MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures

Girotto, Samuele; Pizzi, Cinzia; Comin, Matteo

doi:10.1093/bioinformatics/btw466

Cited by 66 publications

(55 citation statements)

References 43 publications

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“…It is likely that no signature can be effective on very short sequences, due to the little information contained. Some works [16,25,26] dealt successfully with this issue by adopting the following approach: short sequences were grouped, and then the signature was computed on this set as if it were one long sequence. However, this procedure might be risky for metagenomes with high diversity or low species coverage [52].…”

Section: Discussionmentioning

confidence: 99%

“…For instance, the binning tool MetaCluster successfully tested and implemented a signature based on tetranucleotide frequencies ranking [13,25], and showed its effectiveness for clustering metagenomic sequences. RAIphy used a signature computable on sets of sequences [16]; MetaProb computed normalized tetranucleotide frequencies on sequence sets [26]. Signature OFDEG was designed for error-free sequences of at least 8,000 bp [27].…”

Section: Introductionmentioning

confidence: 99%

“…Combination signature is a reordering of the features of the standard tetranucleotide frequencies signature; the adopted reordering makes combination signature strand-independent. Strand-independent signatures currently used in metagenomics [18,26] are derived form the symmetrized signature, that is obtained by summing the frequencies of reverse complementary tetranucleotides [24]. Therefore, combination signature proves that summing frequencies of inverse oligonucleotides, and thus loosing information, is not necessary to achieve a strand-independent signature.…”

Section: Introductionmentioning

confidence: 99%

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The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

Gori

Mavroeidis

Jetten

et al. 2017

Preprint

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An important problem in metagenomic data analysis is to identify the source organism, or at least taxon, of each sequence. Most methods tackle this problem in two steps by using an alignment-free approach: first the DNA sequences are represented as points of a real n-dimensional space via a mapping function then either clustering or classification algorithms are applied. Those mapping functions require to be genomic signatures: the dissimilarity between the mapped points must reflect the degree of phylogenetic similarity of the source species. Designing good signatures for metagenomics can be challenging due to the special characteristics of metagenomic sequences; most of the existing signatures were not designed accordingly and they were tested only on error-free sequences sampled from a few dozens of species.In this work we analyze comparatively the goodness of existing and novel signatures based on tetranucleotide frequencies via statistical models and computational experiments; we also study how they are affected by the generalized Chargaff's second parity rule (GCSPR), which states that in a given sequence longer than 50kbp, inverse oligonucleotides are approximately equally frequent. We analyze 38 million sequences of 150 bp-1,000 bp with 1% base-calling error, sampled from 1,284 microbes. Our models indicate that GCSPR reduces strand-dependence of signatures, that is, their values are less affected by the source strand; GCSPR is further exploited by some signatures to reduce the intra-species dispersion. Two novel signatures stand out both in the models and in the experiments: the combination signature and the operation signature. The former achieves strand-independence without grouping oligonucleotides; this could be valuable for alignment-free sequence comparison methods when distinguishing inverse oligonucleotides matters. Operation signature sums the frequencies of reverse, complement, and inverse tetranucleotides; having 72 features it reduces the computational intensity of the analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

Gori

Mavroeidis

Jetten

et al. 2017

Preprint

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“…Ideally the overlap would be computed using a reads overlap graph [17], but this approach is highly demanding in terms of RAM. Instead we use an alignment-free technique used in de-novo genome assembly, and that has been also successfully applied as an intermediate step in several metagenomic binning studies [14][15][16]19].…”

Section: Grouping Reads Based On Overlapsmentioning

confidence: 99%

“…The literature in this field is growing very rapidly (see, for example, [6,7,[9][10][11][12][13][14]) with a focus on improving the classification precision. Instead, in this study we aim to improve the recall in order to reduce the number of unclassified reads, that is one of the major issues when analyzing real microbiome datasets.…”

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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Fast Approximation of Frequent k-mers and Applications to Metagenomics

Pellegrina

Pizzi

Vandin

2019

Lecture Notes in Computer Science

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MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 66 publications

References 43 publications

The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

Higher recall in metagenomic sequence classification exploiting Overlapping Reads

Fast Approximation of Frequent k-mers and Applications to Metagenomics

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