Reanalyze unassigned reads in Sanger based metagenomic data using conserved gene adjacency

Weng, F. C-H; Su, Chun-Han; Hsu, Ming-Tsung; Wang, Tse−Yi; Tsai, Huai-Kuang; Wang, Daryi

doi:10.1186/1471-2105-11-565

Cited by 12 publications

(19 citation statements)

References 52 publications

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“…Finally, we note that the method used in MetaDomain shares a similar rationale to the recent work by Weng et al 14 Weng et al reported that taxonomic binning tools for metagenomes discard 30-40% of Sanger sequencing data due to the stringency of BLAST cut-offs. Thus, they re-analyzed the discarded reads using less stringent cut-offs.…”

Section: Related Workmentioning

confidence: 53%

Metadomain: A Profile HMM-Based Protein Domain Classification Tool for Short Sequences

Zhang

Sun²

2011

Biocomputing 2012

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Protein homology search provides basis for functional profiling in metagenomic annotation. Profile HMM-based methods classify reads into annotated protein domain families and can achieve better sensitivity for remote protein homology search than pairwise sequence alignment. However, their sensitivity deteriorates with the decrease of read length. As a result, a large number of short reads cannot be classified into their native domain families. In this work, we introduce MetaDomain, a protein domain classification tool designed for short reads generated by next-generation sequencing technologies. MetaDomain uses relaxed position-specific score thresholds to align more reads to a profile HMM while using the distribution of alignment positions as an additional constraint to control false positive matches. In this work MetaDomain is applied to the transcriptomic data of a bacterial genome and a soil metagenomic data set. The experimental results show that it can achieve better sensitivity than the state-of-the-art profile HMM alignment tool in identifying encoded domains from short sequences. The source codes of MetaDomain are available at http://sourceforge.net/projects/metadomain/.

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Section: Related Workmentioning

confidence: 53%

Metadomain: A Profile HMM-Based Protein Domain Classification Tool for Short Sequences

Zhang

Sun²

2011

Biocomputing 2012

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“…The description of these methods here is a summary of the description available in [24]. We next describe a summarization algorithm that uses the UMLS concepts and relations to construct a graph-based representation for the document to be summarized.…”

Section: Methodsmentioning

confidence: 99%

Studying the correlation between different word sense disambiguation methods and summarization effectiveness in biomedical texts

et al. 2011

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BackgroundWord sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in knowledge-based approaches to automatic summarization. However, the correlation between the accuracy of the WSD methods and the summarization performance has never been studied.ResultsWe present three existing knowledge-based WSD approaches and a graph-based summarizer. Both the WSD approaches and the summarizer employ the Unified Medical Language System (UMLS) Metathesaurus as the knowledge source. We first evaluate WSD directly, by comparing the prediction of the WSD methods to two reference sets: the NLM WSD dataset and the MSH WSD collection. We next apply the different WSD methods as part of the summarizer, to map documents onto concepts in the UMLS Metathesaurus, and evaluate the summaries that are generated. The results obtained by the different methods in both evaluations are studied and compared.ConclusionsIt has been found that the use of WSD techniques has a positive impact on the results of our graph-based summarizer, and that, when both the WSD and summarization tasks are assessed over large and homogeneous evaluation collections, there exists a correlation between the overall results of the WSD and summarization tasks. Furthermore, the best WSD algorithm in the first task tends to be also the best one in the second. However, we also found that the improvement achieved by the summarizer is not directly correlated with the WSD performance. The most likely reason is that the errors in disambiguation are not equally important but depend on the relative salience of the different concepts in the document to be summarized.

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“…Application of these new methods would lower the computational cost of the method. Similarity-based taxonomic classifications methods are not robust to horizontal gene transfer events and therefore alternative classification algorithms may be more suitable for contaminant detection than PathoScope (Kunin et al, 2008;Weng et al, 2010). Other methods such as MicrobeGPS and…”

Section: /18mentioning

confidence: 99%

Peer Review #1 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.1)"

Kumar¹

2017

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High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells. Though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of 1 in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in effort to ensure that contaminants in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

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Reanalyze unassigned reads in Sanger based metagenomic data using conserved gene adjacency

Cited by 12 publications

References 52 publications

Metadomain: A Profile HMM-Based Protein Domain Classification Tool for Short Sequences

Metadomain: A Profile HMM-Based Protein Domain Classification Tool for Short Sequences

Studying the correlation between different word sense disambiguation methods and summarization effectiveness in biomedical texts

Peer Review #1 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.1)"

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