Microbe Finder (MiFi®): Implementation of an Interactive Pathogen Detection Tool in Metagenomic Sequence Data

Espindola, Andres S.; Cardwell, Kitty F.

doi:10.3390/plants10020250

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…The results from the Minimap2 application suggest the pathogen presence in a sample; however, the final decision may need additional data such as the pathogen titer and season of the occurrence, which may be relevant to confirm the certainty of the finding. Alternatively, dedicated bioinformatic pipelines that use curated databases (e-probes) [ 15 ] can be used. The e-probes directly provided the result (positive or negative) for TRSV and GLRAv-3 in the sample.…”

Section: Resultsmentioning

confidence: 99%

“…Pathogen abundance has been a concern when using HTS for diagnostics in plants [ 24 ]. Various approaches have been explored at the molecular and bioinformatic levels and with different crops to improve the likelihood of detecting a pathogen [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. The TASPERT protocol allows samples to be enriched in about 45 min before starting a library preparation intended for HTS diagnostics in plants.…”

Section: Discussionmentioning

confidence: 99%

“…To determine if any bias exists in the depth of coverage towards the 3′-terminus when using the TASPERT protocol, Samtools version 1.10 [ 24 ] was used to calculate the depth of coverage at different coordinates of the TRSV and GLRaV-3 genomes. The detection of TRSV and GLRaV-3 was done using the Microbe Finder (MiFi ® ) online system [ 15 ] using e-probes that have been previously developed and validated for TRSV and GLRaV-3. The RT primers (RT-primer control) were aligned to each of the generated metagenomes using the BLASTn algorithm to determine potential cross-contamination.…”

Section: Methodsmentioning

confidence: 99%

“…TASPERT allows obtaining HTS data enriched for any RNA virus of interest. The data can be screened with Microbe Finder (MiFi) or any other bioinformatic software to determine the presence of pathogens in samples of interest [ 15 , 16 , 17 ] with increased statistical significance.…”

Section: Introductionmentioning

confidence: 99%

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TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics

Espindola

Sempertegui-Bayas

Bravo-Padilla

et al. 2021

Viruses

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High-throughput sequencing (HTS) is becoming the new norm of diagnostics in plant quarantine settings. HTS can be used to detect, in theory, all pathogens present in any given sample. The technique’s success depends on various factors, including methods for sample management/preparation and suitable bioinformatic analysis. The Limit of Detection (LoD) of HTS for plant diagnostic tests can be higher than that of PCR, increasing the risk of false negatives in the case of low titer of the target pathogen. Several solutions have been suggested, particularly for RNA viruses, including rRNA depletion of the host, dsRNA, and siRNA extractions, which increase the relative pathogen titer in a metagenomic sample. However, these solutions are costly and time-consuming. Here we present a faster and cost-effective alternative method with lower HTS-LoD similar to or lower than PCR. The technique is called TArget-SPecific Reverse Transcript (TASPERT) pool. It relies on pathogen-specific reverse primers, targeting all RNA viruses of interest, pooled and used in double-stranded cDNA synthesis. These reverse primers enrich the sample for only pathogens of interest. Evidence on how TASPERT is significantly superior to oligodT, random 6-mer, and 20-mer in generating metagenomic libraries containing the pathogen of interest is presented in this proof of concept.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics

Espindola

Sempertegui-Bayas

Bravo-Padilla

et al. 2021

Viruses

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“…Prepared libraries are sequenced using MinION (R9.4 Flow cells), and sequences with an average Q-score of more than 7 are used in the final genome. Pathogens in the metagenome sequence data are identified using the MiFi platform [30] 1 . Using a modified version of the bioinformatics pipeline [31], e-probes, or unique signatures, for each pathogen were developed to identify and label pathogen specific sequences in the metagenome reads.…”

Section: E Implementation Detailsmentioning

confidence: 99%

Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis

Aakur¹,

Indla²,

Indla³

et al. 2021

Preprint

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Advances in next-generation metagenome sequencing have the potential to revolutionize the point-of-care diagnosis of novel pathogen infections, which could help prevent potential widespread transmission of diseases. Given the high volume of metagenome sequences, there is a need for scalable frameworks to analyze and segment metagenome sequences from clinical samples, which can be highly imbalanced. There is an increased need for learning robust representations from metagenome reads since pathogens within a family can have highly similar genome structures (some more than 90%) and hence enable the segmentation and identification of novel pathogen sequences with limited labeled data. In this work, we propose Metagenome2Vec -a contextualized representation that captures the global structural properties inherent in metagenome data and local contextualized properties through self-supervised representation learning. We show that the learned representations can help detect six (6) related pathogens from clinical samples with less than 100 labeled sequences. Extensive experiments on simulated and clinical metagenome data show that the proposed representation encodes compositional properties that can generalize beyond annotations to segment novel pathogens in an unsupervised setting.

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MG-NET: Leveraging Pseudo-imaging for Multi-modal Metagenome Analysis

Aakur

Narayanan

Indla

et al. 2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

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Microbe Finder (MiFi®): Implementation of an Interactive Pathogen Detection Tool in Metagenomic Sequence Data

Cited by 14 publications

References 16 publications

TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics

TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics

Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis

MG-NET: Leveraging Pseudo-imaging for Multi-modal Metagenome Analysis

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