Classification of metagenomic sequences: methods and challenges

Mande, Sharmila S.; Mohammed, Monzoorul Haque; Ghosh, Tarini Shankar

doi:10.1093/bib/bbs054

Cited by 203 publications

(152 citation statements)

References 56 publications

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“…Sequence data can be segregated into bins representing distinct Operational Taxonomic Units (OTU), which may mean an individual organism or species, or a group that shares a certain set of observed characters. Two classes of binning processes include similarity-based (or align-based) and composition-based strategies [59]. Similarity-based binning searches for sequence similarities between samples and reference genomes in existing public databases.…”

Section: Network Inferencementioning

confidence: 99%

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

et al. 2014

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Microorganisms in nature form diverse communities that dynamically change in structure and function in response to environmental variations. As a complex adaptive system, microbial communities show higher-order properties that are not present in individual microbes, but arise from their interactions. Predictive mathematical models not only help to understand the underlying principles of the dynamics and emergent properties of natural and synthetic microbial communities, but also provide key knowledge required for engineering them. In this article, we provide an overview of mathematical tools that include not only current mainstream approaches, but also less traditional approaches that, in our opinion, can be potentially useful. We discuss a broad range of methods ranging from low-resolution supra-organismal to high-resolution individual-based modeling. Particularly, we highlight the integrative approaches that synergistically combine disparate methods. In conclusion, we provide our outlook for the key aspects that should be further developed to move microbial community modeling towards greater predictive power.

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Section: Network Inferencementioning

confidence: 99%

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

et al. 2014

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“…Moreover, we are presented with fragmented assemblies due to insufficient coverage, sequencing errors, sequence repetition, and genetic diversity. Consequently, alignment-free techniques [1], [2] have been introduced as an alternative way to analyse metagenomic data [3] by incorporating species-specific genomic signatures extracted by calculating the normalised frequency of k-mers of a specific size, e.g., commonly k = 4. This frequency is obtained by counting the occurrences of each k-mer combination and represents a feature vector in high-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Marginalised stack denoising autoencoders for metagenomic data binning

Kouchaki

Tirunagari

Tapinos

et al. 2017

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Abstract-Shotgun sequencing has facilitated the analysis of complex microbial communities. Recently we have shown how local binary patterns (LBP) from image processing can be used to analyse the sequenced samples. LBP codes represent the data in a sparse high dimensional space. To improve the performance of our pipeline, marginalised stacked autoencoders are used here to learn frequent LBP codes and map the high dimensional space to a lower dimension dense space. We demonstrate its performance using both low and high complexity simulated metagenomic data and compare the performance of our method with several existing techniques including principal component analysis (PCA) in the dimension reduction step and k-mer frequency in feature extraction step.

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“…In general, there are two methods to detect the taxonomic content of environmental samples: (1) sequencing phylogenetic marker genes, e.g. 16S rRNA, that requires PCR amplicons analysis; (2) Next Generation Sequencing, where all the genomic material of the sample is sequenced.…”

Section: Introductionmentioning

confidence: 99%

Higher recall in metagenomic sequence classification exploiting Overlapping Reads

Girotto

Comin

Pizzi

2016

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

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Background:In recent years several different fields, such as ecology, medicine and microbiology, have experienced an unprecedented development due to the possibility of direct sequencing of microbioimic samples. Among problems that researchers in the field have to deal with, taxonomic classification of metagenomic reads is one of the most challenging. State of the art methods classify single reads with almost 100% precision. However, very often, the performance in terms of recall falls at about 50%. As a consequence, state-of-the-art methods are indeed capable of correctly classify only half of the reads in the sample. How to achieve better performances in terms of overall quality of classification remains a largely unsolved problem. Results: In this paper we propose a method for metagenomics CLassification Improvement with Overlapping Reads (CLIOR), that exploits the information carried by the overlapping reads graph of the input read dataset to improve recall, f-measure, and the estimated abundance of species. In this work, we applied CLIOR on top of the classification produced by the classifier Clark-l. Experiments on simulated and synthetic metagenomes show that CLIOR can lead to substantial improvement of the recall rate, sometimes doubling it. On average, on simulated datasets, the increase of recall is paired with an higher precision too, while on synthetic datasets it comes at expenses of a small loss of precision. On experiments on real metagenomes CLIOR is able to assign many more reads while keeping the abundance ratios in line with previous studies. Conclusions: Our results showed that with CLIOR is possible to boost the recall of a state-of-the-art metagenomic classifier by inferring and/or correcting the assignment of reads with missing or erroneous labeling. CLIOR is not restricted to the reads classification algorithm used in our experiments, but it may be applied to other methods too. Finally, CLIOR does not need large computational resources, and it can be run on a laptop.

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Classification of metagenomic sequences: methods and challenges

Cited by 203 publications

References 56 publications

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

Marginalised stack denoising autoencoders for metagenomic data binning

Higher recall in metagenomic sequence classification exploiting Overlapping Reads

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