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- Swift Biosciences lanza su nuevo kit de biblioteca de larga inserción para mejorar la calidad de los datos en las plataformas de secuenciación PacBio Los resultados de Mount Sinai, Washington University y de Cold Spring Harbor Laboratories se presentarán en AGBT ANN ARBOR, Michigan, 15 de febrero de 2017 /PRNewswire/ -- Swift Biosciences anunció hoy el lanzamiento comercial de su kit de preparación de biblioteca Accel-NGS® XL, la solución de secuenciación más rápida para toda la secuenciación del genoma en las plataformas Pacific Biosciences® (PacBio®). Este kit de preparación de biblioteca, optimizado especialmente para la tecnología de secuenciación Single Molecule, Real-Time (SMRT®) de PacBio, proporciona lecturas de secuenciación mucho más largas con un solo flujo de trabajo de un solo tubo utilizando menos entradas de muestras. Swift Biosciences ya acepta pedidos para el kit Accel-NGS XL— vendido de forma exclusiva por medio de Swift. "Con su flujo de trabajo sencillo de cuatro horas y longitudes de lectura mayores, el kit Accel-NGS XL mejora de forma sustancial todas las aplicaciones de secuenciación del genoma, como el montaje de novo y la secuenciación de haplotipo, en cualquier genoma que incluyen microbiales, planta, animales y humanos", afirmó Haley Fiske, responsable comercial de Swift Biosciences. "Estas mejoras de calidad y flujo de trabajo ayuda a los usuarios de PacBio para generar resultados más destacados desde cada puesta en marcha con una productividad que es del doble". Swift Biosciences y diversos colaboradores científicos presentaron dos posters en la reunión general de la AGBT 2017 mostrando los datos de secuenciación generados con esta nueva química. El primer poster, titulado "A Method to Improve Read Length of SMRT Sequencing", mostró los resultados, generados en colaboración con Mount Sinai y Cold Spring Harbor Laboratories, desde diversos genomas, incluyendo el ADN de referencia de plantas, bacterias y humanos. Los datos de apoyo produjeron unas lecturas medias de hasta 20Kb, con un 50% menos de entrada de muestra y sin dispositivos de adaptador de dímero. En el segundo poster, titulado "Improved Library Construction Methods for the Pacific Biosciences Sequencing Platform Using Swift Accel-NGS XL Library Prep Kit for PacBio Applied to Challenging BAC Clones for Human Genome Reference Improvement", Robert Fulton, director de desarrollo de proyectos y gestión del McDonnell Genome Institute of Washington University, presentó los resultados de la secuenciación de clones humanos BAC, demostrando unos rendimientos de biblioteca superiores con lecturas de secuenciación más largas. "Swift Biosciences es la primera compañía que ofrece soluciones de preparación de biblioteca en las tres principales plataformas de secuenciación, incluyendo Pacific Biosystems, Illumina®, e Ion Torrent™", destacó Timothy Harkins, Ph.D., director general y consejero delegado de Swift Biosciences. "Estamos centrados de forma estratégica en la ampliación del mercado NGS por medio de la simplificación de los flujos de trabajo complejos por medio de las tecnologías de biblioteca innovadoras y por el suministro de aplicaciones nuevas para cada una de las plataformas NGS. Nuestras bibliotecas proporcionan los datos de la mayor calidad en las aplicaciones más complejas. Swift es por ello la 'The NGS library company'".


News Article | March 1, 2017
Site: www.nature.com

The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. ARC-Net, University of Verona: approval number 1885 from the Integrated University Hospital Trust (AOUI) Ethics Committee (Comitato Etico Azienda Ospedaliera Universitaria Integrata) approved in their meeting of 17 November 2010, documented by the ethics committee 52070/CE on 22 November 2010 and formalized by the Health Director of the AOUI on the order of the General Manager with protocol 52438 on 23 November 2010. APGI: Sydney South West Area Health Service Human Research Ethics Committee, western zone (protocol number 2006/54); Sydney Local Health District Human Research Ethics Committee (X11-0220); Northern Sydney Central Coast Health Harbour Human Research Ethics Committee (0612-251M); Royal Adelaide Hospital Human Research Ethics Committee (091107a); Metro South Human Research Ethics Committee (09/QPAH/220); South Metropolitan Area Health Service Human Research Ethics Committee (09/324); Southern Adelaide Health Service/Flinders University Human Research Ethics Committee (167/10); Sydney West Area Health Service Human Research Ethics Committee (Westmead campus) (HREC2002/3/4.19); The University of Queensland Medical Research Ethics Committee (2009000745); Greenslopes Private Hospital Ethics Committee (09/34); North Shore Private Hospital Ethics Committee. Baylor College of Medicine: Institutional Review Board protocol numbers H-29198 (Baylor College of Medicine tissue resource), H-21332 (Genomes and Genetics at the BCM-HGSC), and H-32711(Cancer Specimen Biobanking and Genomics). Patients were recruited and consent obtained for genomic sequencing through the ARC-Net Research Centre at Verona University, Australian Pancreatic Cancer Genome Initiative (APGI), and Baylor College of Medicine as part of the ICGC (www.icgc.org). A patient criterion for admission to the study was that they were clinically sporadic. This information was acquired through direct interviews with participants and a questionnaire regarding their personal history and that of relatives with regard to pancreas cancers and any other cancers during anamnesis. Clinical records were also used to clarify familial history based on patient indications. Samples were prospectively and consecutively acquired through institutions affiliated with the Australian Pancreatic Cancer Genome Initiative. Samples from the ARC-Net biobank are the result of consecutive collections from a single centre. All tissue samples were processed as previously described5151. Representative sections were reviewed independently by at least one additional pathologist with specific expertise in pancreatic diseases. Samples either had full face frozen sectioning performed in optimal cutting temperature (OCT) medium, or the ends excised and processed in formalin to verify the presence of tumour in the sample to be sequenced and to estimate the percentage of neoplastic cells in the sample relative to stromal cells. Macrodissection was performed if required to excise areas that did not contain neoplastic epithelium. Tumour cellularity was determined using SNP arrays (Illumina) and the qpure tool9. PanNET is a rare tumour type and the samples were collected via an international network. We estimate that with 98 unique patients in the discovery cohort, we will achieve 90% power for 90% of genes to detect mutations that occur at a frequency of ~10% above the background rate for PanNET (assuming a somatic mutation frequency of more than 2 per Mb)52. Cancer and matched normal colonic mucosa were collected at the time of surgical resection from the Royal Brisbane and Women’s Hospital and snap frozen in liquid nitrogen. A biallelic germline mutation in the MUTYH gene was detected by restriction fragment length polymorphism analysis and confirmed by automated sequencing to be the G382D mutation (or ENST00000450313.5 G396D, ClinVar#5294) in both alleles53. The primary antibodies used for immunohistochemical staining were: cytokeratin 8/18 (5D3, Novocastra), chromogranin A (DAK-A3, Dako), and CD99 (O13, Biolegend). Antibodies and staining conditions have been described elsewhere39. Whole-genome sequencing with 100-bp paired reads was performed with a HiSEQ2000 (Illumina). Sequence data were mapped to a GRCh37 using BWA and BAM files are available in the EGA (accession number: EGAS00001001732). Somatic mutations and germline variants were detected using a previously described consensus mutation calling strategy11. Mutations were annotated with gene consequence using SNPeff. The pathogenicity of germline variants was predicted using cancer-specific and locus-specific genetic databases, medical literature, computational predictions with ENSEMBL Variant Effect Predictor (VEP) annotation, and second hits identified in the tumour genome. Intogen27 was used to find somatic genes that were significantly mutated. Somatic structural variants were identified using the qSV tool as previously described10, 11, 17. Coding mutations are included in supplementary tables and all mutations have been uploaded to the International Cancer Genome Consortium Data Coordination Center. Mutational signatures were predicted using a published framework14. Essentially, the 96-substitution classification was determined for each sample. The signatures were compared to other validated signatures and the prevalence of each signature per megabase was determined. Somatic copy number was estimated using high density SNP arrays and the GAP tool12. Arm level copy number data were clustered using Ward’s method, Euclidian distance. GISTIC13 was used to identify recurrent regions of copy number change. The whole genome sequence data was used to determine the length of the telomeres in each sample using the qMotif tool. Essentially, qMotif determines telomeric DNA content by calculating the number of reads that harbour the telomere motif (TTAGG), and then estimates the relative length of telomeres in the tumour compared to the normal. qMotif is available online (http://sourceforge.net/projects/adamajava). Telomere length was validated by qPCR as previously described54. RNASeq library preparation and sequencing were performed as previously described55. Essentially, sequencing reads were mapped to transcripts corresponding to ensemble 70 annotations using RSEM. RSEM data were normalized using TMM (weighted trimmed mean of M-values) as implemented in the R package ‘edgeR’. For downstream analyses, normalized RSEM data were converted to counts per million (c.p.m.) and log transformed. Genes without at least 1 c.p.m. in 20% of the sample were excluded from further analysis55. Unsupervised class discovery was performed using consensus clustering as implemented in the ConsensusClusterPlus R package56. The top 2,000 most variable genes were used as input. Differential gene expression analysis between representative samples was performed using the R package ‘edgeR’57. Ontology and pathway enrichment analysis was performed using the R package ‘dnet’58. PanNET class enrichment using published gene signatures44 was performed using Gene Set Variation Analysis (GSVA) as described previously55. Two strategies were used to verify fusion transcripts. For verification of EWSR1–BEND2 fusions, cDNAs were synthesized using the SuperScript VILO cDNA synthesis kit (Thermofisher) with 1 μg purified total RNA. For each fusion sequence, three samples were used: the PanNET sample containing the fusion, the PanNET sample without that fusion, and a non-neoplastic pancreatic sample. The RT–PCR product were evaluated on the Agilent 2100 Bioanalyzer (Agilent Technologies) and verified by sequencing using the 3130XL Genetic Analyzer (Life Technologies). Primers specific for EWSR1–BEND2 fusion genes are available upon request. To identify the EWSR1 fusion partner in the case ITNET_2045, a real-time RT–PCR translocation panel for detecting specific Ewing sarcoma fusion transcripts was applied as described59. Following identification of the fusion partner, PCR amplicons were subjected to sequencing using the 3130XL Genetic Analyzer. EWSR1 rearrangements were assayed on paraffin-embedded tissue sections using a commercial split-signal probe (Vysis LSI EWSR1 (22q12) Dual Colour, Break Apart Rearrangement FISH Probe Kit) that consists of a mixture of two FISH DNA probes. One probe (~500 kb) is labelled in SpectrumOrange and flanks the 5′ side of the EWSR1 gene, extending through intron 4, and the second probe (~1,100 kb) is labelled in SpectrumGreen and flanks the 3′ side of the EWSR1 gene, with a 7-kb gap between the two probes. With this setting, the assay enables the detection of rearrangements with breakpoints spanning introns 7–10 of the EWSR1 gene. Hybridization was performed according to the manufacturer’s instructions and scoring of tissue sections was assessed as described elsewhere60, counting at least 100 nuclei per slide. Recurrently mutated genes identified by whole-genome sequencing were independently evaluated in a series of 62 PaNETs from the ARC-Net Research Centre, University of Verona. Four Ion Ampliseq Custom panels (Thermofisher) were designed to target the entire coding regions and flanking intron–exon junctions of the following genes: MEN1, DAXX, ATRX, PTEN and TSC2 (panel 1); DEPDC5, TSC1 and SETD2 (panel 2); ARID1A and MTOR (panel 3); CHEK2 and MUTYH (panel 4). Twenty nanograms of DNA were used per multiplex PCR amplification. The quality of the obtained libraries was evaluated by the Agilent 2100 Bioanalyzer on chip electrophoresis. Emulsion PCR was performed with the OneTouch system (Thermofisher). Sequencing was run on the Ion Torrent Personal Genome Machine (PGM, Thermofisher) loaded with 316 or 318 chips. Data analysis, including alignment to the hg19 human reference genome and variant calling, was done using Torrent Suite Software v4.0 (Thermofisher). Filtered variants were annotated using a custom pipeline based on the Variant Effector Predictor (VEP) software. Alignments were visually verified with the Integrative Genomics Viewer: IGV v2.3 (Broad Institute). There is no contiguous structure available for CHEK2, so we produced a model of isoform C using PDBid 3i6w61 as a template for predicting the structure of sequence O96017. Modelling was carried out within the YASARA suite of programs62 and consisted of an initial BLAST search for suitable templates followed by alignment, building of loops not present in selected template structure and energy minimization in explicit solvent. Modelling was carried out in the absence of a phosphopeptide ligand, which was added on completion by aligning the model with structure 1GXC and merging the ligand contained therein with the model structure. Similarly, MUTYH is represented by discontinuous structures and so this too was modelled using PDBids 3N5N and 4YPR as templates together with sequence NP_036354.1. Having constructed both models, amino acid substitutions were carried out to make the wild-type sequences conform to the variants described above. Each substitution was carried out independently and the resulting variant structures were subject to simulated annealing energy minimization using the AMBER force field. The resulting energy-minimized structures formed the basis of the predictions. CHEK2 site mutants were generated by site-directed mutagenesis of wild-type pCMV–FLAG CHEK2 (primer sequences in Supplementary Table 16). Proteins were expressed in HEK293T, a highly transfectable derivative of HEK293 cells that were retrieved from the cell culture bank at the QIMR Berghofer medical research institute. Cells were authenticated by STR profiling and were negative for mycoplasma. Transfected cells were lysed in NP-40 modified RIPA with protease and phosphatase inhibitors. Protein expression levels were analysed by western blotting with anti-FLAG antibodies and imaging HRP luminescent signal on a CCD camera (Fuji) and quantifying in MultiGauge software (Fuji). Kinase assays were performed using recombinant GST–CDC25C (amino acids 200–256) as substrate, essentially as described63. Kinase assay quantification was performed by scintillation counting of excised gel bands in OptiPhase scintillant (Perkin Elmer) using a Tri-Carb 2100TR beta counter (Packard). Counts for each reaction set were expressed as a fraction of the wild type. All experiments were performed at least three times. The date of diagnosis and the date and cause of death for each patient were obtained from the Central Cancer Registry and treating clinicians. Median survival was estimated using the Kaplan–Meier method and the difference was tested using the log-rank test. P values of less than 0.05 were considered statistically significant. The hazard ratio and its 95% confidence interval were estimated using Cox proportional hazard regression modelling. The correlation between DAXX or ATRX mutational status and other clinico-pathological variables was calculated using the χ2 test. Statistical analysis was performed using StatView 5.0 Software (Abacus Systems). Disease-specific survival was used as the primary endpoint. Genome sequencing data presented in this study have been submitted to the European Genome-Phenome Archive under accession number EGAS00001001732 (https://www.ebi.ac.uk/ega/search/site/EGAS00001001732).


News Article | March 23, 2016
Site: www.nature.com

Seawater was collected in 2-l diver-deployed Niskin bottles at approximately 10 m depth within 30 cm of the benthos on coral reefs across the Pacific and Atlantic Oceans47. Samples were fixed with 2% final concentration paraformaldehyde within four hours of collection. Pacific Ocean samples were filtered and stained with SYBR Gold (Life Technologies, USA), mounted on slides and analysed by epifluorescence microscopy47. Atlantic Ocean samples were flash frozen and stored in liquid nitrogen until analysis on a BD FACSCalibur flow cytometer48. Investigators were blinded when conducting all counts in this study (environmental or experimental), with sites or incubation samples imaged and analysed in a random order and identified only after analysis. Steady state solutions to the dynamic model of Weitz and Dushoff (2007)8 were calculated under varying carrying capacities (K). The chemostat model of Thingstad et al. (2014)9 was run for varying K, and the final point in the evolution of the system plotted. A standard lytic model49 that incorporates a logistic or trophic-state dependence for the microbe growth rate r is given by the equations δV/δt = (β • ϕ • N/K • N • V) − (m • V) where d and m are, respectively, the trophic-independent death rates for microbes and phage, N and V are, respectively, microbial host and viral abundances, β is the burst size, and ϕ is the adsorption coefficient. This corresponds to the Weitz–Dushoff model8 with their parameter a (the fractional reduction of lysis at carrying capacity term) set equal to 0. In this case the specific viral production rate per microbe is given by the product β • ϕ. In the new PtW model of viral–host interactions proposed here we replace this product with the quantity β • ϕ • N/K, suppressing viral production as the system moves away from K (that is, N/K becomes smaller) to simulate augmentation of lysogeny in eutrophic conditions. In this case β • ϕ has the interpretation as the maximum value for the specific viral production rate per microbe. Steady state solutions of host and viral densities in the PtW model generated herein were calculated across a range of K (Fig. 1b). All models are available as Matlab scripts from https://github.com/benjaminwilliamknowles/Piggyback-the-Winner. The relationships between published VLP and cell abundances from disparate environments were probed from 22 studies17, 28, 44, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68. When abundances were not available, we used the WebPlotDigitizer tool to recover data from graphs (http://arohatgi.info/WebPlotDigitizer/app/). Samples were grouped by habitat: animal-associated, polar lakes, coastal/estuarine, coral reefs, deep ocean, drinking water, open ocean, sediment, soil, soil water and temperate lake/river. We similarly extracted data from published studies and tested the relationship between cell abundance and the frequency of lysogenic cells as studied by mitomycin C induction in previous studies from the Adriatic Basin, Arctic Shelf, Mid Atlantic Ridge and Tampa Bay4, 28, 29, 30. Viral metagenomic samples were collected at 24 reefs (Extended Data Table 2), a subset of sites sampled for counts as previously described47. Pacific viral concentrates were treated with 250 μl of chloroform per 50 ml of concentrate to destroy microbes and purified using CsCl step gradient ultracentrifugation47. Viral DNA was extracted using the formamide/phenol/chloroform isoamyl alcohol technique47 and amplified using the Linker Amplified Sequencing Library approach69 and sequenced on an Illumina MySeq platform (Illumina, USA). Atlantic viral concentrates were passed through a 0.22 μm filter and 250 μl of chloroform per 50 ml of concentrate was added to remove microbes, followed by ultracentrifugation for further concentration. DNA from Atlantic sites was extracted by the phenol/chloroform/isoamyl alcohol technique, amplified using multiple displacement amplification20 and sequenced on an Ion Torrent sequencer (Life Sciences, USA). Microbial metagenomes were prepared by DNA extraction from the >0.22 μm fraction of the microbial community using Nucleospin Tissue Extraction kits (Macherey Nagel, Germany)47 and sequencing on an Illumina MySeq platform (Illumina, USA). Sequences less than 100 bp and with mean quality scores less than 25 were removed using PrinSeq70. Acceptable sequences were then dereplicated with TagCleaner71 and potential contaminants matching lambda or human DNA sequences removed with DeconSeq72. Focusing on microbial reads, microbial metagenomes were taxonomically annotated based on k-mer similarity using FOCUS73. Rank-abundance tables were then used to calculate microbial species-level Shannon (base e) taxonomic diversity. For the virome analysis, protein sequences of all integrase, excisionase, and competence gene sequences on the NCBI RefSeq database (http://www.ncbi.nlm.nih.gov/refseq/) were downloaded and made into BLAST databases (makeblastdb command; BLAST version 2.2.29+, ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/). The Virulence Factors of Pathogenic Bacteria Database (http://www.mgc.ac.cn/VFs/main.htm) was used as a protein database for virulence genes. The percentage of each sequence library composed of integrase, excisionase, competence, or virulence genes was computed as the number of sequences with >60 bp match at a 40% identity to database sequences identified using BLASTx, normalized by the total number of sequences in the virome. CRISPRs were identified in microbiomes using the CRISPR Recognition Tool (https://github.com/ajmazurie/CRT) and hits normalized to parts per million (p.p.m.) against total reads. The fraction of known prophage-like reads in the viromes, normalized by total sequences, was assessed by a stringent (e-value 10−10) BLAST against known prophages in cultured bacteria downloaded from NCBI (hosts (number of prophage): Escherichia coli (36), Shigella flexneri (31), Salmonella enterica (16), Staphylococcus aureus (14), Xylella fastidiosa (12), Yersinia pseudotuberculosis (11), Yersinia pestis (9), Shewanella baltica (8), Streptococcus pyogenes (7), Pseudomonas syringae (7), Salmonella typhimurium (6), Xanthomonas campestris (5), Mycobacterium tuberculosis (4), Yersinia enterocolitica (3), Streptococcus agalactiae (3), Stenotrophomonas maltophilia (3), Pseudomonas putida (3), Staphylococcus haemolyticus (2), Streptomyces avermitilis (1), Streptococcus uberis (1), Listeria monocytogenes (1), Caulobacter sp. (1)). For functional diversity analysis, reads of each virome were assembled using MIRA74 followed by ORF calling using FragGenScan75 and ORF clustering at 85% identity using CD-HIT76 to build protein cluster databases. We then performed BLASTx of reads against clusters databases to assess the number of reads assigned to each protein cluster. An OTU-like table was built using each cluster as a rank unit and read counts as abundance. Shannon (base e) indexes were calculated using the VEGAN package in R (http://cran.r-project.org/web/packages/vegan/index.html). Average viral genome size estimates were performed using GAAS77 and virome clustering was performed using crAss78. The following codes and parameters were used for each step of the viral functional diversity analysis: minimum overlap = 30 and minimum relative score = 90. FragGeneScan code: ./run_FragGeneScan.pl -genome=[seq_file_name] -out=[output_file_name] -complete=0 -train=illumina_10. CD-HIT code: cd-hit –i [input fastafilename.faa] -o [outputfilename]_85 -c 0.85 -n 5 CD-HIT output was used as database for BLASTx with virome reads, and output format 6 was parsed with the following python script to create rank-abundance tables: f="BlastOutput.txt" myfile=open(f) h={};temp="" for line in myfile: line=line.split() if temp!=line[0]: if line[1] not in h: h[line[1]]=0 h[line[1]]+=1 temp=line[0]myfile.close() Water samples were collected at Palmyra Atoll, a pristine coral reef in the central Pacific, and Mission Bay, a degraded embayment in San Diego, CA. Samples were twice filtered through 0.8 μm pre-combusted GF/F filters to remove protists. Palmyra water was subsampled in 100-ml aliquots and distributed in 12 Whirl-Pak bags (Cole-Parmer, IL, USA), divided into two experimental groups and one control, each one containing four randomly chosen replicate bags. For the two experimental groups we added a DOC cocktail containing 48 different labile carbon sources79 at the final concentration of 500 μM or 60 μM (+DOC treatment; Extended Data Fig. 3a), while no DOC was added to the control group (−DOC treatment; Extended Data Fig. 3a). Viral decay in microbe-free incubation bags was monitored as an additional control with 0.22 μm double-filtered water samples (Extended Data Fig. 3b). 1 ml samples were taken at times 0 h, 24 h, 48 h, 72 h, and 120 h from each bag for cell and viral counts. Mission Bay water was filtered and separated in three groups as above. 250 ml aliquots were distributed in each bag and incubated with 0 μM, 1 μM or 100 μM final concentrations by DOC addition. Samples were taken at times 12 h, 24 h, 48 h, and 72 h for counts. All incubations were performed in the dark at 25 °C. Samples were fixed and analysed by epifluorescence microscopy as described above. No statistical methods were used to predetermine sample size. Significance was determined using an alpha of 0.05 when direct counts data were compared, and using an alpha of 0.1 when analysing counts versus bioinformatic analyses to account for the disparate nature of these data sets (although 95% prediction intervals are also shown). The relationship between microbial density and microbial diversity, CRISPR sequences, and competence genes were tested for significant deviation from a slope of 0 by linear regression. The relationship between VLP and microbial densities in Figs 1a, 2 (all except the final panel showing VMR), and 3a, c were tested for slopes significantly different to 1 by t-tests that tested the null hypothesis that the slope is not equal to 1 against the two-sided alternative; the corresponding P value is given for this test. The fdrtool package in R was used to provide false discovery rate-corrected (FDR) P values for the multiple comparisons conducted in Fig. 2 (Extended Data Table 1). Conclusions were similar between FDR and uncorrected analyses. Experimental data in Fig. 3 was complemented by average counts taken from previous studies3, 31 using the WebPlotDigitizer tool. Data was taken from the nutrient added treatment of Hennes et al. (1995)31 as it was described as showing ‘lytic’ dynamics (Fig. 3c, d). Data from the ‘non-lytic’ 30%, 20%, 10%, and 3% dilutions by Wilcox and Fuhrman (1994)3 were used as they had encounter rates (the product of viral and host densities) ~1012 or less at the beginning of the incubations, described as the cutoff below which lytic dynamics were not sustained. A thin plate spline was applied to experimental and literature values for visualization and interpretation (Fig. 3b, d). While some data sets used to examine alternatives to PtW and published values in Figs 1c, d, 2, 3c, d, Extended Data Fig. 1a, and Extended Data Fig. 2, violated the assumptions of linear regression, this analysis was used for comparability. Robust regressions were used in Fig. 4 and Extended Data Fig. 4a analyses in order to accommodate high-leverage samples on parametric statistical models, allowing all samples to be retained in the analysis. Results are presented for robust regression estimation using Tukey’s biweight and corresponding bootstrapped 90th percentile and 95th percentile confidence intervals (90% and 95% CIs) for the slope using 1,000 bootstrap replications. It should be noted for Fig. 4a that even though 95% CI covers 0, the 90% CI does not cover 0 indicating that there is evidence at the 0.1 confidence level that the slope is positive. For subsequent analyses in Fig. 4, 95% CIs do not straddle 0, showing that there is evidence at the 0.05 confidence level that the slope is negative (Fig. 4c) or positive (Fig. 4b, d). To account for error in the y axis we also performed Model II regression analyses with data shown in Figs 1, 2 and 4 using the package lmodel2 in R (Extended Data Table 3). It should be noted, however, that these results should be treated with caution, as error variance and goodness of fit metrics are not obtainable for this analysis.


Les résultats de Mount Sinaï, de l'Université de Washington, et des laboratoires de Cold Spring Harbor seront présentés à AGBT ANN ARBOR, Michigan, 15 février 2017 /PRNewswire/ -- Swift Biosciences a annoncé aujourd'hui la mise sur le marché de son kit de préparation de bibliothèque Accel-NGS® XL, la solution de séquençage la plus rapide pour le séquençage du génome entier sur les plateformes Pacific Biosciences® (PacBio®). Ce kit de préparation de bibliothèque, spécialement optimisé pour la technologie de séquençage d'une simple molécule en temps réel (SMRT®) de PacBio, fournit des lectures de séquençage significativement plus longues avec un simple workflow à tube unique utilisant des introductions d'échantillons réduites. Swift Biosciences accepte dès maintenant des commandes pour le kit Accel-NGS XL —vendu exclusivement par Swift. « Grâce à son workflow convivial de quatre heures et ses plus longues lectures, le kit Accel-NGS XL  améliore considérablement les applications de séquençage de génome entier, telles que le séquençage d'haplotypes et d'assemblages de novo, sur n'importe quel génome y compris le génome microbien, végétal, animal, et humain », a déclaré Haley Fiske, directrice commerciale de Swift Biosciences. « Ces améliorations de la qualité et du workflow aident les utilisateurs de PacBio à obtenir des résultats plus significatifs dans chaque passage tout en doublant leur productivité. » Dans le cadre de l'assemblée générale AGBT 2017, Swift Biosciences et plusieurs collaborateurs scientifiques ont présenté deux affiches dévoilant les données de séquençage générées grâce à cette nouvelle chimie. La première affiche, intitulée « Une méthode améliorant la longueur de lecture du séquençage SMRT », a affiché les résultats, générés en collaboration avec Mount Sinaï et les laboratoires de Cold Spring Harbor, à partir de divers génomes y compris les génomes végétaux, bactériens et l'ADN de référence humain. Les données à l'appui ont produit des lectures moyennes allant jusqu'à 20Kb, avec une introduction d'échantillon réduite de 50%  et sans artefact de dimère adaptateur. Sur la deuxième affiche, intitulée « Méthodes de construction de bibliothèque améliorées pour la plateforme de séquençage Pacific Biosciences utilisant le kit de préparation de bibliothèque Accel-NGS XL  pour PacBio appliqué à des clones BAC problématiques pour l'amélioration de la référence du génome humain », Robert Fulton, directeur du développement et de gestion de projet au McDonnell Genome Institute de l'Université de Washington, a présenté les résultats de séquençage de clones BAC humains qui démontrent des rendements de bibliothèque accrus avec de plus longues lectures de séquençage. « Swift Biosciences est la première société à offrir des solutions de préparation de bibliothèque sur les trois principales plateformes de séquençage, Pacific Biosystems, Illumina® et Ion Torrent™ », a ajouté Timothy Harkins, Ph.D., président-directeur général de Swift Biosciences.  « Nous sommes stratégiquement axés sur l'expansion du marché NGS en simplifiant les workflows complexes avec nos technologies de bibliothèque innovantes et en apportant de nouvelles applications à chacune des plateformes de séquençage de prochaine génération (next generation sequencing, NGS). Nos bibliothèques fournissent les données de la plus haute qualité dans les applications les plus problématiques. Swift est la 'The NGS library company'. »


News Article | November 14, 2016
Site: en.prnasia.com

SEOUL, South Korea, Nov 14, 2016 /PRNewswire/ -- DNA Link, Inc. and Amplicon Express announced that they established an agreement today for a strategic partnership for the delivery of the best quality genomics solutions. This strategic partnership will further strengthen the cooperative relationship between two companies, which have developed over the past several years. DNA Link and Amplicon Express have agreed to cooperate in areas including nucleic acid extraction, library preparation, and PacBio sequencing. Under the terms Amplicon Express will extract HMW DNA samples of NGS-quality from crude materials such as bacteria, plants, animals and humans. These samples will be then forwarded to DNA Link to be made into a long-inserted library ideal for PacBio sequencing for genome or transcriptome analysis by the fleet of PacBio sequencers including Sequel and RSII units at DNA Link. Jongeun Lee, CEO and the founder of DNA Link said, "Amplicon Express is an expert in nucleic acid extraction and library preparation with a long proven track of success, and DNA Link is one of the best sequencing facility that has the most experience with PacBio sequencers. This partnership will bring a tremendous synergy, and the researchers will enjoy the best quality of sequence data using the third-generation sequencers." Robert Bogden, President and founder of Amplicon Express remarked, "DNA Link is one of the few PacBio service providers that can fully realize the added value of having very long starting DNA fragments. PacBio data from DNA Link maximizes the long reads from our HMW DNA preps in 20Kb or 30Kb libraries." DNA Link, Inc. is a genomics company who is a certified service provider for Pacific Bioscience, Illumina, Ion Torrent and Affymetrix. As one of the first companies that adopted Pacific Bioscience RS II system, DNA Link has become one of the world's leading expert in NGS and Bioinformatics, capable of providing an integrated genome analysis service using various platforms. Incorporated in 2000, DNA Link has accumulated profound experiences in various types of projects for sequencing and analysis of diverse organisms such as bacteria, fungi, plants, animals, and human. Headquartered in Seoul, Republic of Korea, it has a branch office and lab in San Diego, US. Amplicon Express Inc since 1996 has made 2,500+ custom BAC libraries, picked 55+ million BAC clones, and made thousands of quality HMW gDNA preps from practically every organism imaginable. The Amplicon Express distribution network includes: Japan, Singapore, Malaysia, Taiwan, Mainland China, India, and offices in the EU. Amplicon is privately held, based in Washington State. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/dna-link-and-amplicon-express-announce-joint-partnership-for-genomics-services-300361245.html


ANN ARBOR, Mich., Feb. 15, 2017 /PRNewswire/ -- Swift Biosciences today announced the commercial release of its Accel-NGS® XL Library Prep Kit, the fastest sequencing solution for whole genome sequencing on Pacific Biosciences® (PacBio®) platforms. This library preparation kit, specially optimized for PacBio's Single Molecule, Real-Time (SMRT®) sequencing technology, provides significantly longer sequencing reads with a simple, single-tube workflow utilizing lower sample inputs. Swift Biosciences is now accepting orders for the Accel-NGS XL kit -- sold exclusively by Swift. "With its easy four-hour workflow and longer read lengths, the Accel-NGS XL kit substantially improves whole genome sequencing applications, such as de novo assembly and haplotype sequencing, on any genome including microbial, plant, animal, and human," said Haley Fiske, Chief Commercial Officer of Swift Biosciences. "These quality and workflow improvements help PacBio users generate more meaningful results from every run with twice the productivity." Swift Biosciences and several scientific collaborators presented two posters at the AGBT 2017 General Meeting showcasing sequencing data generated with this new chemistry. The first poster, entitled "A Method to Improve Read Length of SMRT Sequencing," displayed results, generated in collaboration with Mount Sinai and Cold Spring Harbor Laboratories, from diverse genomes including plant, bacterial and human reference DNA. The supporting data produced average reads up to 20Kb, with 50% less sample input and no adapter dimer artifacts. In the second poster, entitled "Improved Library Construction Methods for the Pacific Biosciences Sequencing Platform Using Swift Accel-NGS XL Library Prep Kit for PacBio Applied to Challenging BAC Clones for Human Genome Reference Improvement," Robert Fulton, Director of Project Development and Management at McDonnell Genome Institute of Washington University, presented results from human BAC clone sequencing, demonstrating higher library yields with longer sequencing reads. "Swift Biosciences is the first company to offer library preparation solutions on all three major sequencing platforms, including Pacific Biosystems, Illumina®, and Ion Torrent™," stated Timothy Harkins, Ph.D., President and CEO of Swift Biosciences.  "We are strategically focused on expanding the NGS market by simplifying complex workflows through our innovative library technologies and bringing new applications to each of the NGS platforms. Our libraries provide the highest quality data in the most challenging of applications. Swift is 'The NGS library company.'"


This report studies Genome Sequencing Equipment in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering  Illumina  Thermo Fisher Scientific  BGI  Roche  Qiagen  Pacific Biosciences  Sequenom  DAAN Gene  Agilent Technologies  Berry Genomics  Hunan China Sun Pharmaceutical Machinery  Jilin Zixin Pharmaceutical Industrial Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Genome Sequencing Equipment in these regions, from 2011 to 2021 (forecast), like  North America  Europe  China  Japan  Southeast Asia  India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into  Pacific Bio  Ion Torrent sequencing  Illumina  SOLiD sequencing Split by application, this report focuses on consumption, market share and growth rate of Genome Sequencing Equipment in each application, can be divided into  Medicine  Biology  Geology  Agriculture  Others 1 Genome Sequencing Equipment Market Overview  1.1 Product Overview and Scope of Genome Sequencing Equipment  1.2 Genome Sequencing Equipment Segment by Type  1.2.1 Global Production Market Share of Genome Sequencing Equipment by Type in 2015  1.2.2 Pacific Bio  1.2.3 Ion Torrent sequencing  1.2.4 Illumina  1.2.5 SOLiD sequencing  1.3 Genome Sequencing Equipment Segment by Application  1.3.1 Genome Sequencing Equipment Consumption Market Share by Application in 2015  1.3.2 Medicine  1.3.3 Biology  1.3.4 Geology  1.3.5 Agriculture  1.3.6 Others  1.4 Genome Sequencing Equipment Market by Region  1.4.1 North America Status and Prospect (2011-2021)  1.4.2 Europe Status and Prospect (2011-2021)  1.4.3 China Status and Prospect (2011-2021)  1.4.4 Japan Status and Prospect (2011-2021)  1.4.5 Southeast Asia Status and Prospect (2011-2021)  1.4.6 India Status and Prospect (2011-2021)  1.5 Global Market Size (Value) of Genome Sequencing Equipment (2011-2021) 2 Global Genome Sequencing Equipment Market Competition by Manufacturers  2.1 Global Genome Sequencing Equipment Production and Share by Manufacturers (2015 and 2016)  2.2 Global Genome Sequencing Equipment Revenue and Share by Manufacturers (2015 and 2016)  2.3 Global Genome Sequencing Equipment Average Price by Manufacturers (2015 and 2016)  2.4 Manufacturers Genome Sequencing Equipment Manufacturing Base Distribution, Sales Area and Product Type  2.5 Genome Sequencing Equipment Market Competitive Situation and Trends  2.5.1 Genome Sequencing Equipment Market Concentration Rate  2.5.2 Genome Sequencing Equipment Market Share of Top 3 and Top 5 Manufacturers  2.5.3 Mergers & Acquisitions, Expansion 3 Global Genome Sequencing Equipment Production, Revenue (Value) by Region (2011-2016)  3.1 Global Genome Sequencing Equipment Production by Region (2011-2016)  3.2 Global Genome Sequencing Equipment Production Market Share by Region (2011-2016)  3.3 Global Genome Sequencing Equipment Revenue (Value) and Market Share by Region (2011-2016)  3.4 Global Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016)  3.5 North America Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016)  3.6 Europe Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016)  3.7 China Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016)  3.8 Japan Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016)  3.9 Southeast Asia Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016)  3.10 India Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2011-2016) 4 Global Genome Sequencing Equipment Supply (Production), Consumption, Export, Import by Regions (2011-2016)  4.1 Global Genome Sequencing Equipment Consumption by Regions (2011-2016)  4.2 North America Genome Sequencing Equipment Production, Consumption, Export, Import by Regions (2011-2016)  4.3 Europe Genome Sequencing Equipment Production, Consumption, Export, Import by Regions (2011-2016)  4.4 China Genome Sequencing Equipment Production, Consumption, Export, Import by Regions (2011-2016)  4.5 Japan Genome Sequencing Equipment Production, Consumption, Export, Import by Regions (2011-2016)  4.6 Southeast Asia Genome Sequencing Equipment Production, Consumption, Export, Import by Regions (2011-2016)  4.7 India Genome Sequencing Equipment Production, Consumption, Export, Import by Regions (2011-2016) 7 Global Genome Sequencing Equipment Manufacturers Profiles/Analysis  7.1 Illumina  7.1.1 Company Basic Information, Manufacturing Base and Its Competitors  7.1.2 Genome Sequencing Equipment Product Type, Application and Specification  7.1.2.1 Type I  7.1.2.2 Type II  7.1.3 Illumina Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016)  7.1.4 Main Business/Business Overview  7.2 Thermo Fisher Scientific  7.2.1 Company Basic Information, Manufacturing Base and Its Competitors  7.2.2 Genome Sequencing Equipment Product Type, Application and Specification  7.2.2.1 Type I  7.2.2.2 Type II  7.2.3 Thermo Fisher Scientific Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016)  7.2.4 Main Business/Business Overview  7.3 BGI  7.3.1 Company Basic Information, Manufacturing Base and Its Competitors  7.3.2 Genome Sequencing Equipment Product Type, Application and Specification  7.3.2.1 Type I  7.3.2.2 Type II  7.3.3 BGI Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016)  7.3.4 Main Business/Business Overview  7.4 Roche  7.4.1 Company Basic Information, Manufacturing Base and Its Competitors  7.4.2 Genome Sequencing Equipment Product Type, Application and Specification  7.4.2.1 Type I  7.4.2.2 Type II  7.4.3 Roche Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016)  7.4.4 Main Business/Business Overview  7.5 Qiagen  7.5.1 Company Basic Information, Manufacturing Base and Its Competitors  7.5.2 Genome Sequencing Equipment Product Type, Application and Specification  7.5.2.1 Type I  7.5.2.2 Type II


This report studies Genome Sequencing Equipment in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering Illumina Thermo Fisher Scientific BGI Roche Qiagen Pacific Biosciences Sequenom DAAN Gene Agilent Technologies Berry Genomics Hunan China Sun Pharmaceutical Machinery Jilin Zixin Pharmaceutical Industrial View Full Report With Complete TOC, List Of Figure and Table: http://globalqyresearch.com/global-genome-sequencing-equipment-market-research-report-2016 Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Genome Sequencing Equipment in these regions, from 2011 to 2021 (forecast), like North America Europe China Japan Southeast Asia India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into Pacific Bio Ion Torrent sequencing Illumina SOLiD sequencing Split by application, this report focuses on consumption, market share and growth rate of Genome Sequencing Equipment in each application, can be divided into Medicine Biology Geology Agriculture Others Global Genome Sequencing Equipment Market Research Report 2016 1 Genome Sequencing Equipment Market Overview 1.1 Product Overview and Scope of Genome Sequencing Equipment 1.2 Genome Sequencing Equipment Segment by Type 1.2.1 Global Production Market Share of Genome Sequencing Equipment by Type in 2015 1.2.2 Pacific Bio 1.2.3 Ion Torrent sequencing 1.2.4 Illumina 1.2.5 SOLiD sequencing 1.3 Genome Sequencing Equipment Segment by Application 1.3.1 Genome Sequencing Equipment Consumption Market Share by Application in 2015 1.3.2 Medicine 1.3.3 Biology 1.3.4 Geology 1.3.5 Agriculture 1.3.6 Others 1.4 Genome Sequencing Equipment Market by Region 1.4.1 North America Status and Prospect (2011-2021) 1.4.2 Europe Status and Prospect (2011-2021) 1.4.3 China Status and Prospect (2011-2021) 1.4.4 Japan Status and Prospect (2011-2021) 1.4.5 Southeast Asia Status and Prospect (2011-2021) 1.4.6 India Status and Prospect (2011-2021) 1.5 Global Market Size (Value) of Genome Sequencing Equipment (2011-2021) 7 Global Genome Sequencing Equipment Manufacturers Profiles/Analysis 7.1 Illumina 7.1.1 Company Basic Information, Manufacturing Base and Its Competitors 7.1.2 Genome Sequencing Equipment Product Type, Application and Specification 7.1.2.1 Type I 7.1.2.2 Type II 7.1.3 Illumina Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.1.4 Main Business/Business Overview 7.2 Thermo Fisher Scientific 7.2.1 Company Basic Information, Manufacturing Base and Its Competitors 7.2.2 Genome Sequencing Equipment Product Type, Application and Specification 7.2.2.1 Type I 7.2.2.2 Type II 7.2.3 Thermo Fisher Scientific Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.2.4 Main Business/Business Overview 7.3 BGI 7.3.1 Company Basic Information, Manufacturing Base and Its Competitors 7.3.2 Genome Sequencing Equipment Product Type, Application and Specification 7.3.2.1 Type I 7.3.2.2 Type II 7.3.3 BGI Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.3.4 Main Business/Business Overview 7.4 Roche 7.4.1 Company Basic Information, Manufacturing Base and Its Competitors 7.4.2 Genome Sequencing Equipment Product Type, Application and Specification 7.4.2.1 Type I 7.4.2.2 Type II 7.4.3 Roche Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.4.4 Main Business/Business Overview 7.5 Qiagen 7.5.1 Company Basic Information, Manufacturing Base and Its Competitors 7.5.2 Genome Sequencing Equipment Product Type, Application and Specification 7.5.2.1 Type I 7.5.2.2 Type II 7.5.3 Qiagen Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.5.4 Main Business/Business Overview 7.6 Pacific Biosciences 7.6.1 Company Basic Information, Manufacturing Base and Its Competitors 7.6.2 Genome Sequencing Equipment Product Type, Application and Specification 7.6.2.1 Type I 7.6.2.2 Type II 7.6.3 Pacific Biosciences Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.6.4 Main Business/Business Overview 7.7 Sequenom 7.7.1 Company Basic Information, Manufacturing Base and Its Competitors 7.7.2 Genome Sequencing Equipment Product Type, Application and Specification 7.7.2.1 Type I 7.7.2.2 Type II 7.7.3 Sequenom Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.7.4 Main Business/Business Overview 7.8 DAAN Gene 7.8.1 Company Basic Information, Manufacturing Base and Its Competitors 7.8.2 Genome Sequencing Equipment Product Type, Application and Specification 7.8.2.1 Type I 7.8.2.2 Type II 7.8.3 DAAN Gene Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.8.4 Main Business/Business Overview 7.9 Agilent Technologies 7.9.1 Company Basic Information, Manufacturing Base and Its Competitors 7.9.2 Genome Sequencing Equipment Product Type, Application and Specification 7.9.2.1 Type I 7.9.2.2 Type II 7.9.3 Agilent Technologies Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.9.4 Main Business/Business Overview 7.10 Berry Genomics 7.10.1 Company Basic Information, Manufacturing Base and Its Competitors 7.10.2 Genome Sequencing Equipment Product Type, Application and Specification 7.10.2.1 Type I 7.10.2.2 Type II 7.10.3 Berry Genomics Genome Sequencing Equipment Production, Revenue, Price and Gross Margin (2015 and 2016) 7.10.4 Main Business/Business Overview 7.11 Hunan China Sun Pharmaceutical Machinery 7.12 Jilin Zixin Pharmaceutical Industrial Global QYResearch ( http://globalqyresearch.com/ ) is the one spot destination for all your research 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CARLSBAD, Calif.--(BUSINESS WIRE)--Thermo Fisher Scientific today announced four new additions to its portfolio of multi-biomarker targeted assays for cancer research, including the Oncomine Immune Response Research Assay, which is designed to interrogate the tumor microenvironment and enable identification of predictive biomarkers for immunotherapy clinical research trials. The very low sample input gene expression assay for Ion Torrent next-generation sequencing (NGS) platforms targets low-ex


News Article | February 22, 2017
Site: www.eurekalert.org

DNA is the hereditary material in our cells and contains the instructions for them to live, behave, grow, and develop. These instructions are based on the order of the DNA bases, called nucleotides. To unlock the instructions, carried by a DNA molecule, we need to read these nucleotide sequences (by performing DNA sequencing). There are various methods for sequencing DNA, including Sanger sequencing, Illumina, 454, Ion Torrent sequencing, SMRT sequencing (Pacific Biosciences), and Nanopore sequencing. Nanopore sequencing is a modern and promising technique, in which many researchers are interested. This method benefits from the potential advantages of label-free sequencing as well as the long reads, both of which help in easing the sequencing requirements. In this method, the DNA zips through a tiny pore (nanopore) in a membrane. Each nucleotide which passes through the nanopore results in a unique characteristic change, uncovering the sequence of the biomolecule. Analyzing the DNA, directly taken from the cell, as opposed to synthesized molecules, is another advantage of this method, enhancing the sequencing accuracy. Nanopore sequencing methods are based on two types of nanopores: (1) solid-state nanopores, and (2) protein-based nanopores. In a review published in the journal, Recent Patents on Nanotechnology, by Roozbeh Abedini-Nassab, recent advances presented in various articles and patents in the field of solid state nanopore sequencing, including sequencing methods, membrane materials and their fabrication techniques, drilling methods, and biomolecule translocation speed control ideas are investigated. This review shows how nanotechnology is helping in revealing crucial biological information, which can be used later in solving problems in biological research. For more information about the article, please visit http://www.

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