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custom read 1  (Bio-Rad)


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    Bio-Rad custom read 1
    Custom Read 1, supplied by Bio-Rad, used in various techniques. Bioz Stars score: 95/100, based on 36 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/custom+read+1/SureCell+ATAC-Seq+Library+Prep+Kit/pm41687613-421-18-21
    Average 95 stars, based on 36 article reviews
    custom read 1 - by Bioz Stars, 2026-10
    95/100 stars

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    Related Articles

    Sequencing:

    Article Title: Enhancer dynamics and cellular architecture in the human spinal cord.
    Article Snippet: In brief Kandror et al. present an innovative framework redefining human spinal cord cellular diversity through epigenetic configuration and spatial organization.. They identify unseen enhancer classes, show cell-type-specific reconfiguration of gene regulatory networks along the rostrocaudal axis, and uncover cellular networks mediated by discrete paracrine signaling, challenging conventional definitions of cellular state.. 1 6 2



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    (a–c) Effects of <t>sequencing</t> depth on (a) the number of detected genes and transcript counts per single cell, (b) the number of detected PLA products and their UMI counts, and (c) the number of detected proteins and protein UMI counts in 10x-based Prox-seq. (d) Effects of sequencing depth on automated cell type annotation based on mRNA data with singleR package. The cell type annotation at the maximum sequencing depth is used as the ground truth annotation. (e) Effects of sequencing depth on the number of detected protein complexes. Clusters were identified using mRNA data (see Extended Data Fig. 7). Clusters 0 and 3 were chosen as examples because they had the most number of cells per cluster. In (a–e), the sequencing results of the mRNA and PLA product libraries from the 10x PBMC experiment were downsampled to 10%, 20%, 40%, 60%, and 80% to simulate different sequencing depths. (f, g) Effects of sequencing depth on (f) the number of detected PLA products and their UMI counts, and (g) the number of detected proteins and protein UMI counts in plate-based Prox-seq. In (f, g), the sequencing results of the mRNA and PLA product libraries from the plate-based PBMC experiment were downsampled to 0.5%, 1%, 5%, 10%, 25%, 50%, and 75% to simulate different sequencing depths. The red dashed lines in (e, f) indicate 10,000 mean reads per cell.
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    (a–c) Effects of sequencing depth on (a) the number of detected genes and transcript counts per single cell, (b) the number of detected PLA products and their UMI counts, and (c) the number of detected proteins and protein UMI counts in 10x-based Prox-seq. (d) Effects of sequencing depth on automated cell type annotation based on mRNA data with singleR package. The cell type annotation at the maximum sequencing depth is used as the ground truth annotation. (e) Effects of sequencing depth on the number of detected protein complexes. Clusters were identified using mRNA data (see Extended Data Fig. 7). Clusters 0 and 3 were chosen as examples because they had the most number of cells per cluster. In (a–e), the sequencing results of the mRNA and PLA product libraries from the 10x PBMC experiment were downsampled to 10%, 20%, 40%, 60%, and 80% to simulate different sequencing depths. (f, g) Effects of sequencing depth on (f) the number of detected PLA products and their UMI counts, and (g) the number of detected proteins and protein UMI counts in plate-based Prox-seq. In (f, g), the sequencing results of the mRNA and PLA product libraries from the plate-based PBMC experiment were downsampled to 0.5%, 1%, 5%, 10%, 25%, 50%, and 75% to simulate different sequencing depths. The red dashed lines in (e, f) indicate 10,000 mean reads per cell.

    Journal: Nature methods

    Article Title: Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing

    doi: 10.1038/s41592-022-01684-z

    Figure Lengend Snippet: (a–c) Effects of sequencing depth on (a) the number of detected genes and transcript counts per single cell, (b) the number of detected PLA products and their UMI counts, and (c) the number of detected proteins and protein UMI counts in 10x-based Prox-seq. (d) Effects of sequencing depth on automated cell type annotation based on mRNA data with singleR package. The cell type annotation at the maximum sequencing depth is used as the ground truth annotation. (e) Effects of sequencing depth on the number of detected protein complexes. Clusters were identified using mRNA data (see Extended Data Fig. 7). Clusters 0 and 3 were chosen as examples because they had the most number of cells per cluster. In (a–e), the sequencing results of the mRNA and PLA product libraries from the 10x PBMC experiment were downsampled to 10%, 20%, 40%, 60%, and 80% to simulate different sequencing depths. (f, g) Effects of sequencing depth on (f) the number of detected PLA products and their UMI counts, and (g) the number of detected proteins and protein UMI counts in plate-based Prox-seq. In (f, g), the sequencing results of the mRNA and PLA product libraries from the plate-based PBMC experiment were downsampled to 0.5%, 1%, 5%, 10%, 25%, 50%, and 75% to simulate different sequencing depths. The red dashed lines in (e, f) indicate 10,000 mean reads per cell.

    Article Snippet: Custom read 1 sequencing primer (SmartPLA_Read1), custom i5 index read primer (SmartPLA_i5Read) and custom i7 index read primer (SmartPLA_i7Read) were used according to Illumina’s instructions.

    Techniques: Sequencing