Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage

The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primer

Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage

The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers

Model bionomii brosznicy jesionówki (Macrophya punctumalbum L.) (Hymenoptera, Tenthredinidae)

The aim of the paper is to construct a model which describes the life expectancy of privet sawfly females (Macrophya punctumalbum L.), including additional information on the number of eggs. The data on life expectancy of females and their fertility were obtained in the course of research on the bionomy of the privet sawfly. A variety of discrete distributions to modelling the lengths of life was provided, namely: the Poisson Distribution, the Negative Binomial Distribution and the Poisson-inverse Gaussian Distribution. The analysis the above distributions were applied along with the GAMLSS (Generalized Additive Models for Location, Scale and Shape) and the resulting models were compared with the use of the Global Deviance criterion, the Akaike information criterion and the Schwarz Bayesian criterion. For the best model the expected value and the standard deviation were defined. The profile deviance plot of this parameters, analysis of the residuals, kernel density and Q-Q plot are presented, too. All analyses were performed in R with the GAMLSS package.Celem prezentowanej pracy jest zaproponowanie modelu statystycznego opisującego długość życia samic brosznicy jesionówki (Macrophya punctumalbum L.). Dane na temat długości życia samic i ich płodności zostały otrzymane w trakcie badania nad bionomią. Do modelowania długości życia z wykorzystaniem informacji o ilości składanych jaj zastosowano uogólniony addytywny model dla lokalizacji, skali i kształtu (GAMLSS) dla trzech rozkładów dyskretnych: rozkład Poissona, rozkład odwrotny Poissona oraz rozkład ujemny binomialny. Otrzymane modele porównano, stosując kryterium ogólnego odchylenia, kryterium Akaike i kryterium bayesowskie Schwarza. Uzyskano w ten sposób informacje, że najlepiej opisującym modelem badany problem jest model z rozkładem odwrotnym Poissona. Ostatnim etapem badań była estymacja wartości oczekiwanej i błędu standardowego dla najlepszego modelu oraz analiza tych wartości za pomocą wykresu dopasowania reszt dla wartości oczekiwanej, wykresu indeksów reszt, wykresu funkcji gęstości oraz wykresu typu Q-Q. Wszystkie analizy zostały wykonane za pomocą platformy obliczeniowej R z wykorzystaniem pakietu GAMLSS

Krzywe wzrostu w modelowaniu procesu suszenia kukurydzy w cienkiej warstwie

Modeling the thin-layer drying process for corn is described using 37 growth curve functions. The most effective functions were qualified by the application of Akaike Information Criterion and Bayesian Information Criterion. Both criteria showed that the thin-layer drying process for corn was best described by the baroreflex five-parameter function.Modelowanie procesu suszenia kukurydzy w cienkiej warstwie zostało opisane przy użyciu 37 krzywych wzrostu. Najlepiej dopasowane krzywe zostały wyłonione w oparciu o Informacyjne Kryterium Akaike oraz Bayesowkie Kryterium Informacyjne Schwarza. Oba współczynniki pokazały, iż proces suszenia kukurydzy w cienkiej warstwie najlepiej odwzorowuje pięcioparametrowa krzywa baroreflex