清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

metaDMG – A Fast and Accurate Ancient DNA Damage Toolkit for Metagenomic Data

古代DNA 基因组 计算机科学 计算生物学 DNA DNA损伤 生物 数据挖掘 进化生物学 遗传学 基因 社会学 人口学 人口
作者
Christian Michelsen,Mikkel Winther Pedersen,Antonio Fernàndez-Guerra,Lei Zhao,T. C. Petersen,Thorfinn Sand Korneliussen
出处
期刊: [Cold Spring Harbor Laboratory]
被引量:26
标识
DOI:10.1101/2022.12.06.519264
摘要

Abstract Motivation Under favourable conditions DNA molecules can persist for hundreds of thousands of years. Such genetic remains make up invaluable resources to study past assemblages, populations, and even the evolution of species. However, DNA is subject to degradation, and hence over time decrease to ultra low concentrations which makes it highly prone to contamination by modern sources. Strict precautions are therefore necessary to ensure that DNA from modern sources does not appear in the final data is authenticated as ancient. The most generally accepted and widely applied authenticity for ancient DNA studies is to test for elevated deaminated cytosine residues towards the termini of the molecules: DNA damage. To date, this has primarily been used for single organisms and recently for read assemblies, however, these methods are not applicable for estimating DNA damage for ancient metagenomes with tens and even hundreds of thousands of species. Methods We present metaDMG , a novel framework and toolkit that allows for the estimation, quantification and visualization of postmortem damage for single reads, single genomes and even metagenomic environmental DNA by utilizing the taxonomic branching structure. It bypasses any need for initial classification, splitting reads by individual organisms, and realignment. We have implemented a Bayesian approach that combines a modified geometric damage profile with a beta-binomial model to fit the entire model to the individual misincorporations at all taxonomic levels. Results We evaluated the performance using both simulated and published environmental DNA datasets and compared to existing methods when relevant. We find metaDMG to be an order of magnitude faster than previous methods and more accurate – even for complex metagenomes. Our simulations show that metaDMG can estimate DNA damage at taxonomic levels down to 100 reads, that the estimated uncertainties decrease with increased number of reads and that the estimates are more significant with increased number of C to T misincorporations. Conclusion metaDMG is a state-of-the-art program for aDNA damage estimation and allows for the computation of nucleotide misincorporation, GC-content, and DNA fragmentation for both simple and complex ancient genomic datasets, making it a complete package for ancient DNA damage authentication.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
金秋发布了新的文献求助10
5秒前
CATH完成签到 ,获得积分10
15秒前
幽默的破茧完成签到 ,获得积分10
20秒前
Liou发布了新的文献求助50
22秒前
科研通AI6.3应助quit123采纳,获得10
23秒前
非洲大象完成签到,获得积分10
41秒前
47秒前
quit123发布了新的文献求助10
1分钟前
小通通完成签到 ,获得积分10
1分钟前
SCI的芷蝶完成签到 ,获得积分10
1分钟前
瘦瘦稀完成签到,获得积分10
1分钟前
激动的似狮完成签到,获得积分0
1分钟前
真君山山长完成签到,获得积分10
1分钟前
科研通AI6.3应助senli2018采纳,获得10
1分钟前
老石完成签到 ,获得积分10
1分钟前
富贵完成签到,获得积分10
1分钟前
梨落南山雪完成签到 ,获得积分10
1分钟前
姚芭蕉完成签到 ,获得积分0
1分钟前
2分钟前
af完成签到,获得积分10
2分钟前
快乐的千兰完成签到 ,获得积分10
2分钟前
2分钟前
senli2018发布了新的文献求助10
2分钟前
2分钟前
Xzx1995完成签到 ,获得积分10
2分钟前
一杯沧海完成签到 ,获得积分10
2分钟前
2分钟前
完美世界应助科研通管家采纳,获得10
2分钟前
自觉的忻完成签到,获得积分10
2分钟前
2分钟前
欣喜的沛芹完成签到 ,获得积分10
2分钟前
Copyright应助自觉的忻采纳,获得10
2分钟前
3分钟前
roro熊完成签到 ,获得积分10
3分钟前
往徕完成签到,获得积分10
3分钟前
慕青应助mirutio采纳,获得10
3分钟前
长尾巴的人类完成签到,获得积分10
3分钟前
4分钟前
疯狂的凡梦完成签到 ,获得积分10
4分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Nondestructive Testing Handbook: Vol. 4, Thermal and Infrared Testing (IR), 4th ed 800
Understanding Acculturation: The Process of Cultural Adjustment as Applied to International Migration 700
作者名:Kristopher P. Plain,悉尼大学的,目前只能查到其四篇论文,想找到其博士论文 590
Évora na Idade Média 555
Soil mites of the family Rhagidiidae (Actinedida: Eupodoidea). Morphology, Systematics, Ecology 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7370562
求助须知:如何正确求助?哪些是违规求助? 8978140
关于积分的说明 19087281
捐赠科研通 7012836
什么是DOI,文献DOI怎么找? 3224959
关于科研通互助平台的介绍 2388544
邀请新用户注册赠送积分活动 2205648