Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence

<p>Abstract</p> <p>Background</p> <p>In real-time PCR, it is necessary to consider the efficiency of amplification (EA) of amplicons in order to determine initial target levels properly. EAs can be deduced from standard curves, but these involve extra effort and cost and may yield invalid EAs. Alternatively, EA can be extracted from individual fluorescence curves. Unfortunately, this is not reliable enough.</p> <p>Results</p> <p>Here we introduce simultaneous non-linear fitting to determine – without standard curves – an optimal common EA for all samples of a group. In order to adjust EA as a function of target fluorescence, and still to describe fluorescence as a function of cycle number, we use an iterative algorithm that increases fluorescence cycle by cycle and thus simulates the PCR process. A Gauss peak function is used to model the decrease of EA with increasing amplicon accumulation. Our approach was validated experimentally with hydrolysis probe or SYBR green detection with dilution series of 5 different targets. It performed distinctly better in terms of accuracy than standard curve, DART-PCR, and LinRegPCR approaches. Based on reliable EAs, it was possible to detect that for some amplicons, extraordinary fluorescence (EA > 2.00) was generated with locked nucleic acid hydrolysis probes, but not with SYBR green.</p> <p>Conclusion</p> <p>In comparison to previously reported approaches that are based on the separate analysis of each curve and on modelling EA as a function of cycle number, our approach yields more accurate and precise estimates of relative initial target levels.</p

Sleep deficits but no metabolic deficits in premanifest Huntington's disease.

OBJECTIVE: Huntington disease (HD) is a fatal autosomal dominant, neurodegenerative condition characterized by progressively worsening motor and nonmotor problems including cognitive and neuropsychiatric disturbances, along with sleep abnormalities and weight loss. However, it is not known whether sleep disturbances and metabolic abnormalities underlying the weight loss are present at a premanifest stage. METHODS: We performed a comprehensive sleep and metabolic study in 38 premanifest gene carrier individuals and 36 age- and sex-matched controls. The study consisted of 2 weeks of actigraphy at home, 2 nights of polysomnography and multiple sleep latency tests in the laboratory, and body composition assessment using dual energy x-ray absorptiometry scanning with energy expenditure measured over 10 days at home by doubly labeled water and for 36 hours in the laboratory by indirect calorimetry along with detailed cognitive and clinical assessments. We performed a principal component analyses across all measures within each studied domain. RESULTS: Compared to controls, premanifest gene carriers had more disrupted sleep, which was best characterized by a fragmented sleep profile. These abnormalities, as well as a theta power (4-7Hz) decrease in rapid eye movement sleep, were associated with disease burden score. Objectively measured sleep problems coincided with the development of cognitive, affective, and subtle motor deficits and were not associated with any metabolic alterations. INTERPRETATION: The results show that among the earliest abnormalities in premanifest HD is sleep disturbances. This raises questions as to where the pathology in HD begins and also whether it could drive some of the early features and even possibly the pathology.The study was funded from a grant from CHDI Foundation, Inc.CHDI-RG50786. RAB received grants from NIHR BRC-RG64473. PS is funded by an MRC Programme grant (Physiological Modelling and Metabolic Risk: MC_UP_A090_1005).This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/ana.2449

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Characteristics of thermal-mineral waters in Backa region (Vojvodina) and their exploitation in spa tourism

Hydropower, biomass, biogas, biofuels, wind power, solar energy and geothermal energy are the major resources to provide Backa region with most of its. Backa extends between 45°16' and 46°22' of the northern latitude and 18°36' and 20°37' of the eastern longitude. It occupies the north-eastern part of Vojvodina, i.e. the most north-western part of the Republic of Serbia. That is historical-geographic territory bordered on the Danube on its western and eastern side, the Tisa on its eastern side and with the state border towards Hungary on the north. In this paper, the focus will be on renewable sources, specifically geothermal energy in Backa region. The paper analyzes the characteristics of thermal-mineral waters in Backa, the condition and possibilities of their exploitation in spa tourism, and in other economic branches. The tradition of thermo-mineral waters exploitation in spas and public baths is rather long. Today, this type of thermo-mineral waters exploitation in Backa is the widest spread. Permanent, i.e. continuous exploiters of thermal-mineral waters in Backa are primarily balneal-rehabilitation centres and exploiters using the water for technological purposes.Renewable energy Thermo-mineral waters Exploitation Spa tourism Backa region Vojvodina

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-1

Rbitrarily chosen; numbering refers to the Excel raw data file [see Additional file ]) was analyzed. After subtraction of linear background, EA was calculated as F/Fratio and plotted against fluorescence (F) as shown. To avoid confusion, points with F< 0.1 are not displayed; most of these, because of large errors in EA, lie outside the y axis range. The line graphs were drawn with the Gauss peak function (upper row) or the logistic peak function (lower row). Note that function parameters were not fitted to the points shown, but determined by our stepwise PCR simulation approach based on the raw fluorescence versus cycle number data. Open circles represent data that was not used for fitting.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-2

SYBR green detection. The diagram shows forward primer, LNA hydrolysis probe with 5' fluorophore (filled circle) and 3' quencher (open circle), and the entire amplicon antisense strand, with reverse primer sequence underlined. A 3' phosphate (P) prevents elongation of the probe.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-4

Rbitrarily chosen; numbering refers to the Excel raw data file [see Additional file ]) was analyzed. After subtraction of linear background, EA was calculated as F/Fratio and plotted against fluorescence (F) as shown. To avoid confusion, points with F< 0.1 are not displayed; most of these, because of large errors in EA, lie outside the y axis range. The line graphs were drawn with the Gauss peak function (upper row) or the logistic peak function (lower row). Note that function parameters were not fitted to the points shown, but determined by our stepwise PCR simulation approach based on the raw fluorescence versus cycle number data. Open circles represent data that was not used for fitting.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p