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基因组测序模拟

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基因组测序模拟
基因组测序模拟

一、摘要

通过熟悉已有的基因组测序模拟和评估程序，加深全基因组鸟枪法测序原理的理解，并且能够编写程序模拟全基因组鸟枪法测序，理解覆盖度、测序深度、拷贝数等概念，设置测序相关参数，生成单端/双端测序结果文件

二、材料和方法

1、硬件平台

处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
安装内存(RAM)：16.0GB

2、系统平台
Windows 8.1，Ubuntu

3、软件平台

art_454
GenomeABC http://crdd.osdd.net/raghava/genomeabc/
Python3.5
Biopython
4、数据库资源

NCBI数据库：https://www.ncbi.nlm.nih.gov/

5、研究对象

酵母基因组Saccharomyces cerevisiae S288c (assembly R64)
ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF_000146045.2_R64/GCF_000146045.2_R64_genomic.fna.gz

6、方法

art_454的使用
首先至art系列软件的官网，下载软件，在ubuntu系统安装，然后阅读相关参数设置的帮助文档，运行程序。
GenomeABC
进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
编程模拟测序
下载安装python，并且安装biopython扩展模块，编写程序，模拟单端/双端测序。
三、结果

1、art_454的运行结果

无参数art_454运行，阅读帮助文档

图表 1无参数art_454运行
对酵母基因组进行基因组单端测序模拟，FOLD_COVERAGE设为20，即覆盖度为20.
下图为模拟单端测序，程序运行过程及结果

图表 2 art454单端测序

图表 3 art454单端模拟结果
双端测序模拟，FOLD_COVERAGE设为20，即覆盖度为20；MEAN_FRAG_LEN设为1500，即平均片段长度为1500；STD_DEV设为20，即长度的标准差为20
下图为模拟双端测序，程序运行过程及结果

图表 4 art454双端测序

图表 5 art454双端模拟结果
2、GenomeABC
下图为设置参数页面

下图为结果下载页面

图表 6 结果下载页面
3、编程模拟测序结果
拷贝数是这里的N值；覆盖度是m，测序深度是宏观的量，在这里与覆盖度意思相同，就是测序仪10X，20X。
单端测序

图表 7 程序模拟单端测序
双端测序

图表 8 程序模拟双端测序
测序结果

图表 9 结果文件

因为期望片段长度是600bp，在片段长度区间200-1000bp内，所以大部分的片段都没有删除。
测序结果统计表

测序方式基因组大小(bp) 片段长度区间 (bp) N值期望片段长度克隆保留率片段数量 Reads长度范围（bp） Reads总数量 Reads总长度覆盖度(m值) 理论丢失率(e-m) 覆盖率(1-e-m)
单端 12157kb 200-1000 10 600 0.95 107378 50-100 101968 7645.541kb 0.62889 0.53318 0.46682
单端 12157kb 200-1000 20 600 0.95 213722 50-100 202996 15227.882kb 1.25259 0.28576 0.71424
双端 12157kb 200-1000 10 600 0.95 106704 50-100 202770 15212.662kb 1.25134 0.28612 0.71388
双端 12157kb 200-1000 20 600 0.95 214212 50-100 407186 30534.265kb 2.51164 0.08114 0.91886
四、讨论和结论

程序运行方法

在类的构造方法init()中，调整参数。
Averagefragmentlength为片段平均的长度；
minfragmentlength和maxfragmentlength是保留片段的范围；
cloneRetainprobability是克隆的保留率；
minreadslength和maxreadslength是测序reads的长度范围

模拟测序的诸多方法都封装成了Sequencing类，只需要创建类，并调用singlereadsequencing()和pairreadsequencing()方法，传入文件名的参数即可。

附录

from Bio import SeqIO
from math import exp
import random

class Sequencing:
# N代表拷贝份数
def __init__(self)
      self.fragmentList = []
      self.readsID = 1
      self.readsList = []
      self.averagefragmentlength = 650
      self.minfragmentlength = 500
      self.maxfragmentlength = 800
      self.cloneRetainprobability = 1
      self.minreadslength = 50
      self.maxreadslength = 150
      self.N = 10
      self.genomeLength = 0
      self.allreadslength = 0

# 生成断裂点
def generatebreakpoint(self, seqlen, averageLength):
      # 假设平均每500bp 产生一个断裂点(averageLength = 500),通过随机函数生成seqlen/500个随机断裂点(1到seqlen之间的随机整数)
      breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
      breakpoint.append(seqlen)
      breakpoint.append(0)
      # 把随机断裂点从小到大排序
      breakpoint.sort()
      return breakpoint

# 沿断裂点打断基因组,并删除不符合长度要求的序列片段,定义片段范围:200-1000bp
def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
      for i in range(len(breakpoint) - 1):
         fragment = seq[breakpoint:breakpoint[i + 1]]7 \/ z7 y2 Y- Z/ x
         if maxfragmentlength > len(fragment) > minfragmentlength:  G, E- J7 C8 H
            self.fragmentList.append(fragment)
# o; ?; D  k7 Q6 ~& _: N: c       return self.fragmentList" X7 w9 ~6 ?6 T# z
2 j- z3 X( `+ s+ B
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率8 B6 I2 w3 v/ K9 B! v0 A( l
def clonefragment(self, fragmentList, cloneRetainprobability):) Y4 s3 R# l7 m% k6 E- ~4 t) ?
      clonedfragmentList = []
$ I# ]& `9 C, }; o% Y1 |; w       Lossprobability = [random.random() for _ in range(len(fragmentList))]
% q) f7 h5 P1 p1 _       for i in range(len(fragmentList)):* t- c% N7 Q: `- o2 _
         if Lossprobability <= cloneRetainprobability:
. P) y% ?# c. T5 a7 m/ d# v             clonedfragmentList.append(fragmentList)
' X6 S* ^# Y, K% K3 G       return clonedfragmentList% U8 D1 t# K# k# S: c1 a

7 D- M; M& g: w1 K+ C' J9 Y5 ^ # 模拟单端测序,并修改reads的ID号6 A& a9 L( Q; r* H
def singleread(self, clonedfragmentList):2 s1 v) s) Y% {( N& p$ u
      for fragment in clonedfragmentList:
! {5 _- ?$ A$ X( q% w& Q3 Q          fragment.id = ""
$ y, B% M! K( ^( h) ~0 |          fragment.name = ""5 E' S0 n- R* o' O% T, Z! q
         fragment.description = fragment.description[12:].split(",")[0]
" B. b0 b8 e7 T# Z          fragment.description = str(self.readsID) + "." + fragment.description
7 o9 c5 z# ~0 G1 v( {) |          self.readsID += 1
; [0 a9 ]9 p: M7 s+ b          readslength = random.randint(self.minreadslength, self.maxreadslength)
  {# `0 i1 w) }/ E          self.allreadslength += readslength
; v0 j1 f1 Q* z& W          self.readsList.append(fragment[:readslength])
6 b. u, f0 k( d4 W+ n$ T; X
def singlereadsequencing(self, genomedata, sequencingResult):
9 b3 W+ T1 F* |( m% v  [  E5 L       for seq_record in SeqIO.parse(genomedata, "fasta"):0 F- a7 g+ h0 d- _; Z( A+ ]) t! P  S
         seqlen = len(seq_record)
, V" l# u. y5 J; T          self.genomeLength += seqlen/ c5 Z: W1 {$ C2 R, a" R
         for i in range(self.N):
$ L/ m  V8 j/ t4 Y/ L             # 生成断裂点( C7 p! \0 ^- |6 m5 A' n2 {$ G( n
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
0 K0 f+ |, W! Z, [% f             # 沿断裂点打断基因组
0 P; ~* a" u, r2 }             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
2 h( v- k$ s( F2 K! v" [2 E# g7 K       # 模拟克隆时的随机丢失情况9 Z; K' B2 H2 U) H& C+ F# @/ L, V
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability); }, ~$ v: f# l! [
      # 模拟单端测序. [: H+ t: n7 z' G  ^& q0 v6 i  ?
      self.singleread(clonedfragmentList)7 `. R- o% b% O7 r( G
      SeqIO.write(self.readsList, sequencingResult, "fasta")4 b  D- I) c0 T: @! U* ?' ?7 {5 Y

5 V2 M' B' X. v6 _) X. j, m' @2 B* n def pairread(self, clonedfragmentList):( R- `) D5 [; T- D0 @: C
      for fragment in clonedfragmentList:+ Z0 m/ z# z6 v& @' y6 l
         fragment.id = ""
) ]4 s4 }& c. P* k          fragment.name = ""
( k% w5 S1 t5 n' c+ F6 g' M7 O          description = fragment.description[12:].split(",")[0]
7 _( c0 y: {) X  E0 k. B, @          fragment.description = str(self.readsID) + "." + description8 n6 Q" G6 X9 I" V
         readslength = random.randint(self.minreadslength, self.maxreadslength)
# c- R# a" x! C          self.allreadslength += readslength# h; t1 ~8 n1 }% k: c9 @) i
         self.readsList.append(fragment[:readslength])
! v8 b. |  m! A2 q  |  Z2 L; B4 T. ^* `9 E+ a) f5 O3 R& u  L7 `
         readslength = random.randint(self.minreadslength, self.maxreadslength)% t2 |/ i" d, E0 e
         self.allreadslength += readslength( E' S1 w6 z. H5 M  y# q4 }
2 K' t& s, j$ a) l, M+ z; g
         fragmentcomplement = fragment.reverse_complement()9 a$ A. @5 C' }" e7 P
         fragmentcomplement.id = ""& f: L* L. z' D; I6 W
         fragmentcomplement.name = ""
7 q' @0 H8 F5 P* p          fragmentcomplement.description = str(self.readsID) + "." + description
* r% M! ]' A2 C$ ?8 J$ J5 }          self.readsList.append(fragmentcomplement[:readslength])6 G3 I1 y2 ]" u0 A- v

$ _: [6 _, Q" v' D& C* Q          self.readsID += 1: H$ a- b( H. U% V, D& o
9 \) L8 S/ K% W
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):: W) u: t/ N% w& c5 e
      for seq_record in SeqIO.parse(genomedata, "fasta"):
2 ^/ l' A& ^8 G4 t: {9 \          seqlen = len(seq_record)( ]* {2 F& I  L% O
         self.genomeLength += seqlen
$ k$ E5 o! |- E$ u          for i in range(self.N):
2 \  B; Q( z/ G' c             # 生成断裂点4 o: R. v, f+ }& n# r, x# I; F% @
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)4 J0 q% `; R8 G6 W9 O/ k
            # 沿断裂点打断基因组7 {& R: m) [2 J* ^/ m! H# S* d
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
) U1 X+ D, M/ U       # 模拟克隆时的随机丢失情况
" J+ D5 L1 J9 n4 E/ Q; x/ H5 Z       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)0 g& v0 R/ p) ]; ]8 `6 g
      # 模拟双端测序7 o, p0 Y$ X  C
      self.pairread(clonedfragmentList)
8 [0 |  r! w# F       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
; O. u0 t; T) b, Z       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]( O9 e2 Q, x/ G7 |
      SeqIO.write(readsList_1, sequencingResult_1, "fasta")
! ^& G/ I2 Z- \) j8 F1 T       SeqIO.write(readsList_2, sequencingResult_2, "fasta")
. f4 M, l6 V& S- D' z
5 O' S  ^7 T+ ]3 H, B def resultsummary(self):
3 f4 L9 J6 g+ p& d# @' F       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
4 f3 t7 |- ^: l4 O! a       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))% ~9 \- \' O$ u# V/ K
      print("N值:" + str(self.N))8 E- _' c* q- a# _5 Q" M& R
      print("期望片段长度:" + str(self.averagefragmentlength))! `# n" w* _6 w4 n0 C0 D
      print("克隆保留率:" + str(self.cloneRetainprobability)): f, i/ T, Z1 w8 V% {
      print("片段数量:" + str(len(self.fragmentList))). P/ F. C4 e  ]. n7 X% c" }8 F. \/ d
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
" X3 [% |% n1 E; q2 a       print("reads总数量:" + str(len(self.readsList)))
0 x" J  w! s/ h, ?2 O4 h0 O       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")9 }2 \. m; r! K: S9 `
      m = self.allreadslength / self.genomeLength$ u+ W- {4 S0 j
      print("覆盖度(m值):" + str(round(m, 5)))5 r. F+ R  Y0 \0 n' }
      print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))/ Z  C- j4 S5 e' |7 C, p
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))9 ~. S7 ^4 {" Y2 v0 i/ Z
# -------------------------------------------主程序-------------------------------------------+ A$ P. A4 x) R9 U8 P# z8 R
# 模拟单端测序
& G& B) g& @0 osequencingObj = Sequencing()
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
4 L  I7 ?7 l+ h, P3 l/ t& ?- isequencingObj.resultsummary()
- X% G+ ~  m0 A: T
% i( k( \* @3 F9 J3 H& a/ s# 模拟双端测序+ }5 a. `: D  k0 X3 X
sequencingObj = Sequencing()
0 m/ z. z& S8 B: m. usequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
: g4 ?: `( P. K/ w; U3 M" OsequencingObj.resultsummary()
! u9 i& J# Q% F6 ^/ m8 mfrom Bio import SeqIO
- Y5 ?4 W& u& a, Sfrom math import exp6 n* p/ w1 k) l
import random
3 H2 _& Q$ |% n/ }3 M/ ^9 |% T5 O, @  J0 h' r, ]
class Sequencing:$ i, H6 W- V& O
# N代表拷贝份数
1 Q3 j% c# r- J. A6 R* i def __init__(self):; R; L% P: }3 h/ h+ c
      self.fragmentList = []- _4 w" D3 C7 {; P; L
      self.readsID = 14 V- m# ?; V$ ?) P9 {/ B2 d5 v2 n1 s
      self.readsList = []  s" L4 c8 B3 D) j& ]& i
      self.averagefragmentlength = 650
2 ^( c7 x& `0 G7 R  Y       self.minfragmentlength = 500$ K/ M8 c# a! n% b; Y$ N4 x  S
      self.maxfragmentlength = 8009 x; |% a: v1 h" M, t8 T
      self.cloneRetainprobability = 1
: C  y3 m' Z& o  c2 \       self.minreadslength = 50* m: X$ x2 K% i/ p
      self.maxreadslength = 150
# }% l0 D5 f% ]  |8 ?( x% A       self.N = 10
% q) D4 d; M5 o# U# j       self.genomeLength = 0% R' r4 \/ b- O4 K" {$ K7 F
      self.allreadslength = 09 e9 u' X5 b+ D  [2 C
7 X6 E: L/ K. t" X
# 生成断裂点& i0 k4 h& c4 [2 a+ U* T! T8 a, }2 l6 \
def generatebreakpoint(self, seqlen, averageLength):
+ h# {' J, m" W! D$ E% \) p       # 假设平均每500bp 产生一个断裂点(averageLength = 500),通过随机函数生成seqlen/500个随机断裂点(1到seqlen之间的随机整数)
. K( Q7 h: |  _+ g9 s       breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]" ]$ [6 x5 P% T8 \) G& x
      breakpoint.append(seqlen)
5 Z! o# D% a4 B, A1 U       breakpoint.append(0), {% [7 W$ e5 x( ?
      # 把随机断裂点从小到大排序
7 N, D0 E& d; r* B       breakpoint.sort()- ~' i7 n" \* h8 J4 j( S/ U
      return breakpoint
% ~! E! M* d0 c8 P. {2 A2 ]4 W9 u$ H
# 沿断裂点打断基因组,并删除不符合长度要求的序列片段,定义片段范围:200-1000bp
  b. A( @; p* H' m6 G, ]9 d/ N def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
9 O% x+ `& ?1 e       for i in range(len(breakpoint) - 1):
; l! e" X& m. b1 |          fragment = seq[breakpoint:breakpoint[i + 1]]
1 k% ^3 L& S8 W2 S; J% T1 P          if maxfragmentlength > len(fragment) > minfragmentlength:3 ]: ^! k) G+ G$ c8 q" P
            self.fragmentList.append(fragment)
0 {! [! U# x' ]; \: ]       return self.fragmentList
3 t" O5 d5 x# s/ x" g, n/ G9 w- a8 @
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率9 p! s1 U$ H/ ]. N
def clonefragment(self, fragmentList, cloneRetainprobability):3 F3 \3 `1 m1 N" N* t6 I# v
      clonedfragmentList = [], w6 K: n7 \. ~! c+ x- I$ N
      Lossprobability = [random.random() for _ in range(len(fragmentList))]
5 c8 K3 h: k; O% i" N       for i in range(len(fragmentList)):
/ c. K+ c% W0 ?& |          if Lossprobability <= cloneRetainprobability:; Q! r8 L# x0 m5 _4 f* N9 _
            clonedfragmentList.append(fragmentList)
' m+ y9 ?* t  d/ _9 ]       return clonedfragmentList
" a$ u, t% H$ d* s
% p, e0 Y- E4 V: [ # 模拟单端测序,并修改reads的ID号, `! ^5 a4 {7 G
def singleread(self, clonedfragmentList):
' D; o% }$ A  v3 J8 Q0 j0 ~  U! l       for fragment in clonedfragmentList:
6 N* @& y+ W& J  M6 x% d          fragment.id = ""
) J/ z- Z6 o, }          fragment.name = ""
3 i9 b1 Y+ }* n) |3 p0 g( n          fragment.description = fragment.description[12:].split(",")[0]
9 J; G: c" A& l, Z$ z          fragment.description = str(self.readsID) + "." + fragment.description
! Q% I1 h# Q: v$ s4 o( B+ K; r( R# ~          self.readsID += 11 s8 A, ^' v8 V/ R& `& Q
         readslength = random.randint(self.minreadslength, self.maxreadslength)
$ O- c, u( B) g$ Z, v) Q          self.allreadslength += readslength/ t3 Z5 X' i1 f5 r1 S1 J& m
         self.readsList.append(fragment[:readslength])7 a- D& X9 A( J2 M+ R
, J- Y2 O) W- n( {
def singlereadsequencing(self, genomedata, sequencingResult):
' E# h6 E: U& r2 c2 i       for seq_record in SeqIO.parse(genomedata, "fasta"):
. l/ s  K2 e$ e) Z          seqlen = len(seq_record)1 A' o+ b* I" Y+ I$ D) r
         self.genomeLength += seqlen
2 a2 |; u3 `- H5 y3 k+ ^          for i in range(self.N):6 W% L( I- a5 J" L7 d4 H! D1 k8 m
            # 生成断裂点- @) K$ c9 L8 O3 Q& A; q
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)9 L2 G% a' a* {9 y% e
            # 沿断裂点打断基因组
8 e$ U9 R, q8 y, W- U             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)" j& G( H" F/ q9 L. d
      # 模拟克隆时的随机丢失情况+ Q9 f2 }' `  i$ v6 h. m9 }
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
/ [) i9 _& O0 c5 K4 |       # 模拟单端测序
* e, y2 F6 ~* W7 Q6 R* V; g  R       self.singleread(clonedfragmentList)
& r: t  y/ c) ^  S       SeqIO.write(self.readsList, sequencingResult, "fasta")% E0 X6 L7 ]7 D) o! `2 z# v
, o/ C9 h/ W5 s, ~
def pairread(self, clonedfragmentList):
& x. D' o. h, C+ m$ I7 g$ o       for fragment in clonedfragmentList:2 [, J4 H( ?0 W3 i
         fragment.id = ""
( C7 \, G* N8 g% n, N          fragment.name = ""9 f  o- t1 `- E8 N) X
         description = fragment.description[12:].split(",")[0]6 y/ @4 T- `! z3 }7 c9 B/ |
         fragment.description = str(self.readsID) + "." + description* J  ]( [3 s- y+ S5 H4 y- g
         readslength = random.randint(self.minreadslength, self.maxreadslength)1 U/ X' y# u' s7 g* d7 w/ {
         self.allreadslength += readslength3 y* f; q9 N/ u) j
         self.readsList.append(fragment[:readslength])
" Z$ U' o0 W" q! d! d. ]9 k3 V) n7 z+ Z) b7 o
         readslength = random.randint(self.minreadslength, self.maxreadslength)
* w4 W5 K$ A8 z% l+ i9 V2 n          self.allreadslength += readslength; f( E" P$ a6 q

, f( C& q& \) z: [4 W& d          fragmentcomplement = fragment.reverse_complement()
1 u7 S# I, ]7 X* k9 M* [- i7 Y$ J          fragmentcomplement.id = ""+ _+ @$ G* a! O4 h3 U
         fragmentcomplement.name = "": D/ f  A+ w% p& O4 E, a  R
         fragmentcomplement.description = str(self.readsID) + "." + description
( q) n, o$ c$ H8 o          self.readsList.append(fragmentcomplement[:readslength]). c+ C7 E5 N6 p2 M0 s/ _
9 p7 }) F! a2 e7 `
         self.readsID += 1
6 _) q! d9 f, K- Z
: e" t3 k- a, N9 Z( l def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):  Z" n8 }: A5 |' S7 r
      for seq_record in SeqIO.parse(genomedata, "fasta"):
  @( a( h  {; S* v          seqlen = len(seq_record)
7 T4 q% p% {) b/ R0 p, e# H          self.genomeLength += seqlen' ]0 F3 d8 H  O1 u+ B' c# m9 E' W
         for i in range(self.N):. y$ {0 ]' v& Q# V6 V5 o
            # 生成断裂点5 a) e) b0 E! m5 Q7 d
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength), L0 c7 k* {4 Y! {3 c& e
            # 沿断裂点打断基因组2 ]/ }; d/ h# ]
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength); z( b  }. Q. u; d7 B
      # 模拟克隆时的随机丢失情况
7 p4 s$ o# }8 M/ i       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
) D: t& ^9 S# j       # 模拟双端测序
6 d$ @  @2 h" [       self.pairread(clonedfragmentList)
, B0 K  t! s: b, `; E       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
8 T" z& f0 L- ^2 ]       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]; v/ E( d0 b% i: [
      SeqIO.write(readsList_1, sequencingResult_1, "fasta")' y. ^# R" V" S2 r
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")9 M: ^5 ^% X+ E* C  h5 v; Y

8 n5 X8 p: T+ a+ a4 [! O def resultsummary(self):
  P0 }) @3 b7 B) `, M       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")8 S4 Y2 u& n0 H% i
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
9 d. J, s* J$ Y2 _2 e       print("N值:" + str(self.N))4 s* h! Z! k9 t/ Z
      print("期望片段长度:" + str(self.averagefragmentlength))
* S! ~. y5 `/ {& p       print("克隆保留率:" + str(self.cloneRetainprobability))1 s4 J4 u, q! u  F! B# u5 H7 Z( p- _
      print("片段数量:" + str(len(self.fragmentList)))/ Z+ b! o5 G8 ~
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))8 i! C9 R; D& B4 z
      print("reads总数量:" + str(len(self.readsList)))! V- L/ k1 s: V4 b8 k
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
/ L6 @' m) f  y" J       m = self.allreadslength / self.genomeLength
* S3 h. {3 Q; e2 f       print("覆盖度(m值):" + str(round(m, 5))). a9 p+ g0 m2 m# w
      print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
& ?& j* i% }# x# ^' J       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))- n2 `' J" b" s, G0 j
# -------------------------------------------主程序-------------------------------------------; @2 Z3 t; P2 k
# 模拟单端测序
$ k" t8 `# _% ssequencingObj = Sequencing(). M; }9 d+ ]  W( o( x
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")% U4 D9 C5 a0 A4 c. c5 g" I% O& x
sequencingObj.resultsummary()
* a3 U0 |. N$ I, ]' m" A: c& s% V( C# M; Y% R9 @
# 模拟双端测序
/ {7 U: X& r4 ?# j$ S( lsequencingObj = Sequencing()
% n% y2 o4 E' O4 t, vsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
# j9 f/ |, c" ]7 l) tsequencingObj.resultsummary(), X' B2 D  O  E+ o3 H. \
1 I7 C* {. l: n$ [2 H2 N- T. Q
3 n- E5 S) ~$ u' b

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