Be aware of the allele-specific bias and compositional effects in multi-template PCR.

High-throughput sequencing of amplicon libraries is the most widespread and one of the most effective ways to study the taxonomic structure of microbial communities, even despite growing accessibility of whole metagenome sequencing. Due to the targeted amplification, the method provides unparalleled resolution of communities, but at the same time perturbs initial community structure thereby reducing data robustness and compromising downstream analyses. Experimental research of the perturbations is largely limited to comparative studies on different PCR protocols without considering other sources of experimental variation related to characteristics of the initial microbial composition itself.

Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules.

Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention.

Investigation of the core microbiome in main soil types from the East European plain.

The main goal of modern microbial ecology is to determine the key factors influencing the global diversity of microorganisms. Because of their complexity, soil communities are largely underexplored in this context. We studied soil genesis (combination of various soil-forming processes, specific to a particular soil type) that is driven by microbial activity.

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data.

The preservation of microbial DNA in archived soils of various genetic types.

This study is a comparative analysis of samples of archived (stored for over 70-90 years) and modern soils of two different genetic types-chernozem and sod-podzolic soils. We revealed a reduction in biodiversity of archived soils relative to their modern state. Particularly, long-term storage in the museum exerted a greater impact on the microbiomes of sod-podzolic soils, while chernozem samples better preserved the native community.

Correction: Comparative Analysis of Prokaryotic Communities Associated with Organic and Conventional Farming Systems.

[This corrects the article DOI: 10.1371/journal.pone.

Comparative Analysis of Prokaryotic Communities Associated with Organic and Conventional Farming Systems.

One of the most important challenges in agriculture is to determine the effectiveness and environmental impact of certain farming practices. The aim of present study was to determine and compare the taxonomic composition of the microbiomes established in soil following long-term exposure (14 years) to a conventional and organic farming systems (CFS and OFS accordingly). Soil from unclared forest next to the fields was used as a control.