基因组测序模拟

杨利霞

基因组测序模拟
5 y, w6 `" }* b; H' `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

4 a; J3 w; ?; Q5 I, |1 W二、材料和方法

4 s  W+ t0 S1 w( K8 ~, D$ d1、硬件平台
( g0 y- i' F: ?5 g1 u) @: w/ [
+ O) l6 j8 v! Q9 [: n9 l处理器:Intel（R） Core（TM）i7-4710MQ CPU @ 2.50GHz
安装内存(RAM)：16.0GB
4 [8 P) w; W* P% z
0 m+ s1 a8 ~/ p  d, `; L# W$ ~2 @2、系统平台
! E2 f) s/ K# A' O7 m" NWindows 8.1，Ubuntu
  F& y' b% M; G" Y/ y2 `0 k+ Q
3、软件平台

art_454
8 ^# N. g% _( u$ dGenomeABC http://crdd.osdd.net/raghava/genomeabc/
Python3.5
$ ^2 U% j7 F5 lBiopython
* L' N( \( z: _& d( n4、数据库资源

3 d3 i1 J' {, j0 j: V; v/ j4 ]& ~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)
$ U4 k2 s- q0 M# L' xftp://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

  E5 v" D+ z9 N9 a2 O. |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
6 j6 T, ]9 Y! U2 H进入GenomeABC(http://crdd.osdd.net/raghava/genomeabc/)，输入参数，获得模拟测序结果。
& N, r1 [! ^3 O0 Z% a8 Y编程模拟测序
  _! a% f2 e: a1 z% c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
0 R0 \/ _3 m& K: o0 w9 @
1、art_454的运行结果
1 f2 J4 p% V/ e$ @
1 U* F3 o. {9 Z1 g. n无参数art_454运行，阅读帮助文档

% m1 a0 g7 p1 F; D4 A& v. }' q( O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

  Y! ]( {; r5 R图表 2 art454单端测序
2 {4 {6 W0 P& S3 c$ {
3 d6 O4 n& {9 {  T- i9 g图表 3 art454单端模拟结果
# q  Q: l6 w1 U4 k/ E: n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
' L8 N; K; H* z2 u4 s
图表 4 art454双端测序

图表 5 art454双端模拟结果
2、GenomeABC
+ M& r, z- O+ h5 }2 d) n0 ?下图为设置参数页面
0 X9 A: M: Z* L/ ^  B
下图为结果下载页面
' k% Z+ ]. L) D0 Z, k* ]
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。
; q: X  S8 `2 n( I8 W- I- B) ]单端测序

图表 7 程序模拟单端测序
/ d$ m: n: q( j3 P4 l' K7 J- e. |0 S双端测序
* ?* ?7 h( @2 n# O* |% h
' {8 o$ Q9 D0 q6 b5 ]图表 8 程序模拟双端测序
测序结果

图表 9 结果文件
0 U/ d- _0 I3 u- J) \
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。
# v7 H& o0 D% O2 Q6 F( D测序结果统计表
$ U6 v. M6 |: \  C1 \; e# v
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
' S! ~6 O" k6 v$ c6 X9 t) o) N单端        12157kb        200-1000        20        600        0.95        213722        50-100        202996        15227.882kb        1.25259        0.28576        0.71424
' d+ S$ g1 G( ~  W) G( `1 ]6 D" C双端        12157kb        200-1000        10        600        0.95        106704        50-100        202770        15212.662kb        1.25134        0.28612        0.71388
, L* r; S' D2 S7 Z+ L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
" ]. ]. ]# g) n! f' S, s
8 S/ v" M9 b- }8 e, k1 G程序运行方法

! p- x* z6 |2 W5 ~在类的构造方法init()中，调整参数。
( D& A, a& u: VAveragefragmentlength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；
: T' X" [8 n0 ~8 Y/ Z8 fminreadslength和maxreadslength是测序reads的长度范围
4 r/ Y6 h- a. L$ o
5 h' v3 P- f3 s& J' Q) E  n. c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
& W9 G; Y& I  K" C9 L
1 E2 E( ]1 P2 T8 Qfrom Bio import SeqIO
7 J9 l4 y. M/ b; ~from math import exp
/ L2 ]3 c( m$ zimport random

+ q1 x& t6 ]+ Q5 a% a  U3 J# Jclass Sequencing:
5 R$ z. F4 U! Y( p  ]* a    # N代表拷贝份数
    def __init__(self)
        self.fragmentList = []
        self.readsID = 1
: z0 `; B5 J! j, F        self.readsList = []
$ G  _* C7 W. R& \/ Q$ `$ M$ R, c        self.averagefragmentlength = 650
. _7 Q5 }9 a+ V; n+ Z# h! _+ X+ B        self.minfragmentlength = 500
4 ?. x: R% a" ?8 K( R+ ~7 K        self.maxfragmentlength = 800
4 h7 C6 R$ g6 n  V3 Z& B        self.cloneRetainprobability = 1
        self.minreadslength = 50
        self.maxreadslength = 150
( l0 p9 p5 h" G! O+ z        self.N = 10
1 U( B+ W3 u( m3 l* [        self.genomeLength = 0
        self.allreadslength = 0

- C& G& t) S7 }- C5 v8 ~$ U    # 生成断裂点
    def generatebreakpoint(self, seqlen, averageLength):
4 x7 S9 N, B  ]0 [& L5 X, ], x+ z        # 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)
* l. @6 q5 f+ [7 W& N' e        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
9 @" ^% D: i+ @/ R$ p8 i' _4 q        breakpoint.append(seqlen)
        breakpoint.append(0)
# C4 ?0 q4 ~4 \4 h0 O- n  o9 B        # 把随机断裂点从小到大排序
0 N" E2 j* ]5 Z9 X! f. p" {        breakpoint.sort()
2 l9 s3 }# t$ q1 n  K" ~        return breakpoint
7 I5 m( b4 {6 f9 L8 h! `% a" X: Q
; {. }9 s; q, w4 W9 m3 n    # 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
7 J) K; T" _! A8 C6 i    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
& T0 T! \# S/ q! y& K% o3 J            if maxfragmentlength > len(fragment) > minfragmentlength:
) t8 J$ ]0 B# l4 Y0 O% C+ `                self.fragmentList.append(fragment)
& {; V: F* p; j  `' d2 h3 z0 G* J3 {        return self.fragmentList
- b8 S  [  J1 q# p
9 n6 F; R2 v4 x6 n9 D    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
    def clonefragment(self, fragmentList, cloneRetainprobability):
  O/ M8 b0 a, K6 o/ [( h. ?& H! ^        clonedfragmentList = []
        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
            if Lossprobability <= cloneRetainprobability:
                clonedfragmentList.append(fragmentList)
, o( n* K: ?4 D( q        return clonedfragmentList

/ h) w% J3 ~1 I, a! Y' d    # 模拟单端测序,并修改reads的ID号
3 k9 |" _8 K% |" @  Z' k* t    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
; z. [& ?: E& x+ p, e. [            fragment.name = ""
            fragment.description = fragment.description[12:].split(",")[0]
) ^0 p: {2 u3 G7 _. V7 S+ C            fragment.description = str(self.readsID) + "." + fragment.description
1 F* M. I3 M+ ~, S) q            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
8 \5 e4 s. o  y/ ?. I2 W2 e# @            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])

% @+ \( F- o- J% P# d; D    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
* P& }4 f- o; Y            self.genomeLength += seqlen
; g9 p* K; S. j+ r            for i in range(self.N):
                # 生成断裂点
& q7 b/ p% z0 {1 u8 Y) A# H0 S9 e                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
3 I$ _! {0 L* W+ H- I, `        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
1 U4 E' g: j7 D  i; k$ Q% G: a$ g5 O        # 模拟单端测序
, c0 ]9 U+ H$ e        self.singleread(clonedfragmentList)
        SeqIO.write(self.readsList, sequencingResult, "fasta")

    def pairread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
. w) H9 q' [9 S            fragment.id = ""
            fragment.name = ""
% z2 G$ I/ k6 f) ?% t# x            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
' L/ p# E+ g+ X& x5 _0 t* U% c            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])

            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength

            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
. p0 E- J: }, l. w0 F9 W            fragmentcomplement.name = ""
, [' `& R- E$ a, D6 H            fragmentcomplement.description = str(self.readsID) + "." + description
            self.readsList.append(fragmentcomplement[:readslength])
! B: W2 E2 p/ b0 [* t! p
            self.readsID += 1
- h, m; [7 w$ |6 a, h; ?1 O  G
2 N' K' R$ c8 a, R    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
  ?6 W. h& H. M0 z1 V7 H        for seq_record in SeqIO.parse(genomedata, "fasta"):
" W1 T$ }+ p% r            seqlen = len(seq_record)
+ ^+ p  h% e: y            self.genomeLength += seqlen
; ]3 w1 [- s0 o            for i in range(self.N):
; {0 H* N* `" H) U3 v: V% \                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
5 }% W' z5 v! W1 o! R                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
        # 模拟克隆时的随机丢失情况
        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
        self.pairread(clonedfragmentList)
        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
- [- H1 A% T' F% J% b$ ^$ X. \2 O        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
' s* l. P+ f& G        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
        SeqIO.write(readsList_2, sequencingResult_2, "fasta")

    def resultsummary(self):
        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
        print("N值:" + str(self.N))
1 G. l$ }. H' g  |% N        print("期望片段长度:" + str(self.averagefragmentlength))
2 M2 h4 A+ E0 {0 i        print("克隆保留率:" + str(self.cloneRetainprobability))
4 q' v6 ]. f7 a) \  T0 t5 B( L        print("片段数量:" + str(len(self.fragmentList)))
, `& q1 f5 [+ C0 M$ ~: t- r. G* ]- G        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
        print("reads总数量:" + str(len(self.readsList)))
        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
! u4 H3 x2 I2 m- F1 ^4 A        m = self.allreadslength / self.genomeLength
        print("覆盖度(m值):" + str(round(m, 5)))
        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
8 Z2 E% N: [4 l0 R' b# W        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
# -------------------------------------------主程序-------------------------------------------
1 ^, Q3 R+ G/ ^; Z/ ?# 模拟单端测序
. E7 Z7 O. g! B9 y0 ^sequencingObj = Sequencing()
+ z8 e, d- v2 W* X2 f$ k2 c: isequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
sequencingObj.resultsummary()
. k% h& D% J6 Z( F7 X: o
# 模拟双端测序
$ @8 h2 z2 Q# R  S0 _3 X. b0 ^2 ]4 c, U( ^sequencingObj = Sequencing()
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()
0 p- b' l/ c: A% V  J0 ~8 @! Kfrom Bio import SeqIO
8 R" i2 }) ]2 ~3 k" j, j4 l* J  Ofrom math import exp
import random
& m/ Y% D, T- I
class Sequencing:
    # N代表拷贝份数
. X8 `7 [; o5 u/ |: A    def __init__(self):
) l" f+ q: t, @3 p        self.fragmentList = []
" [. X# Y7 n; u/ p        self.readsID = 1
1 G7 b+ m# B( y4 o        self.readsList = []
6 ?4 n; ~3 M) k" ^9 Y- O! {        self.averagefragmentlength = 650
        self.minfragmentlength = 500
        self.maxfragmentlength = 800
        self.cloneRetainprobability = 1
        self.minreadslength = 50
$ O# h) c- k/ ?/ D        self.maxreadslength = 150
% _* Z- t$ w( }' @        self.N = 10
        self.genomeLength = 0
        self.allreadslength = 0

) O) Q; K1 n8 p    # 生成断裂点
/ Z' U( X- e# s# O  E. c% g    def generatebreakpoint(self, seqlen, averageLength):
) c; s. K. f/ O3 v/ o+ O        # 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)
* h) R, d7 `, [( @8 i' b( O        breakpoint = [random.randint(0, seqlen) for _ in range(int(seqlen / averageLength))]
6 f+ K5 q0 F3 D        breakpoint.append(seqlen)
        breakpoint.append(0)
- K6 v& ?, p/ ^8 W        # 把随机断裂点从小到大排序
7 C: g/ p) z' m4 W        breakpoint.sort()
% k% ]  O8 ]3 S# k3 J4 t        return breakpoint
5 `* k  h1 C, S7 Z
/ r& ^3 }/ d' n/ Y! `    # 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
6 D- l* S- B' d+ V    def breakgenome(self, seq, breakpoint, minfragmentlength, maxfragmentlength):
        for i in range(len(breakpoint) - 1):
            fragment = seq[breakpoint:breakpoint[i + 1]]
            if maxfragmentlength > len(fragment) > minfragmentlength:
' C. ~8 a5 c0 t" X5 K9 X1 J                self.fragmentList.append(fragment)
        return self.fragmentList

! f3 B& U$ d/ X    # 模拟克隆时的随机丢失情况,random.random()生成0-1的保留概率
* x: r. m! y& ?3 _- u# V    def clonefragment(self, fragmentList, cloneRetainprobability):
5 |" S! q  K2 ~% j, `        clonedfragmentList = []
: K( ^  ~$ b0 l5 N& k; n        Lossprobability = [random.random() for _ in range(len(fragmentList))]
        for i in range(len(fragmentList)):
            if Lossprobability <= cloneRetainprobability:
1 }& E1 H/ p. x! K: e3 f: C0 _                clonedfragmentList.append(fragmentList)
        return clonedfragmentList

/ Q; {+ N" Z0 P* I% U: t! J. K; ]    # 模拟单端测序,并修改reads的ID号
    def singleread(self, clonedfragmentList):
        for fragment in clonedfragmentList:
            fragment.id = ""
            fragment.name = ""
- z5 W; s. X7 S" M! T1 ^- I) F. w            fragment.description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + fragment.description
3 d$ b* x9 A4 U1 `            self.readsID += 1
            readslength = random.randint(self.minreadslength, self.maxreadslength)
# D& v9 C2 ~. z6 O- k5 K( Y: I            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
2 F+ p" L! Z+ \
    def singlereadsequencing(self, genomedata, sequencingResult):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
            seqlen = len(seq_record)
  R& M4 B/ g' `            self.genomeLength += seqlen
! S2 W, k2 p. u            for i in range(self.N):
                # 生成断裂点
- z8 G7 e- ^. i4 n% I! K                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
. V5 G5 [$ z; d; @4 ?# P, H                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
        # 模拟克隆时的随机丢失情况
; s7 Z( c, x: M9 E  P9 a0 R2 v8 A        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
' r3 ^0 j" _+ X- N        # 模拟单端测序
        self.singleread(clonedfragmentList)
- U3 r, u, z3 {0 O5 c# y        SeqIO.write(self.readsList, sequencingResult, "fasta")

    def pairread(self, clonedfragmentList):
- [( G. z3 ~* Q        for fragment in clonedfragmentList:
, m1 [0 ]8 a0 N+ H: l            fragment.id = ""
% d( k" ~: z: [. T            fragment.name = ""
; C4 o  b9 z2 Y- B" [2 e* i0 V; F/ n            description = fragment.description[12:].split(",")[0]
            fragment.description = str(self.readsID) + "." + description
            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength
            self.readsList.append(fragment[:readslength])
9 R, U" W, B( ?
/ k' b+ S) r! X0 Z            readslength = random.randint(self.minreadslength, self.maxreadslength)
            self.allreadslength += readslength

9 j* F) x) f3 Z) q            fragmentcomplement = fragment.reverse_complement()
            fragmentcomplement.id = ""
            fragmentcomplement.name = ""
) r. B& ?) ~7 G9 R- J5 T; `: l            fragmentcomplement.description = str(self.readsID) + "." + description
" Z2 f) C# |* u2 r: r* y/ v! B% J            self.readsList.append(fragmentcomplement[:readslength])
% K0 ^/ K6 G; f7 A+ E
" B: ^. ]3 e, _            self.readsID += 1
0 g- q- Q7 E2 ]
' d# d3 X- W( E$ G    def pairreadsequencing(self,genomedata, sequencingResult_1, sequencingResult_2):
        for seq_record in SeqIO.parse(genomedata, "fasta"):
# \, `3 s( O$ H' K            seqlen = len(seq_record)
9 \: T" [% F6 u& ^3 S5 H8 m/ [. q            self.genomeLength += seqlen
            for i in range(self.N):
                # 生成断裂点
                breakpoint = self.generatebreakpoint(seqlen, self.averagefragmentlength)
                # 沿断裂点打断基因组
                self.breakgenome(seq_record, breakpoint, self.minfragmentlength, self.maxfragmentlength)
. e4 d0 B8 Q9 V9 ~        # 模拟克隆时的随机丢失情况
9 C; J7 d) E# g" J, _" ?) p        clonedfragmentList = self.clonefragment(self.fragmentList, self.cloneRetainprobability)
        # 模拟双端测序
        self.pairread(clonedfragmentList)
$ }( F5 _' ~: ^4 R7 V* V- _        readsList_1 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 0]
; K( w5 Y: G. `3 \        readsList_2 = [self.readsList for i in range(len(self.readsList)) if i % 2 == 1]
4 ^3 D; S7 n5 I1 }$ n        SeqIO.write(readsList_1, sequencingResult_1, "fasta")
5 a  W, Y- D% ~2 {4 E- N( F        SeqIO.write(readsList_2, sequencingResult_2, "fasta")
' ~2 C+ G( g- o( K& p3 w
+ R' Y' Q8 b% B( [( ]1 f5 A3 V    def resultsummary(self):
        print("基因组长度:" + str(self.genomeLength / 1000) + "kb")
, f. z8 p0 \$ h; z        print("片段长度区间:" + str(self.minfragmentlength) + "-" + str(self.maxfragmentlength))
, T+ a& m4 Q# m5 x0 _0 _        print("N值:" + str(self.N))
        print("期望片段长度:" + str(self.averagefragmentlength))
        print("克隆保留率:" + str(self.cloneRetainprobability))
        print("片段数量:" + str(len(self.fragmentList)))
1 R2 i+ k, N+ ?1 s  W; [0 m        print("reads长度:" + str(self.minreadslength) + "-" + str(self.maxreadslength))
9 {) K6 ]" H* ~& S        print("reads总数量:" + str(len(self.readsList)))
" ]# ^& B. {0 d/ o! d) S7 F        print("reads总长度:" + str(self.allreadslength / 1000) + "kb")
        m = self.allreadslength / self.genomeLength
5 Z+ e. t) V9 {8 E        print("覆盖度(m值):" + str(round(m, 5)))
3 T5 _) M. U4 x: J) F3 T        print("理论丢失率(e^-m):" + str(round(exp(-m), 5)))
        print("覆盖率(1-e^-m):" + str(round(1 - exp(-m), 5)))
* A9 u0 Q& ~8 n% R# E/ Z# -------------------------------------------主程序-------------------------------------------
# 模拟单端测序
sequencingObj = Sequencing()
8 f3 X0 n# p- j: U! jsequencingObj.singlereadsequencing("data/NC_025452.fasta", "result/virusSingleRead.fa")
. o6 ?' \& ?9 a$ C' H0 H, N/ B* |sequencingObj.resultsummary()
: v4 |+ @- T/ K8 C1 s5 f% c% X8 _% _
' A* P2 z5 X( b# s* g# 模拟双端测序
sequencingObj = Sequencing()
sequencingObj.pairreadsequencing("data/GCF_000146045.2_R64_genomic.fna", "result/yeastPairRead_1.fa", "result/yeastPairRead_2.fa")
sequencingObj.resultsummary()


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3477959497

不错。。。。。。。。。。。。。。。。。
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