The insufficient standardization of diagnostic next-generation sequencing (NGS) still limits its implementation in clinical practice, with the correct detection of mutations at low variant allele frequencies (VAF) facing particular challenges. We address here the standardization of sequencing coverage depth in order to minimize the probability of false positive and false negative results, the latter being underestimated in clinical NGS. There is currently no consensus on the minimum coverage depth, and so each laboratory has to set its own parameters. To assist laboratories with the determination of the minimum coverage parameters, we provide here a user-friendly coverage calculator. Using the sequencing error only, we recommend a minimum depth of coverage of 1,650 together with a threshold of at least 30 mutated reads for a targeted NGS mutation analysis of ≥3% VAF, based on the binomial probability distribution. Moreover, our calculator also allows adding assay-specific errors occurring during DNA processing and library preparation, thus calculating with an overall error of a specific NGS assay. The estimation of correct coverage depth is recommended as a starting point when assessing thresholds of NGS assay. Our study also points to the need for guidance regarding the minimum technical requirements, which based on our experience should include the limit of detection (LOD), overall NGS assay error, input, source and quality of DNA, coverage depth, number of variant supporting reads, and total number of target reads covering variant region. Further studies are needed to define the minimum technical requirements and its reporting in diagnostic NGS.

Extramedullary disease (EMM) represents a rare, aggressive and mostly resistant phenotype of multiple myeloma (MM). EMM is frequently associated with high-risk cytogenetics, but their complex genomic architecture is largely unexplored. We used whole-genome optical mapping (Saphyr, Bionano Genomics) to analyse the genomic architecture of CD138+ cells isolated from bone-marrow aspirates from an unselected cohort of newly diagnosed patients with EMM (n = 4) and intramedullary MM (n = 7). Large intrachromosomal rearrangements (> 5 Mbp) within chromosome 1 were detected in all EMM samples. These rearrangements, predominantly deletions with/without inversions, encompassed hundreds of genes and led to changes in the gene copy number on large regions of chromosome 1. Compared with intramedullary MM, EMM was characterised by more deletions (size range of 500 bp–50 kbp) and fewer interchromosomal translocations, and two EMM samples had copy number loss in the 17p13 region. Widespread genomic heterogeneity and novel aberrations in the high-risk IGH/IGK/IGL, 8q24 and 13q14 regions were detected in individual patients but were not specific to EMM/MM. Our pilot study revealed an association of chromosome 1 abnormalities in bone marrow myeloma cells with extramedullary progression. Optical mapping showed the potential for refining the complex genomic architecture in MM and its phenotypes.

A global uncertainty analysis is performed for three current mechanisms describing the low temperature oxidation of dimethyl ether (Aramco Mech 1.3, Zheng et al. 2005, Liu et al. 2013) with application to simulations of species concentrations (CH 2 , H 2 O 2 , CH 3 OCHO) corresponding to existing data from an atmospheric pressure flow reactor, and high pressure ignition delays. When incorporating uncertainties in reaction rates within a global sampling approach, the distributions of predicted targets can span several orders of magnitude. The experimental profiles however, fall within the predictive uncertainty limits. A variance based sensitivity analysis is then undertaken using high dimensional model representations. The main contributions to predictive uncertainties come from the CH 3 OCH 2 +O 2 system, with isomerisation, propagation, chain-branching, secondary OH formation and peroxy-peroxy reactions all playing a role. The response surface describing the relationship between sampled reaction rates and predicted outputs is complex in all cases. Higher-order interactions between parameters contribute significantly to output variance, and no single reaction channel dominates for any of the conditions studied. Sensitivity scatter plots illustrate that many different parameter combinations could lead to good agreement with specific sets of experimental data. The Aramco scheme is then updated based on data from a recent study by Eskola et al. which presents quite different temperature and pressure dependencies for the rates of CH 3 OCH 2 O 2 CH 2 OCH 2 O 2 H and CH 2 OCH 2 O 2 H OH+2CH 2 O compared with currently used values, and includes well skipping channels. The updates from Eskola worsen the agreement with experiments when used in isolation. However, if the rate of the CH 2 OCH 2 O 2 H+O 2 channel is subsequently reduced, very good agreement can be achieved. Due to the complex nature of the response surface, the tuning of this channel remains speculative. Further detailed studies of the temperature and pressure dependence of the CH 3 OCH 2 O 2 +O 2 , CH 2 OCH 2 O 2 H+O 2 system are recommended in order to reduce uncertainties within current DME mechanisms for low temperature conditions.

No abstract

There are two classes of algorithms used in data compression. The first class deals directly with compression itself and it represents algorithms such as Huffman coding, Lempel-Ziv family algorithms, PPM and others. In the second class, there are algorithms trying to transform data into one, that are more easily compressed by the first class, for example Burrows-Wheeler transform or MoveToFront Coding. We prepared a second class method that transforms input data into data with lower entropy.Consider an input message, in which we know counts of all pairs of following symbols(bigrams). Suppose two bigrams αβ and αγ, the first bigram αβ doesn't occur in the message and a bigram αγ that occurs in the message. Since there is no occurence of αβ in the former message, we can replace each occurence of αγ for αβ and we note such a replacement. We know, that each time we encounter bigram αβ in the transformed message, it had to be created from αγ, so we are able to reconstruct the former message. Such a replacement we call a context transformation[1].If we add one more assumption to the above model, that probabilities of occurence of symbols β, γ follow p β > p γ , then the context transformation leads to increase of occurence of symbol β and decrease of occurence of symbol γ. If we chose context transformations in a convenient way, then we are able to decrease entropy of the former message and in consequence it leads to better compression attainable by entropy coder, such as Huffman coding.We tested the method on files from Calgary corpus and based on the file type, we were able to decrease entropy in the range 2-13%. There were file types, such as binary or image files, in which the method didn't work. Generally, we can state that using context transformations, we were able to manipulate with symbol's probability distribution to reach a better one that allows entropy coding algorithms to carry out more efficiently. References[1] M. Vasinek, "Kontextove mapy a jejich aplikace," Master's thesis, Vysoka skola banska -Technicka univerzita Ostrava, 2013. [Online]. Available: http: //hdl.handle.net/10084/98662