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An artificial functional family filter in homolog searching in next-generation sequencing metagenomics.

ABSTRACT: In functional metagenomics, BLAST homology search is a common method to classify metagenomic reads into protein/domain sequence families such as Clusters of Orthologous Groups of proteins (COGs) in order to quantify the abundance of each COG in the community. The resulting functional profile of the community is then used in downstream analysis to correlate the change in abundance to environmental perturbation, clinical variation, and so on. However, the short read length coupled with next-generation sequencing technologies poses a barrier in this approach, essentially because similarity significance cannot be discerned by searching with short reads. Consequently, artificial functional families are produced, in which those with a large number of reads assigned decreases the accuracy of functional profile dramatically. There is no method available to address this problem. We intended to fill this gap in this paper. We revealed that BLAST similarity scores of homologues for short reads from COG protein members coding sequences are distributed differently from the scores of those derived elsewhere. We showed that, by choosing an appropriate score cut-off, we are able to filter out most artificial families and simultaneously to preserve sufficient information in order to build the functional profile. We also showed that, by incorporated application of BLAST and RPS-BLAST, some artificial families with large read counts can be further identified after the score cutoff filtration. Evaluated on three experimental metagenomic datasets with different coverages, we found that the proposed method is robust against read coverage and consistently outperforms the other E-value cutoff methods currently used in literatures.

SUBMITTER: Du R

PROVIDER: S-EPMC3597637 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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An artificial functional family filter in homolog searching in next-generation sequencing metagenomics.

Du Ruofei R Mercante Donald D Fang Zhide Z

PloS one 20130314 3

In functional metagenomics, BLAST homology search is a common method to classify metagenomic reads into protein/domain sequence families such as Clusters of Orthologous Groups of proteins (COGs) in order to quantify the abundance of each COG in the community. The resulting functional profile of the community is then used in downstream analysis to correlate the change in abundance to environmental perturbation, clinical variation, and so on. However, the short read length coupled with next-genera ...[more]

PMID: 23516532

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Project description:BackgroundPleural infection is a common and severe disease with high morbidity and mortality worldwide. The knowledge of pleural infection bacteriology remains incomplete, as pathogen detection methods based on culture have insufficient sensitivity and are biased to selected microbes. We designed a study with the aim to discover and investigate the total microbiome of pleural infection and assess the correlation between bacterial patterns and 1-year survival of patients.MethodsWe assessed 243 pleural fluid samples from the PILOT study, a prospective observational study on pleural infection, with 16S rRNA next generation sequencing. 20 pleural fluid samples from patients with pleural effusion due to a non-infectious cause and ten PCR-grade water samples were used as controls. Downstream analysis was done with the DADA2 pipeline. We applied multivariate Cox regression analyses to investigate the association between bacterial patterns and 1-year survival of patients with pleural infection.FindingsPleural infection was predominately polymicrobial (192 [79%] of 243 samples), with diverse bacterial frequencies observed in monomicrobial and polymicrobial disease and in both community-acquired and hospital-acquired infection. Mixed anaerobes and other Gram-negative bacteria predominated in community-acquired polymicrobial infection whereas Streptococcus pneumoniae prevailed in monomicrobial cases. The presence of anaerobes (hazard ratio 0·46, 95% CI 0·24-0·86, p=0·015) or bacteria of the Streptococcus anginosus group (0·43, 0·19-0·97, p=0·043) was associated with better patient survival, whereas the presence (5·80, 2·37-14·21, p<0·0001) or dominance (3·97, 1·20-13·08, p=0·024) of Staphylococcus aureus was linked with lower survival. Moreover, dominance of Enterobacteriaceae was associated with higher risk of death (2·26, 1·03-4·93, p=0·041).InterpretationPleural infection is a predominantly polymicrobial infection, explaining the requirement for broad spectrum antibiotic cover in most individuals. High mortality infection associated with S aureus and Enterobacteriaceae favours more aggressive, with a narrower spectrum, antibiotic strategies.FundingUK Medical Research Council, National Institute for Health Research Oxford Biomedical Research Centre, Wellcome Trust, Oxfordshire Health Services Research Committee, Chinese Academy of Medical Sciences, and John Fell Fund.

Project description:The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.

Dataset Information

An artificial functional family filter in homolog searching in next-generation sequencing metagenomics.

Publications

An artificial functional family filter in homolog searching in next-generation sequencing metagenomics.

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