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Binnacle-估計圖腳手架的覆蓋範圍，並與現有的元基因組合工具集成

Binnacle準確地計算了圖形支架的覆蓋範圍，並與領先的Binning方法（例如Metabat2，Maxbin 2.0和Concotct）無縫集成。使用圖形支架，與重疊群（最常見的方法）相反，用於binning可以改善元基因組垃圾箱的連續性和質量，並可以捕獲重建基因組的更廣泛的輔助元素。

安裝

要運行Binnacle，您將需要Python 3.7.x，BedTools，Samtools，Biopython，NetworkX，Numpy和Pandas。
可以使用一個環境。這裡提供了有關如何安裝這些軟件包的詳細文檔。我們使用掌腳架工具輸出的圖形腳手架，因此您還需要下載和安裝MetAcarvel。有一個逐步安裝指南，用於掌級。

binning宏基因組數據

通常，當您有一個或多個元基因組樣品時，我們需要從每個樣品中組裝，腳手架和垃圾桶/支架來生成元基因組箱。我們建議將Megahit用於組裝，並使用MetaCarvel進行腳手架。我們通過組裝，腳手架和每位基礎覆蓋範圍估算步驟提供了一個輔助指南。

請按照以下步驟生成文件，以便使用圖形支架運行的binning方法：

生成準確的腳手架（脫落的模塊），並估算腳手架跨度和覆蓋範圍。
此步驟從MetAcarvel輸出目錄中獲取圖形支架，並使用與輸入相同樣本的讀取對重疊群進行估計的覆蓋範圍。它輸出了準確的圖形支架集的覆蓋範圍，以及有關其坐標，方向等的其他必要信息。 Contig級別的覆蓋範圍可以作為描述Perbase覆蓋範圍的文本文件或BAM和讀取對齊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

如果您想從其讀取中估算樣本的圖腳手架的覆蓋範圍，則將運行estimate_abundances.py，使用以下參數，

 -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

使用多個樣本時，我們使用同一樣本的讀取來“正確”圖形支架並估計其跨度。但是，我們可以使用所有其他樣品的讀取來估計樣品跨樣品的圖形支架的覆蓋範圍。使用來自多個樣品的信息可以幫助減少二線階段的噪聲，我們強烈建議它。因此，如果您想從另一個樣本的讀取（樣本2）估算圖形支架的覆蓋範圍（樣本1），則可以通過這些修改來運行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

一旦您對所有樣本的圖形支架估算了覆蓋範圍（所有與所有樣本），我們需要組合這些信息並生成文件以運行元基因組混合方法。我們提供可以輕鬆與Metabat2，Concoct和Maxbin2.0一起使用的文件。

為了生成用於群集的功能矩陣，我們將使用bnnacle中的Colalate.py程序。它採取了通往輸出目錄的路徑，其中放置了前一個步驟生成的所有摘要信息以及您希望下一步運行的混合方法。

 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