解析一个gff文件，获取scaffold名称并创建一个新的datafram

2024-05-19 14:32:23 发布

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我解释我的问题。在

我实际上有一个gff文件（Augustus输出） 在这个gff文件中，我有不同支架中基因的定位，例如：

# Predicted genes for sequence number 1 on both strands
# start gene g1
scaffold_0  AUGUSTUS    gene    5629    8516    0.13    +   .   g1
scaffold_0  AUGUSTUS    transcript  5629    8516    0.13    +   .   g1.t1
scaffold_0  AUGUSTUS    start_codon 5629    5631    .   +   0   transcript_id "g1.t1"; gene_id "g1";
scaffold_0  AUGUSTUS    intron  5662    7695    0.18    +   .   transcript_id "g1.t1"; gene_id "g1";
scaffold_0  AUGUSTUS    intron  8219    8424    0.55    +   .   transcript_id "g1.t1"; gene_id "g1";
scaffold_0  AUGUSTUS    CDS 5629    5661    0.41    +   0   transcript_id "g1.t1"; gene_id "g1";
scaffold_0  AUGUSTUS    CDS 7696    8218    0.56    +   0   transcript_id "g1.t1"; gene_id "g1";
scaffold_0  AUGUSTUS    CDS 8425    8516    0.59    +   2   transcript_id "g1.t1"; gene_id "g1";
scaffold_0  AUGUSTUS    stop_codon  8514    8516    .   +   0   transcript_id "g1.t1"; gene_id "g1";
# protein sequence = [MRVKSGNLRENNEARGTSANVALCTVSRGYGGRGARGGGVSGANSGPSGARGGSTRARSRAESVVRLPLSPRTLCAPG
# PVAGPHTGAQAQQHPQQAQSRDHHASQPLTGGAGTGSGLEDGPGSADEPMVHLERPQQAGTWICCVPCYWLRRSKAVHKALLTFAMLLVTSLLVTSPV
# LFLITTLPEAASIRHTYACLFSLSKIPLPPHMKTNGTPF]
# end gene g1

例如，这里的g1.t1位于scaffold 0中。 我还有一个包含以下信息的tab文件：

^{pr2}$

我想做的是用这些信息创建一个数据框，例如，如果我有5个基因：

^{3}$

这些脚手架的信息包括：

scaf_name       scaf_length cov_depth       GC
scaffold 6      56786         79            0.39
scaffold 256    789765        86            0.42
scaffold 890    3456          85            0.40
scaffold 1234   590           90            0.41

我想要一个这样的数据帧：

gene_name   scaf_name       scaf_length cov_depth       GC
g3.t1       scaffold 6      56786         79            0.39
g4.t1       scaffold 6      56786         79            0.39
g1.t1       scaffold 256    789765        86            0.42
g2.t1       scaffold 890    3456          85            0.40
g5.t1       scaffold 1234   590           90            0.41

我想用Python和熊猫是有可能的，不是吗？ 感谢您的帮助：）

以下是我对Ami的感谢：

import pandas as pd

gff_0042="/Users/etudiant/Desktop/Horizon_project/Augustus/Augustus_out/run_augustus_0042.out"
liste=["scaf_name","source","feature","start","end","score","strand","frame","gene"]
gene_info=pd.read_csv(gff_0042,comment='"',sep='\s+',header=None,names=liste)


scaf_info=pd.read_csv("cov_GC.tab",sep='\t')

scaf_info.scaf_name = scaf_info.scaf_name.str.replace(' ', '_')
new=pd.merge(gene_info, scaf_info, on='scaf_name')

print(new)

以下是我得到的部分输出：

    scaf_name   source  feature start   end score   strand  frame   gene    scaf_length cov_depth   GC  
0   scaffold_0  AUGUSTUS    gene    5793    11216   0.13    +   .   g1  541519  79.7321515957889    0.39808760172773294 
1   scaffold_0  AUGUSTUS    transcript  5793    11216   0.13    +   .   g1.t1   541519  79.7321515957889    0.39808760172773294 
2   scaffold_0  AUGUSTUS    start_codon 5793    5795    .   +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
3   scaffold_0  AUGUSTUS    intron  5829    7371    0.41    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
4   scaffold_0  AUGUSTUS    intron  7405    7695    0.8 +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
5   scaffold_0  AUGUSTUS    intron  8248    11054   0.37    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
6   scaffold_0  AUGUSTUS    CDS 5793    5828    0.45    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
7   scaffold_0  AUGUSTUS    CDS 7372    7404    0.81    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
8   scaffold_0  AUGUSTUS    CDS 7696    8247    0.84    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
9   scaffold_0  AUGUSTUS    CDS 11055   11216   0.4 +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
10  scaffold_0  AUGUSTUS    stop_codon  11214   11216   .   +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
11  scaffold_0  AUGUSTUS    gene    38793   39207   0.91    -   .   g2  541519  79.7321515957889    0.39808760172773294 
12  scaffold_0  AUGUSTUS    transcript  38793   39207   0.91    -   .   g2.t1   541519  79.7321515957889    0.39808760172773294 
13  scaffold_0  AUGUSTUS    stop_codon  38793   38795   .   -   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
14  scaffold_0  AUGUSTUS    intron  38904   39066   0.92    -   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
15  scaffold_0  AUGUSTUS    CDS 38793   38903   0.95    -   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
16  scaffold_0  AUGUSTUS    CDS 39067   39207   0.91    -   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
17  scaffold_0  AUGUSTUS    start_codon 39205   39207   .   -   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
18  scaffold_0  AUGUSTUS    gene    50409   58153   0.07    +   .   g3  541519  79.7321515957889    0.39808760172773294 
19  scaffold_0  AUGUSTUS    transcript  50409   58153   0.07    +   .   g3.t1   541519  79.7321515957889    0.39808760172773294 
20  scaffold_0  AUGUSTUS    start_codon 50409   50411   .   +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
21  scaffold_0  AUGUSTUS    intron  50520   50762   0.65    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
22  scaffold_0  AUGUSTUS    intron  50895   51080   0.91    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
23  scaffold_0  AUGUSTUS    intron  51180   51284   0.61    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
24  scaffold_0  AUGUSTUS    intron  51458   55603   0.47    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
25  scaffold_0  AUGUSTUS    intron  55791   56101   0.98    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
26  scaffold_0  AUGUSTUS    intron  56279   56384   1   +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
27  scaffold_0  AUGUSTUS    intron  56486   56573   1   +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
28  scaffold_0  AUGUSTUS    intron  56630   56952   0.97    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
29  scaffold_0  AUGUSTUS    intron  57240   57365   0.57    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
30  scaffold_0  AUGUSTUS    intron  57525   57736   0.81    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
31  scaffold_0  AUGUSTUS    intron  57814   58053   0.75    +   .   transcript_id   541519  79.7321515957889    0.39808760172773294 
32  scaffold_0  AUGUSTUS    CDS 50409   50519   0.39    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
33  scaffold_0  AUGUSTUS    CDS 50763   50894   0.74    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
34  scaffold_0  AUGUSTUS    CDS 51081   51179   0.91    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
35  scaffold_0  AUGUSTUS    CDS 51285   51457   0.57    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
36  scaffold_0  AUGUSTUS    CDS 55604   55790   0.98    +   1   transcript_id   541519  79.7321515957889    0.39808760172773294 
37  scaffold_0  AUGUSTUS    CDS 56102   56278   0.98    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
38  scaffold_0  AUGUSTUS    CDS 56385   56485   1   +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
39  scaffold_0  AUGUSTUS    CDS 56574   56629   0.98    +   1   transcript_id   541519  79.7321515957889    0.39808760172773294 
40  scaffold_0  AUGUSTUS    CDS 56953   57239   0.64    +   2   transcript_id   541519  79.7321515957889    0.39808760172773294 
41  scaffold_0  AUGUSTUS    CDS 57366   57524   0.81    +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
42  scaffold_0  AUGUSTUS    CDS 57737   57813   0.9 +   0   transcript_id   541519  79.7321515957889    0.39808760172773294 
43  scaffold_0  AUGUSTUS    CDS 58054   58153   0.5 +   1   transcript_id   541519  79.7321515957889    0.39808760172773294 
44  scaffold_0  AUGUSTUS    stop_codon  58151   58153   .   +   0   transcript_id   541519  79.7321515957889    0.39808760172773294

如果可能的话我只想得到：

    scaf_name   source  feature start   end score   strand  frame   gene    scaf_length cov_depth   GC  
1   scaffold_0  AUGUSTUS    transcript  5793    11216   0.13    +   .   g1.t1   541519  79.7321515957889    0.39808760172773294 
12  scaffold_0  AUGUSTUS    transcript  38793   39207   0.91    -   .   g2.t1   541519  79.7321515957889    0.39808760172773294 
19  scaffold_0  AUGUSTUS    transcript  50409   58153   0.07    +   .   g3.t1   541519  79.7321515957889    0.39808760172773294

等等。。在


Tags： nameinfoidcovstartscaffoldt1transcript
1条回答
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1楼 · 发布于 2024-05-19 14:32:23

首先，使用^{}读取gff文件。注意以下几点：

  • 设置comment='"'
  • 设置“sep='\s+”
  • 设置header=None
  • 将names=...设置为某个表示名称的列表，第一个名为'scaf_name'，最后一个名为'gene'。在

将其放入gene_info。在

现在使用read_csv读取tab限制文件，设置sep='\t'。将其放入scaf_info。在

跑

scaf_info.scaf_name = scaf_info.scaf_name.str.replace(' ', '_')

为了将scaffold 6等内容改为scaffold_6。在

现在开始吧

^{pr2}$

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