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Illumina Inc hiseq 2000 data
SEQC study design. This figure was modified from b presented in the related research manuscript 13 . Similar to the MAQC-I benchmarks, well characterized RNA samples A and B were augmented by samples C and D comprised of A and B in known mixing ratios 3:1 and 1:3, respectively. These allow tests for titration consistency and the correct recovery of the known mixing ratios. Synthetic RNAs from the External RNA Control Consortium (ERCC) were both pre-added to samples A and B before mixing and also sequenced separately to assess dynamic range (samples E and F). Samples were distributed to independent sites for RNA-Seq library construction and profiling by Illumina’s <t>HiSeq</t> 2000 (3+4x) and Life Technologies’ SOLiD 5500 (3+1x). In addition to the replicate libraries A1…D4 at each site, for each platform, one vendor-prepared library A5…D5 was being sequenced at all three official sites, giving a total of 24 libraries. At each site, each library has a unique barcode sequence and all libraries were pooled before sequencing, so each lane was sequencing the same material, allowing a study of lane specific effects. Samples A and B were also sequenced by Roche 454 GS FLX at different sites with two runs each but no library replicates.
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1) Product Images from "Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq"

Article Title: Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq

Journal: Scientific Data

doi: 10.1038/sdata.2014.20

SEQC study design. This figure was modified from b presented in the related research manuscript 13 . Similar to the MAQC-I benchmarks, well characterized RNA samples A and B were augmented by samples C and D comprised of A and B in known mixing ratios 3:1 and 1:3, respectively. These allow tests for titration consistency and the correct recovery of the known mixing ratios. Synthetic RNAs from the External RNA Control Consortium (ERCC) were both pre-added to samples A and B before mixing and also sequenced separately to assess dynamic range (samples E and F). Samples were distributed to independent sites for RNA-Seq library construction and profiling by Illumina’s HiSeq 2000 (3+4x) and Life Technologies’ SOLiD 5500 (3+1x). In addition to the replicate libraries A1…D4 at each site, for each platform, one vendor-prepared library A5…D5 was being sequenced at all three official sites, giving a total of 24 libraries. At each site, each library has a unique barcode sequence and all libraries were pooled before sequencing, so each lane was sequencing the same material, allowing a study of lane specific effects. Samples A and B were also sequenced by Roche 454 GS FLX at different sites with two runs each but no library replicates.
Figure Legend Snippet: SEQC study design. This figure was modified from b presented in the related research manuscript 13 . Similar to the MAQC-I benchmarks, well characterized RNA samples A and B were augmented by samples C and D comprised of A and B in known mixing ratios 3:1 and 1:3, respectively. These allow tests for titration consistency and the correct recovery of the known mixing ratios. Synthetic RNAs from the External RNA Control Consortium (ERCC) were both pre-added to samples A and B before mixing and also sequenced separately to assess dynamic range (samples E and F). Samples were distributed to independent sites for RNA-Seq library construction and profiling by Illumina’s HiSeq 2000 (3+4x) and Life Technologies’ SOLiD 5500 (3+1x). In addition to the replicate libraries A1…D4 at each site, for each platform, one vendor-prepared library A5…D5 was being sequenced at all three official sites, giving a total of 24 libraries. At each site, each library has a unique barcode sequence and all libraries were pooled before sequencing, so each lane was sequencing the same material, allowing a study of lane specific effects. Samples A and B were also sequenced by Roche 454 GS FLX at different sites with two runs each but no library replicates.

Techniques Used: Modification, Titration, RNA Sequencing Assay, Sequencing

2) Product Images from "A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium"

Article Title: A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium

Journal: Nature biotechnology

doi: 10.1038/nbt.2957

Cross-platform agreement of expression levels. ( a ) Comparison of log2 fold-change estimates for 843 selected genes. Good and similar concordances were observed between relative expression measures from the MAQC-III HiSeq 2000 and SOLiD sequencing platforms, MAQC-I TaqMan and the MAQC-III Affymetrix HuGene2 arrays (Pearson and Spearman correlation coefficients are shown; cf. Supplementary Figure 22 ). ( b ) Comparison of absolute expression levels from HiSeq 2000 and SOLiD in a rank scatter density plot. Expression level ranks for sample A are shown on the x -axis for HiSeq 2000, and on the y -axis for SOLiD. Genes are represented by dots, and areas with several genes are shown in blue, with darker blue corresponding to a higher gene density in the area. Large cross-platform deviations are seen even for highly expressed genes and these variations are systematic. The genes in the vertical ‘spur’, for instance, are not detected by SOLiD RNA-seq but show strong expression levels in HiSeq 2000 RNA-seq, with an analog comparison to 20,801 qPCR measurements giving a similar picture ( Supplementary Figure 25 ). The ERCC spike-ins are shown as red symbols (+). ERCC spike-in signals are systematically lower in the HiSeq 2000 data, which may be explained by their shorter poly-A tails and differences in the library construction protocols. ( c ) The same plot as ( b ) but removing the 11,066 genes that can be affected by the non-stranded nature of the applied Illumina protocol. Although the actual number of genes in the vertical spur that are not detected by SOLiD but show strong expression levels in the HiSeq 2000 is now smaller, it is still substantial. ( d ) Comparison of TaqMan and PrimePCR for 843 selected genes. Expression estimates vary considerably for individual genes, with some genes showing high expression in one platform but are not detected at all by the other.
Figure Legend Snippet: Cross-platform agreement of expression levels. ( a ) Comparison of log2 fold-change estimates for 843 selected genes. Good and similar concordances were observed between relative expression measures from the MAQC-III HiSeq 2000 and SOLiD sequencing platforms, MAQC-I TaqMan and the MAQC-III Affymetrix HuGene2 arrays (Pearson and Spearman correlation coefficients are shown; cf. Supplementary Figure 22 ). ( b ) Comparison of absolute expression levels from HiSeq 2000 and SOLiD in a rank scatter density plot. Expression level ranks for sample A are shown on the x -axis for HiSeq 2000, and on the y -axis for SOLiD. Genes are represented by dots, and areas with several genes are shown in blue, with darker blue corresponding to a higher gene density in the area. Large cross-platform deviations are seen even for highly expressed genes and these variations are systematic. The genes in the vertical ‘spur’, for instance, are not detected by SOLiD RNA-seq but show strong expression levels in HiSeq 2000 RNA-seq, with an analog comparison to 20,801 qPCR measurements giving a similar picture ( Supplementary Figure 25 ). The ERCC spike-ins are shown as red symbols (+). ERCC spike-in signals are systematically lower in the HiSeq 2000 data, which may be explained by their shorter poly-A tails and differences in the library construction protocols. ( c ) The same plot as ( b ) but removing the 11,066 genes that can be affected by the non-stranded nature of the applied Illumina protocol. Although the actual number of genes in the vertical spur that are not detected by SOLiD but show strong expression levels in the HiSeq 2000 is now smaller, it is still substantial. ( d ) Comparison of TaqMan and PrimePCR for 843 selected genes. Expression estimates vary considerably for individual genes, with some genes showing high expression in one platform but are not detected at all by the other.

Techniques Used: Expressing, Sequencing, RNA Sequencing Assay, Real-time Polymerase Chain Reaction

Gene detection and junction discovery. ( a ) The fraction of all reads aligned to gene models from different annotations, RefSeq, Encode and NCBI AceView (green). Reads aligning only to specific annotations are shown in dark green. ( b ) Known genes (left) and exon junctions (right) supported by at least 16 HiSeq 2000 or SOLiD reads are in green; genes or junctions annotated but not observed at this threshold are shown in grey. ( c – e ) show sensitivity as a function of read depth. ( c ) Known genes detected. We show the number and percentage of all AceView annotated genes detected for three RNA-seq analysis pipelines, Subread (yellow), r-make (cyan) and Magic (magenta). The x -axis marks cumulative aligned fragments from all replicates and sites. Vertical lines indicate boundaries between samples A through D. ( d ) Known junctions detected. The numbers and percentages of all exon-exon junctions (supported by 8 or more reads) are shown for different gene model databases (line style). Horizontal lines show the respective total numbers of annotated junctions. ( e ) Unannotated junctions supported by multiple platforms and pipelines. Subsets of unannotated junctions have expression levels with correct titration orders and mixing ratios ( cf. Figs 1b–d and 4a,b ). ( f ) Distribution of junction expression levels. Unannotated junctions, then unannotated junctions supported by multiple platforms and pipelines, and known junctions show increasing expression levels (colors). Subsets expressed with mutual information about the samples and correct titration order and mixing ratio display a further shift towards higher expression levels (dashed lines). ( g , h ) Intra- (blue) and inter-site reproducibility (orange) of detected known genes ( g ) and junctions ( h ). Pairwise agreement is shown by boxplots, where the second set of boxplots (upper group) indicates percentages.
Figure Legend Snippet: Gene detection and junction discovery. ( a ) The fraction of all reads aligned to gene models from different annotations, RefSeq, Encode and NCBI AceView (green). Reads aligning only to specific annotations are shown in dark green. ( b ) Known genes (left) and exon junctions (right) supported by at least 16 HiSeq 2000 or SOLiD reads are in green; genes or junctions annotated but not observed at this threshold are shown in grey. ( c – e ) show sensitivity as a function of read depth. ( c ) Known genes detected. We show the number and percentage of all AceView annotated genes detected for three RNA-seq analysis pipelines, Subread (yellow), r-make (cyan) and Magic (magenta). The x -axis marks cumulative aligned fragments from all replicates and sites. Vertical lines indicate boundaries between samples A through D. ( d ) Known junctions detected. The numbers and percentages of all exon-exon junctions (supported by 8 or more reads) are shown for different gene model databases (line style). Horizontal lines show the respective total numbers of annotated junctions. ( e ) Unannotated junctions supported by multiple platforms and pipelines. Subsets of unannotated junctions have expression levels with correct titration orders and mixing ratios ( cf. Figs 1b–d and 4a,b ). ( f ) Distribution of junction expression levels. Unannotated junctions, then unannotated junctions supported by multiple platforms and pipelines, and known junctions show increasing expression levels (colors). Subsets expressed with mutual information about the samples and correct titration order and mixing ratio display a further shift towards higher expression levels (dashed lines). ( g , h ) Intra- (blue) and inter-site reproducibility (orange) of detected known genes ( g ) and junctions ( h ). Pairwise agreement is shown by boxplots, where the second set of boxplots (upper group) indicates percentages.

Techniques Used: RNA Sequencing Assay, Expressing, Titration

Built-in truths for assessing RNA-seq. ( a ) Titration order A, C, D, B. Log2 fold-change is related to cross-platform titration consistency. At sufficiently strong log2 fold-change, reliable titration is also found across platforms: The dark blue line represents the 22,074 ‘unmissable’ genes showing the correct titration order with no contradiction in at least 14 HiSeq 2000 and 6 SOLiD samples. Most genes with high differential expression are in this class. ( b ) Known A/B mixing ratios in samples C and D. The yellow solid line traces the expected values after mRNA/total-RNA shift correction. The 1%, 10% and 25% most highly expressed genes are shown in red, cyan and magenta, respectively. On average, the most strongly expressed genes recover the expected mixing ratio best. Genes with inconsistent titration ( cf. a ) are colored grey. Black and grey symbols intermixing indicates that consistent titration (black) does not guarantee reliable recovery of the mixing ratio (and vice versa ). ( c ) ERCC spike-in ratios can be recovered increasingly well at higher expression levels. From the response curves, one can calculate signal thresholds for the detection of a change. 50 ( d ) Variation of the total amounts of detected ERCC spikes. The lack of reliable titration indicates that the considerable differences between libraries of a given site and protocol are random, implying limits for absolute expression level estimates, in general, and using spike-ins for the calibration of absolute quantification, in particular. The observed variations likely arise in library construction, as the vendor-prepared libraries (colored cyan or grey) gave constant results across different sites. For ( a ) and ( b ), all 55,674 AceView genes tested.
Figure Legend Snippet: Built-in truths for assessing RNA-seq. ( a ) Titration order A, C, D, B. Log2 fold-change is related to cross-platform titration consistency. At sufficiently strong log2 fold-change, reliable titration is also found across platforms: The dark blue line represents the 22,074 ‘unmissable’ genes showing the correct titration order with no contradiction in at least 14 HiSeq 2000 and 6 SOLiD samples. Most genes with high differential expression are in this class. ( b ) Known A/B mixing ratios in samples C and D. The yellow solid line traces the expected values after mRNA/total-RNA shift correction. The 1%, 10% and 25% most highly expressed genes are shown in red, cyan and magenta, respectively. On average, the most strongly expressed genes recover the expected mixing ratio best. Genes with inconsistent titration ( cf. a ) are colored grey. Black and grey symbols intermixing indicates that consistent titration (black) does not guarantee reliable recovery of the mixing ratio (and vice versa ). ( c ) ERCC spike-in ratios can be recovered increasingly well at higher expression levels. From the response curves, one can calculate signal thresholds for the detection of a change. 50 ( d ) Variation of the total amounts of detected ERCC spikes. The lack of reliable titration indicates that the considerable differences between libraries of a given site and protocol are random, implying limits for absolute expression level estimates, in general, and using spike-ins for the calibration of absolute quantification, in particular. The observed variations likely arise in library construction, as the vendor-prepared libraries (colored cyan or grey) gave constant results across different sites. For ( a ) and ( b ), all 55,674 AceView genes tested.

Techniques Used: RNA Sequencing Assay, Titration, Expressing

Multiple performance metrics for the quantification of genes and alternative transcripts. The y -axes show a Consistency Score. Secondary y -axes mark the percentage of the maximal possible score. Panels show the three official HiSeq 2000 and SOLiD sites and compare a few analysis variants: Green, TopHat2; magenta, TopHat2 guided by known gene models; cyan, Subread; yellow, BitSeq; blue, Magic. Panels a and b consider all AceView annotated genes. Panels c and d focus on a subset of expressed complex genes with multiple alternative transcripts where comparison to a high-resolution test microarray (rightmost bar) can be conducted. ( e ) Comparison of RNA-seq to four different microarrays and data-processing methods (red bars) by plotting the mutual information ( y -axes) at different read depths ( x -axes). For the microarrays, the number of probes used is shown. The numbers given for RNA-seq state the number of fragments mapped to genes as well as the [total fragments]. SOLiD and HiSeq 2000 performed similarly well for comparable effective read depths ( Supplementary Figure 33a ). HiSeq 2000 data is plotted here. Each bar shows the minima and maxima across the three official sites. The read depth for which average RNA-seq performance met or exceeded that of the array is marked by a cyan bar. The corresponding read depths varied widely from 5 M (HGU133plus2 with MAS5) to about 50 M fragments (PrimeView with gcRMA/affyPLM), showing the strong effect of the reference gene set implied by the probes on the respective arrays and the employed microarray data-processing methods. Results are shown for the Subread pipeline. Alternative RNA-seq data analysis pipelines, however, can require up to double the number of fragments (TopHat2+Cufflinks, Supplementary Figure 35 ). See Supplementary Figures 33 and 34 for comparisons of other platforms and read depths.
Figure Legend Snippet: Multiple performance metrics for the quantification of genes and alternative transcripts. The y -axes show a Consistency Score. Secondary y -axes mark the percentage of the maximal possible score. Panels show the three official HiSeq 2000 and SOLiD sites and compare a few analysis variants: Green, TopHat2; magenta, TopHat2 guided by known gene models; cyan, Subread; yellow, BitSeq; blue, Magic. Panels a and b consider all AceView annotated genes. Panels c and d focus on a subset of expressed complex genes with multiple alternative transcripts where comparison to a high-resolution test microarray (rightmost bar) can be conducted. ( e ) Comparison of RNA-seq to four different microarrays and data-processing methods (red bars) by plotting the mutual information ( y -axes) at different read depths ( x -axes). For the microarrays, the number of probes used is shown. The numbers given for RNA-seq state the number of fragments mapped to genes as well as the [total fragments]. SOLiD and HiSeq 2000 performed similarly well for comparable effective read depths ( Supplementary Figure 33a ). HiSeq 2000 data is plotted here. Each bar shows the minima and maxima across the three official sites. The read depth for which average RNA-seq performance met or exceeded that of the array is marked by a cyan bar. The corresponding read depths varied widely from 5 M (HGU133plus2 with MAS5) to about 50 M fragments (PrimeView with gcRMA/affyPLM), showing the strong effect of the reference gene set implied by the probes on the respective arrays and the employed microarray data-processing methods. Results are shown for the Subread pipeline. Alternative RNA-seq data analysis pipelines, however, can require up to double the number of fragments (TopHat2+Cufflinks, Supplementary Figure 35 ). See Supplementary Figures 33 and 34 for comparisons of other platforms and read depths.

Techniques Used: Microarray, RNA Sequencing Assay

The SEQC (MAQC-III) project and experimental design. ( a ) Overview of projects. We report on a group of studies assessing different sequencing platforms in real-world use cases, including transcriptome annotation and other research applications, as well as clinical settings. This paper focuses on the results of a multi-center experiment with built-in ground truths. ( b ) Main study design. Similar to the MAQC-I benchmarks, we analysed RNA samples A to D: Samples C and D were created by mixing the well-characterized samples A and B in 3:1 and 1:3 ratios, respectively. This allows tests for titration consistency ( c ) and the correct recovery of the known mixing ratios ( d ). Synthetic RNAs from the External RNA Control Consortium (ERCC) were both pre-added to samples A and B before mixing and also sequenced separately to assess dynamic range (samples E and F). Samples were distributed to independent sites for RNA-seq library construction and profiling by Illumina’s HiSeq 2000 (3 official + 3 inofficial sites) and Life Technologies’ SOLiD 5500 (3 official sites + 1 inofficial site). Unless mentioned otherwise, data presented shows results from the three official sites ( italics ). In addition to the four replicate libraries each for samples A to D per site, for each platform, one vendor-prepared library A5…D5 was being sequenced at the official sites, giving a total of 120 libraries. At each site, every library has a unique bar-code sequence and all libraries were pooled before sequencing, so each lane was sequencing the same material, allowing a study of lane specific effects. To support a later assessment of gene models, we sequenced samples A and B by Roche 454 (3x, no replicates, see Supplementary Notes Section 2.5 ). ( c ) Schema illustrating tests for titration order consistency. Four examples are shown. The dashed lines represent the ideal mixture of samples A and B (blue and red) expected for samples D and C (magenta and dark purple). ( d ) Schema illustrating a consistency test for recovering the expected sample mixing ratio. The yellow lines mark a 10% deviation from the expected response (black) for a perfect mixing ratio. Both tests ( c ) and ( d ) will reflect both systemic distortions (bias) and random variation (noise).
Figure Legend Snippet: The SEQC (MAQC-III) project and experimental design. ( a ) Overview of projects. We report on a group of studies assessing different sequencing platforms in real-world use cases, including transcriptome annotation and other research applications, as well as clinical settings. This paper focuses on the results of a multi-center experiment with built-in ground truths. ( b ) Main study design. Similar to the MAQC-I benchmarks, we analysed RNA samples A to D: Samples C and D were created by mixing the well-characterized samples A and B in 3:1 and 1:3 ratios, respectively. This allows tests for titration consistency ( c ) and the correct recovery of the known mixing ratios ( d ). Synthetic RNAs from the External RNA Control Consortium (ERCC) were both pre-added to samples A and B before mixing and also sequenced separately to assess dynamic range (samples E and F). Samples were distributed to independent sites for RNA-seq library construction and profiling by Illumina’s HiSeq 2000 (3 official + 3 inofficial sites) and Life Technologies’ SOLiD 5500 (3 official sites + 1 inofficial site). Unless mentioned otherwise, data presented shows results from the three official sites ( italics ). In addition to the four replicate libraries each for samples A to D per site, for each platform, one vendor-prepared library A5…D5 was being sequenced at the official sites, giving a total of 120 libraries. At each site, every library has a unique bar-code sequence and all libraries were pooled before sequencing, so each lane was sequencing the same material, allowing a study of lane specific effects. To support a later assessment of gene models, we sequenced samples A and B by Roche 454 (3x, no replicates, see Supplementary Notes Section 2.5 ). ( c ) Schema illustrating tests for titration order consistency. Four examples are shown. The dashed lines represent the ideal mixture of samples A and B (blue and red) expected for samples D and C (magenta and dark purple). ( d ) Schema illustrating a consistency test for recovering the expected sample mixing ratio. The yellow lines mark a 10% deviation from the expected response (black) for a perfect mixing ratio. Both tests ( c ) and ( d ) will reflect both systemic distortions (bias) and random variation (noise).

Techniques Used: Sequencing, Titration, RNA Sequencing Assay

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Sequencing:

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Article Snippet: .. While the quality control steps above can remove many assembly-confounding errors, certain specific sequence motifs can produce false positive base calling errors in Illumina HiSeq 2000 data [ , ]. .. To remove these systematic sequence read errors we utilized the Reptile v1.1 error correction pipeline ( http://aluru-sun.ece.iastate.edu/doku.php?id=reptile ) [ ].

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    Illumina Inc hiseq 2000 sequencing data
    Comparison of expression profiles of selected genes as determined by Illumina <t>HiSeq</t> 2000 sequencing (black) and qRT-PCR (grey) in WSSV-challenged shrimp. Target gene abbreviations are as follows: CASP—caspase, HSP60—heat shock protein 60, CARC—carcinin, ALF3—anti-lipopolisaccharide factor-3, HSP90—heat shock protein 90, HSP 10—heat shock protein 10, HHAP—haemocyte homeostasis-associated protein, CHF—crustacean hematopoietic factor, HEPKPI—hepatopancreas kazal-type proteinase inhibitor 1A1 and KSPI4—kazal-type serine proteinase inhibitor 4. The results showed validation of the differential expression for each selected genes as determined by Illumina HiSeq 2000 sequencing and qRT-PCR between the survived WSSV-challenged shrimp and control group.
    Hiseq 2000 Sequencing Data, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 88/100, based on 13 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Illumina Inc hiseq 2000 lane
    Sampling and major sources of variation . Strains CT43 and ATCC10792 grown in two medium lots #1091744 and 7220443 in water taken from building 1520 and 1610. Bacteria were cultured on four different dates and four biological replicates were grown to mid-log phase for each date, harvested and then RNA-seq data were generated using an Illumina <t>Hiseq</t> 2000 instrument.
    Hiseq 2000 Lane, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 89/100, based on 58 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Illumina Inc pond sediment metagenome the sequence data
    Differential taxonomic binning of pond sediment. a) Phylogenetic reassignment of metagenomic reads of PS1, PS2 and HCH gradient (1 Km, 5 Km, DS and SolexaDS) against the nr-database ( ftp://ftp.ncbi.nlm.nih.gov/blast/db/nr , June, 2015) (BLASTX, E-value = 1e-10), b) Stack area plot of the most abundant phyla present among PS1 and PS2 samples which were statistically computed using METASTATS (P value ≤ 0.05 and 1000 permutations), c) Depiction of genomic variation in pond sediment by principal component analysis of tetranucleotide frequency of essential single copy genes using R v. 3.2.2 via the ace function of ape. pc.comp1 and pc.comp2 are its two principle components. d) The plot was constructed along two principle components; X1 and X2 were composed of all terminal and internal phylogenetic nodes with the branches connecting adjacent nodes. X1 and X2 axis in the principle component plot are the eigenvectors with the highest eigenvalues for essential single copy genes of the most abundant bacterial phyla. Clustering of nodes represented the genomic variation in pond sediment and Cluster B was found to be abundant in pond sediment <t>metagenome.</t>
    Pond Sediment Metagenome The Sequence Data, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 91/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Comparison of expression profiles of selected genes as determined by Illumina HiSeq 2000 sequencing (black) and qRT-PCR (grey) in WSSV-challenged shrimp. Target gene abbreviations are as follows: CASP—caspase, HSP60—heat shock protein 60, CARC—carcinin, ALF3—anti-lipopolisaccharide factor-3, HSP90—heat shock protein 90, HSP 10—heat shock protein 10, HHAP—haemocyte homeostasis-associated protein, CHF—crustacean hematopoietic factor, HEPKPI—hepatopancreas kazal-type proteinase inhibitor 1A1 and KSPI4—kazal-type serine proteinase inhibitor 4. The results showed validation of the differential expression for each selected genes as determined by Illumina HiSeq 2000 sequencing and qRT-PCR between the survived WSSV-challenged shrimp and control group.

    Journal: PeerJ

    Article Title: A new insight to biomarkers related to resistance in survived-white spot syndrome virus challenged giant tiger shrimp, Penaeus monodon

    doi: 10.7717/peerj.8107

    Figure Lengend Snippet: Comparison of expression profiles of selected genes as determined by Illumina HiSeq 2000 sequencing (black) and qRT-PCR (grey) in WSSV-challenged shrimp. Target gene abbreviations are as follows: CASP—caspase, HSP60—heat shock protein 60, CARC—carcinin, ALF3—anti-lipopolisaccharide factor-3, HSP90—heat shock protein 90, HSP 10—heat shock protein 10, HHAP—haemocyte homeostasis-associated protein, CHF—crustacean hematopoietic factor, HEPKPI—hepatopancreas kazal-type proteinase inhibitor 1A1 and KSPI4—kazal-type serine proteinase inhibitor 4. The results showed validation of the differential expression for each selected genes as determined by Illumina HiSeq 2000 sequencing and qRT-PCR between the survived WSSV-challenged shrimp and control group.

    Article Snippet: Validation of NGS data & comparative transcriptome profiling analysis To validate the Illumina HiSeq 2000 sequencing data, 10 immune-related genes with potential for disease resistance were chosen for quantitative RT-PCR analysis, using the same RNA samples as for the Illumina HiSeq 2000 sequencing ( ).

    Techniques: Expressing, Sequencing, Quantitative RT-PCR

    Frequency distribution of the contig sizes from two  Amorphophallus  species.  The frequency distribution of contig sizes resulting from Illumina HiSeq™ 2000 sequencing, as analyzed using the Trinity software.

    Journal: BMC Genomics

    Article Title: Development of microsatellite markers by transcriptome sequencing in two species of Amorphophallus (Araceae)

    doi: 10.1186/1471-2164-14-490

    Figure Lengend Snippet: Frequency distribution of the contig sizes from two Amorphophallus species. The frequency distribution of contig sizes resulting from Illumina HiSeq™ 2000 sequencing, as analyzed using the Trinity software.

    Article Snippet: Here, we report the generation of a large expressed sequence dataset based on Illumina HiSeq™ 2000 sequencing data from the young leaves of two Amorphophallus species, A. konjac and A. bulbifer .

    Techniques: Sequencing, Software

    Sampling and major sources of variation . Strains CT43 and ATCC10792 grown in two medium lots #1091744 and 7220443 in water taken from building 1520 and 1610. Bacteria were cultured on four different dates and four biological replicates were grown to mid-log phase for each date, harvested and then RNA-seq data were generated using an Illumina Hiseq 2000 instrument.

    Journal: Frontiers in Microbiology

    Article Title: Replicates, Read Numbers, and Other Important Experimental Design Considerations for Microbial RNA-seq Identified Using Bacillus thuringiensis Datasets

    doi: 10.3389/fmicb.2016.00794

    Figure Lengend Snippet: Sampling and major sources of variation . Strains CT43 and ATCC10792 grown in two medium lots #1091744 and 7220443 in water taken from building 1520 and 1610. Bacteria were cultured on four different dates and four biological replicates were grown to mid-log phase for each date, harvested and then RNA-seq data were generated using an Illumina Hiseq 2000 instrument.

    Article Snippet: The data from this well-replicated study with 32 samples, each from one Illumina HiSeq 2000 lane, generated a large number of reads per sample, and significantly differentially expressed genes were detected using DESeq2 (Love et al., ).

    Techniques: Sampling, Cell Culture, RNA Sequencing Assay, Generated

    Differential taxonomic binning of pond sediment. a) Phylogenetic reassignment of metagenomic reads of PS1, PS2 and HCH gradient (1 Km, 5 Km, DS and SolexaDS) against the nr-database ( ftp://ftp.ncbi.nlm.nih.gov/blast/db/nr , June, 2015) (BLASTX, E-value = 1e-10), b) Stack area plot of the most abundant phyla present among PS1 and PS2 samples which were statistically computed using METASTATS (P value ≤ 0.05 and 1000 permutations), c) Depiction of genomic variation in pond sediment by principal component analysis of tetranucleotide frequency of essential single copy genes using R v. 3.2.2 via the ace function of ape. pc.comp1 and pc.comp2 are its two principle components. d) The plot was constructed along two principle components; X1 and X2 were composed of all terminal and internal phylogenetic nodes with the branches connecting adjacent nodes. X1 and X2 axis in the principle component plot are the eigenvectors with the highest eigenvalues for essential single copy genes of the most abundant bacterial phyla. Clustering of nodes represented the genomic variation in pond sediment and Cluster B was found to be abundant in pond sediment metagenome.

    Journal: Journal of Genomics

    Article Title: Metagenomic Analysis of a Complex Community Present in Pond Sediment

    doi: 10.7150/jgen.16685

    Figure Lengend Snippet: Differential taxonomic binning of pond sediment. a) Phylogenetic reassignment of metagenomic reads of PS1, PS2 and HCH gradient (1 Km, 5 Km, DS and SolexaDS) against the nr-database ( ftp://ftp.ncbi.nlm.nih.gov/blast/db/nr , June, 2015) (BLASTX, E-value = 1e-10), b) Stack area plot of the most abundant phyla present among PS1 and PS2 samples which were statistically computed using METASTATS (P value ≤ 0.05 and 1000 permutations), c) Depiction of genomic variation in pond sediment by principal component analysis of tetranucleotide frequency of essential single copy genes using R v. 3.2.2 via the ace function of ape. pc.comp1 and pc.comp2 are its two principle components. d) The plot was constructed along two principle components; X1 and X2 were composed of all terminal and internal phylogenetic nodes with the branches connecting adjacent nodes. X1 and X2 axis in the principle component plot are the eigenvectors with the highest eigenvalues for essential single copy genes of the most abundant bacterial phyla. Clustering of nodes represented the genomic variation in pond sediment and Cluster B was found to be abundant in pond sediment metagenome.

    Article Snippet: De-novo Assembly of Pond Sediment Metagenome The sequence data (Illumina HiSeq 2000 clean data) were assembled using the Velvet_1.2.03 software at an optimized k-mer length of 39 (insert length, 170 bp; expected coverage, auto; minimum contig standard deviation for insert length, 20 bp; and length cutoff, 200 bp).

    Techniques: Significance Assay, Construct

    Functional Annotation from pond sediment and sites across dumpsite. a) Extrapolation of taxonomic diversity of PS1 and PS2 (Fisher's Exact Test, P > 0.01) is the overall representation of phylum profiles using Pfam protein domains and the associated Gene Ontology (GO) in PS1 versus PS2 (R 2 = 0.985), b) Rare fraction analysis performed on the unique Pfam protein domains across pond sediment (PS1 and PS2) and three HCH gradients (1 Km, 5 Km, DS(Dumpsite) and SolexaDS), c) Principle Component Analysis (PCA) plot of NCBI COG categories of three HCH gradients and two samples of pond sediment metagenome to validate the functional profiling of matagenomic samples, d) Plot showing Shannon diversity across pond sediment (PS1 PS2), 1 Km, 5 Km, DS(Dumpsite) and SolexaDS for functional and diversity (by EGT) analysis.

    Journal: Journal of Genomics

    Article Title: Metagenomic Analysis of a Complex Community Present in Pond Sediment

    doi: 10.7150/jgen.16685

    Figure Lengend Snippet: Functional Annotation from pond sediment and sites across dumpsite. a) Extrapolation of taxonomic diversity of PS1 and PS2 (Fisher's Exact Test, P > 0.01) is the overall representation of phylum profiles using Pfam protein domains and the associated Gene Ontology (GO) in PS1 versus PS2 (R 2 = 0.985), b) Rare fraction analysis performed on the unique Pfam protein domains across pond sediment (PS1 and PS2) and three HCH gradients (1 Km, 5 Km, DS(Dumpsite) and SolexaDS), c) Principle Component Analysis (PCA) plot of NCBI COG categories of three HCH gradients and two samples of pond sediment metagenome to validate the functional profiling of matagenomic samples, d) Plot showing Shannon diversity across pond sediment (PS1 PS2), 1 Km, 5 Km, DS(Dumpsite) and SolexaDS for functional and diversity (by EGT) analysis.

    Article Snippet: De-novo Assembly of Pond Sediment Metagenome The sequence data (Illumina HiSeq 2000 clean data) were assembled using the Velvet_1.2.03 software at an optimized k-mer length of 39 (insert length, 170 bp; expected coverage, auto; minimum contig standard deviation for insert length, 20 bp; and length cutoff, 200 bp).

    Techniques: Functional Assay