KOMB: K-core based de novo characterization of copy number variation in microbiomes

Balaji, Advait; Sapoval, Nicolae; Seto, Charlie; Elworth, R. A. Leo; Fu, Yilei; Nute, Michael; Savidge, Tor; Segarra, Santiago; Treangen, Todd J.

doi:10.1016/j.csbj.2022.06.019

Cited by 6 publications

(3 citation statements)

References 96 publications

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“… 11 Additionally, it is also important to note that a crucial downstream application of MAG is their utilization for capturing variations in both sequence and microbiome structure. 12 , 13 …”

Section: Before You Beginmentioning

confidence: 99%

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses

Banerjee,

Papri,

Banerjee

2024

STAR Protocols

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Section: Before You Beginmentioning

confidence: 99%

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses

Banerjee,

Papri,

Banerjee

2024

STAR Protocols

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“…As a reasonable negative control of our FunSoC framework within SeqScreen-Nano, we set out to it on a screened and cleared FMT healthy Donor sample. To accomplish this, we used a FMT Donor sample we previously characterized (Balaji et al 2022b). Briefly, two pediatric patients with a recurrent Clostridioides difficile Infection (CDI) diagnosis received FMT under IRB-approved informed consent (#H-31066) at Baylor College of Medicine.…”

Section: Datasets and Toolsmentioning

confidence: 99%

SeqScreen-Nano: a computational platform for rapid, in-field characterization of previously unseen pathogens

Balaji

Liu

Nute

et al. 2023

Preprint

Self Cite

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The COVID-19 pandemic forever underscored the need for biosurveillance platforms capable of rapid detection of previously unseen pathogens. Oxford Nanopore Technology (ONT) couples long-read sequencing with in-field capability, opening the door to real-time, in-field biosurveillance. Though a promising technology, streaming assignment of accurate functional and taxonomic labels with nanopore reads remains challenging given: (i) individual reads can span multiple genes, (ii) individual reads may contain truncated genes, and pseudogenes, (iii) the error rate of the ONT platform that may introduce frameshifts and missense errors, and (iv) the computational costs of read-by-read analysis may exceed that of in-field computational equipment. Altogether, these challenges highlight a need for novel computational approaches. To this end, we describe SeqSeqscreen-Nano, a novel and portable computational platform for the characterization of novel pathogens. Based on results from simulated and synthetic microbial communities, SeqScreen-Nano can identify Open Reading Frames (ORFs) across the length of raw ONT reads and then use the predicted ORFs for accurate functional characterization and taxonomic classification. SeqScreen-Nano can run efficiently in a memory-constrained environment (less than 32GB of RAM), allowing it to be utilized in resource-limited settings. SeqScreen-Nano can also process reads directly from the ONT MinION sequencing device, enabling rapid, in-field characterization of previously unseen pathogens. SeqScreen-Nano (v4.0) is available on GitLab at:https://gitlab.com/treangenlab/seqscreen

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“…STRONG uses multiple metagenome samples from a time series to identify strains de novo from an assembly graph, and performs coassembly and genome binning ( Quince, 2021 ). The tool spacegraphcats can perform a local search of an assembly graph to identify variation that is not present in reference sequences ( Brown, 2020 ), and the tool KOMB uses assembly graphs to identify copy number variants and structural variants in a metagenomic read set ( Balaji, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Capturing variation in metagenomic assembly graphs with MetaCortex

et al. 2023

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Motivation The assembly of contiguous sequence from metagenomic samples presents a particular challenge, due to the presence of multiple species, often closely related, at varying levels of abundance. Capturing diversity within species, for example viral haplotypes, or bacterial strain-level diversity, is even more challenging. Results We present MetaCortex, a metagenome assembler that captures intra-species diversity by searching for signatures of local variation along assembled sequences in the underlying assembly graph and outputting these sequences in sequence graph format. We show that MetaCortex produces accurate assemblies with higher genome coverage and contiguity than other popular metagenomic assemblers on mock viral communities with high levels of strain level diversity, and on simulated communities containing simulated strains. Availability Source code is freely available to download from https://github.com/SR-Martin/metacortex, is implemented in C and supported on MacOS and Linux. The version used for the results presented in this paper is available at doi.org/10.5281/zenodo.7273627. Supplementary information Supplementary data are available at Bioinformatics online.

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KOMB: K-core based de novo characterization of copy number variation in microbiomes

Cited by 6 publications

References 96 publications

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses

SeqScreen-Nano: a computational platform for rapid, in-field characterization of previously unseen pathogens

Capturing variation in metagenomic assembly graphs with MetaCortex

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