five

Hybrid Enterobacteriaceae assemblies using PacBio+Illumina or ONT+Illumina sequencing|基因组组装数据集|测序技术数据集

收藏
Mendeley Data2024-06-25 更新2024-06-27 收录
基因组组装
测序技术
下载链接:
https://figshare.com/articles/dataset/Hybrid_Enterobacteriaceae_assemblies_using_PacBio_Illumina_or_ONT_Illumina_sequencing/7649051/3
下载链接
链接失效反馈
资源简介:
Data associated with: De Maio, Shaw, et al. on behalf of the REHAB consortium (2019), Comparison of long-read sequencing technologies in the hybrid assembly of complex bacterial genomes. biorxiv 530824 Illumina sequencing allows rapid, cheap and accurate whole genome bacterial analyses, but short reads (<300 bp) do not usually enable complete genome assembly. Long read sequencing greatly assists with resolving complex bacterial genomes, particularly when combined with short-read Illumina data (hybrid assembly). However, it is not clear how different long-read sequencing methods impact on assembly accuracy. In this study, we compared hybrid assemblies for 20 bacterial isolates, including two reference strains, using Illumina sequencing and long reads from either Oxford Nanopore Technologies (ONT) or from SMRT Pacific Biosciences (PacBio) sequencing platforms. This set of files includes all hybrid assemblies produced using Unicycler with different sequencing approaches and strategies. Each isolate has 8 hybrid assemblies = 4 x ONT-Illumina + 4 x PacBio-Illumina. There are a total of 158 hybrid assemblies from the full data as two assemblies did not finish (8x20 - 2 = 160 - 2 = 158). Additionally, there are Assemblies were produced from different long read preparation strategies. Hybrid assemblies with Unicycler (n1 = 158): • Basic: no filtering or correction of reads (i.e. all long reads available used for assembly). • Corrected: Long reads were error-corrected and subsampled (preferentially selecting longest reads) to 30-40x coverage using Canu (v1.5, https://github.com/marbl/canu) with default options. • Filtered: long reads were filtered using Filtlong (v0.1.1, https://github.com/rrwick/Filtlong) by using Illumina reads as an external reference for read quality and either removing 10% of the worst reads or by retaining 500Mbp in total, whichever resulted in fewer reads. We also removed reads shorter than 1kb and used the --trim and --split 250 options. • Subsampled: we randomly subsampled long reads to leave approximately 600Mbp (corresponding to a long read coverage around 100x). Long-read only assemblies (n2 = 20 x 2 x 2 = 80):• Flye: we ran Flye (https://github.com/fenderglass/Flye) with the options --plasmids --meta, which have been shown to improve the assemblies of plasmids in bacterial genomes (see: https://github.com/rrwick/Long-read-assembler-comparison) • Pilon: the Flye assemblies were then polished with Illumina short-reads using Pilon (https://github.com/broadinstitute/pilon). Assembly file names have the following format: ${sample-name}_${preparation-strategy}_${long-read-sequencing}.fastae.g. for sample CFT073 the filtered PacBio-Illumina assembly is: CFT073_filtered_pacbio.fasta Also included are assemblies produced after subsampling long-read data to ~10X genome coverage for the following strategies: "basic" (hybrid) and long-read ("flye" and "pilon"). There are n3 = 20 x 3 x 2 = 120 of these assemblies. These have a '10X' preceding the preparation strategy. The total number of assemblies is n1+n2+n3=158+80+120=358. See the associated preprint for more details: https://doi.org/10.1101/530824
创建时间:
2023-06-28
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4099个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

Figshare

Figshare是一个在线数据共享平台,允许研究人员上传和共享各种类型的研究成果,包括数据集、论文、图像、视频等。它旨在促进科学研究的开放性和可重复性。

figshare.com 收录

中国区域交通网络数据集

该数据集包含中国各区域的交通网络信息,包括道路、铁路、航空和水路等多种交通方式的网络结构和连接关系。数据集详细记录了各交通节点的位置、交通线路的类型、长度、容量以及相关的交通流量信息。

data.stats.gov.cn 收录

2022_长沙市标准地图行政区划示意版32开

基于湖南省基础地理信息数据库,依据湖南省行政区划界线标准画法和最新境界、标准地名成果,采用其他自然地理要素和人文专题要素的现势性资料编制而成。

湖南大数据交易所 收录

中国1km分辨率逐月降水量数据集(1901-2024)

该数据集为中国逐月降水量数据,空间分辨率为0.0083333°(约1km),时间为1901.1-2024.12。数据格式为NETCDF,即.nc格式。该数据集是根据CRU发布的全球0.5°气候数据集以及WorldClim发布的全球高分辨率气候数据集,通过Delta空间降尺度方案在中国降尺度生成的。并且,使用496个独立气象观测点数据进行验证,验证结果可信。本数据集包含的地理空间范围是全国主要陆地(包含港澳台地区),不含南海岛礁等区域。为了便于存储,数据均为int16型存于nc文件中,降水单位为0.1mm。 nc数据可使用ArcMAP软件打开制图; 并可用Matlab软件进行提取处理,Matlab发布了读入与存储nc文件的函数,读取函数为ncread,切换到nc文件存储文件夹,语句表达为:ncread (‘XXX.nc’,‘var’, [i j t],[leni lenj lent]),其中XXX.nc为文件名,为字符串需要’’;var是从XXX.nc中读取的变量名,为字符串需要’’;i、j、t分别为读取数据的起始行、列、时间,leni、lenj、lent i分别为在行、列、时间维度上读取的长度。这样,研究区内任何地区、任何时间段均可用此函数读取。Matlab的help里面有很多关于nc数据的命令,可查看。数据坐标系统建议使用WGS84。

国家青藏高原科学数据中心 收录

The sex [Male (1) and female (2)] age (in years), weight (in lbs), #GPS Points (total after filtering), and the 100% MCP, 95% KDE, and 50% KDE home ranges (ha) for all cats sampled in the study. All cats were desexed. The personality scores (shown as a percent), were obtained from a survey, based on the “Feline Five” (Litchfield et al., 2017), that evaluated how much owners agreed or disagreed that their cats showed certain traits. Traits were then summed and converted into percentages. Bold cats are considered to have a low neuroticism score. Road density was estimated by summing the road lengths, measured in meters, within a fixed boundary centred on each cat’s mean latitude and longitude coordinates. The variable “major road” indicated the presence (1) or absence (0) of a major road near the cat’s home range. Roads were labeled as “major” based on Google Maps’ classification, related to traffic rates, and through “ground-truthing”.

Domestic cats (<i>Felis catus</i>) play a dual role in society as both companion animals and predators. When provided with unsupervised outdoor access, cats can negatively impact native wildlife and create public health and animal welfare challenges. The effective implementation of management strategies, such as buffer zones or curfews, requires an understanding of home range size, the factors that influence their movement, and the types of habitats they use. Here, we used a community/citizen scientist approach to collect movement and habitat use data using GPS collars on owned outdoor cats in the Kitchener-Waterloo-Cambridge-Guelph region, southwestern Ontario, Canada.

DataCite Commons 收录