Abstract
Background The use of DNA barcoding allows unprecedented advances in biodiversity assessments and monitoring schemes of freshwater ecosystems; nevertheless, it requires the construction of comprehensive reference collections of DNA sequences that represent the existing biodiversity. Plecoptera are considered particularly good ecological indicators and one of the most endangered groups of insects, but very limited information on their DNA barcodes is available in public databases. Currently, less than 50% of the Iberian species are represented in BOLD. New information The InBIO Barcoding Initiative Database: contribution to the knowledge on DNA barcodes of Iberian Plecoptera dataset contains records of 71 specimens of Plecoptera. All specimens have been morphologically identified to species level and belong to 29 species in total. This dataset contributes to the knowledge on the DNA barcodes and distribution of Plecoptera from the Iberian Peninsula and it is one of the IBI database public releases that makes available genetic and distribution data for a series of taxa. The species represented in this dataset correspond to an addition to public databases of 17 species and 21 BINs. Fifty-eight specimens were collected in Portugal and 18 in Spain during the period of 2004 to 2018. All specimens are deposited in the IBI collection at CIBIO, Research Center in Biodiversity and Genetic Resources and their DNA barcodes are publicly available in the Barcode of Life Data System (BOLD) online database. The distribution dataset can be freely accessed through the Global Biodiversity Information Facility (GBIF).

Abstract
Background The InBIO Barcoding Initiative (IBI) Diptera 01 dataset contains records of 203 specimens of Diptera. All specimens have been morphologically identified to species level, and belong to 154 species in total. The species represented in this dataset correspond to about 10% of continental Portugal dipteran species diversity. All specimens were collected north of the Tagus river in Portugal. Sampling took place from 2014 to 2018, and specimens are deposited in the IBI collection at CIBIO, Research Center in Biodiversity and Genetic Resources. New information This dataset contributes to the knowledge on the DNA barcodes and distribution of 154 species of Diptera from Portugal and is the first of the planned IBI database public releases, which will make available genetic and distribution data for a series of taxa. All specimens have their DNA barcodes made publicly available in the Barcode of Life Data System (BOLD) online database and the distribution dataset can be freely accessed through the Global Biodiversity Information Facility (GBIF).

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A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected –LTBI- or uninfected –NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas. © Copyright © 2020 Estévez, Anibarro, Garet, Pallares, Barcia, Calviño, Maueia, Mussá, Fdez-Riverola, Glez-Peña, Reboiro-Jato, López-Fernández, Fonseca, Reljic and González-Fernández.

Abstract
Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities. We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.