Page 127 - Read Online
P. 127
Pham et al. Microbiome Res Rep 2024;3:25 https://dx.doi.org/10.20517/mrr.2024.01 Page 15 of 16
12. Chiang A, Dekker JP. From the pipeline to the bedside: advances and challenges in clinical metagenomics. J Infect Dis
2020;221:S331-40. DOI PubMed PMC
13. Sharpton TJ. An introduction to the analysis of shotgun metagenomic data. Front Plant Sci 2014;5:209. DOI PubMed PMC
14. Piro VC, Lindner MS, Renard BY. DUDes: a top-down taxonomic profiler for metagenomics. Bioinformatics 2016;32:2272-80. DOI
PubMed
15. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. Metagenomic microbial community profiling using
unique clade-specific marker genes. Nat Methods 2012;9:811-4. DOI PubMed PMC
16. Tran Q, Pham DT, Phan V. Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities. BMC
Bioinformatics 2017;18:499. DOI PubMed PMC
17. Popic V, Kuleshov V, Snyder M, Batzoglou S. Fast metagenomic binning via hashing and bayesian clustering. J Comput Biol
2018;25:677-88. DOI PubMed
18. Qian J, Comin M. MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage. BMC
Bioinformatics 2019;20:367. DOI PubMed PMC
19. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics
assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674-6. DOI PubMed
20. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017;27:824-
34. DOI PubMed PMC
21. Lindner MS, Renard BY. Metagenomic profiling of known and unknown microbes with microbeGPS. PLoS One 2015;10:e0117711.
DOI PubMed PMC
22. Pham DT, Gao S, Phan V. An accurate and fast alignment-free method for profiling microbial communities. J Bioinform Comput Biol
2017;15:1740001. DOI PubMed
23. Müller A, Hundt C, Hildebrandt A, Hankeln T, Schmidt B. MetaCache: context-aware classification of metagenomic reads using
minhashing. Bioinformatics 2017;33:3740-8. DOI PubMed
24. Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using
discriminative k-mers. BMC Genomics 2015;16:236. DOI PubMed PMC
25. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014;15:R46.
DOI PubMed PMC
26. Lindgreen S, Adair KL, Gardner PP. An evaluation of the accuracy and speed of metagenome analysis tools. Sci Rep 2016;6:19233.
DOI PubMed PMC
27. Stranneheim H, Käller M, Allander T, Andersson B, Arvestad L, Lundeberg J. Classification of DNA sequences using Bloom filters.
Bioinformatics 2010;26:1595-600. DOI PubMed PMC
28. Srikakulam SK, Keller S, Dabbaghie F, Bals R, Kalinina OV. MetaProFi: an ultrafast chunked Bloom filter for storing and querying
protein and nucleotide sequence data for accurate identification of functionally relevant genetic variants. Bioinformatics 2023;39:btad101.
DOI PubMed PMC
29. Bradley P, den Bakker HC, Rocha EPC, McVean G, Iqbal Z. Ultrafast search of all deposited bacterial and viral genomic data. Nat
Biotechnol 2019;37:152-9. DOI PubMed PMC
30. Bingmann T, Bradley P, Gauger F, Iqbal Z. COBS: a compact bit-sliced signature index. In: Brisaboa N, Puglisi S, editors. String
processing and information retrieval. Cham: Springer; 2019. pp. 285-303. DOI
31. Lemane T, Medvedev P, Chikhi R, Peterlongo P. kmtricks: efficient and flexible construction of Bloom filters for large sequencing
data collections. Bioinform Adv 2022;2:vbac029. DOI PubMed PMC
32. Bloom BH. Space/time trade-offs in hash coding with allowable errors. Commun ACM 1970;13:422-6. DOI
33. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825-30.
Available from: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf?ref=https:/. [Last accessed on 28 March
2024].
34. Buitinck L, Louppe G, Blondel M, et al. API design for machine learning software: experiences from the scikit-learn project. arXiv.
[Preprint.] Sep 1, 2013 [accessed 2024 Mar 28]. Available from: https://arxiv.org/abs/1309.0238.
35. Mende DR, Waller AS, Sunagawa S, et al. Assessment of metagenomic assembly using simulated next generation sequencing data.
PLoS One 2012;7:e31386. DOI PubMed PMC
36. Sczyrba A, Hofmann P, Belmann P, et al. Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software.
Nat Methods 2017;14:1063-71. DOI PubMed PMC
37. Ye SH, Siddle KJ, Park DJ, Sabeti PC. Benchmarking metagenomics tools for taxonomic classification. Cell 2019;178:779-94. DOI
PubMed PMC
38. Salzberg SL, Breitwieser FP, Kumar A, et al. Next-generation sequencing in neuropathologic diagnosis of infections of the nervous
system. Neurol Neuroimmunol Neuroinflamm 2016;3:e251. DOI PubMed PMC
39. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019;20:257. DOI PubMed PMC
40. Breitwieser FP, Baker DN, Salzberg SL. KrakenUniq: confident and fast metagenomics classification using unique k-mer counts.
Genome Biol 2018;19:198. DOI PubMed PMC
41. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res
2016;26:1721-9. DOI PubMed PMC