AMBER: Assessment of Metagenome BinnERs

GigaScience

Volumn 7, Issue 6, 2018, Pages

  Hofmann, Fernando Meyer Peter ^a,b   Belmann, Peter ^a,b,c   Garrido Oter, Ruben ^d,e   Fritz, Adrian ^a,b   Sczyrba, Alexander ^c   McHardy, Alice C ^a,b  

^a HELMHOLTZ CENTRE FOR INFECTION RESEARCH   (Germany)

^b Technische Universität Braunschweig and Braunschweig Integrated Centre of Systems Biology   (Germany)

^c BIELEFELD UNIVERSITY   (Germany)

^d MAX PLANCK INSTITUTE FOR PLANT BREEDING RESEARCH   (Germany)

^e Heinrich Heine Universität and Cluster of Excellence on Plant Sciences   (Germany)

Author keywords

Benchmarking; Binning; Bioboxes; Metagenomics; Performance metrics

Indexed keywords

BENCHMARKING; HUMAN; LICENCE; METAGENOME; METAGENOMICS; NOTE; CHROMOSOMAL MAPPING; GENETIC DATABASE; SOFTWARE;

CHROMOSOME MAPPING; DATABASES, GENETIC; METAGENOME; SOFTWARE;

EID: 85050894511     PISSN: None     EISSN: 2047217X     Source Type: Journal    
DOI: 10.1093/gigascience/giy069     Document Type: Note

References (28)

1. Sczyrba A, Hofmann P, Belmann P, et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat Methods 2017;14(11):1063-71
2. Belmann P, Bremges A, Dahms E, et al. Benchmark data sets, software results and reference data for the first CAMI challenge. GigaScience Database 2017. http://dx.doi.org/10.5524/100344
3. Parks HD, Imelfort M, Skennerton T, et al. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015;25(7):1043-55
4. CAMISIM metagenome simulator. https://github.com/CAMIchallenge/CAMISIM. Accessed 27 Apr 2018
5. Fritz A, Hofmann P, Majda S, et al. CAMISIM: Simulating metagenomes and microbial communities. bioRxiv 2018;300970
6. Langmead B, Trapnell C, Pop M, et al. Ultrafast and memory efficient alignment of short DNA sequences to the human genome. Genome Biol 2009;10(3):R25-10
7. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25(14):1754-60
8. Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 2016;32(7):1088-90
9. Belmann P, Dröge J, Bremges A, et al. Bioboxes: standardised containers for interchangeable bioinformatics software. GigaScience 2015;4:47
10. Bioboxes binning format. https://github.com/bioboxes/rfc/tree/master/data-format. Accessed 27 Apr 2018
11. Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016;32:605-7
12. AMBER GitHub repository. https://github.com/CAMI-challe nge/AMBER. Accessed 27 Apr 2018
13. Baldi P, Brunak S, Chauvin Y, et al. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000;16:412-24
14. Tsoumakas G, Katakis I, Vlahavas I. Mining multi-label data. In: Maimon O, Rokach L, eds. Data Mining and Knowledge Discovery Handbook. Springer-Verlag, 2010
15. Rand WM. Objective criteria for the evaluation of clustering methods. J Am Statist Assoc 1971;66(336):846-50
16. Alneberg J, Bjarnason BS, de Bruijn I, et al. Binning metagenomic contigs by coverage and composition. Nat Methods 2014;11:1144-6
17. AMBER example HTML visualization of calculated metrics. https://cami-challenge.github.io/AMBER/. Accessed 30 May 2018
18. National Center for Biotechnology Information [Internet]. Bethesda, MD: National Library of Medicine (US), National Center for Biotechnology Information, 1988
19. Parks DH, Rinke C, Chuvochina M, et al. Recovery of nearly 8, 000 metagenome-assembled genomes substantially expands the tree of life. Nature Microbiology 2017;2:1533-42
20. Kang DD, Froula J, Egan R, et al. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165
21. Strous M, Kraft B, Bisdorf R, et al. The binning of metagenomic contigs for microbial physiology of mixed cultures. Frontiers in Microbiology 2012;3:410
22. Lin HH, Liao YC. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep 2016;6:24175
23. Graham ED, Heidelberg JF, Tully BJ. BinSanity: unsupervised clustering of environmentalmicrobial assemblies using coverage and affinity propagation. PeerJ 2016;5:e3035
24. Lu YY, Chen T, Fuhrman JA, et al. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge. Bioinformatics 2017;33(6):791-8
25. Sieber CMK, Probst J, Sharrar A, et al. Recovery of genomes from metagenomes via a dereplication, aggregation, and scoring strategy. bioRxiv 2017;107789
26. Critical Assessment of Metagenome Interpretation (CAMI). http://www.cami-challenge.org. Accessed 27 Apr 2018
27. CAMI benchmarking portal. https://data.cami-challenge.or g. Accessed 27 Apr 2018
28. Meyer F, Hofmann P, Belmann P et al. Supporting data for "AMBER: Assessment of Metagenome BinnERs." GigaScience Database 2018. http://dx.doi.org/10.5524/100454