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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]]
6 B8 V  n. j5 g          if maxfragmentlength > len(fragment) > minfragmentlength:/ G) V5 Y% P, w7 P  c, m  e% r
            self.fragmentList.append(fragment)
8 R$ c% j8 p6 Q! `5 r, j3 F       return self.fragmentList
/ O' e; L$ B  {* A  k, V: X( Z7 N6 }4 f
# 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
  o* p/ B! {$ e3 ^ def clonefragment(self, fragmentList, cloneRetainprobability):) @' y( [+ Y" @& D* g) K1 Y& k
      clonedfragmentList = []
  C' q5 `% L1 ^! n" V1 z       Lossprobability = [random.random() for _ in range(len(fragmentList))]
  p: P8 x1 e; q8 |" s       for i in range(len(fragmentList)):
* }) G/ N+ A( m9 b: S          if Lossprobability <= cloneRetainprobability:3 S; ~  p" Q5 i$ m
            clonedfragmentList.append(fragmentList)) ~  {: }  m: t: X& W
      return clonedfragmentList
# m& S: v' b8 T7 T/ C% ?" @! o% X
. O+ m, _- H6 `  N # 模拟单端测序,并修改reads的ID号0 k  t. _) i% W+ z
def singleread(self, clonedfragmentList):4 _7 s& p/ g( o5 @
      for fragment in clonedfragmentList:
. m( N5 S7 l! D' G: u5 F, D          fragment.id = ""
/ F2 A  ~' \  a0 ^          fragment.name = "". B, B1 o6 C( _: g
         fragment.description = fragment.description[12:].split(",")[0]
1 q; v( S! R. s6 z4 }2 i          fragment.description = str(self.readsID) + "." + fragment.description) G# @4 s( ^; q
         self.readsID += 1
0 d: ^. \8 p3 V; T, Z: L& r8 q9 A          readslength = random.randint(self.minreadslength, self.maxreadslength)$ p! q, o; ~: C" R3 D4 b0 ]' s
         self.allreadslength += readslength5 b; N6 ^; A2 R: t
         self.readsList.append(fragment[:readslength])  Y- h; a4 M  K& {8 N( a1 T5 y' C! R

9 o$ D+ \+ ~* ~- q- I& K, q def singlereadsequencing(self, genomedata, sequencingResult):1 ^% I  a% C& K: z$ J3 H# O
      for seq_record in SeqIO.parse(genomedata, "fasta"):
# X8 j$ E0 h3 Z( W! b! K5 |) B          seqlen = len(seq_record)
/ u6 @" ?; @5 z' G& _0 k9 x4 j- v2 Y          self.genomeLength += seqlen& L1 L5 {6 ~; D7 u% m. N% t9 Y5 r
         for i in range(self.N):
: R6 ^- J% \1 F5 P. [) T, p7 p2 W             # 生成断裂点
) v7 k  {# U" a             breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
+ {( `* L; w9 Y, f% T1 l) {             # 沿断裂点打断基因组
! [! B3 H. E; q9 F9 @- H             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
4 w* l) d! U! Q5 L' R4 Y/ [7 n       # 模拟克隆时的随机丢失情况: l2 ~9 u" S! ?8 h
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
7 V1 J$ p3 ?& n  W, |% u. m& i       # 模拟单端测序- y+ g8 \& k, @" I6 {5 |
      self.singleread(clonedfragmentList)/ F) H' |" @0 l% v1 X/ ~7 o
      SeqIO.write(self.readsList, sequencingResult, "fasta")0 @& d% Y5 ^" g

/ R% y2 j7 `) R# | def pairread(self, clonedfragmentList):
. X' G- |# T9 B" W% K       for fragment in clonedfragmentList:0 s6 o; w0 m8 e. O; c( C* u
         fragment.id = ""0 D& p* n, b$ f. K( A
         fragment.name = ""
" t, ]: ~  I2 Y* I0 C$ h          description = fragment.description[12:].split(",")[0]1 r2 ?/ T. A" x, _$ H
         fragment.description = str(self.readsID) + "." + description% J/ h9 u0 t4 u5 L# x; p
         readslength = random.randint(self.minreadslength, self.maxreadslength)
2 V# n: G0 E" D9 B7 B2 Z* X2 v          self.allreadslength += readslength
6 Z$ _# H; x3 z5 B$ d          self.readsList.append(fragment[:readslength])
1 ^; D' Q2 b  C5 b$ K' |' }) k2 d' o( e: Z' C, h6 z
         readslength = random.randint(self.minreadslength, self.maxreadslength)6 I  c4 a9 x1 c3 s3 h1 `6 s; S
         self.allreadslength += readslength
+ s# R: j$ I0 ]3 l- I' {4 B. ~5 D- Z& I, ]8 G- U
         fragmentcomplement = fragment.reverse_complement()- J  G1 V2 B, x$ H
         fragmentcomplement.id = ""% j. q( a7 [# V" @& S  P
         fragmentcomplement.name = ""
; E9 M1 K; W. V          fragmentcomplement.description = str(self.readsID) + "." + description
  _* k$ I3 G: P; ^& D1 p# _          self.readsList.append(fragmentcomplement[:readslength])
$ H) B: L. N5 P7 S3 ]/ J9 v6 c- |' a9 E% n& M; z- s
         self.readsID += 16 q# X6 {" C/ v* d% H
2 [: e( u/ G1 g0 x4 x: q) J
def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
8 c" ?% l; Q% D$ |9 Y       for seq_record in SeqIO.parse(genomedata, "fasta"):$ O+ f' P& c' B5 u' F0 ]: O' D
         seqlen = len(seq_record)
- f1 Z5 m- E( S          self.genomeLength += seqlen
( O0 H" [: d' C2 m9 Q1 D" b          for i in range(self.N):
1 O0 V8 D1 ?; n0 D: L2 Q4 k1 N6 u2 m             # 生成断裂点' _4 [% U" }9 T4 H: g& m! H
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)6 f" a9 u+ l' {% B" a
            # 沿断裂点打断基因组
; W2 q( }: E; a1 X4 |, u* m             self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
" f' w$ ~/ j$ s5 h, p       # 模拟克隆时的随机丢失情况
7 T/ h& z! _. s6 J; e       clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)4 w. q) s% f7 s. O, C3 X( b2 L' h
      # 模拟双端测序# q+ @2 \0 y, T' c" z1 L
      self.pairread(clonedfragmentList)
" V9 N% d/ S) M. H- d4 _0 |. _       readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]( S9 C* f' |" }, l$ ]1 @% j+ c
      readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]) O$ D$ n- Q4 X+ E) x2 I
      SeqIO.write(readsList_1, sequencingResult_1, "fasta")
( o0 G  v6 t) t- b: N  X       SeqIO.write(readsList_2, sequencingResult_2, "fasta")4 c# W/ G3 g/ j
0 M9 d0 Z; T' N4 k: H  `
def resultsummary(self):
! B4 y0 t: |* W; I9 A0 s       print("基因组长度:" + str(self.genomeLength / 1000) + "kb")  R3 g7 f5 X7 d& u: o) D
      print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))' }3 d- \$ j- _* V$ }6 B: p% L
      print("N值:" + str(self.N))& a, f2 ?+ k% L9 G; l1 ]
      print("期望片段长度:" + str(self.averagefragmentlength))% x4 s% D# }0 z3 i- w% |8 j
      print("克隆保留率:" + str(self.cloneRetainprobability)): I/ F- g% h/ u0 |5 \7 A  B
      print("片段数量:" + str(len(self.fragmentList)))/ k5 x5 s& z  J
      print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength)), F: v4 n1 q; V  |' t5 q5 ~2 t+ F
      print("reads总数量:" + str(len(self.readsList)))
5 x8 b6 E5 E- s4 \       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
3 }8 t1 v( d) L       m = self.allreadslength / self.genomeLength/ X. C: y( J! o: A# }
      print("覆盖度(m值):" + str(round(m, 5))): t& L$ d& P% [) S
      print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
3 h8 S0 d, `' c       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))* N- w& R% m! |* E& s5 _
# -------------------------------------------主程序-------------------------------------------% e5 O; R0 i: Q5 \% R4 j% n
# 模拟单端测序
+ s! l. R; ?. N: ^  E+ T1 ?& P4 nsequencingObj = Sequencing(): |% U) X$ M; g( B- f+ g5 V
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
. ^& m1 R1 L1 v1 r2 w. \8 b% j8 vsequencingObj.resultsummary()
# Y! d1 c  \, l# _& ]" K0 d+ M: e" r7 T4 Z! n0 f
# 模拟双端测序
, u: x$ v, a2 p+ |# x- |sequencingObj = Sequencing()
. S9 E( K9 E5 RsequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
1 s/ K0 m# i& Y; CsequencingObj.resultsummary()
3 c' }  [0 m% `5 W6 ^from Bio import SeqIO6 m" ?0 G* Z9 B- E" ~' a8 X$ e' S
from math import exp+ l* K, `0 `- u4 c2 O! H
import random
/ i: c+ T: s6 u; Z( f* L1 l8 C+ G/ f4 P, S8 c( \  w1 c  d7 O$ P" @' d/ S
class Sequencing:
: N; C* T9 r$ J2 M  n # N代表拷贝份数
1 @' h  q4 H6 S2 K& m3 l% k def __init__(self):
, W' \  o! c% o$ [- P       self.fragmentList = []
0 {+ ?, [% w- f9 t9 g. d       self.readsID = 1" G$ S9 k. p" @9 @& l
      self.readsList = []
  s2 O" O& x/ R- r* l       self.averagefragmentlength = 650, p- B$ U; ]' ]6 e" f% Y0 C
      self.minfragmentlength = 500
      self.maxfragmentlength = 800
5 U# Z  E2 `& D       self.cloneRetainprobability = 1
+ F+ T& Z# F+ }+ d1 `       self.minreadslength = 50( J; n5 n4 Y8 Y, W
      self.maxreadslength = 150
* ^! m" H) }, d       self.N = 10* P8 F% T: ~* J6 {8 ?, s- g' F9 w
      self.genomeLength = 0" l9 y6 h' Z& k) s6 x1 Q9 K: x
      self.allreadslength = 0' J2 S( X& e/ O! y) M- m

6 k) P, a  s5 }5 V* w! ^6 B # 生成断裂点
& V2 N. J' Z* g$ ]- ^ def generatebreakpoint(self, seqlen, averageLength):* _/ a7 b0 ]$ f" O% c; s8 C8 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)
3 G9 H( t- r; ^% Y$ Q! ~( ?       breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]# {9 s4 e' }- Y9 V3 k; K
      breakpoint.append(seqlen)  y$ ]2 R+ K4 J9 {
      breakpoint.append(0)
" F9 g7 f8 E  F+ w" U7 _: e       # 把随机断裂点从小到大排序
1 z. C6 Q, e1 D7 H* j       breakpoint.sort()
5 ?/ S: d( y, N5 p/ P8 K2 y       return breakpoint
2 i: G0 X: ^$ o# D$ Q
( t0 X) @  A# b. [4 a # 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: a- z8 z9 C, E9 z5 u2 n
def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
; L- ?: Y/ c" L/ X       for i in range(len(breakpoint) - 1):
6 U, [. A* b4 V! e  J1 L3 N          fragment = seq[breakpoint:breakpoint[i + 1]]
; F; ~0 g9 G- U7 t          if maxfragmentlength > len(fragment) > minfragmentlength:
, ?1 `% f, S: ^7 e+ e             self.fragmentList.append(fragment)7 C! _6 i* A' t) t( F
      return self.fragmentList
: n/ C6 W1 ^$ m7 E
( A9 a. n# E3 P7 b7 [. q" ^  n # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率# ~) e- k; n6 f1 z4 a# }9 A9 t: x
def clonefragment(self, fragmentList, cloneRetainprobability):# f$ b& h( }; {5 `
      clonedfragmentList = []
* d  t" H, {# B* A( r       Lossprobability = [random.random() for _ in range(len(fragmentList))]; R4 F; }' i; A  y# ^
      for i in range(len(fragmentList)):" ?. V6 E) D( o) F4 j. W
         if Lossprobability <= cloneRetainprobability:
+ p4 {  J5 `( w( c9 n             clonedfragmentList.append(fragmentList)
6 v# k' |" O# E" r0 S$ L       return clonedfragmentList4 o! Z2 s2 S) I+ B' K2 [

- v7 M& N- c$ ]) W9 Z9 i6 S5 e # 模拟单端测序,并修改reads的ID号% J5 @8 i: U4 B- W0 M' K' ~
def singleread(self, clonedfragmentList):: J1 {; l7 h! ]9 M
      for fragment in clonedfragmentList:- W* ^# }) \7 R: K5 i3 G' Y
         fragment.id = ""
9 ]  X& E+ _$ |2 W8 e1 T8 \          fragment.name = ""
3 I6 J$ D1 O! e  Z( f# s$ X* Q          fragment.description = fragment.description[12:].split(",")[0]- ?# p: J: G- J8 L( f, W+ a
         fragment.description = str(self.readsID) + "." + fragment.description( S8 v& W9 x7 Q6 X1 e' a
         self.readsID += 1" b  v/ J5 t5 u7 `  Z% ]7 m
         readslength = random.randint(self.minreadslength, self.maxreadslength)
0 d8 c  @0 F+ Y# R3 R" {  c          self.allreadslength += readslength
9 P: w2 Z' d; e& L: z$ s          self.readsList.append(fragment[:readslength])* Y1 A% \5 m; R

% C& e- l$ m- U def singlereadsequencing(self, genomedata, sequencingResult):
. f7 P' B; c- d! T$ S0 Z       for seq_record in SeqIO.parse(genomedata, "fasta"):
; a+ s5 R: Y1 f! U2 j          seqlen = len(seq_record)6 b& G- p9 C% f) R
         self.genomeLength += seqlen
0 l4 S/ d) R, a, Z          for i in range(self.N):
" {3 l+ C* k2 @, ?# c1 x% g             # 生成断裂点$ @! F8 v+ v- r- |/ L) q. o
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
( K& v  y, ^/ T$ H             # 沿断裂点打断基因组
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
, k# q$ W7 b5 x. U  h       # 模拟克隆时的随机丢失情况3 w. H) h% Q/ _9 C! @5 v/ k4 p
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)" \) }0 X, `1 t+ y
      # 模拟单端测序
; i5 x7 p& @1 J2 o5 f3 s1 k       self.singleread(clonedfragmentList)) U! {7 H8 T# @& F1 j
      SeqIO.write(self.readsList, sequencingResult, "fasta")) f, p7 P7 Q! S3 k
: t5 K* J1 P9 m  M+ W0 f
def pairread(self, clonedfragmentList):
6 X# m- j# e- h- R       for fragment in clonedfragmentList:
3 z2 D, }/ y! P          fragment.id = ""1 L  y9 b! t/ O# I$ N
         fragment.name = ""- g! J; Z! h6 \: }. m: G
         description = fragment.description[12:].split(",")[0]
& Z4 m2 {/ G3 k4 J9 v          fragment.description = str(self.readsID) + "." + description
- g: s; m4 j) K5 X2 B1 o, q& A          readslength = random.randint(self.minreadslength, self.maxreadslength)7 M3 x% d' b" z- z4 m" t+ v
         self.allreadslength += readslength
  @* j+ v; |: [7 `2 t          self.readsList.append(fragment[:readslength])
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- k/ ~0 W' L0 d3 _# l9 o          readslength = random.randint(self.minreadslength, self.maxreadslength)
$ ^0 m; B2 n; @4 d- _& Q( \5 n( s8 j0 O          self.allreadslength += readslength: f4 g7 e7 {- c$ N7 a
" |) v" h: P# C4 n$ [! M
         fragmentcomplement = fragment.reverse_complement()& t4 k+ h( I* n" V, C, S8 ]& b2 y5 }
         fragmentcomplement.id = ""
% K) c1 }2 U  S3 J( h- P! ]          fragmentcomplement.name = ""
" u) |0 U! e' _' y8 m  P) G          fragmentcomplement.description = str(self.readsID) + "." + description
* q$ ^: K! I. l2 s          self.readsList.append(fragmentcomplement[:readslength])
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         self.readsID += 1
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- u( Q7 q% R( D6 [# q! c3 k def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
. p/ @# X/ L; r0 \2 F/ X/ D7 i5 Z       for seq_record in SeqIO.parse(genomedata, "fasta"):# o8 R0 W- ]# E( e4 f
         seqlen = len(seq_record)7 _* V& x" b9 m; r
         self.genomeLength += seqlen# s/ E' C3 r6 _* P
         for i in range(self.N):
0 ]) r+ \  s+ c- ?             # 生成断裂点( k( x( z  P( g. d# k0 M3 f
            breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)# \6 ~  l+ H6 t, u- k* c
            # 沿断裂点打断基因组2 B% p3 P/ L% }9 N) ]" F
            self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)+ j5 `. K0 N, Z& }$ R4 n( j, I
      # 模拟克隆时的随机丢失情况5 _0 O( q% E1 k8 E* |0 A) F
      clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability); S# S+ y) R$ H; u9 @- ]2 |- V* H
      # 模拟双端测序
# U9 o, W1 z$ [8 l$ X6 j' E5 v       self.pairread(clonedfragmentList): W0 @( w) p$ A  K
      readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
$ H/ x) a! G  t9 @) g0 |" B       readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
6 t  a' N: U: I, c' Z       SeqIO.write(readsList_1, sequencingResult_1, "fasta")8 Z) q, {. @" ~& [8 Z! \
      SeqIO.write(readsList_2, sequencingResult_2, "fasta")
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. z5 y  H+ Q4 G+ u; M! h def resultsummary(self):0 @) ?3 b2 F2 P0 o! m) I
      print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
1 \" x+ N9 o3 U# ~7 ]- `0 l* L! L       print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength)): K1 Y' e* Y5 G6 u8 j2 J1 v
      print("N值:" + str(self.N))
; ]7 B5 V9 W8 _6 Z       print("期望片段长度:" + str(self.averagefragmentlength))/ b% ~: A* F; a& c
      print("克隆保留率:" + str(self.cloneRetainprobability))
. w! Y1 F/ _& S) f       print("片段数量:" + str(len(self.fragmentList)))
* G7 I- R/ |8 O! y& D! @2 l6 _2 [, M       print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength)): I9 D; c( u8 K* D  y
      print("reads总数量:" + str(len(self.readsList)))
8 l2 R" @* z! a' W3 \0 B       print("reads总长度:" + str(self.allreadslength / 1000) + "kb")2 |+ O! N* \1 i3 g$ I' u/ q
      m = self.allreadslength / self.genomeLength
; _1 H: r* o+ g  _" a! q4 e: E       print("覆盖度(m值):" + str(round(m, 5)))! x+ d' `& A0 n% B3 D2 j% `, ~% J
      print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
* r6 X7 W) a  x3 R5 a' X0 \# B       print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
: \* {" P0 b% a) \) E! _/ K1 P# -------------------------------------------主程序-------------------------------------------
0 H* A4 z5 i+ \; w* o# 模拟单端测序7 _' v, c. z1 c) K$ @2 I) _
sequencingObj = Sequencing()7 f/ L+ N/ x8 V! y. ]* z6 a
sequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
5 `/ j9 X- n7 c3 ]sequencingObj.resultsummary()
! H3 [! y. v% Z9 N- `+ C1 \2 f' Z+ P% @( |3 W$ r
# 模拟双端测序! i2 y- N! Z: C/ I0 D9 i
sequencingObj = Sequencing()
  @+ n/ V1 I/ ~sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")# l  r. Q9 o! @; S; m
sequencingObj.resultsummary()
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