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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]]
; r7 F1 |+ |* G4 O          if maxfragmentlength > len(fragment) > minfragmentlength:. w) F" L5 K: b' j5 D6 i7 v. C; \
            self.fragmentList.append(fragment)
" S) r4 H" t4 H# \3 x, ?) d1 i8 m/ P       return self.fragmentList+ h" B% D' U& ^, A: z4 ]* W% H

5 A  n& b+ N) O+ h # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率$ \" I* F" z* P# u5 d+ |. p0 @. {
def clonefragment(self, fragmentList, cloneRetainprobability):+ h/ S7 v4 B7 R+ A
      clonedfragmentList = []! ^/ ]. l. F" v, b& n# ~; z
      Lossprobability = [random.random() for _ in range(len(fragmentList))]1 r  o) d/ O& v, P' |
      for i in range(len(fragmentList)):- Z) W6 H- E! F! ^: V5 }+ V5 S
         if Lossprobability <= cloneRetainprobability:
: j. d3 `! T* z2 |: m0 w             clonedfragmentList.append(fragmentList)7 p6 u3 f+ C* d+ n
      return clonedfragmentList9 Y7 r! r5 p% |. c1 @& y# E$ P

  i0 V* U( T. ]( e # 模拟单端测序,并修改reads的ID号$ ]. h) r2 x1 ~$ K5 z
def singleread(self, clonedfragmentList):. @" L! K" n) d, J9 n( }; F4 ~
      for fragment in clonedfragmentList:( p) C( U: Z5 ?0 e
         fragment.id = ""
6 @, O3 M7 k& X7 P; J( n' T. y          fragment.name = ""
3 a1 H! W) M- T4 I) [1 M          fragment.description = fragment.description[12:].split(",")[0]$ Y+ @0 \4 d# @8 d4 b
         fragment.description = str(self.readsID) + "." + fragment.description
! n$ e5 q: A6 w- W' |          self.readsID += 1
9 c( e( k: a8 B& _7 B          readslength = random.randint(self.minreadslength, self.maxreadslength)
+ T8 }# ]% K5 O6 C* L          self.allreadslength += readslength6 g8 M" m, X# K+ M, R
         self.readsList.append(fragment[:readslength])
5 }* X# L# ^( E/ V) s( G1 K. u3 q! s2 x" \
def singlereadsequencing(self, genomedata, sequencingResult):! B4 c( Z/ H+ }5 A. [/ w
      for seq_record in SeqIO.parse(genomedata, "fasta"):. Q; y0 r- e8 T% M) h* p
         seqlen = len(seq_record)0 b8 B; b2 p! [2 B7 w( x
         self.genomeLength += seqlen; j- J. `' C+ e$ P
         for i in range(self.N):1 ~$ o* m" E6 r& l. X# E5 Z
            # 生成断裂点
" Y/ p$ _! u; V5 d- f             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
& q& |3 v+ L' X3 H2 w, k% ]             # 沿断裂点打断基因组& d# a! J; d" ~2 E5 D0 O3 G. D
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)+ s( |& [5 ?$ Y% n! g5 i/ W& e6 g
      # 模拟克隆时的随机丢失情况
# I! `& o. w( t" L: y' l2 |$ U       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)% R9 v- \/ i; Z3 Q5 B
      # 模拟单端测序  ]- `: g$ m: L) _
      self.singleread(clonedfragmentList)
9 u; ]6 w8 ]3 H7 R3 G4 ?       SeqIO.write(self.readsList, sequencingResult, "fasta")/ G; Y$ o! t* m: h4 b. h$ q
1 V# X; _$ f! N& q' Z' l2 Z
def pairread(self, clonedfragmentList):
+ [/ f$ K, w1 c( v  \4 N. h       for fragment in clonedfragmentList:
  S5 X# L4 a- E8 Z3 Y# `          fragment.id = ""
$ T/ I: y9 o% H( u; ^1 H          fragment.name = ""
! V* _  L0 V* X0 y& u% u: ]1 E          description = fragment.description[12:].split(",")[0]  b* C+ p1 V  N# `4 r" Q7 `+ Z
         fragment.description = str(self.readsID) + "." + description# V9 i7 h  ]% ]( r  _
         readslength = random.randint(self.minreadslength, self.maxreadslength)
. a+ f7 T6 X+ V' H0 ?          self.allreadslength += readslength+ O9 p  ^' ?) P. r5 q6 r& j( D
         self.readsList.append(fragment[:readslength])) \$ E) ^8 I! }% B5 J" K+ S4 R
0 M! l2 m6 V, F* K+ d8 u
         readslength = random.randint(self.minreadslength, self.maxreadslength)0 ?1 S+ f8 [: G1 f8 _. p8 n
         self.allreadslength += readslength( z" Z3 ?! s8 h4 t- S5 O8 S" p* O

0 u1 z) c  d: q. f          fragmentcomplement = fragment.reverse_complement()$ o$ I8 j8 A! M+ L
         fragmentcomplement.id = ""( u2 E& w$ H7 u# d9 v
         fragmentcomplement.name = ""$ [# i/ A6 I' Y9 ]5 Q( @+ I, \5 U6 V
         fragmentcomplement.description = str(self.readsID) + "." + description
4 g5 ?; z8 F0 [- N1 w          self.readsList.append(fragmentcomplement[:readslength]), b# H9 k* O+ o/ D. G5 M% s

4 S1 Q# B9 n* |6 V4 i' M6 k          self.readsID += 17 b5 g/ C+ H, Q0 S) k

) Q7 q0 t9 P# H6 [5 S def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):) K+ ^/ R3 K' L- O
      for seq_record in SeqIO.parse(genomedata, "fasta"):0 v7 \$ B0 k5 D& `! S
         seqlen = len(seq_record)# n4 ~$ z9 I# C. B
         self.genomeLength += seqlen
% x9 A6 M8 P- G& P% L. ?  H0 N          for i in range(self.N):+ B( |& w) F7 w( y' d; S  X) }9 t
            # 生成断裂点
# @9 E$ {7 g& X% n. S# W             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)- F  L+ r6 Y# T9 t: y
            # 沿断裂点打断基因组6 g5 ]8 b- B7 Q# S4 j
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
  }! ~; o$ Z: X. b, ^       # 模拟克隆时的随机丢失情况& I0 g; D' u9 K
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability), |. `' k4 N3 ?$ M
      # 模拟双端测序
; r! L- f6 ^) e% r       self.pairread(clonedfragmentList)6 W$ {% v) K1 Q+ s) n
      readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
/ @, e3 k( ^8 m4 o       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]  B0 t9 ]8 ]" r# ]3 [
      SeqIO.write(readsList_1, sequencingResult_1, "fasta")
. @3 s" I$ O" {: ]6 l+ ^  }       SeqIO.write(readsList_2, sequencingResult_2, "fasta")
5 g8 g/ V; V. I1 p1 b! E5 V- w
3 N1 N8 R* Q3 J+ n7 h; \* [" F def resultsummary(self):
& T, _  T/ w: ?8 l/ e6 h; ?       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")9 u9 a4 y$ R) C$ S% I2 y& h3 i5 _
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))# ~1 |) Q% |! f; ~' }5 r
      print("N值:" + str(self.N))
5 n4 L, V& L" E$ G       print("期望片段长度:" + str(self.averagefragmentlength))
+ N4 {- H: ?. ~5 J7 Z1 |1 j# z5 K       print("克隆保留率:" + str(self.cloneRetainprobability))
& m, r7 Y. s9 L       print("片段数量:" + str(len(self.fragmentList)))& u- o$ F2 g  k% O2 S
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))4 g& `" L0 p0 z* e. F
      print("reads总数量:" + str(len(self.readsList)))
4 r- u1 J% g6 Y5 o  o. n       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
3 ]% U# v6 r7 `       m = self.allreadslength / self.genomeLength% R  Z( {$ u% K+ _% f
      print("覆盖度(m值):" + str(round(m, 5)))
; D2 c. t- i7 M6 @' c: n       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
% p( Z/ C! W1 O& d3 a       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))% d  K% E2 \# F( Y9 p
# -------------------------------------------主程序-------------------------------------------
  W- P6 a! W+ I7 A/ A& A( o# 模拟单端测序
1 e* s5 ?% F  H' k' }sequencingObj = Sequencing()5 E6 K0 _, \" x2 @2 s9 v
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")# I% q5 h2 N9 X' f. p
sequencingObj.resultsummary()
* d2 d7 R  r: A2 ~$ Z) d& k# R2 Q6 S- h2 P' Q7 t- q
# 模拟双端测序
! N' M4 F! `: usequencingObj = Sequencing()
' g! a( b- l: A% W" SsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")- t2 X/ m: J6 {8 J- n
sequencingObj.resultsummary()
( c9 s# C1 Q9 W2 M3 g# Qfrom Bio import SeqIO5 j9 j, U* R, J# [" c- l' D, f; o( e
from math import exp8 \! J9 o0 e8 D0 o
import random) R& s1 f( l( m
2 s& C8 b' K. i8 \5 O- K
class Sequencing:
; P% t) E6 J3 L% E" k # N代表拷贝份数% Q4 G/ K: @6 `% l8 J* ~
def __init__(self):2 o( S8 a6 l  P" Y- ?
      self.fragmentList = []5 d! |3 Z' X; R( b  M0 w7 Q7 G
      self.readsID = 1
9 Y2 F3 S* o) A$ M       self.readsList = []
8 q  u) x* F0 B1 ~; s6 D/ Z' C; O       self.averagefragmentlength = 6508 W; b/ l1 P9 A8 S3 Q4 `
      self.minfragmentlength = 500
: u! N) D- w: i* b       self.maxfragmentlength = 8006 a/ d' I0 Q2 ^8 L/ [' d& h0 Y! _, R
      self.cloneRetainprobability = 13 R( ?! ^6 J8 F8 \( ~
      self.minreadslength = 50
9 m9 u# q8 @+ x7 _( X! v+ Z0 J       self.maxreadslength = 150
7 C  A7 z1 y3 @; Z8 u- {       self.N = 10# R+ R; X) x$ {3 K* M
      self.genomeLength = 04 e* c3 Z- @' x
      self.allreadslength = 0
6 P6 ~  [0 e$ E7 I; L  W- B. e- j/ m8 {' s
# 生成断裂点; m* g- O3 }6 p; I8 q  v" O' ]
def generatebreakpoint(self, seqlen, averageLength):
! F: G. \* f! k, \       # 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)
# U/ f3 z2 G6 I& V, o/ ^: p' f8 X       breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]5 o. r' y3 X7 ]. Y# p7 t
      breakpoint.append(seqlen)+ _" \, E9 t' b0 |" C
      breakpoint.append(0)" u" ?2 h  [3 I9 `9 ]- K
      # 把随机断裂点从小到大排序
  y. r% u/ T4 d/ u: \3 K       breakpoint.sort()
- v2 D. r. X$ X. X0 |. ^* H4 ?4 T       return breakpoint
% Q- K# A! p# V  n/ K* D* j  f' H- z/ {7 o3 }) q0 m3 `
# 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:200-1000bp2 P+ @. M4 l) r$ Z  e
def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
8 ]# ]. ~& I) ~       for i in range(len(breakpoint) - 1):# e' D: P# Z1 j8 v* P5 x
         fragment = seq[breakpoint:breakpoint[i + 1]]# o" ]; h( M6 S+ c- \' A$ `2 y
         if maxfragmentlength > len(fragment) > minfragmentlength:
6 h) \0 [7 b9 m; r  ~+ h             self.fragmentList.append(fragment)
3 H) l' c2 m# }4 j; W$ y( Y  w9 }       return self.fragmentList
# q; w: q5 a+ Y5 Y; e8 K
* E0 J% g3 d, k9 i # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
2 K' v5 b; [  d. d0 I$ l) \9 _! H- _ def clonefragment(self, fragmentList, cloneRetainprobability):
, b9 w; C) Z0 r8 u4 U0 z1 ?" o2 p2 H+ C       clonedfragmentList = []% z; K0 [( x1 O* a4 |) U
      Lossprobability = [random.random() for _ in range(len(fragmentList))]
! p0 p6 B, D$ V, `9 T       for i in range(len(fragmentList)):, |$ }7 h5 n( t' H* _+ z
         if Lossprobability <= cloneRetainprobability:3 Y6 C  ?/ T' n$ v# P! W% N
            clonedfragmentList.append(fragmentList)
- Q: t  c8 h: L+ c" R/ `+ |       return clonedfragmentList
. y( O$ L4 L" P# H9 V: J& J7 k. \4 t8 q* U4 l! t, @) g7 Q, ?6 P) s2 H: G
# 模拟单端测序,并修改reads的ID号% C, }+ z! v( M5 O5 r
def singleread(self, clonedfragmentList):- [, e& G; t2 R9 u+ @( ~. w
      for fragment in clonedfragmentList:
  u+ u, e/ u$ b0 L$ w8 |6 w          fragment.id = ""
  W9 @5 A+ u! U9 q- o5 A          fragment.name = ""
+ c7 O* j# J! n3 [9 X          fragment.description = fragment.description[12:].split(",")[0]1 j5 p6 q+ d7 x$ @# |5 P  k0 A1 g. P
         fragment.description = str(self.readsID) + "." + fragment.description9 ]# v9 G. Z5 W* a' y
         self.readsID += 1" q; Z' b- `5 ?" g
         readslength = random.randint(self.minreadslength, self.maxreadslength)/ Q+ q+ F4 o) Q" |
         self.allreadslength += readslength
1 g9 X# I% x! S4 s9 n2 b* T          self.readsList.append(fragment[:readslength]): l2 g/ i& }& {3 z* B3 k& ?7 V
& |, v' {1 C; Q; |6 e
def singlereadsequencing(self, genomedata, sequencingResult):
- U2 N8 v4 }& m+ M       for seq_record in SeqIO.parse(genomedata, "fasta"):3 N  n+ x* S4 g5 K* @. k2 o- h3 p
         seqlen = len(seq_record)) S1 B! \' D; F; Z  K' A
         self.genomeLength += seqlen' K* R& x. G; A' z* v+ G, W- I  z
         for i in range(self.N):# _/ P8 O3 H' [+ r8 Q5 u( a! F
            # 生成断裂点
3 {$ m; e) ^+ ]9 `! a/ W6 Y# H             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
1 q4 m; o. Y/ g8 v- a; x             # 沿断裂点打断基因组
, A4 _; _4 r7 ]. G! I7 O8 p             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)( I1 ?; b) p' n
      # 模拟克隆时的随机丢失情况
8 V$ P! _3 W3 d9 C, l3 `7 S- l, q       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
* y* J+ q  k& g. z/ l       # 模拟单端测序
* o/ D: O$ m. V/ d4 j1 j7 {       self.singleread(clonedfragmentList)
3 t+ L, f, M" y. [       SeqIO.write(self.readsList, sequencingResult, "fasta")  s0 ~, m( v9 @  r% A3 v
2 h" i6 o  I1 h0 ^) q
def pairread(self, clonedfragmentList):. h5 M* h. B; ?$ m2 ?4 ~' D
      for fragment in clonedfragmentList:) g. Y5 ]6 B( i
         fragment.id = ""
& r. r6 D1 a# G) |: @  }. w+ }          fragment.name = ""2 {1 m1 W* J4 D7 m; l3 G1 s
         description = fragment.description[12:].split(",")[0]
* b/ B* g2 [+ }0 c          fragment.description = str(self.readsID) + "." + description
- J; |0 q* q8 G* |# X- A: K          readslength = random.randint(self.minreadslength, self.maxreadslength)
2 R0 _# [, |3 N7 E0 S+ C* @          self.allreadslength += readslength6 ?8 G( [1 C, n: D, S9 J
         self.readsList.append(fragment[:readslength])* X4 V/ x0 @1 X+ T% p/ c
) O3 ^0 v9 C" R+ f  a
         readslength = random.randint(self.minreadslength, self.maxreadslength)
8 E; S/ Q  M1 d5 n" R5 Z          self.allreadslength += readslength
7 C1 }% D3 U7 }, h3 c4 Z0 c
/ c$ D* N* u6 j5 T* ^          fragmentcomplement = fragment.reverse_complement()
$ U+ o$ o3 [8 u7 Z          fragmentcomplement.id = ""
, M; h: @3 f& J0 ~# S6 T4 e; j$ `          fragmentcomplement.name = ""
8 L/ R1 ?: R+ h( D          fragmentcomplement.description = str(self.readsID) + "." + description) B! y- T3 o( k5 S
         self.readsList.append(fragmentcomplement[:readslength])
) z  H) y8 E3 A1 \. i% x1 H$ b; Z# R) D& d, V
         self.readsID += 1; Y5 m8 [3 Y" d" o" O1 W: N
2 E- o- ?' W; p) I
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):) t9 J5 F8 k! V; D" Q6 I7 M
      for seq_record in SeqIO.parse(genomedata, "fasta"):
) {3 a  X' r0 M2 w          seqlen = len(seq_record)
: M2 M, x% F3 V6 |' l! J6 s% u          self.genomeLength += seqlen
  z% T# y, i! z; s8 _          for i in range(self.N):2 \0 R( }7 {; I0 I# {
            # 生成断裂点
" P  `) c- Q5 J. H" j8 ]             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength). x- L" e0 `  r$ Z: A1 L- ?  B& h
            # 沿断裂点打断基因组8 V1 Z! v0 s: E. \0 m* ^: |
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
- ^4 u/ `/ D6 u( u& u: Y+ h) [1 R       # 模拟克隆时的随机丢失情况- M/ D- H( ?( \, I  A
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)0 S. C" C" l9 h7 h! I
      # 模拟双端测序
      self.pairread(clonedfragmentList)
! Y+ @/ x( u4 o3 u0 E2 ^5 B       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
: [: ]  U  g( ?* R( j. x5 [       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]$ T; |  y; P( w9 [, d! O( O# q
      SeqIO.write(readsList_1, sequencingResult_1, "fasta")
7 N$ l; v+ x6 d+ V       SeqIO.write(readsList_2, sequencingResult_2, "fasta")
7 f! a& K" Q* V2 R  c2 M* U  r" n3 a! B& R! C# L
def resultsummary(self):
; ~* Z7 ~% U1 b* p+ d% ?! b       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
( g! f& C- `8 C       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))" I" x; ?7 `" Z& k8 H) x' b
      print("N值:" + str(self.N))* u% i4 I, i5 u8 c6 a/ Y
      print("期望片段长度:" + str(self.averagefragmentlength))% V3 ], E6 ^- @1 a" e4 }/ S- \# a) j5 H
      print("克隆保留率:" + str(self.cloneRetainprobability))
' p1 X) E* j, e2 d  n. R       print("片段数量:" + str(len(self.fragmentList)))# l" J, e  c5 @
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
- `+ \4 u+ Q  ~       print("reads总数量:" + str(len(self.readsList)))
. ^( X( `4 O: y* t0 K       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")+ |9 g6 C/ p; b3 y0 U. I
      m = self.allreadslength / self.genomeLength4 {' v  W0 c5 O! Z
      print("覆盖度(m值):" + str(round(m, 5)))
7 B3 ]  {5 H) |       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))* ]( x' Z0 Q" T5 \& @3 V$ k
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
2 D' G. D  J8 l# -------------------------------------------主程序-------------------------------------------
2 h' u1 c2 B: B% S# 模拟单端测序
1 l, c, l. K" D" _( b( [1 w" hsequencingObj = Sequencing()* [; ?0 v( t3 ~4 L; B: L# q. R
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")" G/ t" _7 c8 Y
sequencingObj.resultsummary()
' r% B3 s- \7 I4 p0 M3 G
) X' }' Z6 i, z; P8 B- p# 模拟双端测序
, C- t% F; o% ?) \: gsequencingObj = Sequencing()) q. M% {, w4 z
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
, i! J; w; ]+ m8 p- |5 @sequencingObj.resultsummary()
2 j* @7 N. n/ M0 R6 z4 o
* P  @" }+ M, B  @: i4 \& l, k
5 w. j4 l6 Q: I+ ?, u5 S7 T0 Q6 d
! K/ ^3 o, y1 f! T3 N2 h( R6 O7 L 8 v4 u" C: O/ I9 @$ d: A, G) M