Davide Albanese

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R20.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

minerva and minepy

UNLABELLED We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large ( = 1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. AVAILABILITY AND IMPLEMENTATION Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX).

Integrating gene expression profiling and clinical data

We propose a combination of machine learning techniques to integrate predictive profiling from gene expression with clinical and epidemiological data. Starting from BioDCV, a complete software setup for predictive classification and feature ranking without selection bias, we apply semisupervised profiling for detecting outliers and deriving informative subtypes of patients. During the profiling process, sampletracking curves are extracted, and then clustered according to a distance derived from dynamic time warping. Sampletracking allows also the identification of outlier cases, whose removal is shown to improve predictive accuracy and stability of derived gene profiles. Here we propose to employ clinical features to validate the semisupervising procedure. The procedure is demonstrated in the analysis of a liver cancer dataset of 213 samples described by 1993 genes and by pathological features.

Diversity and Cyclical Seasonal Transitions in the Bacterial Community in a Large and Deep Perialpine Lake

High-throughput sequencing (HTS) was used to analyze the seasonal variations in the bacterioplankton community composition (BCC) in the euphotic layer of a large and deep lake south of the Alps (Lake Garda). The BCC was analyzed throughout two annual cycles by monthly samplings using the amplification and sequencing of the V3–V4 hypervariable region of the 16S rRNA gene by the MiSeq Illumina platform. The dominant and most diverse bacterioplankton phyla were among the more frequently reported in freshwater ecosystems, including the Proteobacteria, Cyanobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, and Planctomycetes. As a distinctive feature, the development of the BCC showed a cyclical temporal pattern in the two analyzed years and throughout the euphotic layer. The recurring temporal development was controlled by the strong seasonality in water temperature and thermal stratification, and by cyclical temporal changes in nutrients and, possibly, by the remarkable annual cyclical development of cyanobacteria and eukaryotic phytoplankton hosting bacterioplankton that characterizes Lake Garda. Further downstream analyses of operational taxonomic units associated to cyanobacteria allowed confirming the presence of the most abundant taxa previously identified by microscopy and/or phylogenetic analyses, as well as the presence of other small Synechococcales/Chroococcales and rare Nostocales never identified so far in the deep lakes south of the Alps. The implications of the high diversity and strong seasonality are relevant, opening perspectives for the definition of common and discriminating patterns characterizing the temporal and spatial distribution in the BCC, and for the application of the new sequencing technologies in the monitoring of water quality in large and deep lakes.

A practical tool for maximal information coefficient analysis

Abstract Background The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual information-based measures of association are particularly promising, in particular after the recent introduction of the TICe and MICe estimators, which combine computational efficiency with superior bias/variance properties. An open-source software implementation of these two measures providing a complete procedure to test their significance would be extremely useful. Findings Here, we present MICtools, a comprehensive and effective pipeline that combines TICe and MICe into a multistep procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. Conclusions We show that MICtools, combining TICe and MICe, is able to highlight associations that would not be captured by conventional strategies.

TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data

Many are the sources of variability that can affect reproducibility of disease biomarkers from time-of-flight (TOF) Mass Spectrometry (MS) data. Here we present TOFwave, a complete software pipeline for TOF-MS biomarker identification, that limits the impact of parameter tuning along the whole chain of preprocessing and model selection modules. Peak profiles are obtained by a preprocessing based on Continuous Wavelet Transform (CWT), coupled with a machine learning protocol aimed at avoiding selection bias effects. Only two parameters (minimum peak width and a signal to noise cutoff) have to be explicitly set. The TOFwave pipeline is built on top of the mlpy Python package. Examples on Matrix-Assisted Laser Desorption and Ionization (MALDI) TOF datasets are presented. Software prototype, datasets and details to replicate results in this paper can be found at http://mlpy.sf.net/tofwave/.

Reduced diversity of gut microbiota in two Aedes mosquitoes species in areas of recent invasion

Aedes mosquitoes are considered highly successful global invasive species and vectors of several pathogens of relevance for public health. Their midgut’s microbiota can play an important role in affecting not only their vectorial competence but also their fitness, physiology, food digestion, metabolism, immunity and adaptation to new environmental conditions. Using high-throughput sequencing we compared the microbiota of Aedes albopictus collected in Italy with those reported in populations from France and Vietnam. We also analysed Aedes koreicus gut microbiota for the first time. We found remarkable individual difference along with common bacterial taxa in both species. Ae. albopictus collected in Italy had a lower richness and a different composition of microbiota in respect to specimens collected in France and Vietnam. It also showed a core microbiota formed mainly of bacteria of the genus Pseudomonas. Overall, the two Aedes species (Ae. albopictus and Ae. koreicus) collected in Italy, showed a large core microbiota with 75.98% of the identified Operational Taxonomic Units. Furthermore, Ae. albopictus had 2.5% prevalence of Wolbachia and 0.07% of Asaia spp, while Ae. koreicus had 14.42% of Asaia spp. and no Wolbachia. This study provides new informations on the spatial variation of the midgut bacterial communities in mosquitoes of medical relevance within areas of recent invasion and provide the basis for further studies aimed at assessing the effects of such variation on vectorial capacity for a range of pathogens.