vlachos I.S. (2022). A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients. bioRxiv.

+ Open protocol

+ Expand

Single-cell gene expression library generation

Cited 1 time

Check if the same lab product or an alternative is used in the 5 most similar protocols

Single cell and nucleus gene expression libraries were generated on the 10x Genomics Chromium platform using the Chromium Next GEM Single Cell 3′ Library & Gel Bead Kit v2 (scRNAseq) or v3.1 (snRNAseq) and Chromium Next GEM Chip G Single Cell Kit (10x Genomics) according to the manufacturer’s protocol. Hashtag libraries were amplified and barcoded as in Stoeckius et al.^{131 (link)} Gene expression and hashtag libraries were sequenced on a NextSeq500 or NovaSeq 6000 S4 flow cell using v1 Chemistry (Illumina) and FASTQ files were generated using Cell Ranger mkfastq (10x Genomics).

van der Valk W.H., van Beelen E.S., Steinhart M.R., Nist-Lund C., Osorio D., de Groot J.C., Sun L., van Benthem P.P., Koehler K.R, & Locher H. (2023). A single-cell level comparison of human inner ear organoids with the human cochlea and vestibular organs. Cell reports, 42(6), 112623.

+ Open protocol

+ Expand

Multimodal Profiling of Tonsillar Immune Cells

Check if the same lab product or an alternative is used in the 5 most similar protocols

Tonsillar immune cells were loaded on to the 10X Genomics Chromium according to the manufacturer’s protocol using either the single-cell 3’ kit (v3) or the single-cell ATAC kit (v1). Cell surface labelling for scADT-seq libraries was performed with 12 oligo-labelled TotalSeq^™ antibodies (BioLegend; Table S2). Library preparation was performed according to the manufacturer’s protocol prior to sequencing on either the Illumina NovaSeq 6000 or NextSeq 500 platforms. scRNA-seq libraries were sequenced with 28/10/10/90 bp cycles while scATAC-seq libraries were sequenced with 70/8/16/70 bp read configurations. BaseCall files were used to generate FASTQ files with either cellranger mkfastq (v3; 10X Genomics) or cellranger-atac (v1; 10X Genomics) prior to running cellranger count with the cellranger-GRCh38–3.0.0 reference or cellranger-atac count with the cellranger-atac-GRCh38–1.1.0 reference for scRNA-seq and scATAC-seq libraries respectively.

King H.W., Wells K.L., Shipony Z., Kathiria A.S., Wagar L.E., Lareau C., Orban N., Capasso R., Davis M.M., Steinmetz L.M., James L.K, & Greenleaf W.J. (2021). Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci. Science immunology, 6(64), eabh3768.

+ Open protocol

+ Expand

Transgenic Mouse scRNA-Seq Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

For full-length scRNA-Seq SMART-Seq2 data, raw sequencing data was demultiplexed to fastq files using bcl2fastq (v.2.17.1.14) and aligned to a custom reference using RSEM (v.1.2.8); the custom reference included the mm10 genome and the sequence for the human genes KIR2DL1 and HLA-C*05 (as described in the transgenic mice generation above), and was generated using rsem-prepare-reference. Quality control was assessed and summaries generated using STAR. We used RSEM (v1.2.8) to quantify gene counts and TPM (we converted TPM to TP100K after initial quality control filtering, described below).
For droplet-based scRNA-Seq data, raw sequencing data was demultiplexed to fastq files with cellranger mkfastq (version 2.1.0, 10x Genomics) and reads were aligned and count matrices were generated with cellranger count (version 2.0.1). A custom reference was generated to include the HLA-C*05 and KIR2DL1 transgenic construct sequences into the mm10 reference using cellranger mkref (version 2.1.0). The alignment of HLA-C*05 sequence was limited to the human DNA sequence region to minimize the risk of misalignment with the mouse H-2K^b gene.

Kaur G., Porter C.B., Ashenberg O., Lee J., Riesenfeld S.J., Hofree M., Aggelakopoulou M., Subramanian A., Kuttikkatte S.B., Attfield K.E., Desel C.A., Davies J.L., Evans H.G., Avraham-Davidi I., Nguyen L.T., Dionne D.A., Neumann A.E., Jensen L.T., Barber T.R., Soilleux E., Carrington M., McVean G., Rozenblatt-Rosen O., Regev A, & Fugger L. (2022). Mouse fetal growth restriction through parental and fetal immune gene variation and intercellular communications cascade. Nature Communications, 13, 4398.

+ Open protocol

+ Expand

Single-cell RNA-seq analysis pipeline

Check if the same lab product or an alternative is used in the 5 most similar protocols

Sequencing data were analyzed using the Cellranger pipeline (10x Genomics, version 3.0, https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) to generate gene count matrices. Cellranger mkfastq (10x Genomics) was used to demultiplex raw base call files from the Illumina NextSeq 500 machine into sample-specific fastq files. Next, fastq files for each sample were processed with Cellranger count (10x Genomics), which takes fastq files and performs alignment, filtering, barcode counting, and unique molecular identifier counting. The expected cell numbers for each sample were 10⁴. Samples were aligned to the murine genome (mm10), filtered, and quantified. The resulting analysis files for each sample were aggregated using the cellranger aggr pipeline, which performed a between-sample normalization step and merged four samples into one.

Zhou Y., Medik Y.B., Patel B., Zamler D.B., Chen S., Chapman T., Schneider S., Park E.M., Babcock R.L., Chrisikos T.T., Kahn L.M., Dyevoich A.M., Pineda J.E., Wong M.C., Mishra A.K., Cass S.H., Cogdill A.P., Johnson D.H., Johnson S.B., Wani K., Ledesma D.A., Hudgens C.W., Wang J., Wadud Khan M.A., Peterson C.B., Joon A.Y., Peng W., Li H.S., Arora R., Tang X., Raso M.G., Zhang X., Foo W.C., Tetzlaff M.T., Diehl G.E., Clise-Dwyer K., Whitley E.M., Gubin M.M., Allison J.P., Hwu P., Ajami N.J., Diab A., Wargo J.A, & Watowich S.S. (2022). Intestinal toxicity to CTLA-4 blockade driven by IL-6 and myeloid infiltration. The Journal of Experimental Medicine, 220(2), e20221333.

+ Open protocol

+ Expand

Single-cell gene expression library generation

Cited 1 time

Check if the same lab product or an alternative is used in the 5 most similar protocols

Valentin H.E., Mitsky T.A., Mahadeo D.A., Tran M, & Gruys K.J. (2000). Application of a Propionyl Coenzyme A Synthetase for Poly(3-Hydroxypropionate-co-3-Hydroxybutyrate) Accumulation in Recombinant Escherichia coli. Applied and Environmental Microbiology, 66(12), 5253-5258.

+ Open protocol

+ Expand

Illumina NovaSeq RNA Sequencing

Check if the same lab product or an alternative is used in the 5 most similar protocols

Libraries were sequenced on Illumina NovaSeq to an average depth of 54,609 reads per cell to achieve an average of ∼75% barcode saturation. Fastqs were generated using ‘cellranger mkfastq’ (10X Genomics, version 3.1.0, default parameters). Because libraries were generated and sequenced across multiple time points, we applied ‘cellranger aggr’ to aggregate and normalize libraries across all samples and replicates. Average read depth and barcode saturation were calculated using ‘cellranger aggr’.

Reyes J.M., Tovy A., Zhang L., Bortoletto A.S., Rosas C., Chen C.W., Waldvogel S.M., Guzman A.G., Aguilar R., Gupta S., Liu L., Buckley M.T., Patel K.R., Marcogliese A.N., Li Y., Curry C.V., Rando T., Brunet A., Parchem R.J., Rau R.E, & Goodell M.A. (2024). Hematologic DNMT3A reduction and high-fat diet synergize to promote weight gain and tissue inflammation. iScience, 27(3), 109122.

+ Open protocol

+ Expand

Single-cell RNA-seq data processing

Check if the same lab product or an alternative is used in the 5 most similar protocols

Illumina BCL files were de-multiplexed and converted to FASTQ files using Cell Ranger mkfastq (version 5.0.1, 10X Genomics https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest). These FASTQ files were then used to quantify gene expression using Cell Ranger count (version 5.0.1) and the GRCh38 (version refdata-gex-GRCh38-2020-A, 10X Genomics) human genome reference to generate cell by gene count matrices for each sample.

Burchill M.A., Salomon M.P., Golden-Mason L., Wieland A., Maretti-Mira A.C., Gale M J.r, & Rosen H.R. (2021). Single-cell transcriptomic analyses of T cells in chronic HCV-infected patients dominated by DAA-induced interferon signaling changes. PLoS Pathogens, 17(8), e1009799.

+ Open protocol

+ Expand

Bulk Tumor-Infiltrating Immune Cell Profiling

Cited 1 time

Check if the same lab product or an alternative is used in the 5 most similar protocols

Raw scRNA-sequencing reads were demultiplexed to FASTQ files using Cell Ranger mkfastq (v3.0.0, 10x Genomics), and the Cumulus cellranger-workflow implementation of Cell Ranger count (v4.0.0) on Terra (https://terra.bio/) was used to align reads to the human reference genome (GRCh38-2020-A) and generate a raw counts matrix (Li et al., 2020 (link)). Cumulus was used to filter cells from the Cell Ranger raw counts matrix with fewer than 400 UMIs, 200 genes, or greater than 20% of UMIs mapped to mitochondrial genes. Count matrices from each sample were pooled and initial clusters were identified based on the Seurat clustering analysis pipeline (described below). Two clusters corresponding to endothelial and stromal cells were excluded. Then, the potential doublets included in tumor were also filtered out by using the same TCR-related strategies as used in the blood data. Finally, the remaining 74,557 tumor-infiltrating immune cells were used for the following analysis.

Luoma A.M., Suo S., Wang Y., Gunasti L., Porter C.B., Nabilsi N., Tadros J., Ferretti A.P., Liao S., Gurer C., Chen Y.H., Criscitiello S., Ricker C.A., Dionne D., Rozenblatt-Rosen O., Uppaluri R., Haddad R.I., Ashenberg O., Regev A., Van Allen E.M., MacBeath G., Schoenfeld J.D, & Wucherpfennig K.W. (2022). Tissue-resident Memory and Circulating T cells are Early Responders to Pre-surgical Cancer Immunotherapy. Cell, 185(16), 2918-2935.e29.

+ Open protocol

+ Expand

ScRNA-seq Clonality Analysis Pipeline

Check if the same lab product or an alternative is used in the 5 most similar protocols

Raw reads were demultiplexed to FASTQ files using Cell Ranger mkfastq (v7.0.0, 10X Genomics), and the Cumulus cellranger-workflow implementation of Cell Ranger multi (v7.0.0) was used to align reads to the human reference genome (GRCh38-2020-A). The filtered contig annotations, which contained high-level annotations of each high-confidenT cellular contig, were further filtered by removing records with ‘raw_consensus_id’ as ‘none’. Differences in clonality and Morisita indices between different stages and cohorts were evaluated by two-sided t-test. The Immunarch (v0.7.0, R package) was used to calculate the size distribution of tumor TCR clonotypes by using ‘repClonality’ function.

Liu W., You W., Lan Z., Ren Y., Gao S., Li S., Chen W.W., Huang C., Zeng Y., Xiao N., Wang Z., Xie H., Ma H., Chen Y., Wang G., Chen C, & Li H. (2024). An immune cell map of human lung adenocarcinoma development reveals an anti-tumoral role of the Tfh-dependent tertiary lymphoid structure. Cell Reports Medicine, 5(3), 101448.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!