scholarly journals Seeker: Alignment-free identification of bacteriophage genomes by deep learning

Author(s):  
Noam Auslander ◽  
Ayal B. Gussow ◽  
Sean Benler ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin

SummaryAdvances in metagenomics enable massive discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. The existing methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct bacteriophage families. We present Seeker, a deep-learning tool for reference-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and clean differentiation of phage sequences from bacterial ones, even for phages with little sequence similarity to established phage families. We comprehensively validate Seeker’s ability to identify unknown phages and employ Seeker to detect unknown phages, some of which are highly divergent from known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly python package (https://github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

2020 ◽  
Vol 48 (21) ◽  
pp. e121-e121
Author(s):  
Noam Auslander ◽  
Ayal B Gussow ◽  
Sean Benler ◽  
Yuri I Wolf ◽  
Eugene V Koonin

Abstract Recent advances in metagenomic sequencing have enabled discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. Most of the existing detection methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct, highly divergent bacteriophage families. Here we present Seeker, a deep-learning tool for alignment-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and differentiation of phage sequences from bacterial ones, even when those phages exhibit little sequence similarity to established phage families. We comprehensively validate Seeker's ability to identify previously unidentified phages, and employ this method to detect unknown phages, some of which are highly divergent from the known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly Python package (github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.


2002 ◽  
Vol 365 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Suren AGHAJANIAN ◽  
D.Margaret WORRALL

The final two enzymes in the CoA biosynthetic pathway, phosphopantetheine adenylyltransferase (PPAT; EC 2.7.7.3) and dephospho-CoA kinase (DPCK; EC 2.7.1.24), are separate proteins in prokaryotes, but exist as a bifunctional enzyme in pig liver. In the present study we have obtained sequence information from purified pig-liver enzyme, and identified the corresponding cDNA in a number of species. The human gene localizes to chromosome 17q12-21 and contains regions with sequence similarity to the monofunctional Escherichia coli DPCK and PPAT. The recombinant 564-amino-acid human protein confirmed the associated transferase and kinase activities, and gave similar kinetic properties to the wild-type pig enzyme.


2020 ◽  
Vol 32 (29) ◽  
pp. 2000953 ◽  
Author(s):  
Bingnan Han ◽  
Yuxuan Lin ◽  
Yafang Yang ◽  
Nannan Mao ◽  
Wenyue Li ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lianyu Lin ◽  
Anupma Sharma ◽  
Qingyi Yu

Abstract Background Miniature inverted-repeat transposable elements (MITEs) are non-autonomous DNA transposable elements that play important roles in genome organization and evolution. Genome-wide identification and characterization of MITEs provide essential information for understanding genome structure and evolution. Results We performed genome-wide identification and characterization of MITEs in the pineapple genome. The top two MITE families, accounting for 29.39% of the total MITEs and 3.86% of the pineapple genome, have insertion preference in (TA) n dinucleotide microsatellite regions. We therefore named these MITEs A. comosus microsatellite-associated MITEs (Ac-mMITEs). The two Ac-mMITE families, Ac-mMITE-1 and Ac-mMITE-2, shared sequence similarity in the terminal inverted repeat (TIR) regions, suggesting that these two Ac-mMITE families might be derived from a common or closely related autonomous elements. The Ac-mMITEs are frequently clustered via adjacent insertions. Among the 21,994 full-length Ac-mMITEs, 46.1% of them were present in clusters. By analyzing the Ac-mMITEs without (TA) n microsatellite flanking sequences, we found that Ac-mMITEs were likely derived from Mutator-like DNA transposon. Ac-MITEs showed highly polymorphic insertion sites between cultivated pineapples and their wild relatives. To better understand the evolutionary history of Ac-mMITEs, we filtered and performed comparative analysis on the two distinct groups of Ac-mMITEs, microsatellite-targeting MITEs (mt-MITEs) that are flanked by dinucleotide microsatellites on both sides and mutator-like MITEs (ml-MITEs) that contain 9/10 bp TSDs. Epigenetic analysis revealed a lower level of host-induced silencing on the mt-MITEs in comparison to the ml-MITEs, which partially explained the significantly higher abundance of mt-MITEs in pineapple genome. The mt-MITEs and ml-MITEs exhibited differential insertion preference to gene-related regions and RNA-seq analysis revealed their differential influences on expression regulation of nearby genes. Conclusions Ac-mMITEs are the most abundant MITEs in the pineapple genome and they were likely derived from Mutator-like DNA transposon. Preferential insertion in (TA) n microsatellite regions of Ac-mMITEs occurred recently and is likely the result of damage-limiting strategy adapted by Ac-mMITEs during co-evolution with their host. Insertion in (TA) n microsatellite regions might also have promoted the amplification of mt-MITEs. In addition, mt-MITEs showed no or negligible impact on nearby gene expression, which may help them escape genome control and lead to their amplification.


Development ◽  
2020 ◽  
Vol 147 (24) ◽  
pp. dev194589
Author(s):  
Benoit Aigouy ◽  
Claudio Cortes ◽  
Shanda Liu ◽  
Benjamin Prud'Homme

ABSTRACTEpithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually involves extensive manual correction, even with semi-automated tools. Here, we present EPySeg, an open-source, coding-free software that uses deep learning to segment membrane-stained epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a Python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By substantially reducing human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.


Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Gianpaolo Francesco Trotta ◽  
Leonarda Carnimeo ◽  
Francescomaria Marino ◽  
...  

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, thus avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: Materials consist in abdominal CT hepatic lesion from 18 patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. Several approaches are implemented to segment the region of liver and, then, detect the lesion ROI. Results: A Deep Learning approach using Convolutional Neural Network is followed for HCC grading. The obtained good results confirm the robustness of the segmentation algorithms leading to a more accurate classification.


Virology ◽  
2017 ◽  
Vol 509 ◽  
pp. 159-166 ◽  
Author(s):  
Klaudia Chrzastek ◽  
Dong-hun Lee ◽  
Diane Smith ◽  
Poonam Sharma ◽  
David L. Suarez ◽  
...  

2011 ◽  
Vol 74 (1) ◽  
pp. 13-17 ◽  
Author(s):  
V. LORUSSO ◽  
A. DAMBROSIO ◽  
N. C. QUAGLIA ◽  
A. PARISI ◽  
G. LASALANDRA ◽  
...  

Verocytotoxin-producing Escherichia coli (VTEC) O26 is an emergent pathotype that has caused an increasing number of sporadic cases and outbreaks of gastroenteritis, hemorrhagic colitis, and hemolytic uremic syndrome in the United States and Europe. Many cases are associated with the consumption of milk and undercooked or fermented meats. The stx2 strains of VTEC O26 seem to be more likely to cause human infections than isolates expressing only stx1. The isolation and identification of VTEC O26 from foods is labor intensive and time-consuming. We developed a multiplex PCR (M-PCR) assay for the identification and characterization of E. coli O26 VTEC and its detection in raw milk and ground beef. The method is based on the amplification of the wzx, stx1, and stx2 genes for the simultaneous detection of the O26 antigen and verocytotoxin types 1 and 2. This M-PCR assay had a sensitivity of 108 CFU/ml when applied to a bacterial suspension and of 106 CFU/ml or g when applied to both inoculated milk and minced beef samples. This M-PCR assay also was highly specific, and results were consistently negative for negative controls (nonpathogenic E. coli strains, uninoculated milk and beef samples, and samples inoculated with the nontarget microorganisms). This method could be used for the rapid detection of E. coli O26 VTEC from foods and for the rapid identification and characterization of clinical and environmental isolates.


2020 ◽  
Author(s):  
Benoit Aigouy ◽  
Benjamin Prud’Homme

AbstractEpithelia are dynamic tissues that self-remodel during their development. At morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually implies extensive manual correction, even with semi-automated tools. Here we present EPySeg, an open source, coding-free software that uses deep learning to segment epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By alleviating human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.


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