Biases in genome reconstruction from metagenomic data

10.7287/peerj.preprints.2953 ◽

2017 ◽

Cited By ~ 1

Author(s):

William C Nelson ◽

Jennifer M Mobberley

Keyword(s):

Mobile Element ◽

Nucleotide Composition ◽

Metagenomic Data ◽

Data Sets ◽

Genome Sequences ◽

Reconstruction Process ◽

Genome Reconstruction ◽

Nucleotide Content ◽

Technological Advances ◽

Species Specific

Background: Technological advances in sequencing, assembly and segregation of resulting contigs into species-specific bins has enabled the reconstruction of individual genomes from environmental metagenomic data sets. Though a powerful technique, it is shadowed by an inability to truly determine whether assembly and binning techniques are accurate, specific, and sensitive due to a lack of complete reference genome sequences against which to check the data. Errors in genome reconstruction, such as missing or mis-attributed activities, can have a detrimental effect on downstream metabolic and ecological modeling, and thus it is important to assess the accuracy of the process. Methods: We compared genomes reconstructed from metagenomic data to complete genome sequences of 10 organisms isolated from the same community to identify regions not captured by typical binning techniques. The nucleotide content, as %G+C and tetranucleotide frequencies, and sequence redundancy within both the genome and across the metagenome were determined for both the captured and uncaptured regions. This direct comparison allowed us to evaluate the efficacy of nucleotide composition and coverage profiles as elements of binning protocols and look for biases in sequence characteristics and gene content in regions missing from the reconstructions. Results: We found that repeated sequences were frequently missed in the reconstruction process as were short sequences with variant nucleotide composition. Genes encoded on the missing regions were strongly biased towards ribosomal RNAs, transfer RNAs, mobile element functions and genes of unknown function. Conclusions: Our observation of increased mis-binning of short regions, especially those with variant nucleotide content, and repeated regions implies that factors which affect assembly efficiency also impact binning accuracy. To a large extent, mis-binned regions appear to derive from mobile elements. Our results support genome reconstruction as a robust process, and suggest that reconstructions determined to be >90% complete are likely to effectively represent organismal function.

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Biases in genome reconstruction from metagenomic data

10.7287/peerj.preprints.2953v1 ◽

2017 ◽

Author(s):

William C Nelson ◽

Jennifer M Mobberley

Keyword(s):

Mobile Element ◽

Nucleotide Composition ◽

Metagenomic Data ◽

Data Sets ◽

Genome Sequences ◽

Reconstruction Process ◽

Genome Reconstruction ◽

Nucleotide Content ◽

Technological Advances ◽

Species Specific

Background: Technological advances in sequencing, assembly and segregation of resulting contigs into species-specific bins has enabled the reconstruction of individual genomes from environmental metagenomic data sets. Though a powerful technique, it is shadowed by an inability to truly determine whether assembly and binning techniques are accurate, specific, and sensitive due to a lack of complete reference genome sequences against which to check the data. Errors in genome reconstruction, such as missing or mis-attributed activities, can have a detrimental effect on downstream metabolic and ecological modeling, and thus it is important to assess the accuracy of the process. Methods: We compared genomes reconstructed from metagenomic data to complete genome sequences of 10 organisms isolated from the same community to identify regions not captured by typical binning techniques. The nucleotide content, as %G+C and tetranucleotide frequencies, and sequence redundancy within both the genome and across the metagenome were determined for both the captured and uncaptured regions. This direct comparison allowed us to evaluate the efficacy of nucleotide composition and coverage profiles as elements of binning protocols and look for biases in sequence characteristics and gene content in regions missing from the reconstructions. Results: We found that repeated sequences were frequently missed in the reconstruction process as were short sequences with variant nucleotide composition. Genes encoded on the missing regions were strongly biased towards ribosomal RNAs, transfer RNAs, mobile element functions and genes of unknown function. Conclusions: Our observation of increased mis-binning of short regions, especially those with variant nucleotide content, and repeated regions implies that factors which affect assembly efficiency also impact binning accuracy. To a large extent, mis-binned regions appear to derive from mobile elements. Our results support genome reconstruction as a robust process, and suggest that reconstructions determined to be >90% complete are likely to effectively represent organismal function.

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Genome Sequences of Three Cruciviruses Found in the Willamette Valley (Oregon)

Microbiology Resource Announcements ◽

10.1128/mra.00447-19 ◽

2019 ◽

Vol 8 (23) ◽

Cited By ~ 4

Author(s):

Ignacio de la Higuera ◽

Ellis L. Torrance ◽

Alyssa A. Pratt ◽

George W. Kasun ◽

Amberlee Maluenda ◽

...

Keyword(s):

Gene Transfer ◽

Rna Viruses ◽

Metagenomic Data ◽

Data Sets ◽

Willamette Valley ◽

Genome Sequences ◽

Single Stranded Dna ◽

Dna And Rna ◽

Ssdna Viruses

Cruciviruses are single-stranded DNA (ssDNA) viruses whose genomes suggest the possibility of gene transfer between DNA and RNA viruses. Many crucivirus genome sequences have been found in metagenomic data sets, although no crucivirus has been isolated.

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virMine: automated detection of viral sequences from complex metagenomic samples

PeerJ ◽

10.7717/peerj.6695 ◽

2019 ◽

Vol 7 ◽

pp. e6695 ◽

Cited By ~ 14

Author(s):

Andrea Garretto ◽

Thomas Hatzopoulos ◽

Catherine Putonti

Keyword(s):

Software Tool ◽

Metagenomic Data ◽

Data Sets ◽

Data Repositories ◽

Viral Genomes ◽

Manual Curation ◽

Public Data ◽

Control Assembly ◽

Signature Sequences ◽

Viral Sequences

Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments.

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Thaumarchaea Genome Sequences from a High Arctic Active Layer

Microbiology Resource Announcements ◽

10.1128/mra.00326-20 ◽

2020 ◽

Vol 9 (21) ◽

Author(s):

Emily Wei-Hsin Sun ◽

Sassan Hajirezaie ◽

Mackenzie Dooner ◽

Tatiana A. Vishnivetskaya ◽

Alice Layton ◽

...

Keyword(s):

Active Layer ◽

High Arctic ◽

Metagenomic Data ◽

Data Sets ◽

Ammonia Oxidizers ◽

Canadian High Arctic ◽

Genome Sequences ◽

Terrestrial Environments

ABSTRACT The role of archaeal ammonia oxidizers often exceeds that of bacterial ammonia oxidizers in marine and terrestrial environments but has been understudied in permafrost, where thawing has the potential to release ammonia. Here, three thaumarchaea genomes were assembled and annotated from metagenomic data sets from carbon-poor Canadian High Arctic active-layer cryosols.

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MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3451392 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-24

Author(s):

Yaojin Lin ◽

Qinghua Hu ◽

Jinghua Liu ◽

Xingquan Zhu ◽

Xindong Wu

Keyword(s):

Empirical Studies ◽

Feature Space ◽

Training Data ◽

Data Sets ◽

Learning Framework ◽

Feature Spaces ◽

Public Data ◽

Margin Distribution ◽

Label Correlations ◽

Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

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metaXplor: an interactive viral and microbial metagenomic data manager

GigaScience ◽

10.1093/gigascience/giab001 ◽

2021 ◽

Vol 10 (2) ◽

Author(s):

Guilhem Sempéré ◽

Adrien Pétel ◽

Magsen Abbé ◽

Pierre Lefeuvre ◽

Philippe Roumagnac ◽

...

Keyword(s):

Heterogeneous Data ◽

Metagenomic Data ◽

Online Data ◽

Data Repositories ◽

Ongoing Research ◽

Efficient Management ◽

Public Data ◽

Reference Databases ◽

Interactive Data ◽

User Friendly

Abstract Background Efficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g., Qiime, Mothur, Megan, IMG/VR, Anvi'o, Qiita, MetaVir), scientists lack comprehensive software for easily building scalable, searchable, online data repositories on which they can rely during their ongoing research. Results metaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing, and exploring metagenomic data. Being based on a flexible NoSQL data model, it has few constraints regarding dataset contents and thus proves useful for handling outputs from both shotgun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data. Conclusion metaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud, or even on personal computers, will facilitate its adoption.

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Improvements for research data repositories: The case of text spam

Journal of Information Science ◽

10.1177/0165551521998636 ◽

2021 ◽

pp. 016555152199863

Author(s):

Ismael Vázquez ◽

María Novo-Lourés ◽

Reyes Pavón ◽

Rosalía Laza ◽

José Ramón Méndez ◽

...

Keyword(s):

Web Application ◽

Research Data ◽

Data Sets ◽

Data Repositories ◽

Software Applications ◽

Public Data ◽

Protection Mechanisms ◽

Experimental Protocols ◽

Learning Research ◽

Processing Steps

Current research has evolved in such a way scientists must not only adequately describe the algorithms they introduce and the results of their application, but also ensure the possibility of reproducing the results and comparing them with those obtained through other approximations. In this context, public data sets (sometimes shared through repositories) are one of the most important elements for the development of experimental protocols and test benches. This study has analysed a significant number of CS/ML ( Computer Science/ Machine Learning) research data repositories and data sets and detected some limitations that hamper their utility. Particularly, we identify and discuss the following demanding functionalities for repositories: (1) building customised data sets for specific research tasks, (2) facilitating the comparison of different techniques using dissimilar pre-processing methods, (3) ensuring the availability of software applications to reproduce the pre-processing steps without using the repository functionalities and (4) providing protection mechanisms for licencing issues and user rights. To show the introduced functionality, we created STRep (Spam Text Repository) web application which implements our recommendations adapted to the field of spam text repositories. In addition, we launched an instance of STRep in the URL https://rdata.4spam.group to facilitate understanding of this study.

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Screening Metagenomic Data for Viruses Using the E-Probe Diagnostic Nucleic Acid Assay

Phytopathology ◽

10.1094/phyto-11-13-0310-r ◽

2014 ◽

Vol 104 (10) ◽

pp. 1125-1129 ◽

Cited By ~ 11

Author(s):

A. H. Stobbe ◽

W. L. Schneider ◽

P. R. Hoyt ◽

U. Melcher

Keyword(s):

Nucleic Acid ◽

Mosaic Virus ◽

Yellow Mosaic Virus ◽

Metagenomic Data ◽

Data Sets ◽

Virus Species ◽

Data Set ◽

Golden Yellow ◽

Nucleic Acid Assay ◽

Ngs Data

Next generation sequencing (NGS) is not used commonly in diagnostics, in part due to the large amount of time and computational power needed to identify the taxonomic origin of each sequence in a NGS data set. By using the unassembled NGS data sets as the target for searches, pathogen-specific sequences, termed e-probes, could be used as queries to enable detection of specific viruses or organisms in plant sample metagenomes. This method, designated e-probe diagnostic nucleic acid assay, first tested with mock sequence databases, was tested with NGS data sets generated from plants infected with a DNA (Bean golden yellow mosaic virus, BGYMV) or an RNA (Plum pox virus, PPV) virus. In addition, the ability to detect and differentiate among strains of a single virus species, PPV, was examined by using probe sets that were specific to strains. The use of probe sets for multiple viruses determined that one sample was dually infected with BGYMV and Bean golden mosaic virus.

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Fast and accurate detection of surface defect based on improved YOLOv4

Assembly Automation ◽

10.1108/aa-04-2021-0044 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Jiawei Lian ◽

Junhong He ◽

Yun Niu ◽

Tianze Wang

Keyword(s):

Feature Extraction ◽

Real Time ◽

Surface Defect ◽

Steel Ingot ◽

Industrial Applications ◽

Data Sets ◽

Data Set ◽

Processing Technologies ◽

Content Type ◽

Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

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