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Illumina Inc miseq
HoCoRT performance on simulated gut microbiome datasets. Boxplots of HoCoRT runtime in seconds (top) and classification accuracy (bottom) using several different classification modules and parameters on HiSeq (yellow, left), <t>MiSeq</t> (cyan, middle) and Nanopore data (red, right). The datasets contain 1% human reads and 99% microbial reads. The human reads are generated from the Genome Reference Consortium Human Build 38 patch release 13 (GRCh38.p13), whereas the microbial reads are generated from a mix of bacterial, fungal and viral sequences pseudo-randomly extracted from NCBI GenBank ( Sayers et al., 2022 ). Each boxplot represents 7 different datasets, each consisting of 5 million reads randomly generated using InSilicoSeq ( Gourlé et al., 2019 ) (HiSeq 125bp and MiSeq 300bp, paired-end) and NanoSim ( Yang et al., 2017 ) (Nanopore, average 2159bp, range 54-98320bp, single-end). For Illumina data the following 15 pipelines were <t>examined:</t> <t>Bowtie2</t> version 2.4.5 in end-to-end and local mode, both with and without the “un_conc” option, HISAT2 version 2.2.1, Kraken2 version 2.1.2, BBMap version 38.96 in default and fast mode, BWA-MEM2 version 2.2.1, Kraken2 followed by Bowtie2 in end-to-end mode, Kraken2 followed by HISAT2, Bowtie2 in end-to-end mode followed by local mode, Bowtie2 in end-to-end mode followed by HISAT2, Minimap2 version 2.24, and Kraken2 followed by Minimap2. For Nanopore data the following 3 pipelines were examined: Minimap2, Kraken2 followed by Minimap2, and Kraken2. When the “un_conc” option is given, Bowtie2 requires both reads in a pair to map concordantly to the genome. Performance analysis was carried out using a Snakemake pipeline ( Koster Rahmann, 2012 ) on a laptop PC with a AMD Ryzen 7 1700X 8 core 3.4 GHz CPU, 64GB RAM and 4TB HDD running Linux. No quality filtering or other pre-processing was performed. Supplementary table S1 shows additional results, including those for Bowtie2 with the “un_conc” option, BBMap in default mode and BWA-MEM2, which were excluded from this boxplot due to outliers.
Miseq, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 88/100, based on 40 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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miseq - by Bioz Stars, 2022-11
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1) Product Images from "HoCoRT: Host contamination removal tool"

Article Title: HoCoRT: Host contamination removal tool

Journal: bioRxiv

doi: 10.1101/2022.11.18.517030

HoCoRT performance on simulated gut microbiome datasets. Boxplots of HoCoRT runtime in seconds (top) and classification accuracy (bottom) using several different classification modules and parameters on HiSeq (yellow, left), MiSeq (cyan, middle) and Nanopore data (red, right). The datasets contain 1% human reads and 99% microbial reads. The human reads are generated from the Genome Reference Consortium Human Build 38 patch release 13 (GRCh38.p13), whereas the microbial reads are generated from a mix of bacterial, fungal and viral sequences pseudo-randomly extracted from NCBI GenBank ( Sayers et al., 2022 ). Each boxplot represents 7 different datasets, each consisting of 5 million reads randomly generated using InSilicoSeq ( Gourlé et al., 2019 ) (HiSeq 125bp and MiSeq 300bp, paired-end) and NanoSim ( Yang et al., 2017 ) (Nanopore, average 2159bp, range 54-98320bp, single-end). For Illumina data the following 15 pipelines were examined: Bowtie2 version 2.4.5 in end-to-end and local mode, both with and without the “un_conc” option, HISAT2 version 2.2.1, Kraken2 version 2.1.2, BBMap version 38.96 in default and fast mode, BWA-MEM2 version 2.2.1, Kraken2 followed by Bowtie2 in end-to-end mode, Kraken2 followed by HISAT2, Bowtie2 in end-to-end mode followed by local mode, Bowtie2 in end-to-end mode followed by HISAT2, Minimap2 version 2.24, and Kraken2 followed by Minimap2. For Nanopore data the following 3 pipelines were examined: Minimap2, Kraken2 followed by Minimap2, and Kraken2. When the “un_conc” option is given, Bowtie2 requires both reads in a pair to map concordantly to the genome. Performance analysis was carried out using a Snakemake pipeline ( Koster Rahmann, 2012 ) on a laptop PC with a AMD Ryzen 7 1700X 8 core 3.4 GHz CPU, 64GB RAM and 4TB HDD running Linux. No quality filtering or other pre-processing was performed. Supplementary table S1 shows additional results, including those for Bowtie2 with the “un_conc” option, BBMap in default mode and BWA-MEM2, which were excluded from this boxplot due to outliers.
Figure Legend Snippet: HoCoRT performance on simulated gut microbiome datasets. Boxplots of HoCoRT runtime in seconds (top) and classification accuracy (bottom) using several different classification modules and parameters on HiSeq (yellow, left), MiSeq (cyan, middle) and Nanopore data (red, right). The datasets contain 1% human reads and 99% microbial reads. The human reads are generated from the Genome Reference Consortium Human Build 38 patch release 13 (GRCh38.p13), whereas the microbial reads are generated from a mix of bacterial, fungal and viral sequences pseudo-randomly extracted from NCBI GenBank ( Sayers et al., 2022 ). Each boxplot represents 7 different datasets, each consisting of 5 million reads randomly generated using InSilicoSeq ( Gourlé et al., 2019 ) (HiSeq 125bp and MiSeq 300bp, paired-end) and NanoSim ( Yang et al., 2017 ) (Nanopore, average 2159bp, range 54-98320bp, single-end). For Illumina data the following 15 pipelines were examined: Bowtie2 version 2.4.5 in end-to-end and local mode, both with and without the “un_conc” option, HISAT2 version 2.2.1, Kraken2 version 2.1.2, BBMap version 38.96 in default and fast mode, BWA-MEM2 version 2.2.1, Kraken2 followed by Bowtie2 in end-to-end mode, Kraken2 followed by HISAT2, Bowtie2 in end-to-end mode followed by local mode, Bowtie2 in end-to-end mode followed by HISAT2, Minimap2 version 2.24, and Kraken2 followed by Minimap2. For Nanopore data the following 3 pipelines were examined: Minimap2, Kraken2 followed by Minimap2, and Kraken2. When the “un_conc” option is given, Bowtie2 requires both reads in a pair to map concordantly to the genome. Performance analysis was carried out using a Snakemake pipeline ( Koster Rahmann, 2012 ) on a laptop PC with a AMD Ryzen 7 1700X 8 core 3.4 GHz CPU, 64GB RAM and 4TB HDD running Linux. No quality filtering or other pre-processing was performed. Supplementary table S1 shows additional results, including those for Bowtie2 with the “un_conc” option, BBMap in default mode and BWA-MEM2, which were excluded from this boxplot due to outliers.

Techniques Used: Generated

2) Product Images from "Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome"

Article Title: Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome

Journal: Applied Microbiology and Biotechnology

doi: 10.1007/s00253-022-12251-z

Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus
Figure Legend Snippet: Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus

Techniques Used:

The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features
Figure Legend Snippet: The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features

Techniques Used:

The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq
Figure Legend Snippet: The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq

Techniques Used:

The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster
Figure Legend Snippet: The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster

Techniques Used: Sequencing, Sampling

The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters
Figure Legend Snippet: The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters

Techniques Used:

3) Product Images from "Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome"

Article Title: Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome

Journal: Applied Microbiology and Biotechnology

doi: 10.1007/s00253-022-12251-z

Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus
Figure Legend Snippet: Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus

Techniques Used:

The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features
Figure Legend Snippet: The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features

Techniques Used:

The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq
Figure Legend Snippet: The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq

Techniques Used:

The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster
Figure Legend Snippet: The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster

Techniques Used: Sequencing, Sampling

The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters
Figure Legend Snippet: The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters

Techniques Used:

4) Product Images from "Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome"

Article Title: Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome

Journal: Applied Microbiology and Biotechnology

doi: 10.1007/s00253-022-12251-z

Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus
Figure Legend Snippet: Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus

Techniques Used:

The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features
Figure Legend Snippet: The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features

Techniques Used:

The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq
Figure Legend Snippet: The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq

Techniques Used:

The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster
Figure Legend Snippet: The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster

Techniques Used: Sequencing, Sampling

The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters
Figure Legend Snippet: The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters

Techniques Used:

5) Product Images from "Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome"

Article Title: Comparison of iSeq and MiSeq as the two platforms for 16S rRNA sequencing in the study of the gut of rat microbiome

Journal: Applied Microbiology and Biotechnology

doi: 10.1007/s00253-022-12251-z

Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus
Figure Legend Snippet: Different taxonomic levels differences in the actual abundance (the taxa were merged based on the sum of their counts across all samples categorized to the experimental groups) of samples sequenced on MiSeq and iSeq at different taxonomic levels, A phylum, B class, C genus

Techniques Used:

The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features
Figure Legend Snippet: The statistically significant biomarkers discovery results with regard to genus – L6 ( A ), and species – L7 level ( B ) for the same samples sequenced using iSeq and MiSeq systems demonstrated by colored bar plots. The graphical output of the LDA score demonstrates both the negative and positive values, with the cutoff of 2.0 determining the most significant features

Techniques Used:

The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq
Figure Legend Snippet: The pie charts show the abundance profiles of one randomly selected sample (ME9okMiSeq) at two taxonomic levels: phylum ( A ) and family ( B ) of the two selected phyla: Firmicutes and Actinobacteria sequenced with MiSeq and iSeq

Techniques Used:

The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster
Figure Legend Snippet: The rarefaction curves for each group in two separate plots show the species richness in samples sequenced in iSeq and MiSeq machines. First, the data and raw sequencing reads were mapped against the complete gene catalog to assess sampling depth and sparsity variability. Then the gene recovery for different numbers of reads was calculated and plotted in the form of a rarefaction curve. The rarefaction curves created for samples sequenced in two different machines show the increasing numbers of raw sequencing reads and much more variation in discovered gene content in samples analyzed by MiSeq than variation attributed to samples sequenced in iSeq. The rarefaction curves of iSeq results reach a plateau earlier than those of MiSeq, which means that only a few new sequences are detected with increasing sequencing depth in iSeq. There was a difference in unequal sequencing depths in MiSeq and iSeq, influencing the richness between microbial communities measured in the samples. Rarefaction curves were obtained based on the R statistical programming language run on a high-performance computing cluster

Techniques Used: Sequencing, Sampling

The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters
Figure Legend Snippet: The dendrograms demonstrate the concordance in beta diversity MiSeq and iSeq at phylum levels ( A ) and species levels ( B ). The x-axis shows the cluster distances. The x-axis shows the cluster distances. The distance between two clusters is the average of the distances between all the features in those clusters

Techniques Used:

6) Product Images from "Cross-Reactive T Cell Response Exists in Chronic Lymphocytic Choriomeningitis Virus Infection upon Pichinde Virus Challenge"

Article Title: Cross-Reactive T Cell Response Exists in Chronic Lymphocytic Choriomeningitis Virus Infection upon Pichinde Virus Challenge

Journal: Viruses

doi: 10.3390/v14102293

No differences were detectable in the NP205-specific T cell receptor (TCR) repertoire between chronic lymphocytic choriomeningitis virus clone 13 mice sequentially infected with Pichinde virus (LCMVcl13+PICV) and lymphocytic choriomeningitis mice sequentially infected with Pichinde virus (LCMV+PICV). Dextramer + NP38- and NP205-specific T cells were sorted and TCRβ clonotypes were sequenced using the Illumina MiSeq platform. Number of ( a ) cross-reactive NP205- and ( b ) NP38-specific TCRβ clonotypes, frequency of TOP3 clonotypes and the diversity, calculated by the Shannon–Wiener index, are depicted. ( c ) Vβ and Jβ matching within the NP205-specific T cell response is depicted by lines connecting both chains, with colors from top to bottom referring to relative abundance within each individual group (ribbon plots; taking clonotype frequencies into account). ( d ) NP205- and NP38-specific TCRβ clonotypes from each individual mouse are depicted. Clonotypes were grouped in regard to their individual relative frequency: Sum of relative frequency of all hyperexpanded clonotypes (defined as ≥25% in relative frequency) are depicted in dark blue, “large” clonotypes (20 to 25%) in light blue, “medium” clonotypes (10 to 20%) in turquoise, “small” clonotypes (1 to 10%) in orange, and “rare” clonotypes (≤1%) in red. ( a – c ) Results are pooled from two independent experiments with n = 3–6 mice/group.
Figure Legend Snippet: No differences were detectable in the NP205-specific T cell receptor (TCR) repertoire between chronic lymphocytic choriomeningitis virus clone 13 mice sequentially infected with Pichinde virus (LCMVcl13+PICV) and lymphocytic choriomeningitis mice sequentially infected with Pichinde virus (LCMV+PICV). Dextramer + NP38- and NP205-specific T cells were sorted and TCRβ clonotypes were sequenced using the Illumina MiSeq platform. Number of ( a ) cross-reactive NP205- and ( b ) NP38-specific TCRβ clonotypes, frequency of TOP3 clonotypes and the diversity, calculated by the Shannon–Wiener index, are depicted. ( c ) Vβ and Jβ matching within the NP205-specific T cell response is depicted by lines connecting both chains, with colors from top to bottom referring to relative abundance within each individual group (ribbon plots; taking clonotype frequencies into account). ( d ) NP205- and NP38-specific TCRβ clonotypes from each individual mouse are depicted. Clonotypes were grouped in regard to their individual relative frequency: Sum of relative frequency of all hyperexpanded clonotypes (defined as ≥25% in relative frequency) are depicted in dark blue, “large” clonotypes (20 to 25%) in light blue, “medium” clonotypes (10 to 20%) in turquoise, “small” clonotypes (1 to 10%) in orange, and “rare” clonotypes (≤1%) in red. ( a – c ) Results are pooled from two independent experiments with n = 3–6 mice/group.

Techniques Used: Mouse Assay, Infection

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