read sequencing data Search Results


90
Weyer GmbH long-read nlr sequence data
Long Read Nlr Sequence Data, supplied by Weyer GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Pacific Biosciences high-fidelity pacbio hifi sequence data
High Fidelity Pacbio Hifi Sequence Data, supplied by Pacific Biosciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Pacific Biosciences long-read sequencing data
Genomic features from outer ring to the inner ring are described in the key to the left, where the innermost two rings correspond to the GC skew (inner), and GC plot (outer). CDS: coding <t>sequence.</t> GI: genomic island. The circular map was generated using DNAPlotter .
Long Read Sequencing Data, supplied by Pacific Biosciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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10X Genomics linked read sequencing data
Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read <t>sequencing</t> data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)
Linked Read Sequencing Data, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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NanoPack Inc visualizing and processing long-read sequencing data
Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read <t>sequencing</t> data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)
Visualizing And Processing Long Read Sequencing Data, supplied by NanoPack Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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10X Genomics long-read cloud sequencing data
Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read <t>sequencing</t> data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)
Long Read Cloud Sequencing Data, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Oxford Nanopore third-generation long-read sequencing data
Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read <t>sequencing</t> data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)
Third Generation Long Read Sequencing Data, supplied by Oxford Nanopore, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Keio University Press Inc sequencing read data
Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read <t>sequencing</t> data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)
Sequencing Read Data, supplied by Keio University Press Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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AQUAGEN LTD whole-genome long-read nanopore sequencing data
Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read <t>sequencing</t> data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)
Whole Genome Long Read Nanopore Sequencing Data, supplied by AQUAGEN LTD, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Macrogen all rna-seq data are deposited in the sequence read archive (sra) under accession number prjna511617.
Transciptomic changes during photosynthetic induction. (a) Principal component analysis for the time series <t>RNA-Seq</t> of Koshihikari and Takanari after illumination. (b) The numbers of differentially expressed genes (DEGs) in Koshihikari and Takanari between before (0 min) and after (1, 5, 10, 30, and 60 min) irradiation. False discovery rate=0.05. (c) The numbers of DEGs between Koshihikari and Takanari at each time point after irradiation. (d) Expression of genes relating to photosynthesis during photosynthetic induction. (e) Expression of OsCKX2 during photosynthetic induction. Values are means ±SE ( n =6). *indicates that the gene was differentially expressed between Koshihikari and Takanari (adjusted P -value <0.05). (This figure is available in colour at JXB online.)
All Rna Seq Data Are Deposited In The Sequence Read Archive (Sra) Under Accession Number Prjna511617., supplied by Macrogen, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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all rna-seq data are deposited in the sequence read archive (sra) under accession number prjna511617. - by Bioz Stars, 2026-04
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Oxford Nanopore oxford nanopore (ont) long-read transcriptomic sequencing data
Transciptomic changes during photosynthetic induction. (a) Principal component analysis for the time series <t>RNA-Seq</t> of Koshihikari and Takanari after illumination. (b) The numbers of differentially expressed genes (DEGs) in Koshihikari and Takanari between before (0 min) and after (1, 5, 10, 30, and 60 min) irradiation. False discovery rate=0.05. (c) The numbers of DEGs between Koshihikari and Takanari at each time point after irradiation. (d) Expression of genes relating to photosynthesis during photosynthetic induction. (e) Expression of OsCKX2 during photosynthetic induction. Values are means ±SE ( n =6). *indicates that the gene was differentially expressed between Koshihikari and Takanari (adjusted P -value <0.05). (This figure is available in colour at JXB online.)
Oxford Nanopore (Ont) Long Read Transcriptomic Sequencing Data, supplied by Oxford Nanopore, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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GrandOmics Biosciences long-read sequencing data
Gene structure of PICK1 . (A) Chromosome location, exon, and intron abundance based on transcriptome <t>sequencing;</t> (B) PICK1 gene coding sequence and amino acid sequence.
Long Read Sequencing Data, supplied by GrandOmics Biosciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Genomic features from outer ring to the inner ring are described in the key to the left, where the innermost two rings correspond to the GC skew (inner), and GC plot (outer). CDS: coding sequence. GI: genomic island. The circular map was generated using DNAPlotter .

Journal: bioRxiv

Article Title: A high quality reference genome for the fish pathogen Streptococcus iniae

doi: 10.1101/2019.12.17.880476

Figure Lengend Snippet: Genomic features from outer ring to the inner ring are described in the key to the left, where the innermost two rings correspond to the GC skew (inner), and GC plot (outer). CDS: coding sequence. GI: genomic island. The circular map was generated using DNAPlotter .

Article Snippet: Long-read sequencing data from Pacific Biosciences and Oxford Nanopore bring complete bacterial genomes within reach of most laboratories, but here also significant care is often required to avoid misassembly.

Techniques: Sequencing, Generated

Graphical representation of spacers in CRISPR between YSFST01-82, ISET0901, ISNO, SF1, and QMA0248. Each box represents a spacer, where the same colour and number indicate identical spacers. *: A spacer with an additional direct repeat sequence.

Journal: bioRxiv

Article Title: A high quality reference genome for the fish pathogen Streptococcus iniae

doi: 10.1101/2019.12.17.880476

Figure Lengend Snippet: Graphical representation of spacers in CRISPR between YSFST01-82, ISET0901, ISNO, SF1, and QMA0248. Each box represents a spacer, where the same colour and number indicate identical spacers. *: A spacer with an additional direct repeat sequence.

Article Snippet: Long-read sequencing data from Pacific Biosciences and Oxford Nanopore bring complete bacterial genomes within reach of most laboratories, but here also significant care is often required to avoid misassembly.

Techniques: CRISPR, Sequencing

Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read sequencing data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)

Journal: Genome Biology

Article Title: Comprehensive identification of somatic nucleotide variants in human brain tissue

doi: 10.1186/s13059-021-02285-3

Figure Lengend Snippet: Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read sequencing data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)

Article Snippet: We used haplotype information provided by 10X Genomics linked read sequencing data to eliminate false-positive mosaic SNV calls and to provide support for bona fide mosaic SNVs.

Techniques: Biomarker Discovery, Generated, Sequencing, Amplification

Summary of validation results for 400 candidate mosaic SNVs. Vertical lines represent candidate mosaic SNVs. Shaded rectangles to the right of the figure provide the keys to interpret the shading presented for each candidate SNV. There was concordance in true-positive mosaic SNV calls (PASS; green rectangle at bottom of figure) in multiple datasets and secondary validation experiments. Chromium linked read haplotype phasing and single-cell sequencing datasets also were effective in supporting a subset of bona fide mosaic SNV calls. By comparison, the VAFs of false-positive calls (red rectangle) are inconsistent across different datasets and often occur within or near insertion/deletion (indel) mutations, short tandem repeat sequences (STRs), homopolymeric nucleotide stretches, or copy number variants (CNVs). Importantly, the panel of normal (PON) filter, but not the comparison to WGS data from a control sample (i.e., to NA12878), was highly effective at identifying contaminating false-positive SNV calls (orange rectangle) and germline SNPs (gray rectangle). We lacked sufficient data to evaluate a subset of candidate SNVs (purple rectangle, NED—not enough data). The two green triangles at the top of the figure denote mosaic SNVs that validation experiments deemed to be false-positive calls; however, cell lineage analyses demonstrated that they are likely bona fide mosaic SNVs (see text and Fig. )

Journal: Genome Biology

Article Title: Comprehensive identification of somatic nucleotide variants in human brain tissue

doi: 10.1186/s13059-021-02285-3

Figure Lengend Snippet: Summary of validation results for 400 candidate mosaic SNVs. Vertical lines represent candidate mosaic SNVs. Shaded rectangles to the right of the figure provide the keys to interpret the shading presented for each candidate SNV. There was concordance in true-positive mosaic SNV calls (PASS; green rectangle at bottom of figure) in multiple datasets and secondary validation experiments. Chromium linked read haplotype phasing and single-cell sequencing datasets also were effective in supporting a subset of bona fide mosaic SNV calls. By comparison, the VAFs of false-positive calls (red rectangle) are inconsistent across different datasets and often occur within or near insertion/deletion (indel) mutations, short tandem repeat sequences (STRs), homopolymeric nucleotide stretches, or copy number variants (CNVs). Importantly, the panel of normal (PON) filter, but not the comparison to WGS data from a control sample (i.e., to NA12878), was highly effective at identifying contaminating false-positive SNV calls (orange rectangle) and germline SNPs (gray rectangle). We lacked sufficient data to evaluate a subset of candidate SNVs (purple rectangle, NED—not enough data). The two green triangles at the top of the figure denote mosaic SNVs that validation experiments deemed to be false-positive calls; however, cell lineage analyses demonstrated that they are likely bona fide mosaic SNVs (see text and Fig. )

Article Snippet: We used haplotype information provided by 10X Genomics linked read sequencing data to eliminate false-positive mosaic SNV calls and to provide support for bona fide mosaic SNVs.

Techniques: Biomarker Discovery, Sequencing, Comparison, Control

Transciptomic changes during photosynthetic induction. (a) Principal component analysis for the time series RNA-Seq of Koshihikari and Takanari after illumination. (b) The numbers of differentially expressed genes (DEGs) in Koshihikari and Takanari between before (0 min) and after (1, 5, 10, 30, and 60 min) irradiation. False discovery rate=0.05. (c) The numbers of DEGs between Koshihikari and Takanari at each time point after irradiation. (d) Expression of genes relating to photosynthesis during photosynthetic induction. (e) Expression of OsCKX2 during photosynthetic induction. Values are means ±SE ( n =6). *indicates that the gene was differentially expressed between Koshihikari and Takanari (adjusted P -value <0.05). (This figure is available in colour at JXB online.)

Journal: Journal of Experimental Botany

Article Title: High-yielding rice Takanari has superior photosynthetic response to a commercial rice Koshihikari under fluctuating light

doi: 10.1093/jxb/erz304

Figure Lengend Snippet: Transciptomic changes during photosynthetic induction. (a) Principal component analysis for the time series RNA-Seq of Koshihikari and Takanari after illumination. (b) The numbers of differentially expressed genes (DEGs) in Koshihikari and Takanari between before (0 min) and after (1, 5, 10, 30, and 60 min) irradiation. False discovery rate=0.05. (c) The numbers of DEGs between Koshihikari and Takanari at each time point after irradiation. (d) Expression of genes relating to photosynthesis during photosynthetic induction. (e) Expression of OsCKX2 during photosynthetic induction. Values are means ±SE ( n =6). *indicates that the gene was differentially expressed between Koshihikari and Takanari (adjusted P -value <0.05). (This figure is available in colour at JXB online.)

Article Snippet: Single-end 50 bp reads were sequenced on a Hiseq 2500 sequencer (Illumina, Hayward, CA, USA) by Macrogen Co. All RNA-Seq data are deposited in the Sequence Read Archive (SRA) under accession number PRJNA511617.

Techniques: RNA Sequencing Assay, Irradiation, Expressing

Gene structure of PICK1 . (A) Chromosome location, exon, and intron abundance based on transcriptome sequencing; (B) PICK1 gene coding sequence and amino acid sequence.

Journal: Animal Reproduction

Article Title: Long-read and short-read RNA-seq reveal the transcriptional regulation characteristics of PICK1 in Baoshan pig testis

doi: 10.1590/1984-3143-AR2024-0047

Figure Lengend Snippet: Gene structure of PICK1 . (A) Chromosome location, exon, and intron abundance based on transcriptome sequencing; (B) PICK1 gene coding sequence and amino acid sequence.

Article Snippet: Long-read sequencing data were obtained from Wuhan GrandOmics Co., Ltd. China, while short-read sequencing data were obtained from Tianjin Novogene Co., Ltd., China.

Techniques: Sequencing