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RefSeq 数据库的增长影响了基于 k-mer 的最低共同祖先物种鉴定的准确性。

RefSeq database growth influences the accuracy of k-mer-based lowest common ancestor species identification.

机构信息

Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.

Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, MD, USA.

出版信息

Genome Biol. 2018 Oct 30;19(1):165. doi: 10.1186/s13059-018-1554-6.

DOI:10.1186/s13059-018-1554-6
PMID:30373669
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6206640/
Abstract

In order to determine the role of the database in taxonomic sequence classification, we examine the influence of the database over time on k-mer-based lowest common ancestor taxonomic classification. We present three major findings: the number of new species added to the NCBI RefSeq database greatly outpaces the number of new genera; as a result, more reads are classified with newer database versions, but fewer are classified at the species level; and Bayesian-based re-estimation mitigates this effect but struggles with novel genomes. These results suggest a need for new classification approaches specially adapted for large databases.

摘要

为了确定数据库在分类序列分类中的作用，我们考察了数据库随时间的变化对基于 k-mer 的最小编辑共同祖先分类的影响。我们得出了三个主要发现：添加到 NCBI RefSeq 数据库中的新物种数量大大超过了新属的数量；因此，更多的读取内容可以使用更新的数据库版本进行分类，但在物种水平上的分类却更少；基于贝叶斯的重新估计可以缓解这种影响，但对于新的基因组则较为困难。这些结果表明，需要专门针对大型数据库开发新的分类方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/2f215c542f4a/13059_2018_1554_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/e1dd687ea002/13059_2018_1554_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/f1755d6e58f6/13059_2018_1554_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/2f215c542f4a/13059_2018_1554_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/e1dd687ea002/13059_2018_1554_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/f1755d6e58f6/13059_2018_1554_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03fc/6206640/2f215c542f4a/13059_2018_1554_Fig5_HTML.jpg

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本文引用的文献

[1]
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts.

Genome Biol. 2018-11-16

[2]
A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life.

Nat Biotechnol. 2018-8-27

[3]
Taxonomy annotation and guide tree errors in 16S rRNA databases.

PeerJ. 2018-6-12

[4]
ReprDB and panDB: minimalist databases with maximal microbial representation.

Microbiome. 2018-1-18

[5]
VecScreen_plus_taxonomy: imposing a tax(onomy) increase on vector contamination screening.

Bioinformatics. 2018-3-1

[6]
Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software.

Nat Methods. 2017-11

[7]
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

Genome Biol. 2017-9-21

[8]
Draft Genome Sequences from a Novel Clade of Strains, Isolated from the International Space Station.

Genome Announc. 2017-8-10

[9]
Metagenomes of Soil Samples from an Established Perennial Cropping System of Asparagus Treated with Biostimulants in Southern France.

Genome Announc. 2017-6-15

[10]
The Public Health Impact of a Publically Available, Environmental Database of Microbial Genomes.

Front Microbiol. 2017-5-9

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