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Binnacle-估计图脚手架的覆盖范围，并与现有的元基因组合工具集成

Binnacle准确地计算了图形支架的覆盖范围，并与领先的Binning方法（例如Metabat2，Maxbin 2.0和Concotct）无缝集成。使用图形支架，与重叠群（最常见的方法）相反，用于binning可以改善元基因组垃圾箱的连续性和质量，并可以捕获重建基因组的更广泛的辅助元素。

安装

要运行Binnacle，您将需要Python 3.7.x，BedTools，Samtools，Biopython，NetworkX，Numpy和Pandas。
可以使用一个环境。这里提供了有关如何安装这些软件包的详细文档。我们使用掌脚架工具输出的图形脚手架，因此您还需要下载和安装MetAcarvel。有一个逐步安装指南，用于掌级。

binning宏基因组数据

通常，当您有一个或多个元基因组样品时，我们需要从每个样品中组装，脚手架和垃圾桶/支架来生成元基因组箱。我们建议将Megahit用于组装，并使用MetaCarvel进行脚手架。我们通过组装，脚手架和每位基础覆盖范围估算步骤提供了一个辅助指南。

请按照以下步骤生成文件，以便使用图形支架运行的binning方法：

生成准确的脚手架（脱落的模块），并估算脚手架跨度和覆盖范围。
此步骤从MetAcarvel输出目录中获取图形支架，并使用与输入相同样本的读取对重叠群进行估计的覆盖范围。它输出了准确的图形支架集的覆盖范围，以及有关其坐标，方向等的其他必要信息。 Contig级别的覆盖范围可以作为描述Perbase覆盖范围的文本文件或BAM和读取对齐的床文件的文本文件。

 python Estimate_Abundances.py -g [ORIENTED.gml] -a [COVERAGE_SORTED.txt] -c [CONTIGS.fa] -d [OUTPUT_DIRECTORY]
usage: Estimate_Abundances.py [-h] [-g ASSEMBLY] [-a COVERAGE] [-bam BAMFILE]
                              [-bed BEDFILE] [-c CONTIGS] -d DIR [-o COORDS]
                              [-w WINDOW_SIZE] [-t THRESHOLD]
                              [-n NEIGHBOR_CUTOFF] [-p POSCUTOFF]
                              [-pre PREFIX]

binnacle: A tool for binning metagenomic datasets using assembly graphs and
scaffolds generated by metacarvel. Estimate_Abundances.py estimates abundance
for scaffolds generated by MetaCarvel. If the coordinates computed by binnacle
is specified then the abundance for each scaffold is estimated based on the
contig abundances and the coordinates. If the coordinates are not specified
then binnacle etimates the abundance from scratch. While calculating all vs
all abundances please specify the coordinates(Coordinates_After_Delinking.txt)
through the "coords" parameter. The abundances can be provided as a bed file,
bam file or a text file describing the per base coverage obtained by running
the genomeCoverageBed program of the bedtools suite.

optional arguments:
  -h, --help            show this help message and exit
  -g ASSEMBLY, --assembly ASSEMBLY
                        Assembly Graph generated by Metacarvel
  -a COVERAGE, --coverage COVERAGE
                        Output generated by running genomecov -d on the bed
                        file generated by MetaCarvel.
  -bam BAMFILE, --bamfile BAMFILE
                        Bam file from aligning reads to contigs
  -bed BEDFILE, --bedfile BEDFILE
                        Bed file from aligning reads to contigs. If bed file
                        is provided please provide a fasta file of the contigs
  -c CONTIGS, --contigs CONTIGS
                        Contigs generated by the assembler, contigs.fasta
  -d DIR, --dir DIR     output directory for results
  -o COORDS, --coords COORDS
                        Coordinate file generated by Binnacle
  -w WINDOW_SIZE, --window_size WINDOW_SIZE
                        Size of the sliding window for computing test
                        statistic to identify changepoints in coverages
                        (Default=1500)
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold to identify outliers (Default=99)
  -n NEIGHBOR_CUTOFF, --neighbor_cutoff NEIGHBOR_CUTOFF
                        Filter size to identify outliers within (Defualt=100)
  -p POSCUTOFF, --poscutoff POSCUTOFF
                        Position cutoff to consider delinking (Default=100)
  -pre PREFIX, --prefix PREFIX
                        Prefix to be attached to all outputs

如果您想从其读取中估算样本的图脚手架的覆盖范围，则将运行estimate_abundances.py，使用以下参数，

 -g Path to oriented.gml from running metacarvel on sample
-c Path to contigs obtained by assembling reads of sample
-a Coverage of contigs in ths sample by mapping to its reads -- See Wiki for how to calculate coverage information
-d Output directory

使用多个样本时，我们使用同一样本的读取来“正确”图形支架并估计其跨度。但是，我们可以使用所有其他样品的读取来估计样品跨样品的图形支架的覆盖范围。使用来自多个样品的信息可以帮助减少二线阶段的噪声，我们强烈建议它。因此，如果您想从另一个样本的读取（样本2）估算图形支架的覆盖范围（样本1），则可以通过这些修改来运行estimate_abundances.py。

 -a Coverage of contigs in Sample 1 by mapping reads of Sample 2 -- See Wiki for how to calculate coverage information
-o Coordinates of scaffolds from Sample 1 that you would have generated from the previous step.
-d Same output directory as Sample 1

一旦您对所有样本的图形支架估算了覆盖范围（所有与所有样本），我们需要组合这些信息并生成文件以运行元基因组混合方法。我们提供可以轻松与Metabat2，Concoct和Maxbin2.0一起使用的文件。

为了生成用于群集的功能矩阵，我们将使用bnnacle中的Colalate.py程序。它采取了通往输出目录的路径，其中放置了前一个步骤生成的所有摘要信息以及您希望下一步运行的混合方法。

 python Collate.py -h                        
usage: Collate.py [-h] -d DIR [-m METHOD] [-k KEEP]

binnacle: A tool for binning metagenomic datasets using assembly graphs and
scaffolds generated by metacarvel.Estimate_Abundances.py estimates abundance
for scaffolds generated by MetaCarvel. The program Collate.py collects the
summary files generated by Estimate_Abundances.py

optional arguments:
  -h, --help            show this help message and exit
  -d DIR, --dir DIR     Output directory that contains the summary files
                        generated by running Estimate_Abundances.py
  -m METHOD, --method METHOD
                        Binning method to format the output to. Presently we
                        support 1. Metabat 2. Maxbin 3. Concoct 4. Binnacle
                        (Default)
  -k KEEP, --keep KEEP  Retain the summary files generated by
                        Estimate_Abundances.py. Defaults to True