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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]]
& q* }# W6 V# i, m3 z          if maxfragmentlength > len(fragment) > minfragmentlength:
" P/ D0 [, [& t7 A             self.fragmentList.append(fragment)
5 J7 i6 P! c8 L3 p& `       return self.fragmentList. W; s2 i# m# j/ V+ Y
3 j8 U! T) J% ^! ~2 q/ y. z
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率' s: [, S9 L8 V. o7 i' ^
def clonefragment(self, fragmentList, cloneRetainprobability):) x4 t; P4 L, J0 i& [  O2 G  `
      clonedfragmentList = []
, z# P9 a, j8 \1 y5 X2 u       Lossprobability = [random.random() for _ in range(len(fragmentList))]
9 N/ v1 x# ~6 o3 M9 z; z/ B- E* B       for i in range(len(fragmentList)):1 H3 C/ E9 C3 w/ }. f+ C' k3 Q
         if Lossprobability <= cloneRetainprobability:( W7 W% W; _3 j" }7 h6 O& n. n; i- L
            clonedfragmentList.append(fragmentList)+ A& e! m1 }% E% a4 v
      return clonedfragmentList
/ J0 k3 `4 x% t+ D
! d$ m" G/ b9 g' Z6 s/ [ # 模拟单端测序,并修改reads的ID号
, h7 j1 G5 [1 d, S def singleread(self, clonedfragmentList):
7 f& {, S+ z0 }* s7 [       for fragment in clonedfragmentList:
& F+ K8 `/ ?) m7 o( P3 i: d" E) q          fragment.id = ""
3 }6 S; r3 C1 G6 F; A# w% H: G, z* R          fragment.name = ""% _& R; t# o- i$ i0 {
         fragment.description = fragment.description[12:].split(",")[0]: N& b1 v9 s  c2 x2 G
         fragment.description = str(self.readsID) + "." + fragment.description5 S; {6 a. _8 A) V1 }1 ]9 }2 B
         self.readsID += 18 C% a$ q7 N- I( G
         readslength = random.randint(self.minreadslength, self.maxreadslength)1 w) X; c* T6 q9 F5 p
         self.allreadslength += readslength
! \5 i$ E2 \& R; x& ~" C" b          self.readsList.append(fragment[:readslength])
5 u  R/ B- M7 C; d7 }% g; R+ N' n8 o& F/ E: D( ~3 i2 d4 q
def singlereadsequencing(self, genomedata, sequencingResult):
, v0 G( e) V8 W       for seq_record in SeqIO.parse(genomedata, "fasta"):
+ [: I( U- {! F  a. f          seqlen = len(seq_record)& P$ h- f1 x& K4 [
         self.genomeLength += seqlen5 [, K1 w/ s9 Q. Y8 H
         for i in range(self.N):
. R- O/ J  S4 N1 s             # 生成断裂点
9 W4 g7 ?" w8 c9 L& E# z. Z             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength): r5 X9 q$ ^, h) ~1 d; ]- A" a4 x
            # 沿断裂点打断基因组
/ P. ~$ l; g( ?+ k             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
0 ^3 p; a3 J' j+ U       # 模拟克隆时的随机丢失情况
" O: ?, W3 Z/ E. J- S       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)9 i( [! b5 x$ w4 k- A, @) I6 x
      # 模拟单端测序/ ]7 v5 b1 O- r+ k- s. W  r4 }
      self.singleread(clonedfragmentList)  V& u* s3 f/ q* ~) S8 r! k
      SeqIO.write(self.readsList, sequencingResult, "fasta")4 Y: P$ t' k' ~$ ]0 d/ H
/ X/ {1 R! k) @8 {& X
def pairread(self, clonedfragmentList):; ~$ }- w: ?! e* p
      for fragment in clonedfragmentList:
- p) d+ l# |( a          fragment.id = ""
! v$ D% b& q, V  |3 ?          fragment.name = ""
  K) V8 z4 o* P$ @( t% M          description = fragment.description[12:].split(",")[0]
8 V3 }$ l, @) B: R8 S          fragment.description = str(self.readsID) + "." + description- B, O9 a1 Y& W3 f
         readslength = random.randint(self.minreadslength, self.maxreadslength)
# G! x- M2 q1 n, y& q& Y& k          self.allreadslength += readslength
6 F+ ]8 A" E' U4 p          self.readsList.append(fragment[:readslength])! z  _& _& F, ^7 K/ D' s+ j
+ |2 s9 [3 d) k
         readslength = random.randint(self.minreadslength, self.maxreadslength)0 v1 w' m1 e+ }" c# t0 k' o2 G8 Q
         self.allreadslength += readslength
. r3 A9 w5 `9 X' b; [; J, Y% e( [' e* a- r- B4 F' J& L& ?
         fragmentcomplement = fragment.reverse_complement()
( y. ^2 |5 N+ Z9 W& x/ V          fragmentcomplement.id = ""
1 ?2 u* X" a0 v1 J& i0 q% n) I          fragmentcomplement.name = ""0 B+ a. W0 e, q. K1 Q  r; H# t
         fragmentcomplement.description = str(self.readsID) + "." + description( G, Q) y; `1 ^) [1 E& V0 {  Q
         self.readsList.append(fragmentcomplement[:readslength])
, n# t# B4 f0 N3 k) q
( b0 v/ n( z* D5 z1 `8 ]5 u$ c          self.readsID += 1$ h! `+ v- @1 k3 N
& r' d# b  ~& }/ c6 e; a
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
% w# U, K" p9 A8 G9 q       for seq_record in SeqIO.parse(genomedata, "fasta"):" S3 F' q# R$ a# N  X2 }, ]' U
         seqlen = len(seq_record)6 a& C4 L. [( F0 N8 \9 T
         self.genomeLength += seqlen2 a( j5 s& Y( j0 W0 c
         for i in range(self.N):1 s2 ?  H: B9 g( N8 E  d" {
            # 生成断裂点
+ Z0 s, n* y) c* K# H' l$ }7 X             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
$ @; L" e3 ?8 n. h% J5 ^7 G             # 沿断裂点打断基因组: E! s) J0 G5 w- j, X
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
6 D: H  e3 z& @; d       # 模拟克隆时的随机丢失情况/ O2 n# ^; E0 O5 ^
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
3 R% `' x1 X+ Q9 U       # 模拟双端测序
, \% \# D) a* [# c" d9 G, ~( r       self.pairread(clonedfragmentList)
  C, S4 p6 c2 A       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]- d# J4 Q1 t0 k. F
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
7 \; A+ Y" A. J9 }. W* i7 E( z       SeqIO.write(readsList_1, sequencingResult_1, "fasta")8 H/ ?+ x; Y. d9 B& p0 q
      SeqIO.write(readsList_2, sequencingResult_2, "fasta"); b$ s- D+ u. h9 b; z: f# T5 s- \
) @# j$ y# z4 y% J. |% W$ i
def resultsummary(self):
5 q" U7 R2 A* Z3 [       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
3 a. N6 T3 \& w9 |3 p       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
. p' X3 ]! k  E" L$ m1 ~  R       print("N值:" + str(self.N))
4 r' W" L3 c2 s- a       print("期望片段长度:" + str(self.averagefragmentlength))
  p% k) W0 l: R7 Y2 a" {! s8 G       print("克隆保留率:" + str(self.cloneRetainprobability))
, ^# [& `8 U: {, Z5 V- D8 o       print("片段数量:" + str(len(self.fragmentList)))
: X% [: n  \1 l5 @$ Q! i       print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))4 W- i" m1 n$ g7 ]
      print("reads总数量:" + str(len(self.readsList)))
2 A" }0 h+ s8 s* a' v9 V! l       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")+ H3 c2 ^7 O. N1 N
      m = self.allreadslength / self.genomeLength
# s  D2 [8 u2 S  ^       print("覆盖度(m值):" + str(round(m, 5)))( Q! c1 I, x; l4 R& C
      print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
/ c& N, E3 J; b* v       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
+ ]: @9 b5 K8 m& e  W# -------------------------------------------主程序-------------------------------------------3 X5 }8 O4 ~" h7 o1 E
# 模拟单端测序
$ _4 ]3 L0 x/ c  a; I! XsequencingObj = Sequencing()6 n" @" I- V$ @* z
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
  B0 z4 j8 M) I' C5 W; Z% ZsequencingObj.resultsummary()! @: u4 h" V  {

; w$ Y/ n$ s1 N6 F& ]6 I( |# 模拟双端测序
0 D1 ]% g! V( G9 p/ }7 T5 F. o, ^sequencingObj = Sequencing()' F, U& U( @, M2 w7 C! C; K$ B
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")/ q1 @4 @  V' K, i5 v$ s
sequencingObj.resultsummary()
6 t$ A. Z3 m1 `0 A  X$ o$ |. x" ifrom Bio import SeqIO
7 g% K7 B8 g8 G  l2 Y# n) J3 `from math import exp0 ?# u' M) U. B3 G' A( W
import random' [5 i" H$ W. h8 x2 }
0 @' y" G+ h$ `/ ^* B7 t
class Sequencing:
6 Z3 ~/ A: G0 p  \$ m! C # N代表拷贝份数
1 Q$ v- J! M$ o# U5 T def __init__(self):
1 a4 z" N6 [) @# y       self.fragmentList = []7 }. H7 f+ V( A  k% |! S, X: ^
      self.readsID = 1
0 h8 @; ?2 R4 c" _, Y* K9 v       self.readsList = []
! d3 s4 K2 @! [* }" W       self.averagefragmentlength = 650- R! g) h5 P6 _  I5 j# ^
      self.minfragmentlength = 5005 G1 q- F, q# l3 c: ^: C7 Z
      self.maxfragmentlength = 800- f$ i& h$ k: [
      self.cloneRetainprobability = 1
8 ~0 }) N$ ?; Y* g% L. @9 V2 K       self.minreadslength = 500 e3 o9 }% s" L2 C0 J! X' A0 T
      self.maxreadslength = 150
! @; ]% P# P3 V/ b3 V+ w       self.N = 101 j; t3 p9 q$ K1 \
      self.genomeLength = 01 I; O5 L7 w4 ^7 p( r
      self.allreadslength = 0
1 q: u% Z! G7 m
; w' l" p9 @; O! z9 F( f3 D # 生成断裂点0 j* y: x) i0 d
def generatebreakpoint(self, seqlen, averageLength):2 y2 L2 [3 ]/ F7 T2 @4 |
      # 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)
. j7 u( b1 w  U) N7 u; ^. z& s       breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]5 x! k6 x8 }5 b# z$ U+ }# q
      breakpoint.append(seqlen)- {: B8 D5 m& S
      breakpoint.append(0); M+ @2 s7 `6 c3 b0 F5 B1 m
      # 把随机断裂点从小到大排序4 V. |0 R8 _+ z+ L- L1 ?7 l$ q0 d0 x8 R
      breakpoint.sort()3 R" l9 F7 f, o
      return breakpoint2 Y; t5 ^, e# d

1 ?4 Q3 a; `/ H7 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:200-1000bp4 J1 W# m; C, E# T6 s; v
def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):$ ~4 L1 V0 ?  e4 V0 c+ t* t
      for i in range(len(breakpoint) - 1):8 ?. S  C( b: U! ]5 I3 f6 ^5 ~
         fragment = seq[breakpoint:breakpoint[i + 1]]
; E# y1 D, d" T& y          if maxfragmentlength > len(fragment) > minfragmentlength:( H/ t6 t4 {- e% v1 w
            self.fragmentList.append(fragment)
' `. y; X4 P2 W& |- K       return self.fragmentList$ @( K+ N$ ?/ P/ j: t( W
+ o/ f5 B; T" F) g
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
# {. Z* g* b& q# Y def clonefragment(self, fragmentList, cloneRetainprobability):5 F( I6 d! K; c: o, Y
      clonedfragmentList = []* U9 W* B; z+ [3 ~' I- r- p
      Lossprobability = [random.random() for _ in range(len(fragmentList))]- x2 c6 J) _4 T8 i) i, [5 q
      for i in range(len(fragmentList)):
% Q, `, g1 b& Z5 P- G" g          if Lossprobability <= cloneRetainprobability:- j! J: L) e: S# D! p
            clonedfragmentList.append(fragmentList)
4 y" b8 d. J4 G, v0 D# m       return clonedfragmentList2 h' S% q7 h  Q1 m& G% ?
2 U) p; n/ }& T
# 模拟单端测序,并修改reads的ID号
$ ?: T# \3 [, f8 Z) g def singleread(self, clonedfragmentList):
. e& Z+ _( E# \; l, q( |       for fragment in clonedfragmentList:. o0 d; i& U% ?
         fragment.id = ""
2 c% r. I+ x; Z          fragment.name = ""( C" m+ V: ~5 y4 [2 p
         fragment.description = fragment.description[12:].split(",")[0]
% J# l1 H. r* O: e, f+ N          fragment.description = str(self.readsID) + "." + fragment.description
* [  ~  t/ o3 C) `0 A) Q" p          self.readsID += 13 C  k# N$ H) _! M6 e+ M7 G
         readslength = random.randint(self.minreadslength, self.maxreadslength)
$ w* N; j1 {3 }, P          self.allreadslength += readslength
' K& {3 ?1 H3 r8 b/ _: t0 [          self.readsList.append(fragment[:readslength])
5 L+ [) f; |" v+ Q9 N' f6 ]; j8 I
  \0 ^7 R3 \8 O& X def singlereadsequencing(self, genomedata, sequencingResult):
, E3 w' I$ j" G! @  P       for seq_record in SeqIO.parse(genomedata, "fasta"):/ G4 d2 K; Y/ A& W6 R
         seqlen = len(seq_record)
' i5 R- @0 z0 n          self.genomeLength += seqlen5 E/ I/ T4 @, q0 i' t# R
         for i in range(self.N):" g$ ?8 c# o' E3 R7 d2 z
            # 生成断裂点5 l: P- n0 H# b- W1 k" s' ?
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)2 W" [4 Y$ \5 C' t2 E/ c3 W2 o
            # 沿断裂点打断基因组
1 p, Y& Y# {' e/ m5 `$ Z+ u             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)4 K4 [: f: |6 F& A' {) l* W
      # 模拟克隆时的随机丢失情况
0 N7 }4 l0 w) ]1 r1 m  u2 p       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)8 f4 b: S0 e* O! P3 o  C' K8 G
      # 模拟单端测序$ K, z" ]# _$ i: l3 d2 F
      self.singleread(clonedfragmentList); w  g& u$ T' R
      SeqIO.write(self.readsList, sequencingResult, "fasta")
" x: `: e! f+ q9 v
7 a! O: m" C: g def pairread(self, clonedfragmentList):
6 h0 X/ X7 g9 j       for fragment in clonedfragmentList:! x( G4 O) V' Y) e1 b
         fragment.id = ""( K$ ~+ j) @* U1 ~  @
         fragment.name = ""
8 V+ p  x; V  ^$ f$ m( f7 O          description = fragment.description[12:].split(",")[0]
4 R( }0 G5 m% A          fragment.description = str(self.readsID) + "." + description
" J' b; j* |" k# o9 |4 R          readslength = random.randint(self.minreadslength, self.maxreadslength)
; }, D$ w; r" y2 d          self.allreadslength += readslength
. B% ?# i6 f4 u& V: z# T2 \8 w1 T          self.readsList.append(fragment[:readslength])  o+ [) j0 _# Z8 ^
7 L/ F# ~0 J2 [3 G6 f, E
         readslength = random.randint(self.minreadslength, self.maxreadslength)
& k5 m1 y5 N: a. b          self.allreadslength += readslength
2 L: V* ]. U& O; ]5 D& l$ `$ p( S4 C5 `- v1 H
         fragmentcomplement = fragment.reverse_complement()
( a9 t/ T% v6 Y9 `/ z8 S5 d2 n          fragmentcomplement.id = ""
  L3 ~! [& g6 k          fragmentcomplement.name = ""
/ @( B0 y) a- u- H1 L$ C+ x, {          fragmentcomplement.description = str(self.readsID) + "." + description' T4 I2 T; p# o
         self.readsList.append(fragmentcomplement[:readslength])
! V8 J5 O8 v/ P2 e: L5 [, l# Y$ N( A  n3 a+ c' z" i( `% T
         self.readsID += 1' O  ]& V  [0 i# [1 u

3 x' `, o2 K% a$ n def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):9 v1 y. |$ b' C8 K
      for seq_record in SeqIO.parse(genomedata, "fasta"):+ d" Q  [$ R2 V# q& U6 t: L' h. |
         seqlen = len(seq_record)$ b# W& _8 T5 h
         self.genomeLength += seqlen
" A' {) o5 K, o6 t4 m' C- @          for i in range(self.N):
: u- B2 e2 I1 V; k) Z             # 生成断裂点$ @0 }: i2 c8 ~" {
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
" F7 x5 U& `# R6 y; N4 g$ I             # 沿断裂点打断基因组
3 F& s$ Q5 Z; e* X- H, [: H             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
3 I! A% V+ R- J6 J/ U       # 模拟克隆时的随机丢失情况
, s5 h' @# _+ m8 `+ R- Q4 J% F       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
# U+ q& ]6 ?3 h% g2 ~2 [) Q       # 模拟双端测序
1 _* {" C/ b5 e, x3 U9 O( C       self.pairread(clonedfragmentList)" Z6 y: ?0 @/ H0 ?- G$ G
      readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]7 W3 F. ~% i5 q
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]# A( @9 Y2 u' W/ }0 K
      SeqIO.write(readsList_1, sequencingResult_1, "fasta")/ [  [& H$ o( W% M' e
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")
# C: N8 a7 C/ t( G
. N8 Y9 B! a/ W8 i def resultsummary(self):
9 I. ?* r) o! ?- ~       print("基因组长度:" + str(self.genomeLength / 1000) + "kb"), t5 P  q- a1 J; G. @+ i5 J
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
) m# b3 y9 _, ?% _       print("N值:" + str(self.N)). N. I: S. ~4 K, O) g
      print("期望片段长度:" + str(self.averagefragmentlength))! e. s3 L: D) X$ K3 S) }+ r
      print("克隆保留率:" + str(self.cloneRetainprobability))
4 L3 M& s- Z# N) [* w       print("片段数量:" + str(len(self.fragmentList)))
/ S- T1 Q/ S; `       print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))' o* i" O: q6 i1 V; w* W
      print("reads总数量:" + str(len(self.readsList)))- Y4 x9 k  M% v1 ~
      print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
$ f* x& g+ h& K# b. d6 y       m = self.allreadslength / self.genomeLength& z/ v+ f/ ], d' \- B
      print("覆盖度(m值):" + str(round(m, 5)))
, [( d3 h, C5 Q0 W( e+ Y       print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))% e, W( U0 @$ }4 `" [+ A& V
      print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
9 Z8 N/ [1 ^' w0 x: h  Q  s: Q# -------------------------------------------主程序-------------------------------------------4 p0 t" F7 {5 j/ O4 {: @. Y5 m
# 模拟单端测序* ^0 C" e4 l- z
sequencingObj = Sequencing()9 ?% F3 [9 D) N, U5 L
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")9 J2 p& G$ |) z- n2 \
sequencingObj.resultsummary()
5 f' B9 M, v6 m% J0 h, P8 t7 Q" A
# 模拟双端测序8 n  m( f: z" ~7 W. p9 z0 L4 X
sequencingObj = Sequencing()
: s2 @2 p! h- UsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
/ O" h& w+ Q: U. W. ^0 w0 ksequencingObj.resultsummary()+ U- W- H1 |: S. F/ o
. B/ l( R; \% ^: G

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