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基因组测序模拟

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	开心 2021-8-11 17:59

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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]]
- N6 ^0 y; z7 @, j0 r          if maxfragmentlength > len(fragment) > minfragmentlength:
4 i% B6 j2 r# W# }: V             self.fragmentList.append(fragment)- C! F% x: j4 E4 Y% S4 t! s
      return self.fragmentList; b# r; \& ^) z, K% P/ {% g

( T4 r1 w; n3 h- e, W& A: `! n& { # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
+ s+ ^+ N8 s) `& M4 a2 e( E def clonefragment(self, fragmentList, cloneRetainprobability):
  J6 ?# a. H8 n' c8 g  m' k$ ?       clonedfragmentList = []
! n& X' R5 D0 D+ D5 l       Lossprobability = [random.random() for _ in range(len(fragmentList))]
; m- x6 L; I- t4 l+ p# k       for i in range(len(fragmentList)):; u, U8 K+ ^1 x9 H$ Z/ V
         if Lossprobability <= cloneRetainprobability:
0 q, y6 r) X% P! B1 n, ]- v6 ]% P             clonedfragmentList.append(fragmentList)
8 L( C0 T. O5 A7 s       return clonedfragmentList
- l0 z1 l2 @, c2 B: c% B0 n" V! V7 ?8 _
# 模拟单端测序,并修改reads的ID号7 V# c2 P9 @& I, ^7 {" u
def singleread(self, clonedfragmentList):0 @+ p5 W2 M2 _4 ]# w" F$ s. e% n
      for fragment in clonedfragmentList:9 D: \* f+ S/ P
         fragment.id = ""
$ j  u+ w1 i# j          fragment.name = ""# D* }/ Z  M8 `% |
         fragment.description = fragment.description[12:].split(",")[0]
7 q$ [1 B; O. Q& `1 o' l          fragment.description = str(self.readsID) + "." + fragment.description4 U4 s/ _! v- b4 u  _) g( O! U& n; m
         self.readsID += 1
. h8 O/ y+ g0 d- A) l          readslength = random.randint(self.minreadslength, self.maxreadslength)
6 D1 \& C$ n3 B+ l$ v# M4 n" O          self.allreadslength += readslength% ^: D6 S" F) e0 X9 @! i
         self.readsList.append(fragment[:readslength])9 q0 e. }3 |  i' \. o
0 Z7 z, u% E# m7 t
def singlereadsequencing(self, genomedata, sequencingResult):) L! U9 l+ ~4 `/ @0 O9 l! D' G
      for seq_record in SeqIO.parse(genomedata, "fasta"):9 G) t  T, }$ b! z$ {9 O+ I
         seqlen = len(seq_record)
) n5 n# U1 p$ ~# Y* G9 {          self.genomeLength += seqlen
0 d) f: k1 @  y6 K! ~) ^          for i in range(self.N):# W' x: d$ _2 c% t' A; C
            # 生成断裂点
& I) @, E3 \1 u/ s& g% v             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
2 x" u! r1 m1 e7 y: R) ?6 B, w             # 沿断裂点打断基因组" t* H  F0 r% X  g4 h
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
4 r5 P& U0 [) T  l- |* g       # 模拟克隆时的随机丢失情况' e0 L* h! s. i' z# y7 t" b
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)9 P7 A6 V$ A) K. w, O
      # 模拟单端测序" T$ q: Y  w# p) D/ t! y
      self.singleread(clonedfragmentList)$ E$ L: b1 c! u! Q
      SeqIO.write(self.readsList, sequencingResult, "fasta")( j$ N8 v, r0 W9 Q+ |9 [. f9 t

* }3 T9 P" e0 B def pairread(self, clonedfragmentList):* h( P6 V7 k1 I4 B) T
      for fragment in clonedfragmentList:
5 E; p  |2 y! _* C3 ~! Q          fragment.id = ""
" X0 Q; ]  N0 q3 F          fragment.name = ""
- t3 r" U6 k' G/ B- `8 C0 m          description = fragment.description[12:].split(",")[0]! K: f3 g2 ~9 \  r: L0 k8 P
         fragment.description = str(self.readsID) + "." + description
" o1 a6 F5 d* u9 l' m3 Y' W          readslength = random.randint(self.minreadslength, self.maxreadslength)
# ~" b2 U8 [) _& I0 @3 k- w/ `" k          self.allreadslength += readslength6 M4 V+ Y0 Y; t% E' o" s1 q& }  t
         self.readsList.append(fragment[:readslength])$ F: p" Y+ W/ d( z. i

2 q; k, v! J8 m. ~3 [$ ?          readslength = random.randint(self.minreadslength, self.maxreadslength)
+ U( j4 \/ L2 O          self.allreadslength += readslength7 u. b1 p* v- D& l5 w  h% ?, @( A

; C& ^: n+ M* Y          fragmentcomplement = fragment.reverse_complement()& T9 D/ L) i0 a+ d
         fragmentcomplement.id = ""0 L% m. p8 K, H$ m( G& C) K, s
         fragmentcomplement.name = ""
0 z" f. S/ m+ v' Z2 @: o          fragmentcomplement.description = str(self.readsID) + "." + description
  S0 R: w& o. w5 j5 n, |          self.readsList.append(fragmentcomplement[:readslength]); J4 h: x$ I& Y9 I. i
) c& @3 R3 G' {$ l* A. ^8 ]1 ~8 f  v
         self.readsID += 12 l9 [8 Y2 X$ g4 R* e" B5 a
! }; h. K% K: P
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
' h6 b" F" X  E( T1 Y% K7 m       for seq_record in SeqIO.parse(genomedata, "fasta"):
! C# Z/ ^9 l" a: R& R; \          seqlen = len(seq_record)+ M& M: ~2 m7 i! Z+ S( S
         self.genomeLength += seqlen4 l) o& `5 k: F
         for i in range(self.N):
% Q9 P" Q% C; z1 Y- ^& k" `             # 生成断裂点: J% ^+ Z, a% Y+ o8 M; X; C" A+ d
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)# B% U3 L/ m2 M& g6 d
            # 沿断裂点打断基因组
: j3 b& v$ y3 Q8 C7 {             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)$ O& C* ]: }2 Y& E7 _5 v/ V4 Z6 J
      # 模拟克隆时的随机丢失情况4 Y% l+ D1 u  J" }+ j1 U4 Q3 F# ~
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
( Q; O  q! W1 Z* a. k  Q       # 模拟双端测序
  Z# Q# n+ y4 I1 S8 S       self.pairread(clonedfragmentList)' w' n. M- ]- d
      readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]. ]7 R, n4 X# B0 {/ P& t$ t
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
2 e. b# W/ w% P/ d       SeqIO.write(readsList_1, sequencingResult_1, "fasta"): `* l2 d/ v7 W7 E* w
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")6 k% z9 o4 @3 z' @0 k! R2 M8 f
# v( ~' o# d; m, O, j5 l4 a
def resultsummary(self):
) l, p" c4 a1 b0 g       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
/ u# M/ V" I9 b       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))5 r% K3 k0 Y: @4 M  @& {6 X
      print("N值:" + str(self.N))
* L" `) ^6 |+ D& C9 D5 ^4 L       print("期望片段长度:" + str(self.averagefragmentlength))6 q/ B! f3 S9 q# j% }
      print("克隆保留率:" + str(self.cloneRetainprobability))
. Z5 m/ r  J$ e) N  z: B       print("片段数量:" + str(len(self.fragmentList)))
1 H0 }2 p* x( }+ ?$ D1 O       print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))4 @2 g5 m% v; M, u: O
      print("reads总数量:" + str(len(self.readsList)))" l- [+ \6 l9 {) u3 F0 d! U
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
7 q6 g* A( z4 G5 u: o/ f  }       m = self.allreadslength / self.genomeLength) `: i' H# D; V# {3 L+ Q
      print("覆盖度(m值):" + str(round(m, 5)))
$ K9 g9 F+ H4 [+ h  Z7 i# D       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))' t" D8 j% i8 W( A8 C/ k! ]4 i% K3 r
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
9 A" U: J. l( b! w% d# -------------------------------------------主程序-------------------------------------------
6 F8 M: I$ d% X# 模拟单端测序6 R. E9 Y1 C- ?5 J, m8 K
sequencingObj = Sequencing()1 J5 d! R1 o- h, g9 L) J, Q
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
1 Z4 _: d( n  V- LsequencingObj.resultsummary(): M$ C/ }3 z# _4 b7 F, B8 |5 N
( j3 h& @0 E( |7 e9 T1 U, r
# 模拟双端测序
" q( j3 P* C; e: c% G* csequencingObj = Sequencing()# i% d6 i* U! K. v! e
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
4 L1 r7 R7 [9 O' x1 ysequencingObj.resultsummary()
, C& F+ x0 k# i6 `from Bio import SeqIO
8 n' J& a! W: i* I0 m; I/ v/ nfrom math import exp1 _5 h, e6 o  m
import random
4 j( b6 o0 |4 C; }% F& M; d
5 \! E& i2 \0 Fclass Sequencing:
# m9 U1 B- a- o3 M) l4 ~+ [- G # N代表拷贝份数& b! {+ f' W/ M3 [6 Y" W, t
def __init__(self):
+ m" j* Q( E+ u) M3 t, Z% m       self.fragmentList = []
" ^5 ^% L+ P! i2 }       self.readsID = 1' H) N# u' \) E2 _8 O! D1 t
      self.readsList = []' X. J* p* ]; R1 M% m& Z1 W* L. \. _. }
      self.averagefragmentlength = 650# Q. o# z2 X5 u: [
      self.minfragmentlength = 5001 B6 h- _* q: p& o
      self.maxfragmentlength = 800
+ h' ~) f7 B. J6 h       self.cloneRetainprobability = 1
& {0 ?! D/ F6 c. s       self.minreadslength = 50$ \* w) l& I1 g# e
      self.maxreadslength = 150
& q+ ~5 n- C) m$ v. y       self.N = 100 p4 q8 H. t; O/ Q0 N+ M
      self.genomeLength = 0) g% L" H1 G* Q" F
      self.allreadslength = 00 |" d9 t7 F$ [% \+ u0 a
$ [) Z2 Z3 P/ n8 N
# 生成断裂点! d' J' j8 C; ~! _/ A
def generatebreakpoint(self, seqlen, averageLength):
: U" T) t2 g* D3 q       # 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)
8 D- d( M1 e6 L& x       breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
; Y; N: J1 Y# v* x/ K, Z       breakpoint.append(seqlen)
( o- @% s5 n! m4 G$ U0 ~5 j: j       breakpoint.append(0)
% y5 H2 H- ~& U0 j' B1 d       # 把随机断裂点从小到大排序
( D: Y: e- r7 O8 g0 i, D       breakpoint.sort()8 r* |4 N, ?( J: j. Q
      return breakpoint
  y/ l2 ]( C, v$ ]+ V! b/ d$ ~5 i* M6 j2 J+ u- K2 a" r  n& S5 P
# 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:200-1000bp6 m0 \" `: B" ]% t- q
def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):- i, ?3 g$ C0 r( M9 f! b
      for i in range(len(breakpoint) - 1):
: V  R. Y) O6 u$ z3 Y# z          fragment = seq[breakpoint:breakpoint[i + 1]]# o$ h% H7 R0 {; T
         if maxfragmentlength > len(fragment) > minfragmentlength:# E% x0 b, W( t4 e8 d& r
            self.fragmentList.append(fragment)
( A& G7 ?/ b) g0 y       return self.fragmentList% t$ L1 |. g. i$ p; l" i4 @' G
! S# m: W! q2 g( s. j1 v& `6 l
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
1 p1 R' q0 ~, B  r- z4 A2 A def clonefragment(self, fragmentList, cloneRetainprobability):
1 ?; ^4 i1 V7 ~( K       clonedfragmentList = []' N+ g5 B! o3 Q& t& m9 }* v1 z5 y
      Lossprobability = [random.random() for _ in range(len(fragmentList))]4 ?8 n! f3 X, S, z. L
      for i in range(len(fragmentList)):6 i! W1 z. z5 L7 C) x5 E3 N
         if Lossprobability <= cloneRetainprobability:- y8 b+ d, F0 k7 [
            clonedfragmentList.append(fragmentList)) X6 n9 i( e) ~  F% E3 B
      return clonedfragmentList! ~. V. e: w& h

5 G( i$ X0 V) U0 m: Z# f2 f # 模拟单端测序,并修改reads的ID号
- O0 |- s, H: q& X8 a* V7 T4 F def singleread(self, clonedfragmentList):% C% j. b3 {9 R0 O! s  J) G
      for fragment in clonedfragmentList:9 s' R- m, _' h  ^
         fragment.id = ""0 r9 G; U9 ~$ a+ X$ P7 U
         fragment.name = ""
1 V& ~, R6 O  U1 q/ l: k          fragment.description = fragment.description[12:].split(",")[0]2 T1 v- S" ^% P# e& `0 ]
         fragment.description = str(self.readsID) + "." + fragment.description
8 X& }. R. m4 L! {& ^! {4 C          self.readsID += 1
; R; b$ Q, X" X% J1 ^          readslength = random.randint(self.minreadslength, self.maxreadslength)
' P% O& L% S* T/ K( f2 h2 O          self.allreadslength += readslength
% t# V& D+ K2 A* p' p4 f+ f$ z          self.readsList.append(fragment[:readslength])
, u; }! b3 |0 Z7 u* b4 Y
8 n2 F) U5 Y! t: N" Y def singlereadsequencing(self, genomedata, sequencingResult):
# M; x  [: t9 G1 V* b6 h       for seq_record in SeqIO.parse(genomedata, "fasta"):3 T* V( H: `4 T. ]4 y
         seqlen = len(seq_record)& w: b! f6 T1 t. b+ ^* b, ]7 n
         self.genomeLength += seqlen
6 A% p" F7 h0 Z, E; n5 I3 e          for i in range(self.N):
3 A; R& B9 i# _+ r1 j' p             # 生成断裂点
' j$ ]6 X% ^" c; B& j5 Q             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)* q+ \5 M$ b$ n+ `+ z' |. h' j
            # 沿断裂点打断基因组# s, w% u3 O' Z7 r8 f% s
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
, n3 w3 K! D+ R$ v       # 模拟克隆时的随机丢失情况9 u$ ~4 M" R0 S8 y( p' f" D
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)0 r7 z+ |/ y1 T. W" g
      # 模拟单端测序
5 t9 L$ i4 n$ V1 S       self.singleread(clonedfragmentList)" e6 p0 o9 g1 f$ [* {8 j* u
      SeqIO.write(self.readsList, sequencingResult, "fasta")
4 G% D" _* j1 }0 i9 B. }- b' T' I) G7 q
def pairread(self, clonedfragmentList):
( R% F$ ?8 L: j) }* C3 O       for fragment in clonedfragmentList:
. g  f6 I% E1 X' F9 P& q. |7 j  L          fragment.id = ""
  m% z" B, F1 Z% m0 @0 ?          fragment.name = ""7 h( r' t  r: W, w  }) y4 s% m) R
         description = fragment.description[12:].split(",")[0]  l$ |3 Q0 m& w. Y9 J" J
         fragment.description = str(self.readsID) + "." + description/ z$ Z+ H- e& e- t
         readslength = random.randint(self.minreadslength, self.maxreadslength)
8 g- |- e+ P4 Q+ D, o1 K* k$ z          self.allreadslength += readslength
         self.readsList.append(fragment[:readslength])
: W) _9 V# R  E; x- ?  L& c7 [; s. j2 t) r5 k. O5 Z
         readslength = random.randint(self.minreadslength, self.maxreadslength)
! k4 z; S( Y# X, Q) s8 t3 u9 X          self.allreadslength += readslength
" C! j$ k% B8 ]/ P7 K0 d5 L! O2 N, |8 ]! Q
         fragmentcomplement = fragment.reverse_complement()
/ F% |0 \; R4 F5 n          fragmentcomplement.id = ""
7 U8 d9 g8 L5 g+ R( b$ K  \$ M0 F          fragmentcomplement.name = ""
% d- f5 {6 w8 D4 `          fragmentcomplement.description = str(self.readsID) + "." + description5 S& B* ^8 [# J6 b
         self.readsList.append(fragmentcomplement[:readslength])$ D6 U8 _, Y* p% t9 K) C! i/ B! o

, k2 I" q8 B! w          self.readsID += 1
9 m8 }* I& n' L# R5 X) v7 M
$ h  o6 i# l# ~ def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):$ O' P# }$ W6 s; {: i& a6 V) i
      for seq_record in SeqIO.parse(genomedata, "fasta"):3 C" e4 [: n0 {0 ~" {
         seqlen = len(seq_record)0 i! q6 y& u; ?8 q* ^9 ]/ a/ n
         self.genomeLength += seqlen) v# r5 \( k* N- j0 b& r5 l
         for i in range(self.N):  o- X6 l8 }7 H2 U
            # 生成断裂点
. }5 r8 q, i; m4 Y* s             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)7 E0 X' |& O; x3 q- B* C( q$ w/ z
            # 沿断裂点打断基因组
& h; b4 K/ c- G/ D/ C3 M0 ^. v             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
+ ]! a3 h; p8 q  G3 F& M7 c! S1 d       # 模拟克隆时的随机丢失情况
% x8 ]; G* H* o: L       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)9 h2 B& Y8 B: M" m; {
      # 模拟双端测序3 G% J" ~% M6 O& P, ^
      self.pairread(clonedfragmentList)
# c" C  v! Q5 T# {       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0], j( C- N# F: L
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
0 V8 e9 z# K, R  h3 p; p$ u       SeqIO.write(readsList_1, sequencingResult_1, "fasta")7 ]# H2 g5 L0 |
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")) A9 s* L  t+ V4 v! y9 [

2 i! A( Q) k# o def resultsummary(self):
6 s4 F4 t3 {- x  U3 ^6 ~       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
# p, ~5 l4 @# s( X: H2 B, |4 c       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
$ `- k$ H7 I6 t$ Z. C0 {       print("N值:" + str(self.N))
5 k' u6 E) f; G0 e) i* P       print("期望片段长度:" + str(self.averagefragmentlength)); U7 N5 s% g! ~* P1 r
      print("克隆保留率:" + str(self.cloneRetainprobability))  s. U% n4 B9 b% x2 h1 h- j
      print("片段数量:" + str(len(self.fragmentList)))
+ S8 b7 P$ I' o9 _" g  X       print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
0 [8 G0 R6 g" h  M       print("reads总数量:" + str(len(self.readsList)))' Y! {; V  g: i$ B# S% x8 K6 d
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
  b+ @0 Z1 j& G6 r! w- m# g       m = self.allreadslength / self.genomeLength! f2 m4 T7 b! v9 }
      print("覆盖度(m值):" + str(round(m, 5)))
  W3 \& |$ i- h* f, Y- U5 J# [       print("理论丢失率(e^-m):" + str(round(exp(-m), 5))); f% n. Q% Q' S. B
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
* `) h$ y4 t6 J4 c4 i) U5 Y  c# -------------------------------------------主程序-------------------------------------------
8 y. P0 M! H7 L# 模拟单端测序
; R8 {' ~! e; C7 e+ TsequencingObj = Sequencing()8 Q4 U8 d; Y& O
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")- S: \& q$ z# w  r
sequencingObj.resultsummary()
& ~' k' e$ N$ i: R3 W. v6 e5 N# |  Q5 z5 t% m5 R6 e, h$ T; p
# 模拟双端测序
2 J, F( c7 ^. l4 w! ?sequencingObj = Sequencing()4 Y2 ]9 c1 F; y- Y, @5 C. M, G
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
4 f4 d! P( X! A2 W: UsequencingObj.resultsummary()
7 [( W) H" R/ g' T! ^3 n: t( [6 M
, x$ ^: X9 U" H  H$ D8 f, y5 E
  r5 M) b* l, Y# e  P- [! W6 _+ b! R( h4 I0 b4 A. G/ f, r/ B
  ]; D1 z. a" ]