Cross-species transmission of viruses from wildlife animal reservoirs poses a marked threat to human and animal health . Bats have been recognized as one of the most important reservoirs for emerging viruses and the transmission of a coronavirus that originated in bats to humans via intermediate hosts was responsible for the high-impact emerging zoonosis, severe acute respiratory syndrome (SARS) . Here we provide virological, epidemiological, evolutionary and experimental evidence that a novel HKU2-related bat coronavirus, swine acute diarrhoea syndrome coronavirus (SADS-CoV), is the aetiological agent that was responsible for a large-scale outbreak of fatal disease in pigs in China that has caused the death of 24,693 piglets across four farms. Notably, the outbreak began in Guangdong province in the vicinity of the origin of the SARS pandemic. Furthermore, we identified SADS-related CoVs with 96-98% sequence identity in 9.8% (58 out of 591) of anal swabs collected from bats in Guangdong province during 2013-2016, predominantly in horseshoe bats (Rhinolophus spp.) that are known reservoirs of SARS-related CoVs. We found that there were striking similarities between the SADS and SARS outbreaks in geographical, temporal, ecological and aetiological settings. This study highlights the importance of identifying coronavirus diversity and distribution in bats to mitigate future outbreaks that could threaten livestock, public health and economic growth.

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.deep learning | MS | de novo sequencing P roteomics research focuses on large-scale studies to characterize the proteome, the entire set of proteins, in a living organism (1-5). In proteomics, de novo peptide sequencing from tandem MS data plays the key role in the characterization of novel protein sequences. This field has been actively studied over the past 20 y, and many de novo sequencing tools have been proposed, such as PepNovo, PEAKS, NovoHMM, MSNovo, pNovo, UniNovo, and Novor among others (6-19). The recent "gold rush" into mAbs has undoubtedly elevated the application of de novo sequencing to a new horizon (20-23). However, computational challenges still remain, because MS/MS spectra contain much noise and ambiguity that require rigorous global optimization with various forms of dynamic programming that have been developed over the past decade (8-10, 12, 13, 15-19, 24).In this study, we introduce neural networks and deep learning to de novo peptide sequencing and achieve major breakthroughs on this well-studied problem. Deep learning has recently brought about a revolution in many research fields (25), repeatedly breaking state of the art records in image processing (26, 27), speech recognition (28), and natural language processing (29). It now forms the core of the artificial intelligence platforms of several technology giants, such as Google, Facebook, and Microsoft, as well as many startups in the industry. Deep learning has also made its way into biological sciences (30) [for instance, in the field of genomics, where deep neural network models have been developed for predicting the effects of noncoding single-nucleotide ...

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The genome sequences of 175 Ebola virus from five districts in Sierra Leone, collected during September–November 2014, show that the rate of virus evolution seems to be similar to that observed during previous outbreaks and that the genetic diversity of the virus has increased substantially, with the emergence of several novel lineages. Supplementary information The online version of this article (doi:10.1038/nature14490) contains supplementary material, which is available to authorized users.

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Recently, several thousand people have been killed by the Ebolavirus disease (EVD) in West Africa, yet no current antiviral medications and treatments are available. Systematic investigation of ebolavirus whole genomes during the 2014 outbreak may shed light on the underlying mechanisms of EVD development. Here, using the genome-wide screening in ebolavirus genome sequences, we predicted four putative viral microRNA precursors (pre-miRNAs) and seven putative mature microRNAs (miRNAs). Combing bioinformatics analysis and prediction of the potential ebolavirus miRNA target genes, we suggest that two ebolavirus coding possible miRNAs may be silence and down-regulate the target genes NFKBIE and RIPK1, which are the central mediator of the pathways related with host cell defense mechanism. Additionally, the ebolavirus exploits the miRNAs to inhibit the NF-kB and TNF factors to evade the host defense mechanisms that limit replication by killing infected cells, or to conversely trigger apoptosis as a mechanism to increase virus spreading. This is the first study to use the genome-wide scanning to predict microRNAs in the 2014 outbreak EVD and then to apply systematic bioinformatics to analyze their target genes. We revealed a potential mechanism of miRNAs in ebolavirus infection and possible therapeutic targets for Ebola viral infection treatment.

This research demonstrates that intranasal application of bacteriophage is viable, and could provide complete protection from pneumonia caused by A. baumannii.

Bovine mastitis is one of the most costly diseases in dairy cows worldwide. It can be caused by over 150 different microorganisms, where Staphylococcus aureus is the most frequently isolated and a major pathogen responsible for heavy economic losses in dairy industry. Although antibiotic therapy is most widely used, alternative treatments are necessary due to the increasing antibiotic resistance. Using phage for pathogen control is a promising tool in the fight against antibiotic resistance. Mainly using high-throughput sequencing, bioinformatics and our proposed phage termini identification method, we have isolated and characterized a novel virulent phage, designated as vB_SauS_IMEP5, from manure collected from dairy farms in Shihezi, Xinjiang, China, for use as a biocontrol agent against Staphylococcus aureus infections. Its latent period was about 30 min and its burst size was approximately 272PFU/cell. Phage vB_SauS_IMEP5 survives in a wide pH range between 3 and 12. A treatment at 70 °C for 20 min can inactive the phage. Morphological analysis of vB_SauS_IMEP5 revealed that phage vB_SauS_IMEP5 morphologically resembles phages in the family Siphoviridae. Among our tested multiplicity of infections (MOIs), the optimal multiplicity of infection (MOI) of this phage was determined to be 0.001, suggesting that phage vB_SauS_IMEP5 has high bacteriolytic potential and good efficiency for reducing bacterial growth. The complete genome of IME-P5 is a 44,677-bp, linear, double-stranded DNA, with a G+C content of 34.26%, containing 69 putative ORFs. The termini of genome were determined with next-generation sequencing data using our previously proposed termini identification method, which suggests that this phage has non-redundant termini with 9nt 3' protruding cohesive ends. The genomic and proteomic characteristics of IMEP5 demonstrate that this phage does not belong to any of the previously recognized Siphoviridae Staphylococcus phage groups, suggesting the creation of a new lineage, thus adding to the knowledge on the diversity of Staphylococcus phages. An N-acetylmuramoyl-L-alanine amidase gene and several conserved genes were predicted, while no virulence or antibiotic resistance genes were identified. This study isolated and characterized a novel S. aureus phage vB_SauS_IMEP5, and our findings suggest that this phage may be potentially utilized as a therapeutic or prophylactic candidate against S.aureus infections.