Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing

BACKGROUND: Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. METHODS: We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. RESULTS: Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. CONCLUSIONS: We propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts

Detecting macroecological patterns in bacterial communities across independent studies of global soils

This study and participants were funded in part by ERC Adv grant 26055290 (KSR, WHvdP); BBSRC David Phillips Fellowship (BB/L02456X/1) (FTDV); ERC Grant Agreements 242658 [BIOCOM] and 647038 [BIODESERT] (FTM); the European Regional Development Fund (Centre of Excellence EcolChange) (JD); Yorkshire Agricultural Society, Nafferton Ecological Farming Group, and the Northumbria University Research Development Fund (CHO); BBSRC Training Grant (BB/K501943/1) (CH); Wallenberg Academy Fellowship (KAW 2012.0152), Formas (214-2011-788) and Vetenskapsrådet (612-2011-5444) (ED); the Glastir Monitoring & Evaluation Programme (Contract reference: C147/2010/11) and the full support of the GMEP team on the Glastir project (DLJ, SC, DAR)

Stratifying measuring requirements and tools for cloud services monitoring

Monitoring plays an essential role in the management and engineering of today’s communication networks. Facing paradigms such as cloud computing and network virtualization, the challenges of monitoring are even more demanding, attending to the variety and dynamics of services and resources involved. The lack of standards regarding cloud services monitoring makes even more difficult to reach a common ground on how to assess these services. In this context, this paper takes a high-level stratified view of the problem to clarify and systematise the involved pieces in cloud services monitoring, from the physical/virtual infrastructure to the customer/provider layer. In each layer, relevant measuring requirements and metrics are discussed, following existing guidelines for the evaluation of performance, reliability, security, and service level agreements fulfilment. In addition, representative cloud services in the market are aggregated per service model and relevant monitoring tools are surveyed and mapped into the proposed functional layers. The present study is a step forward contributing to achieve a more modular, flexible and consensual view to cloud services monitoring.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2019