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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 V7 d( u+ l% ^& x7 T7 R9 g
         if maxfragmentlength > len(fragment) > minfragmentlength:
! `$ w9 Y* }# d/ @             self.fragmentList.append(fragment)
" i, q8 y' K6 {5 N& J       return self.fragmentList% |3 i' d1 ]% S) a9 A7 P: ^
. X; ]4 }' l/ f% h8 p& k
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率6 _8 C# F9 @4 A8 y2 ]& w
def clonefragment(self, fragmentList, cloneRetainprobability):2 t+ w. g$ n: r9 i9 e8 F6 ]
      clonedfragmentList = []
, C9 ]! ^! _( e. D       Lossprobability = [random.random() for _ in range(len(fragmentList))]
" {7 }& v: Y+ e1 C, ?+ ]       for i in range(len(fragmentList)):& ^( k, ^0 {0 M+ y
         if Lossprobability <= cloneRetainprobability:
. M2 K; \( e6 n. E  p) ~             clonedfragmentList.append(fragmentList)
( L' E4 W% B3 D7 y  ]- }* J: o, z       return clonedfragmentList# U( S$ p& z% a
6 p* E, S0 H% a; K: H. k. S
# 模拟单端测序,并修改reads的ID号4 I2 X/ o0 R/ R
def singleread(self, clonedfragmentList):1 h: s; D! n& {- X! p, c8 o+ v. H
      for fragment in clonedfragmentList:
# N3 l' a- I4 G" P          fragment.id = ""+ |& j# _! E1 ]' s3 f0 o
         fragment.name = ""4 p. F. {& F# X
         fragment.description = fragment.description[12:].split(",")[0]5 }$ |* c, g& b- i  r
         fragment.description = str(self.readsID) + "." + fragment.description
4 @3 t3 ?' f7 _          self.readsID += 1" Q" V# T' b2 ?! r, O- ^0 t
         readslength = random.randint(self.minreadslength, self.maxreadslength)' x4 Z# O+ h8 L1 L
         self.allreadslength += readslength
6 }# m" I3 ~/ Z. O  `9 w          self.readsList.append(fragment[:readslength])* a! W' V( f' ?0 @2 Q1 S- S
4 g6 }+ ?- h; B% ?, N
def singlereadsequencing(self, genomedata, sequencingResult):! |8 K' V6 y2 _) M7 Q$ m8 U
      for seq_record in SeqIO.parse(genomedata, "fasta"):$ p2 `) ~: n* E: w6 A
         seqlen = len(seq_record)6 c' T  I- z& z2 R
         self.genomeLength += seqlen$ {, e  N% z* U' }- H
         for i in range(self.N):4 z3 U! ~5 \3 C$ G. O' h2 H' a* d
            # 生成断裂点
' W; ]& m' S6 P4 Q3 n& H             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)4 a3 Q$ V, k/ Z, z: W
            # 沿断裂点打断基因组
: C: E2 c& v; y  q/ T* H             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)7 V+ _1 S8 L7 m; Z0 `0 \
      # 模拟克隆时的随机丢失情况+ r+ C% |$ S) Z* U- x
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
; P8 x0 Q. d, c, C, F       # 模拟单端测序
3 B; O6 I9 N0 l3 X% A       self.singleread(clonedfragmentList)
6 x" d: n; p! J  r       SeqIO.write(self.readsList, sequencingResult, "fasta")
8 z; z8 [" M' h& D% s: u7 b
( J% G4 `5 h- J) l# [4 i def pairread(self, clonedfragmentList):5 u& ~' ]. }+ d' V" k' a7 o
      for fragment in clonedfragmentList:
/ _+ s& n" G4 I, Z, G5 W          fragment.id = "": }3 a; T, _- u+ ^3 J( x
         fragment.name = ""4 ]% E7 D  T7 P1 P. C+ k# R
         description = fragment.description[12:].split(",")[0]
& K' _& s+ m% _" g0 W          fragment.description = str(self.readsID) + "." + description7 o+ S4 K0 N5 ?; z  ^! X
         readslength = random.randint(self.minreadslength, self.maxreadslength)# W! c: a3 c- ^) |
         self.allreadslength += readslength2 D# C( m6 @( j4 _# |1 I
         self.readsList.append(fragment[:readslength])$ p) n# q2 o* K# a7 V2 a: P& P  o5 Y
, j7 S8 Z% {/ W6 F/ D% ]
         readslength = random.randint(self.minreadslength, self.maxreadslength)8 `) Q% N+ j0 S3 N
         self.allreadslength += readslength' }9 D% \& g5 V9 n. t
, Q& l0 `9 P! ^2 q; B8 t2 z
         fragmentcomplement = fragment.reverse_complement()
, @. N# ~  M" M+ A" z3 p/ U- O          fragmentcomplement.id = ""
% {! _5 n  _! }          fragmentcomplement.name = ""' w/ Z9 f  I, R% K: ~; R" |
         fragmentcomplement.description = str(self.readsID) + "." + description
0 |7 {' E2 A" g1 J+ b, d7 u4 e) R          self.readsList.append(fragmentcomplement[:readslength])4 g* M; D. t8 W4 p6 d- v

4 }( d& u5 w1 h/ w4 V2 u          self.readsID += 1$ d/ [7 Q- }9 k$ i9 d% h
) d+ J  ^9 m6 h$ A" G
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):6 M) l3 q3 e% u
      for seq_record in SeqIO.parse(genomedata, "fasta"):
. T! ?" R* L* o          seqlen = len(seq_record)" m) t+ n) Y) h- }* ?6 d
         self.genomeLength += seqlen, [7 V0 W5 C8 Y
         for i in range(self.N):, U, i8 F4 ?1 j& }" p
            # 生成断裂点6 \8 T0 T. U, _5 s) R+ x/ X3 C( C8 v
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)/ B0 L# [2 k+ y7 O- w
            # 沿断裂点打断基因组- Q( u( V) }. c7 i$ K( ?
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength); W9 L; @! ]9 B7 }& k* v
      # 模拟克隆时的随机丢失情况! V. r& U' M+ y) b# `% E9 b
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
5 `5 ^9 ]! q+ O5 }) e' j' W       # 模拟双端测序
6 F/ a+ r  t' u& Q' R       self.pairread(clonedfragmentList)$ z0 v7 i% j( t9 C# m
      readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
' Y: v/ n, x7 }+ C( }       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
+ J) u! ~# K+ F/ g2 o, ?0 [( ?       SeqIO.write(readsList_1, sequencingResult_1, "fasta")
/ l. r6 A4 T# W' Q- x       SeqIO.write(readsList_2, sequencingResult_2, "fasta")
! e4 x0 F) a! ]; h$ t: c5 C3 ]% z4 V8 y* A2 Y& i
def resultsummary(self):- p2 {6 V) Q, l
      print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
1 R# b' d. T0 a! {# ?       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))+ K1 @* R/ b2 u% I
      print("N值:" + str(self.N))
$ V0 v; n7 J2 ^3 \       print("期望片段长度:" + str(self.averagefragmentlength))
* a/ |8 R5 k0 `: I       print("克隆保留率:" + str(self.cloneRetainprobability))
# r# d/ U5 z1 J9 S% L       print("片段数量:" + str(len(self.fragmentList)))8 u  |; Q6 y4 a& }
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
$ |! Q' f$ m; N5 A6 e7 @5 ?/ d5 D       print("reads总数量:" + str(len(self.readsList)))
, ?9 D  X8 O* W% g" o       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
: j# |/ q; }, g% `8 I0 S: E       m = self.allreadslength / self.genomeLength+ c' q9 c+ C- H& E/ z
      print("覆盖度(m值):" + str(round(m, 5)))1 V  A  b8 y; Q- |4 R9 b. |
      print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
# @7 ?- q3 g/ i0 D4 m       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
5 F) X) i+ L+ B# e1 e$ y# -------------------------------------------主程序-------------------------------------------
/ A' K  x$ w- L& q% u* \9 K! E0 R# 模拟单端测序
7 G: C* r9 J0 ~) M, d- JsequencingObj = Sequencing()
% \7 A$ y$ T+ O4 d# tsequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")9 G$ k& N# _% _" x% q  @
sequencingObj.resultsummary()
( ]' x! e2 K- L! Z, z  ~  S6 }+ U' G# @9 ]
# 模拟双端测序  E) {$ V! L# c- f& q
sequencingObj = Sequencing()8 ]' u. \: N7 I( `9 e
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()
: _: ]8 _) T: q5 z+ v8 Sfrom Bio import SeqIO: z6 Z* U+ q' d& D! q
from math import exp; f. {! K  L& N( @* ^
import random. z* H  s7 \' f! f- p! @% j
& [- R. @6 F: J3 ?. y5 d% g$ K
class Sequencing:
1 _4 L6 y# X: c  ~6 g  M8 B # N代表拷贝份数# j! e. ^3 F  {& Z5 u0 P! e: R
def __init__(self):5 |/ P" d# F# K5 Q
      self.fragmentList = []2 V/ @7 E; r5 c* t' s* d
      self.readsID = 18 T+ u% d' U7 z3 C7 M0 Y
      self.readsList = []& ?$ o& c# W. U. o$ ~
      self.averagefragmentlength = 650, q' K! k0 E# q# d8 ?4 g
      self.minfragmentlength = 500: h3 x. s/ a+ O9 P# k$ c7 b
      self.maxfragmentlength = 800, d2 A- d5 s' j3 g
      self.cloneRetainprobability = 1
! E) D; k; h0 K$ g       self.minreadslength = 508 v8 I. ^7 @& J  _
      self.maxreadslength = 150
; j3 u, q. [4 Z" p+ Z  Q       self.N = 10
' Z4 N! r' u% r) P  G       self.genomeLength = 0
. o0 i( J% v/ m9 l% I+ r3 R       self.allreadslength = 08 h$ J+ O- {) Y9 C5 r; n7 I+ V
* {; X5 M8 t- e) z
# 生成断裂点" ?7 I) J' J7 l" C7 s
def generatebreakpoint(self, seqlen, averageLength):
% N! M2 {! ~3 K  U: F, i9 [2 ]       # 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)
! Z& N( H! {# m" X0 \% S3 P! J- a       breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
- W/ W9 X: m; ~8 t) k       breakpoint.append(seqlen)2 [2 \$ b9 U3 E, Q
      breakpoint.append(0)1 }6 W( q( @8 T  u. i! W
      # 把随机断裂点从小到大排序
/ L' M; S$ h9 U8 b* q5 P       breakpoint.sort()
# r& A7 ]( z/ o1 W9 I       return breakpoint
& K+ w) y: Y. r0 \( b: u' M: A; T5 p- J4 R
# 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% W& q7 g3 t! e2 Q( i3 I
def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):, r$ r2 b& I! m8 V' S
      for i in range(len(breakpoint) - 1):. I3 _7 H. R$ }
         fragment = seq[breakpoint:breakpoint[i + 1]]
, F8 ?- X# O: B9 E! R. \          if maxfragmentlength > len(fragment) > minfragmentlength:
0 Q* ]1 y2 p) x# H4 D             self.fragmentList.append(fragment)
9 B' F+ Z/ r2 z% j* h1 Z6 Y6 P$ @) V       return self.fragmentList
$ d) k8 E# ?8 X* t5 _) c4 ?
) T( _) O7 t5 L! f; [ # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率0 K& `" H. [5 O, @
def clonefragment(self, fragmentList, cloneRetainprobability):4 b! `3 y0 x6 v  x
      clonedfragmentList = []' i# W: x( R4 G* o; K0 V, U
      Lossprobability = [random.random() for _ in range(len(fragmentList))]
) q7 q9 G, U0 M9 L' V- Z8 u! n       for i in range(len(fragmentList)):6 K" k( g: z: i3 Q* q1 @
         if Lossprobability <= cloneRetainprobability:- T* O& j) j6 l; e3 i+ I
            clonedfragmentList.append(fragmentList)
- @" _& e* ?/ s4 j       return clonedfragmentList
. L& P# K# K( i* R3 ~
2 C% V! P3 T( l+ r! \ # 模拟单端测序,并修改reads的ID号: D& `5 `! g' H5 j/ j0 O
def singleread(self, clonedfragmentList):
. j7 W9 g  Q: N. s0 W       for fragment in clonedfragmentList:
. Y3 d2 L5 S9 t8 h# g          fragment.id = ""
3 J7 H/ _, n+ X+ [          fragment.name = ""* c6 H4 ^) k( Z: I/ f0 o
         fragment.description = fragment.description[12:].split(",")[0]
; G: e8 Q: y8 s. P, |  B  X: V          fragment.description = str(self.readsID) + "." + fragment.description% I6 l3 ]* s. X8 p
         self.readsID += 1
9 u* j8 Y3 d; [. l& v          readslength = random.randint(self.minreadslength, self.maxreadslength)0 z2 S/ J5 u: G
         self.allreadslength += readslength
0 A" Z4 z# X1 {/ o          self.readsList.append(fragment[:readslength])
! @- j$ W# A0 j+ K6 h. S
/ P9 ?1 Y. D0 x/ j) g1 Y" ~! ~% W( o def singlereadsequencing(self, genomedata, sequencingResult):' w- B7 T' s/ |( }9 b# H8 B$ |
      for seq_record in SeqIO.parse(genomedata, "fasta"):
; t! t8 x1 F  `) `% F          seqlen = len(seq_record)
" W4 D) o+ m# B; w8 x; }          self.genomeLength += seqlen
         for i in range(self.N):# c9 d$ K+ ~) o/ k5 w1 F* p
            # 生成断裂点
% I- @" @2 m6 c9 |             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
6 m- \# J9 j8 W, l( b" V             # 沿断裂点打断基因组; @( l; g7 ^5 e( c7 J% w6 p  z
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
  s" x3 K8 A6 z+ c       # 模拟克隆时的随机丢失情况: H9 X0 S; H/ e& @9 g! X  g- j
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)# _. `% H& g  K2 i
      # 模拟单端测序
7 u9 ?9 b. L1 m2 W. k6 f       self.singleread(clonedfragmentList), ~/ x" Q( n9 H
      SeqIO.write(self.readsList, sequencingResult, "fasta")2 b- c# G# Q0 u* k2 Q
* K( O& r9 k7 i
def pairread(self, clonedfragmentList):7 }# B3 x9 i2 ?) y  [4 K
      for fragment in clonedfragmentList:
. E% N" g3 u8 Z2 Y          fragment.id = ""
8 c, |+ u* f  u) S          fragment.name = ""
( P8 T& f, P0 [% ?          description = fragment.description[12:].split(",")[0]6 v' C- _- D$ M5 o* b" Q
         fragment.description = str(self.readsID) + "." + description$ X6 I3 c- L2 s+ U, p5 M9 L( r
         readslength = random.randint(self.minreadslength, self.maxreadslength)1 \% k" y& ]4 I6 u* o, Y
         self.allreadslength += readslength
- d6 d  U$ R$ }, c          self.readsList.append(fragment[:readslength])& p1 g: N% o0 U! L
1 Z3 F+ a, O6 H' D1 B( R1 D4 ]
         readslength = random.randint(self.minreadslength, self.maxreadslength)
/ g( n/ A2 O% r; s8 P/ Z! ?' ^          self.allreadslength += readslength
: s* V) I" f0 c% N0 x, _  I/ T2 O
+ J1 u0 f& D" ?3 R$ |          fragmentcomplement = fragment.reverse_complement()2 Y- s; \0 z2 \7 l  A. y7 h9 n
         fragmentcomplement.id = ""
1 s1 g: g3 R( b+ |          fragmentcomplement.name = ""/ [' p5 m! s- Y
         fragmentcomplement.description = str(self.readsID) + "." + description0 V6 b1 B' [. K0 r
         self.readsList.append(fragmentcomplement[:readslength])
1 E5 I4 x; n  R% J! \% C8 }2 o3 E5 E1 v" P1 K
         self.readsID += 1) b$ B9 H+ K8 U! ^, o  U
/ q1 {( g' y7 s$ E9 g
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
1 ~2 ~% |- I0 Q  W4 w       for seq_record in SeqIO.parse(genomedata, "fasta"):, i$ Z8 R7 A+ }1 M5 f: V5 C9 O
         seqlen = len(seq_record)
& Y, A0 M; E4 d8 S5 s7 [; {          self.genomeLength += seqlen
# A0 ]3 z& r" X3 l2 p. m3 |6 @          for i in range(self.N):
7 j6 S4 ]- |8 t6 v- T6 a+ d/ B             # 生成断裂点
: Y4 D2 b+ y5 c/ D             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
) T  ?. z, Y) l             # 沿断裂点打断基因组2 T! W! f5 R9 D4 i  g. o* T# l
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)$ a) Q' m; X1 ?) J2 s/ m0 S
      # 模拟克隆时的随机丢失情况
: I; O8 n8 Y- Y3 d% a       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)) N5 N+ Y$ m* ]% G
      # 模拟双端测序5 k+ B, {. n+ x6 `4 v
      self.pairread(clonedfragmentList)2 C9 \1 t6 q- i) r- U( K8 d
      readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]0 T8 a- H# f  N, R1 l& i+ ~) S+ Z: V
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
' m; T* F! m& e       SeqIO.write(readsList_1, sequencingResult_1, "fasta")1 m  D" w7 N% V" g
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")
. K0 Y/ U- m" M$ q# M' Q
  ^$ m- E8 P' w& k def resultsummary(self):
$ {7 r, w" a! O! L: b" P8 B1 `       print("基因组长度:" + str(self.genomeLength / 1000) + "kb"); s0 B7 p4 x: x, B7 a& t
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
, D( S- Y5 {7 k: I" ~       print("N值:" + str(self.N))* h: \/ a9 O3 f& o5 v& i& o
      print("期望片段长度:" + str(self.averagefragmentlength))
! e+ ^2 j$ ^7 I! v, a       print("克隆保留率:" + str(self.cloneRetainprobability))" h/ Z2 }; o- t
      print("片段数量:" + str(len(self.fragmentList)))
4 I# a0 `+ G/ _; Z- T       print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength)), N6 }4 r. }3 x) ~6 y. p! L* H
      print("reads总数量:" + str(len(self.readsList)))( X( v5 }9 C8 T5 Z9 l6 o+ k: N- v
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")" I' _+ X% `! G. Q8 w0 T* M
      m = self.allreadslength / self.genomeLength: F+ r4 X4 M2 ?! C' [
      print("覆盖度(m值):" + str(round(m, 5)))
) _# \/ l( `  E2 w% M       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))% Z$ B- t/ _: v. V8 [% d) U3 Q9 l
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))2 w6 S! y; D; J3 H. k, v3 }
# -------------------------------------------主程序-------------------------------------------
& t( f% ^3 c" o! @' m+ [# 模拟单端测序% B) E( h0 J) b- [. E& Q
sequencingObj = Sequencing()" V+ @5 h' w6 y& D0 k3 D* P" o
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")% E7 c" W: e1 M: k. b0 H
sequencingObj.resultsummary()& m+ y# e7 y  S8 @/ ]
: ^$ u8 v1 h0 j4 F" m2 v) N) O
# 模拟双端测序  n& S/ f" L, |& ^' d; \
sequencingObj = Sequencing(), I% i! h* B0 s' l% e
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
0 ^0 I' P: p( D1 ]! m  e1 ]7 s5 tsequencingObj.resultsummary()2 h2 R; f! ]5 t3 X
( G: h1 L* L1 H. |! P+ d4 f
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