• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

HabiSign:一种用于比较宏基因组和快速鉴定特定生境序列的新方法。

HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences.

机构信息

Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, 1 Software Units Layout, Madhapur, Hyderabad - 500081, Andhra Pradesh, India.

出版信息

BMC Bioinformatics. 2011;12 Suppl 13(Suppl 13):S9. doi: 10.1186/1471-2105-12-S13-S9. Epub 2011 Nov 30.

DOI:10.1186/1471-2105-12-S13-S9
PMID:22373355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3278849/
Abstract

BACKGROUND

One of the primary goals of comparative metagenomic projects is to study the differences in the microbial communities residing in diverse environments. Besides providing valuable insights into the inherent structure of the microbial populations, these studies have potential applications in several important areas of medical research like disease diagnostics, detection of pathogenic contamination and identification of hitherto unknown pathogens. Here we present a novel and rapid, alignment-free method called HabiSign, which utilizes patterns of tetra-nucleotide usage in microbial genomes to bring out the differences in the composition of both diverse and related microbial communities.

RESULTS

Validation results show that the metagenomic signatures obtained using the HabiSign method are able to accurately cluster metagenomes at biome, phenotypic and species levels, as compared to an average tetranucleotide frequency based approach and the recently published dinucleotide relative abundance based approach. More importantly, the method is able to identify subsets of sequences that are specific to a particular habitat. Apart from this, being alignment-free, the method can rapidly compare and group multiple metagenomic data sets in a short span of time.

CONCLUSIONS

The proposed method is expected to have immense applicability in diverse areas of metagenomic research ranging from disease diagnostics and pathogen detection to bio-prospecting. A web-server for the HabiSign algorithm is available at http://metagenomics.atc.tcs.com/HabiSign/.

摘要

背景

比较宏基因组项目的主要目标之一是研究居住在不同环境中的微生物群落的差异。除了深入了解微生物种群的固有结构外,这些研究在医学研究的几个重要领域具有潜在的应用价值,如疾病诊断、检测致病性污染和鉴定以前未知的病原体。在这里,我们提出了一种新颖而快速的、无需比对的方法,称为 HabiSign,它利用微生物基因组中四核苷酸使用模式来突出不同和相关微生物群落组成的差异。

结果

验证结果表明,与基于平均四核苷酸频率的方法和最近发表的基于二核苷酸相对丰度的方法相比,使用 HabiSign 方法获得的宏基因组特征能够准确地在生物群落、表型和物种水平上对宏基因组进行聚类。更重要的是,该方法能够识别特定栖息地特有的序列子集。除此之外,由于该方法是无需比对的,因此可以在短时间内快速比较和分组多个宏基因组数据集。

结论

该方法有望在从疾病诊断和病原体检测到生物勘探的宏基因组研究的各个领域具有广泛的适用性。HabiSign 算法的网络服务器可在 http://metagenomics.atc.tcs.com/HabiSign/ 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/566d10eb20cd/1471-2105-12-S13-S9-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/0cb68dcd50cd/1471-2105-12-S13-S9-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/dcb3ddee2b9b/1471-2105-12-S13-S9-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/566d10eb20cd/1471-2105-12-S13-S9-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/0cb68dcd50cd/1471-2105-12-S13-S9-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/dcb3ddee2b9b/1471-2105-12-S13-S9-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8cf/3278849/566d10eb20cd/1471-2105-12-S13-S9-3.jpg

相似文献

1
HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences.HabiSign:一种用于比较宏基因组和快速鉴定特定生境序列的新方法。
BMC Bioinformatics. 2011;12 Suppl 13(Suppl 13):S9. doi: 10.1186/1471-2105-12-S13-S9. Epub 2011 Nov 30.
2
Eu-Detect: an algorithm for detecting eukaryotic sequences in metagenomic data sets.Eu-Detect:一种用于在宏基因组数据集检测真核序列的算法。
J Biosci. 2011 Sep;36(4):709-17. doi: 10.1007/s12038-011-9105-2.
3
Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.用于宏基因组差异分析的k-mer谱适用性评估。
BMC Bioinformatics. 2016 Jan 16;17:38. doi: 10.1186/s12859-015-0875-7.
4
Metagenomic signatures of 86 microbial and viral metagenomes.86 个微生物和病毒宏基因组的宏基因组特征。
Environ Microbiol. 2009 Jul;11(7):1752-66. doi: 10.1111/j.1462-2920.2009.01901.x. Epub 2009 Mar 18.
5
i-rDNA: alignment-free algorithm for rapid in silico detection of ribosomal gene fragments from metagenomic sequence data sets.i-rDNA:一种无需序列比对的算法,可用于快速从宏基因组序列数据集中检测核糖体基因片段。
BMC Genomics. 2011 Nov 30;12 Suppl 3(Suppl 3):S12. doi: 10.1186/1471-2164-12-S3-S12.
6
Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes.社区分析器:一个用于可视化和比较微生物组中微生物群落结构的平台。
Genomics. 2013 Oct;102(4):409-18. doi: 10.1016/j.ygeno.2013.08.004. Epub 2013 Aug 24.
7
DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.DectICO:一种基于特征提取和动态选择的无比对监督宏基因组分类方法。
BMC Bioinformatics. 2015 Oct 7;16:323. doi: 10.1186/s12859-015-0753-3.
8
INDUS - a composition-based approach for rapid and accurate taxonomic classification of metagenomic sequences.INDUS-一种基于组合的方法,用于快速准确地对宏基因组序列进行分类。
BMC Genomics. 2011 Nov 30;12 Suppl 3(Suppl 3):S4. doi: 10.1186/1471-2164-12-S3-S4.
9
CS-SCORE: Rapid identification and removal of human genome contaminants from metagenomic datasets.CS-SCORE:从宏基因组数据集中快速识别和去除人类基因组污染物
Genomics. 2015 Aug;106(2):116-21. doi: 10.1016/j.ygeno.2015.04.005. Epub 2015 May 2.
10
Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes.通过验证的视角看宏基因组组装:评估和提高宏基因组组装基因组质量的最新进展。
Brief Bioinform. 2019 Jul 19;20(4):1140-1150. doi: 10.1093/bib/bbx098.

引用本文的文献

1
Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques.使用关联规则挖掘技术从宏基因组数据集中推断群落内微生物相互作用模式
PLoS One. 2016 Apr 28;11(4):e0154493. doi: 10.1371/journal.pone.0154493. eCollection 2016.
2
DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.DectICO:一种基于特征提取和动态选择的无比对监督宏基因组分类方法。
BMC Bioinformatics. 2015 Oct 7;16:323. doi: 10.1186/s12859-015-0753-3.
3
Comparison of metatranscriptomic samples based on k-tuple frequencies.

本文引用的文献

1
DiScRIBinATE: a rapid method for accurate taxonomic classification of metagenomic sequences.DiScRIBINATE:一种用于宏基因组序列准确分类的快速方法。
BMC Bioinformatics. 2010 Oct 15;11 Suppl 7(Suppl 7):S14. doi: 10.1186/1471-2105-11-S7-S14.
2
SPHINX--an algorithm for taxonomic binning of metagenomic sequences.SPHINX——一种用于宏基因组序列分类-bin 划分的算法。
Bioinformatics. 2011 Jan 1;27(1):22-30. doi: 10.1093/bioinformatics/btq608. Epub 2010 Oct 28.
3
SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences.
基于k元组频率的宏转录组样本比较。
PLoS One. 2014 Jan 2;9(1):e84348. doi: 10.1371/journal.pone.0084348. eCollection 2014.
4
Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots.块状体学:使用分类群注释的 GC 覆盖图探索原始基因组数据中的污染物、共生体和寄生虫。
Front Genet. 2013 Nov 29;4:237. doi: 10.3389/fgene.2013.00237. eCollection 2013.
5
Alignment-free supervised classification of metagenomes by recursive SVM.基于递归 SVM 的无比对监督宏基因组分类。
BMC Genomics. 2013 Sep 22;14:641. doi: 10.1186/1471-2164-14-641.
6
Comparison of metagenomic samples using sequence signatures.基于序列特征比较宏基因组样本。
BMC Genomics. 2012 Dec 27;13:730. doi: 10.1186/1471-2164-13-730.
7
Reference-independent comparative metagenomics using cross-assembly: crAss.基于交叉组装的参考独立比较宏基因组学:crAss。
Bioinformatics. 2012 Dec 15;28(24):3225-31. doi: 10.1093/bioinformatics/bts613. Epub 2012 Oct 16.
8
Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference.展望未来十年的大数据科学:来自 InCoB 十年和第一届 ISCB-Asia 联合会议。
BMC Bioinformatics. 2011;12 Suppl 13(Suppl 13):S1. doi: 10.1186/1471-2105-12-S13-S1. Epub 2011 Nov 30.
SOrt-ITEMS:基于序列直系同源性的方法,用于改进宏基因组序列的分类学估计。
Bioinformatics. 2009 Jul 15;25(14):1722-30. doi: 10.1093/bioinformatics/btp317. Epub 2009 May 13.
4
Metagenomic signatures of 86 microbial and viral metagenomes.86 个微生物和病毒宏基因组的宏基因组特征。
Environ Microbiol. 2009 Jul;11(7):1752-66. doi: 10.1111/j.1462-2920.2009.01901.x. Epub 2009 Mar 18.
5
A genealogical approach to quantifying lineage divergence.一种用于量化谱系分化的谱系学方法。
Evolution. 2008 Sep;62(9):2411-22. doi: 10.1111/j.1558-5646.2008.00442.x. Epub 2008 Jun 28.
6
Functional metagenomic profiling of nine biomes.九个生物群落的功能宏基因组分析
Nature. 2008 Apr 3;452(7187):629-32. doi: 10.1038/nature06810. Epub 2008 Mar 12.
7
The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific.“魔法师二号”全球海洋采样探险:从西北大西洋到东热带太平洋
PLoS Biol. 2007 Mar;5(3):e77. doi: 10.1371/journal.pbio.0050077.
8
The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families.“魔法师二号”全球海洋采样考察:拓展蛋白质家族的范畴
PLoS Biol. 2007 Mar;5(3):e16. doi: 10.1371/journal.pbio.0050016.
9
MEGAN analysis of metagenomic data.宏基因组数据的MEGAN分析
Genome Res. 2007 Mar;17(3):377-86. doi: 10.1101/gr.5969107. Epub 2007 Jan 25.
10
An obesity-associated gut microbiome with increased capacity for energy harvest.一种与肥胖相关的肠道微生物群,其能量获取能力增强。
Nature. 2006 Dec 21;444(7122):1027-31. doi: 10.1038/nature05414.