MOCAT2: a metagenomic assembly, annotation and profiling framework

Kultima, Jens Roat; Coelho, Luís Pedro; Forslund, Kristoffer; Huerta-Cepas, Jaime; Li, Simone S.; Driessen, Marja; Voigt, Anita Y.; Zeller, Georg; Sunagawa, Shinichi; Bork, Peer

doi:10.1093/bioinformatics/btw183

Cited by 165 publications

(137 citation statements)

References 42 publications

(27 reference statements)

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“…This benchmarking indicates that the best‐hit approach is more than 95% accurate (Appendix Fig S4). Annotation of the reconstructed gene complements was transferred from a published annotation of the IGC (Kultima et al , 2016). From this study, we specifically used annotations to eggNOG (Huerta‐Cepas et al , 2016b), KEGG (Kanehisa & Goto, 2000), and SEED (Overbeek et al , 2005) as indicated in the main text.…”

Section: Methodsmentioning

confidence: 99%

Subspecies in the global human gut microbiome

Costea

Coelho

Sunagawa

et al. 2017

Molecular Systems Biology

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Population genomics of prokaryotes has been studied in depth in only a small number of primarily pathogenic bacteria, as genome sequences of isolates of diverse origin are lacking for most species. Here, we conducted a large‐scale survey of population structure in prevalent human gut microbial species, sampled from their natural environment, with a culture‐independent metagenomic approach. We examined the variation landscape of 71 species in 2,144 human fecal metagenomes and found that in 44 of these, accounting for 72% of the total assigned microbial abundance, single‐nucleotide variation clearly indicates the existence of sub‐populations (here termed subspecies). A single subspecies (per species) usually dominates within each host, as expected from ecological theory. At the global scale, geographic distributions of subspecies differ between phyla, with Firmicutes subspecies being significantly more geographically restricted. To investigate the functional significance of the delineated subspecies, we identified genes that consistently distinguish them in a manner that is independent of reference genomes. We further associated these subspecies‐specific genes with properties of the microbial community and the host. For example, two of the three Eubacterium rectale subspecies consistently harbor an accessory pro‐inflammatory flagellum operon that is associated with lower gut community diversity, higher host BMI, and higher blood fasting insulin levels. Using an additional 676 human oral samples, we further demonstrate the existence of niche specialized subspecies in the different parts of the oral cavity. Taken together, we provide evidence for subspecies in the majority of abundant gut prokaryotes, leading to a better functional and ecological understanding of the human gut microbiome in conjunction with its host.

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Section: Methodsmentioning

confidence: 99%

Subspecies in the global human gut microbiome

Costea

Coelho

Sunagawa

et al. 2017

Molecular Systems Biology

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131

143

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“…Functional annotation of shotgun metagenomic reads can be accomplished by a variety of recently introduced frameworks 113–118 , and is typically based on mapping these reads to genes or protein domains with known functional classifications. Read mapping is done either by aligning each read to a reference database of gene or protein sequences or by using probabilistic models (such as Hidden Markov Models; HMMs) to evaluate the likelihood that a given read belongs to a specific protein family or domain.…”

Section: High-resolution Characterization Of the Microbiome's Functiomentioning

confidence: 99%

High-resolution characterization of the human microbiome

Noecker

McNally

Eng

et al. 2017

Translational Research

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The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically employ targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically- and clinically-relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.

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“…These analyses can be performed using locally installed software; alternatively, for users with no bioinformatic training, there are different suites for analysis, such as MG-RAST (Wilke et al, 2016), IMG/M (Markowitz et al, 2012; Wilke et al, 2016), JCVI and Metagenomics Reports (METAREP) (Goll et al, 2010; Markowitz et al, 2012; Wilke et al, 2016) or MEGAN (Huson and Weber, 2013), MetAMOS (Treangen et al, 2013), MOCAT2 (Kultima et al, 2016), and MetaTrans (Martinez et al, 2016) which are software designed to simplify all metagenomics or metatranscriptomics pipeline; preprocessing, assembly, annotation and analysis.…”

Section: Characterization Of the Microbiome Using High-throughput Tecmentioning

confidence: 99%

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

et al. 2016

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It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

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MOCAT2: a metagenomic assembly, annotation and profiling framework

Cited by 165 publications

References 42 publications

Subspecies in the global human gut microbiome

Subspecies in the global human gut microbiome

High-resolution characterization of the human microbiome

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

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