Draft genome sequence of Streptomyces sp Tü6176
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Project description:We present a draft genome assembly that includes 200 Gb of Illumina reads, 4 Gb of Moleculo synthetic long-reads and 108 Gb of Chicago libraries, with a final size matching the estimated genome size of 2.7 Gb, and a scaffold N50 of 4.8 Mb. We also present an alternative assembly including 27 Gb raw reads generated using the Pacific Biosciences platform. In addition, we sequenced the proteome of the same individual and RNA from three different tissue types from three other species of squid species (Onychoteuthis banksii, Dosidicus gigas, and Sthenoteuthis oualaniensis) to assist genome annotation. We annotated 33,406 protein coding genes supported by evidence and the genome completeness estimated by BUSCO reached 92%. Repetitive regions cover 49.17% of the genome.
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Project description:Next-generation sequencing (NGS) technology applications like RNA-sequencing (RNA-seq) have dramatically expanded the potential for novel genomics discoveries, but the proliferation of various platforms and protocols for RNA-seq has created a need for reference data sets to help gauge the performance characteristics of these disparate methods. Here we describe the results of the ABRF-NGS Study on RNA-seq, which leverages replicate experiments across multiple sites using two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (IlluminaM-bM-^@M-^Ys HiSeqs, Life TechnologiesM-bM-^@M-^Y Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). These results show high (R2 >0.9) intra-platform consistency across test sites, high inter-platform concordance (R2 >0.8) for transcriptome profiling, and a large set of novel splice junctions observed across all platforms. Also, we observe that protocols using ribosomal RNA depletion can both salvage degraded RNA samples and also be readily compared to polyA-enriched fractions. These data provide a broad foundation for standardization, evaluation and improvement of RNA-seq methods. Two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (IlluminaM-bM-^@M-^Ys HiSeqs, Life TechnologiesM-bM-^@M-^Y Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). Please note that the samples were named following the ABRF-Platform-Site-Sample-Replicate# format. For example, ABRF-454-CNL-A-1 means Sample A was run on 454 platform at Cornell and this is the first replicate, and ABRF-454-CNL-A-2 means the same exact sample was ran with same machine at same location and is 2nd replicate.
2014-05-14 | E-GEOD-48032 | biostudies-arrayexpress
Project description:Next-generation sequencing (NGS) technology applications like RNA-sequencing (RNA-seq) have dramatically expanded the potential for novel genomics discoveries, but the proliferation of various platforms and protocols for RNA-seq has created a need for reference data sets to help gauge the performance characteristics of these disparate methods. Here we describe the results of the ABRF-NGS Study on RNA-seq, which leverages replicate experiments across multiple sites using two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). These results show high (R2 >0.9) intra-platform consistency across test sites, high inter-platform concordance (R2 >0.8) for transcriptome profiling, and a large set of novel splice junctions observed across all platforms. Also, we observe that protocols using ribosomal RNA depletion can both salvage degraded RNA samples and also be readily compared to polyA-enriched fractions. These data provide a broad foundation for standardization, evaluation and improvement of RNA-seq methods. Two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). Please note that the samples were named following the ABRF-Platform-Site-Sample-Replicate# format. For example, ABRF-454-CNL-A-1 means Sample A was run on 454 platform at Cornell and this is the first replicate, and ABRF-454-CNL-A-2 means the same exact sample was ran with same machine at same location and is 2nd replicate.
2014-05-14 | E-GEOD-48033 | biostudies-arrayexpress
Project description:Next-generation sequencing (NGS) technology applications like RNA-sequencing (RNA-seq) have dramatically expanded the potential for novel genomics discoveries, but the proliferation of various platforms and protocols for RNA-seq has created a need for reference data sets to help gauge the performance characteristics of these disparate methods. Here we describe the results of the ABRF-NGS Study on RNA-seq, which leverages replicate experiments across multiple sites using two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). These results show high (R2 >0.9) intra-platform consistency across test sites, high inter-platform concordance (R2 >0.8) for transcriptome profiling, and a large set of novel splice junctions observed across all platforms. Also, we observe that protocols using ribosomal RNA depletion can both salvage degraded RNA samples and also be readily compared to polyA-enriched fractions. These data provide a broad foundation for standardization, evaluation and improvement of RNA-seq methods. Two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). Please note that the samples were named following the ABRF-Platform-Site-Sample-Replicate# format. For example, ABRF-454-CNL-A-1 means Sample A was run on 454 platform at Cornell and this is the first replicate, and ABRF-454-CNL-A-2 means the same exact sample was ran with same machine at same location and is 2nd replicate.
2014-05-14 | E-GEOD-48035 | biostudies-arrayexpress
Project description:Next-generation sequencing (NGS) technology applications like RNA-sequencing (RNA-seq) have dramatically expanded the potential for novel genomics discoveries, but the proliferation of various platforms and protocols for RNA-seq has created a need for reference data sets to help gauge the performance characteristics of these disparate methods. Here we describe the results of the ABRF-NGS Study on RNA-seq, which leverages replicate experiments across multiple sites using two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). These results show high (R2 >0.9) intra-platform consistency across test sites, high inter-platform concordance (R2 >0.8) for transcriptome profiling, and a large set of novel splice junctions observed across all platforms. Also, we observe that protocols using ribosomal RNA depletion can both salvage degraded RNA samples and also be readily compared to polyA-enriched fractions. These data provide a broad foundation for standardization, evaluation and improvement of RNA-seq methods. Two reference RNA standards tested with four protocols (polyA selected, ribo-depleted, size selected, and degraded RNA), and examined across five NGS platforms (Illumina’s HiSeqs, Life Technologies’ Personal Genome Machine and Proton, Roche 454 GS FLX, and Pacific Biosciences RS). Please note that the samples were named following the ABRF-Platform-Site-Sample-Replicate# format. For example, ABRF-454-CNL-A-1 means Sample A was run on 454 platform at Cornell and this is the first replicate, and ABRF-454-CNL-A-2 means the same exact sample was ran with same machine at same location and is 2nd replicate.
2014-05-14 | E-GEOD-48034 | biostudies-arrayexpress
Project description:We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for sequence discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcriptlevel profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
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Project description:We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for sequence discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcriptlevel profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
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| PRJNA534110 | ENA