Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome

Bikel, Shirley; Valdez-Lara, Alejandra; Cornejo‐Granados, Fernanda; Rico, Karina; Canizales‐Quinteros, Samuel; Soberón, Xavier; Pozo‐Yauner, Luis del; Ochoa-Leyva, Adrián

doi:10.1016/j.csbj.2015.06.001

Cited by 200 publications

(165 citation statements)

References 180 publications

(241 reference statements)

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“…The percentage of sequence reads with similarity to known viral sequences depends on the database used and on how well sequences have been filtered. This percentage is less than 0.01% (Yang et al, 2011; Bikel et al, 2015). MetaVir, VIROME, and iVirus are few publicly available databases (Sullivan, 2015).…”

Section: “Which Entities Are Present?”mentioning

confidence: 77%

“…Thus, sequencing technologies that prioritize long-read lengths over those of short-read lengths are preferred (Wommack et al, 2008). However, sequencing technologies of long-read lengths, such as 454/Roche pyrosequencing, are about to be discontinued (Bikel et al, 2015). Bioinformatics tools that have been developed to analyze viruses from short sequence reads have accepted this challenge (Liu et al, 2007).…”

Section: “Which Entities Are Present?”mentioning

confidence: 99%

“…To tackle chimeras, the overlap–layout–consensus algorithms have achieved the assembly of viral genomes. Newbler has been extensively used in viral and bacterial shotgun metagenomic projects (Qin et al, 2010; Reyes et al, 2010; Bikel et al, 2015). Yet, it remains to be determined, if Newbler will be discontinued with the 454/Roche in 2016 (Bikel et al, 2015).…”

Section: “Which Entities Are Present?”mentioning

confidence: 99%

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Pharmacometabolomics Informs Viromics toward Precision Medicine

Balasopoulou¹,

Patrinos²,

Κάτσιλα³

2016

Front. Pharmacol.

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Nowadays, we are experiencing the big data era with the emerging challenge of single data interpretation. Although the advent of high-throughput technologies as well as chemo- and bio-informatics tools presents pan-omics data as the way forward to precision medicine, personalized health care and tailored-made therapeutics can be only envisaged when interindividual variability in response to/toxicity of xenobiotics can be interpreted and thus, predicted. We know that such variability is the net outcome of genetics (host and microbiota) and environmental factors (diet, lifestyle, polypharmacy, and microbiota) and for this, tremendous efforts have been made to clarify key-molecules from correlation to causality to clinical significance. Herein, we focus on the host–microbiome interplay and its direct and indirect impact on efficacy and toxicity of xenobiotics and we inevitably wonder about the role of viruses, as the least acknowledged ones. We present the emerging discipline of pharmacometabolomics-informed viromics, in which pre-dose metabotypes can assist modeling and prediction of interindividual response to/toxicity of xenobiotics. Such features, either alone or in combination with host genetics, can power biomarker discovery so long as the features are variable among patients, stable enough to be of predictive value, and better than pre-existing tools for predicting therapeutic efficacy/toxicity.

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Section: “Which Entities Are Present?”mentioning

confidence: 77%

Section: “Which Entities Are Present?”mentioning

confidence: 99%

Section: “Which Entities Are Present?”mentioning

confidence: 99%

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Pharmacometabolomics Informs Viromics toward Precision Medicine

Balasopoulou¹,

Patrinos²,

Κάτσιλα³

2016

Front. Pharmacol.

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“…This approach is somewhat similar to novel approaches in metagenomics, metatranscriptomics, and viromics [66], but there are also substantial differences such as that metatranscriptomics is based on RNA rather than DNA and that it groups sequences based on functionality rather than oligomer composition.…”

Section: Methodsmentioning

confidence: 99%

Additive methods for genomic signatures

et al. 2016

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BackgroundStudies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as “genomic signatures” (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar DNA sequences of the same organism can be very different. While the hypothesis that CGRs of mitochondrial DNA sequences can act as genomic signatures was validated for a snapshot of all sequenced mitochondrial genomes available in the NCBI GenBank sequence database, to our knowledge no such extensive analysis of CGRs of nuclear DNA sequences exists to date.ResultsWe analyzed an extensive dataset, totalling 1.45 gigabase pairs, of nuclear/nucleoid genomic sequences (nDNA) from 42 different organisms, spanning all major kingdoms of life. Our computational experiments indicate that CGR signatures of nDNA of two different origins cannot always be differentiated, especially if they originate from closely-related species such as H. sapiens and P. troglodytes or E. coli and E. fergusonii. To address this issue, we propose the general concept of additive DNA signature of a set (collection) of DNA sequences. One particular instance, the composite DNA signature, combines information from nDNA fragments and organellar (mitochondrial, chloroplast, or plasmid) genomes. We demonstrate that, in this dataset, composite DNA signatures originating from two different organisms can be differentiated in all cases, including those where the use of CGR signatures of nDNA failed or was inconclusive. Another instance, the assembled DNA signature, combines information from many short DNA subfragments (e.g., 100 basepairs) of a given DNA fragment, to produce its signature. We show that an assembled DNA signature has the same distinguishing power as a conventionally computed CGR signature, while using shorter contiguous sequences and potentially less sequence information.ConclusionsOur results suggest that, while CGR signatures of nDNA cannot always play the role of genomic signatures, composite and assembled DNA signatures (separately or in combination) could potentially be used instead. Such additive signatures could be used, e.g., with raw unassembled next-generation sequencing (NGS) read data, when high-quality sequencing data is not available, or to complement information obtained by other methods of species identification or classification.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1157-8) contains supplementary material, which is available to authorized users.

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“…Metagenome information does not tell us which subsets of genes are expressed at any given time. Keeping this in mind, whole metagenome sequencing is now being coupled with metatranscriptome sequencing to reveal the community gene expression profile 4 . This dynamic picture of community function can help identify the consequences of dysbiosis.…”

Section: Community Functionmentioning

confidence: 99%