RNA_seq表达分析

输入文件

input_v1.0.txt （三列，分别是 *.1.fastq.gz,*2.fastq.gz , *.sam）

hisat2运行参数与流程（hisat2_IWGSCv1.0.py）

#!/usr/bin/env python# -*- coding: utf-8 -*-__author__ = 'shengwei ma'__author_email__ = '[e-mail]'import subprocesswith open('input_v1.0_1.txt', 'r') as f:    for line in f:        lin = line.strip().split()        fq1, fq2, sam = lin[0], lin[1], lin[2]        proc = subprocess.Popen(            ['hisat2', '-p', '20', '--dta', '-x', '/data2/Fshare/IWGSCv1.0_hisat2/IWGSCv1.0_hiast2', '--known-splicesite-infile',             '/data2/Fshare/IWGSCv1.0_hisat2/TGACv1.ss', '--novel-splicesite-infile', '/data2/masw_data/rna_seq/all.ss',             '-t', '--no-discordant', '--no-mixed', '-1', fq1, '-2', fq2, '-S', sam], shell=False)        proc.wait()        print sam        new = sam[:-3] + 'unmap.txt'        mat = sam[:-3] + 'match.sam'        mis = sam[:-3] + 'mismatch.sam'        unmap = open(new, 'w')        mat1 = open(mat, 'w')        mis1 = open(mis, 'w')        with open(sam, 'r') as f1:            for (num, value) in enumerate(f1):                lin = value.strip().split()                if value.startswith('@'):                    mat1.writelines(value)                    mis1.writelines(value)                else:                    if '*' in lin[2]:                        unmap.writelines(lin[0])                    else:                        if 'I' not in lin[5] and 'D' not in lin[5] and 'XM:i:0' in value: #筛选完全匹配的reads，但是对于softclip 无效                            mat1.writelines(value)                        if 'I' not in lin[5] and 'D' not in lin[5] and 'XM:i:0' not in value:                            mis1.writelines(value)        mat1.close()        unmap.close()        mis1.close()        proc = subprocess.Popen(['samtools', 'view', '-@', '10', '-b', '-o', mat[:-3] + 'bam', mat], shell=False)        proc.wait()        proc = subprocess.Popen(['samtools', 'sort', '-@', '10', '-o', mat[:-3] + 'sorted.bam', mat[:-3] + 'bam'], shell=False)        proc.wait()        proc = subprocess.Popen(['shred', '-u', '-z', mat, sam, mat[:-3] + 'bam'], shell=False)        proc.wait()

hisat2输出信息，也可见该目录下的mapping_information.txt

Sample total_reads unmapped_reads uniquely-mapped_reads Multimapped_reads ATW_AAOSW_6 45401788(100.00%) 1625896(3.58%) 36054911(79.41%) 7720981(17.01%) ATW_ABOSW_7 43317025(100.00%) 3384472(7.81%) 32613798(75.29%) 7318755(16.90%) ATW_ACOSW 51224256(100.00%) 3682442(7.19%) 37964008(74.11%) 9577806(18.70%) ATW_ADOSW 85237299(100.00%) 5327852(6.25%) 63649972(74.67%) 16259475(19.08%) ATW_AEOSW 44470405(100.00%) 3714550(8.35%) 33422930(75.16%) 7332925(16.49%) ATW_AFOSW_2 38815740(100.00%) 2344586(6.04%) 30028428(77.36%) 6442726(16.60%) ATW_AGOSW_2 35749803(100.00%) 3298492(9.23%) 26017323(72.78%) 6433988(18.00%) ATW_AHOSW_3 52146021(100.00%) 4219037(8.09%) 39229076(75.23%) 8697908(16.68%) ATW_AIOSW_2 67283195(100.00%) 19946431(29.65%) 31218902(46.40%) 16117862(23.96%) ATW_AKOSW_2 75347431(100.00%) 24014018(31.87%) 33383763(44.31%) 17949650(23.82%) ATW_ALOSW_3 42039096(100.00%) 2031197(4.83%) 32613783(77.58%) 7394116(17.59%) ATW_AMOSW_4 38844640(100.00%) 7661962(19.72%) 25025383(64.42%) 6157295(15.85%) ATW_ANOSW 89075171(100.00%) 11783105(13.23%) 63333349(71.10%) 13958717(15.67%) ATW_AOSW 50836846(100.00%) 1967671(3.87%) 40375224(79.42%) 8493951(16.71%) ATW_COSW 45388739(100.00%) 4336069(9.55%) 33513277(73.84%) 7539393(16.61%) ATW_DOSW_2 48400597(100.00%) 1782615(3.68%) 38184280(78.89%) 8433702(17.42%) ATW_FOSW_2 47627837(100.00%) 11084697(23.27%) 29081034(61.06%) 7462106(15.67%) ATW_GOSW_3 47851480(100.00%) 2025594(4.23%) 38041640(79.50%) 7784246(16.27%) ATW_HOSW_3 46349244(100.00%) 3243306(7.00%) 35294358(76.15%) 7811580(16.85%) ATW_IOSW_4 53653235(100.00%) 2427235(4.52%) 42707453(79.60%) 8518547(15.88%) ATW_KOSW_4 39894644(100.00%) 3043191(7.63%) 30655324(76.84%) 6196129(15.53%) ATW_LOSW_5 40476784(100.00%) 2375565(5.87%) 31157278(76.98%) 6943941(17.16%) ATW_MOSW_5 49643196(100.00%) 6008219(12.10%) 34849848(70.20%) 8785129(17.70%) ATW_NOSW_6 45463315(100.00%) 5519168(12.14%) 32357850(71.17%) 7586297(16.69%) ATW_POSW_6 42820604(100.00%) 4444437(10.38%) 31740451(74.12%) 6635716(15.50%) ATW_QOSW_7 45189058(100.00%) 2519770(5.58%) 35464036(78.48%) 7205252(15.94%) ATW_ROSW_7 41964678(100.00%) 2418292(5.76%) 32377059(77.15%) 7169327(17.08%) ATW_SOSW_8 46010346(100.00%) 2071554(4.50%) 36664030(79.69%) 7274762(15.81%) ATW_TOSW_8 41117096(100.00%) 3337900(8.12%) 30307865(73.71%) 7471331(18.17%) ATW_VOSW_6 44532829(100.00%) 1723299(3.87%) 34529586(77.54%) 8279944(18.59%) ERR392055 26786162(100.00%) 4047245(15.11%) 15990303(59.70%) 6748614(25.19%) ERR392056 29879250(100.00%) 4692482(15.70%) 17363629(58.11%) 7823139(26.18%) ERR392057 30226502(100.00%) 3185007(10.54%) 19814488(65.55%) 7227007(23.91%) ERR392058 18558499(100.00%) 1998085(10.77%) 12203151(65.76%) 4357263(23.48%) ERR392059 30793173(100.00%) 2344455(7.61%) 22029424(71.54%) 6419294(20.85%) ERR392060 22417889(100.00%) 2366252(10.56%) 14884436(66.40%) 5167201(23.05%) ERR392061 20355570(100.00%) 3719356(18.27%) 11386270(55.94%) 5249944(25.79%) ERR392062 25108363(100.00%) 3797487(15.12%) 14548403(57.94%) 6762473(26.93%) ERR392063 29965698(100.00%) 4989790(16.65%) 16957287(56.59%) 8018621(26.76%) ERR392064 34565714(100.00%) 4811540(13.92%) 20727298(59.96%) 9026876(26.12%) ERR392064 34565714(100.00%) 4811540(13.92%) 20727298(59.96%) 9026876(26.12%) ERR392065 27177427(100.00%) 4520724(16.63%) 15412488(56.71%) 7244215(26.66%) ERR392066 37515837(100.00%) 7292639(19.44%) 19656276(52.39%) 10566922(28.17%) ERR392067 29548095(100.00%) 5994021(20.29%) 15715972(53.19%) 7838102(26.53%) ERR392068 30476750(100.00%) 3991164(13.10%) 18606572(61.05%) 7879014(25.85%) ERR392069 31424506(100.00%) 2679321(8.53%) 21520594(68.48%) 7224591(22.99%) ERR392070 29660313(100.00%) 4427930(14.93%) 17592218(59.31%) 7640165(25.76%) ERR392071 29373530(100.00%) 4610961(15.70%) 17098379(58.21%) 7664190(26.09%) ERR392072 33407315(100.00%) 4025192(12.05%) 21596921(64.65%) 7785202(23.30%) ERR392073 28004777(100.00%) 3852619(13.76%) 17378994(62.06%) 6773164(24.19%) ERR392074 26658599(100.00%) 5465146(20.50%) 13906279(52.16%) 7287174(27.34%) ERR392075 27595485(100.00%) 4936815(17.89%) 15247427(55.25%) 7411243(26.86%) ERR392076 29674240(100.00%) 5400293(18.20%) 16416891(55.32%) 7857056(26.48%) ERR392077 31078820(100.00%) 3574955(11.50%) 20081821(64.62%) 7422044(23.88%) ERR392078 32327629(100.00%) 2421810(7.49%) 23264665(71.97%) 6641154(20.54%) ERR392079 23704655(100.00%) 3131080(13.21%) 14112639(59.54%) 6460936(27.26%) ERR392080 27746131(100.00%) 2992584(10.79%) 18525078(66.77%) 6228469(22.45%) ERR392081 31358914(100.00%) 3746073(11.95%) 20709901(66.04%) 6902940(22.01%) ERR392082 32871524(100.00%) 2346792(7.14%) 23727550(72.18%) 6797182(20.68%) ERR392083 32240850(100.00%) 3128224(9.70%) 21232287(65.86%) 7880339(24.44%) ERR392084 32641566(100.00%) 2632926(8.07%) 23252740(71.24%) 6755900(20.70%) NG-5789_1A_lib7482 34356521(100.00%) 2743533(7.99%) 26100505(75.97%) 5512483(16.04%) NG-5789_1B_lib7486 42444661(100.00%) 2276170(5.36%) 32538608(76.66%) 7629883(17.98%) NG-5789_2A_lib7483 58377229(100.00%) 1980165(3.39%) 46325570(79.36%) 10071494(17.25%) NG-5789_2B_lib7487 45374776(100.00%) 1459680(3.22%) 35121823(77.40%) 8793273(19.38%) NG-5789_3A_lib7484 39082313(100.00%) 2105348(5.39%) 30799509(78.81%) 6177456(15.81%) NG-5789_3B_lib7488 39159546(100.00%) 1543503(3.94%) 30324689(77.44%) 7291354(18.62%) NG-5789_4A_lib7485 28215511(100.00%) 1319564(4.68%) 22135951(78.45%) 4759996(16.87%) NG-5789_4B_lib7489 34385209(100.00%) 2265183(6.59%) 26117900(75.96%) 6002126(17.46%) SRR1175868 62225856(100.00%) 4434070(7.13%) 46741787(75.12%) 11049999(17.76%) SRR1177760 68599252(100.00%) 4784428(6.97%) 51327798(74.82%) 12487026(18.20%) SRR1177761 77573636(100.00%) 5418773(6.99%) 58236525(75.07%) 13918338(17.94%) SRR1460549 30618243(100.00%) 2210006(7.22%) 20459229(66.82%) 7949008(25.96%) SRR1460550 72955159(100.00%) 5074113(6.96%) 48678341(66.72%) 19202705(26.32%) SRR1460551 27179120(100.00%) 2914078(10.72%) 17292077(63.62%) 6972965(25.66%) SRR1460552 37693050(100.00%) 2304774(6.11%) 25576360(67.85%) 9811916(26.03%) SRR1460553 23942473(100.00%) 1441952(6.02%) 16313271(68.14%) 6187250(25.84%) SRR1460554 17985663(100.00%) 1158728(6.44%) 12218492(67.93%) 4608443(25.62%) Wheat_Room1_AL_20DPA_RNA_Extra2 32685090(100.00%) 3056423(9.35%) 21550138(65.93%) 8078529(24.72%) Wheat_Room_SE_30DPA_RNA 23711650(100.00%) 3477993(14.67%) 13233266(55.81%) 7000391(29.52%)

使用featurecount计算reads数

其中-a 是输入文件，-o 是输出结果，每次运行注意修改。

featureCounts -T 20 -t exon -g Name --readExtension5 70  --readExtension3 70 -p -O --donotsort -C -a /data2/masw_data/transcript/TGACv1.cdna.gff3 -o /data2/masw_data/transcript/TGACv1.cdna.reformat_expression_new.txt ATW_AOSW.match.sorted.bam ATW_AAOSW_6.match.sorted.bam ATW_ANOSW.match.sorted.bam ATW_LOSW_5.match.sorted.bam ATW_ADOSW.match.sorted.bam ATW_AEOSW.match.sorted.bam ATW_DOSW_2.match.sorted.bam ATW_POSW_6.match.sorted.bam ATW_IOSW_4.match.sorted.bam ATW_KOSW_4.match.sorted.bam ATW_ROSW_7.match.sorted.bam ATW_ALOSW_3.match.sorted.bam ATW_TOSW_8.match.sorted.bam ATW_VOSW_6.match.sorted.bam ATW_MOSW_5.match.sorted.bam ATW_NOSW_6.match.sorted.bam ATW_COSW.match.sorted.bam ATW_AGOSW_2.match.sorted.bam ATW_GOSW_3.match.sorted.bam ATW_HOSW_3.match.sorted.bam ATW_ABOSW_7.match.sorted.bam ATW_ACOSW.match.sorted.bam ATW_QOSW_7.match.sorted.bam ATW_AHOSW_3.match.sorted.bam SRR1175868.match.sorted.bam SRR1177760.match.sorted.bam SRR1177761.match.sorted.bam NG-5789_1A_lib7482.match.sorted.bam NG-5789_1B_lib7486.match.sorted.bam NG-5789_2A_lib7483.match.sorted.bam NG-5789_2B_lib7487.match.sorted.bam NG-5789_3A_lib7484.match.sorted.bam NG-5789_3B_lib7488.match.sorted.bam NG-5789_4A_lib7485.match.sorted.bam NG-5789_4B_lib7489.match.sorted.bam ATW_SOSW_8.match.sorted.bam ATW_AFOSW_2.match.sorted.bam ATW_AIOSW_2.match.sorted.bam ATW_AKOSW_2.match.sorted.bam ATW_FOSW_2.match.sorted.bam ATW_AMOSW_4.match.sorted.bam ERR392061.match.sorted.bam ERR392055.match.sorted.bam ERR392057.match.sorted.bam ERR392072.match.sorted.bam ERR392082.match.sorted.bam ERR392059.match.sorted.bam ERR392080.match.sorted.bam ERR392081.match.sorted.bam ERR392078.match.sorted.bam ERR392084.match.sorted.bam ERR392063.match.sorted.bam ERR392076.match.sorted.bam ERR392074.match.sorted.bam ERR392075.match.sorted.bam ERR392058.match.sorted.bam ERR392077.match.sorted.bam ERR392056.match.sorted.bam ERR392070.match.sorted.bam ERR392064.match.sorted.bam ERR392068.match.sorted.bam ERR392073.match.sorted.bam ERR392083.match.sorted.bam ERR392079.match.sorted.bam ERR392065.match.sorted.bam ERR392066.match.sorted.bam ERR392062.match.sorted.bam ERR392069.match.sorted.bam ERR392060.match.sorted.bam ERR392067.match.sorted.bam ERR392071.match.sorted.bam SRR1460549.match.sorted.bam SRR1460550.match.sorted.bam SRR1460551.match.sorted.bam SRR1460552.match.sorted.bam SRR1460553.match.sorted.bam SRR1460554.match.sorted.bam

计算FPKM。使用 fpkm.py .输入文件为上述featurecount输出文件

#!/usr/bin/env python# -*- coding: utf-8 -*- __author__ = 'shengwei ma'__author_email__ = '[e-mail]'import numpy as npraw_total = [('root_Z10_rep1', 48869175), ('root_Z10_rep2', 43775892), ('root_Z13_rep1', 78098556),             ('root_Z13_rep2', 38101219), ('root_Z39_rep1', 79909447), ('root_Z39_rep2', 40755855),             ('stem_Z30_rep1', 46617982), ('stem_Z30_rep2', 38376167), ('stem_Z32_rep1', 51226000),             ('stem_Z32_rep2', 36851453), ('stem_Z65_rep1', 39546386), ('stem_Z65_rep2',40007899),             ('leaf_Z10_rep1', 37779196), ('leaf_Z10_rep2', 42809530), ('leaf_Z23_rep1', 43634977),             ('leaf_Z23_rep2', 39944147), ('leaf_Z71_rep1', 41052670), ('leaf_Z71_rep2', 32451311),             ('spike_Z32_rep1', 45825886), ('spike_Z32_rep2', 43105938), ('spike_Z39_rep1', 39932553),             ('spike_Z39_rep2', 47541814), ('spike_Z65_rep1', 42669288), ('spike_Z65_rep2', 47926984),             ('carpel', 47926984), ('carpel-like structure', 63914055), ('stamen', 72154863),             ('latent_lepto_rep1', 31612988), ('latent_lepto_rep2', 40168491), ('diplo_dia_rep1', 56397064),             ('diplo_dia_rep2', 43915096), ('zygo_pachy_rep1', 36976965), ('zygo_pachy_rep2', 37616043),             ('metaphaseI_rep1', 26895947), ('metaphaseI_rep2', 32120026), ('grain_Z71_rep1', 43938792),             ('grain_Z71_rep2', 36471154), ('grain_Z75_rep1', 47336764), ('grain_Z75_rep2', 51333413),             ('grain_Z85_rep1', 36543140), ('grain_Z85_rep2', 31182678), ('Wheat_Room1_10DPA', 16712256),             ('Wheat_Room1_10DPA_Rep', 22819483), ('Wheat_Room2_10DPA', 27121510), ('Wheat_Room2_10DPA_Rep', 29453109),             ('Wheat_Room1_AL_20DPA', 30598515), ('Wheat_Room1_AL_20DPA_Rep', 28518937), ('Wheat_Room2_AL_20DPA', 24838220),             ('Wheat_Room2_AL_20DPA_Rep', 27715580), ('Wheat_Room1_AL_20DPA_Extra1', 29978007), ('Wheat_Room1_AL_20DPA_Extra2', 30079461),             ('Wheat_Room1_SE_20DPA', 25140145), ('Wheat_Room1_SE_20DPA_Rep', 24446796), ('Wheat_Room2_SE_20DPA', 21339690),             ('Wheat_Room2_SE_20DPA_Rep', 22815780),             ('Wheat_Room1_TC_20DPA', 16629117), ('Wheat_Room1_TC_20DPA_Rep', 27612315), ('Wheat_Room2_TC_20DPA', 25304622),             ('Wheat_Room2_TC_20DPA_Rep', 25352139), ('Wheat_Room1_REF_20DPA', 29929219), ('Wheat_Room1_REF_20DPA_Rep', 26636425),             ('Wheat_Room2_REF_20DPA', 24316737), ('Wheat_Room2_REF_20DPA_Rep', 29330096), ('Wheat_Room1_SE_30DPA', 20661506),             ('Wheat_Room1_SE_30DPA_Rep', 22777481), ('Wheat_Room2_SE_30DPA', 30513836), ('Wheat_Room2_SE_30DPA_Rep', 21486098),             ('Wheat_Room1_AL_SE_30DPA', 28821672), ('Wheat_Room1_AL_SE_30DPA_Rep', 20134665), ('Wheat_Room2_AL_SE_30DPA', 23721856),             ('Wheat_Room2_AL_SE_30DPA_Rep', 24896811), ('wheat_23_1', 28444918), ('wheat_23_2', 67968193),             ('wheat_23_3', 24321425), ('wheat_4_1', 35430306), ('wheat_4_2', 22527710), ('wheat_4_3', 16848204)]organs = open('1.txt', 'w')organs.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % ('Geneid', 'Chr', 'root_max', 'stem_max', 'leaf_max', 'spike_max', 'grain_max'                                                       , 'stamen_max', 'new_carpel'))with open('specific_gene_expression_new.txt', 'r') as f: # 此处注意修改输入文件    print "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \                  "\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \                  "\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \                  "\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t" % \                  ('Geneid', 'Chr', 'Start', 'End', 'Strand', 'Length', 'root_Z10', 'root_Z13','root_Z39',                   'stem_Z30', 'stem_Z32', 'stem_Z65', 'leaf_Z10', 'leaf_Z23', 'leaf_Z71',                   'spike_Z32', 'spike_Z39', 'spike_Z65', 'carpel', 'carpel_like_structure',                   'stamen', 'latet_lepto', 'diplo_dia', 'zygo_pachy', 'metaphaseI',                   'grain_Z71', 'grain_Z75', 'grain_Z85', 'Wheat_10DPA', 'Wheat_AL_20DPA',                   'Wheat_SE_20DPA', 'Wheat_TC_20DPA', 'Wheat_REF_20DPA', 'Wheat_SE_30DPA',                   'Wheat_AL.SE_30DPA', 'wheat_23', 'wheat_4', 'root_Z10_std', 'root_Z13_std', 'root_Z39_std',                   'stem_Z30_std', 'stem_Z32_std', 'stem_Z65_std', 'leaf_Z10_std', 'leaf_Z23_std', 'leaf_Z71_std',                   'spike_Z32_std', 'spike_Z39_std', 'spike_Z65_std', 'carpel_std', 'carpel-like_std', 'stamen_std',                   'latet_lepto_std', 'diplo_dia_std', 'zygo_pachy_std', 'metaphaseI_std', 'grain_Z71_std',                   'grain_Z75_std', 'grain_Z85_std','Wheat_10DPA_std', 'Wheat_AL_20DPA_std','Wheat_SE_20DPA_std',                   'Wheat_TC_20DPA_std', 'Wheat_REF_20DPA_std', 'Wheat_SE_30DPA_std',                   'Wheat_AL.SE_30DPA_std', 'wheat_23_std', 'wheat_4_std')    for line in f:        if line.startswith('#') or line.startswith('Geneid'):            pass        else:            new = line.strip().split('\t')            (Geneid, Chr, Start, End, Strand, Length, root_Z10_rep1, root_Z10_rep2, root_Z13_rep1, root_Z13_rep2,             root_Z39_rep1, root_Z39_rep2, stem_Z30_rep1, stem_Z30_rep2, stem_Z32_rep1, stem_Z32_rep2, stem_Z65_rep1,             stem_Z65_rep2, leaf_Z10_rep1, leaf_Z10_rep2, leaf_Z23_rep1, leaf_Z23_rep2, leaf_Z71_rep1, leaf_Z71_rep2,             spike_Z32_rep1, spike_Z32_rep2, spike_Z39_rep1, spike_Z39_rep2, spike_Z65_rep1, spike_Z65_rep2, carpel,             carpel_like_structure, stamen, latet_lepto_rep1, latent_lepto_rep2, diplo_dia_rep1, diplo_dia_rep2,             zygo_pachy_rep1, zygo_pachy_rep2, metaphaseI_rep1, metaphaseI_rep2, grain_Z71_rep1, grain_Z71_rep2,             grain_Z75_rep1, grain_Z75_rep2, grain_Z85_rep1, grain_Z85_rep2, Wheat_Room1_10DPA, Wheat_Room1_10DPA_Rep,             Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep, Wheat_Room1_AL_20DPA, Wheat_Room1_AL_20DPA_Rep,             Wheat_Room2_AL_20DPA, Wheat_Room2_AL_20DPA_Rep, Wheat_Room1_AL_20DPA_Extra1, Wheat_Room1_AL_20DPA_Extra2,             Wheat_Room1_SE_20DPA, Wheat_Room1_SE_20DPA_Rep, Wheat_Room2_SE_20DPA, Wheat_Room2_SE_20DPA_Rep,             Wheat_Room1_TC_20DPA, Wheat_Room1_TC_20DPA_Rep, Wheat_Room2_TC_20DPA, Wheat_Room2_TC_20DPA_Rep,             Wheat_Room1_REF_20DPA, Wheat_Room1_REF_20DPA_Rep, Wheat_Room2_REF_20DPA, Wheat_Room2_REF_20DPA_Rep,             Wheat_Room1_SE_30DPA, Wheat_Room1_SE_30DPA_Rep, Wheat_Room2_SE_30DPA, Wheat_Room2_SE_30DPA_Rep,             Wheat_Room1_AL_SE_30DPA, Wheat_Room1_AL_SE_30DPA_Rep, Wheat_Room2_AL_SE_30DPA, Wheat_Room2_AL_SE_30DPA_Rep,             wheat_23_1, wheat_23_2, wheat_23_3, wheat_4_1, wheat_4_2, wheat_4_3) = new            new_root_Z10_rep1 = int(root_Z10_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[0][-1]))            new_root_Z10_rep2 = int(root_Z10_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[1][-1]))            new_root_Z13_rep1 = int(root_Z13_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[2][-1]))            new_root_Z13_rep2 = int(root_Z13_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[3][-1]))            new_root_Z39_rep1 = int(root_Z39_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[4][-1]))            new_root_Z39_rep2 = int(root_Z39_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[5][-1]))            new_stem_Z30_rep1 = int(stem_Z30_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[6][-1]))            new_stem_Z30_rep2 = int(stem_Z30_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[7][-1]))            new_stem_Z32_rep1 = int(stem_Z32_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[8][-1]))            new_stem_Z32_rep2 = int(stem_Z32_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[9][-1]))            new_stem_Z65_rep1 = int(stem_Z65_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[10][-1]))            new_stem_Z65_rep2 = int(stem_Z65_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[11][-1]))            new_leaf_Z10_rep1 = int(leaf_Z10_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[12][-1]))            new_leaf_Z10_rep2 = int(leaf_Z10_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[13][-1]))            new_leaf_Z23_rep1 = int(leaf_Z23_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[14][-1]))            new_leaf_Z23_rep2 = int(leaf_Z23_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[15][-1]))            new_leaf_Z71_rep1 = int(leaf_Z71_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[16][-1]))            new_leaf_Z71_rep2 = int(leaf_Z71_rep2) * pow(10.0 , 9) / (int(Length) * int(raw_total[17][-1]))            new_spike_Z32_rep1 = int(spike_Z32_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[18][-1]))            new_spike_Z32_rep2 = int(spike_Z32_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[19][-1]))            new_spike_Z39_rep1 = int(spike_Z39_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[20][-1]))            new_spike_Z39_rep2 = int(spike_Z39_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[21][-1]))            new_spike_Z65_rep1 = int(spike_Z65_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[22][-1]))            new_spike_Z65_rep2 = int(spike_Z65_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[23][-1]))            new_carpel = int(carpel) * pow(10.0, 9) / (int(Length) * int(raw_total[24][-1]))            new_carpel_like_structure = int(carpel_like_structure) * pow(10.0, 9) / (int(Length) * int(raw_total[25][-1]))            new_stamen = int(stamen) * pow(10.0, 9) / (int(Length) * int(raw_total[26][-1]))            new_latet_lepto_rep1 = int(latet_lepto_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[27][-1]))            new_latet_lepto_rep2 = int(latent_lepto_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[28][-1]))            new_diplo_dia_rep1 = int(diplo_dia_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[29][-1]))            new_diplo_dia_rep2 = int(diplo_dia_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[30][-1]))            new_zygo_pachy_rep1 = int(zygo_pachy_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[31][-1]))            new_zygo_pachy_rep2 = int(zygo_pachy_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[32][-1]))            new_metaphaseI_rep1 = int(metaphaseI_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[33][-1]))            new_metaphaseI_rep2 = int(metaphaseI_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[34][-1]))            new_grain_Z71_rep1 = int(grain_Z71_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[35][-1]))            new_grain_Z71_rep2 = int(grain_Z71_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[36][-1]))            new_grain_Z75_rep1 = int(grain_Z75_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[37][-1]))            new_grain_Z75_rep2 = int(grain_Z75_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[38][-1]))            new_grain_Z85_rep1 = int(grain_Z85_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[39][-1]))            new_grain_Z85_rep2 = int(grain_Z85_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[40][-1]))            Wheat_Room1_10DPA = int(Wheat_Room1_10DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[41][-1]))            Wheat_Room1_10DPA_Rep = int(Wheat_Room1_10DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[42][-1]))            Wheat_Room2_10DPA = int(Wheat_Room2_10DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[43][-1]))            Wheat_Room2_10DPA_Rep = int(Wheat_Room2_10DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[44][-1]))            Wheat_Room1_AL_20DPA = int(Wheat_Room1_AL_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[45][-1]))            Wheat_Room1_AL_20DPA_Rep = int(Wheat_Room1_AL_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[46][-1]))            Wheat_Room2_AL_20DPA = int(Wheat_Room2_AL_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[47][-1]))            Wheat_Room2_AL_20DPA_Rep = int(Wheat_Room2_AL_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[48][-1]))            Wheat_Room1_AL_20DPA_Extra1 = int(Wheat_Room1_AL_20DPA_Extra1) * pow(10.0, 9) / (int(Length) * int(raw_total[49][-1]))            Wheat_Room1_AL_20DPA_Extra2 = int(Wheat_Room1_AL_20DPA_Extra2) * pow(10.0, 9) / (int(Length) * int(raw_total[50][-1]))            Wheat_Room1_SE_20DPA = int(Wheat_Room1_SE_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[51][-1]))            Wheat_Room1_SE_20DPA_Rep = int(Wheat_Room1_SE_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[52][-1]))            Wheat_Room2_SE_20DPA = int(Wheat_Room2_SE_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[53][-1]))            Wheat_Room2_SE_20DPA_Rep = int(Wheat_Room2_SE_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[54][-1]))            Wheat_Room1_TC_20DPA = int(Wheat_Room1_TC_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[55][-1]))            Wheat_Room1_TC_20DPA_Rep = int(Wheat_Room1_TC_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[56][-1]))            Wheat_Room2_TC_20DPA = int(Wheat_Room2_TC_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[57][-1]))            Wheat_Room2_TC_20DPA_Rep = int(Wheat_Room2_TC_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[58][-1]))            Wheat_Room1_REF_20DPA = int(Wheat_Room1_REF_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[59][-1]))            Wheat_Room1_REF_20DPA_Rep = int(Wheat_Room1_REF_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[60][-1]))            Wheat_Room2_REF_20DPA = int(Wheat_Room2_REF_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[61][-1]))            Wheat_Room2_REF_20DPA_Rep = int(Wheat_Room2_REF_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[62][-1]))            Wheat_Room1_SE_30DPA = int( Wheat_Room1_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[63][-1]))            Wheat_Room1_SE_30DPA_Rep = int(Wheat_Room1_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[64][-1]))            Wheat_Room2_SE_30DPA = int(Wheat_Room2_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[65][-1]))            Wheat_Room2_SE_30DPA_Rep = int(Wheat_Room2_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[66][-1]))            Wheat_Room1_AL_SE_30DPA = int(Wheat_Room1_AL_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[67][-1]))            Wheat_Room1_AL_SE_30DPA_Rep = int(Wheat_Room1_AL_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[68][-1]))            Wheat_Room2_AL_SE_30DPA = int(Wheat_Room2_AL_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[69][-1]))            Wheat_Room2_AL_SE_30DPA_Rep = int(Wheat_Room2_AL_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[70][-1]))            wheat_23_1 = int(wheat_23_1) * pow(10.0, 9) / (int(Length) * int(raw_total[71][-1]))            wheat_23_2 = int(wheat_23_2) * pow(10.0, 9) / (int(Length) * int(raw_total[72][-1]))            wheat_23_3 = int(wheat_23_3) * pow(10.0, 9) / (int(Length) * int(raw_total[73][-1]))            wheat_4_1 = int(wheat_4_1) * pow(10.0, 9) / (int(Length) * int(raw_total[74][-1]))            wheat_4_2 = int(wheat_4_2) * pow(10.0, 9) / (int(Length) * int(raw_total[75][-1]))            wheat_4_3 = int(wheat_4_3) * pow(10.0, 9) / (int(Length) * int(raw_total[76][-1]))            root_Z10_mean = np.mean(np.array([new_root_Z10_rep1, new_root_Z10_rep2]))            root_Z10_std = np.std(np.array([new_root_Z10_rep1, new_root_Z10_rep2]))            root_Z13_mean = np.mean(np.array([new_root_Z13_rep1, new_root_Z13_rep2]))            root_Z13_std = np.std(np.array([new_root_Z13_rep1, new_root_Z13_rep2]))            root_Z39_mean = np.mean(np.array([new_root_Z39_rep1, new_root_Z39_rep2]))            root_Z39_std = np.std(np.array([new_root_Z39_rep1, new_root_Z39_rep2]))            stem_Z30_mean = np.mean(np.array([new_stem_Z30_rep1, new_stem_Z30_rep2]))            stem_Z30_std = np.std(np.array([new_stem_Z30_rep1, new_stem_Z30_rep2]))            stem_Z32_mean = np.mean(np.array([new_stem_Z32_rep1, new_stem_Z32_rep2]))            stem_Z32_std = np.std(np.array([new_stem_Z32_rep1, new_stem_Z32_rep2]))            stem_Z65_mean = np.mean(np.array([new_stem_Z65_rep1, new_stem_Z65_rep2]))            stem_Z65_std = np.std(np.array([new_stem_Z65_rep1, new_stem_Z65_rep2]))            leaf_Z10_mean = np.mean(np.array([new_leaf_Z10_rep1, new_leaf_Z10_rep2]))            leaf_Z10_std = np.std(np.array([new_leaf_Z10_rep1, new_leaf_Z10_rep2]))            leaf_Z23_mean = np.mean(np.array([new_leaf_Z23_rep1, new_leaf_Z23_rep2]))            leaf_Z23_std = np.std(np.array([new_leaf_Z23_rep1, new_leaf_Z23_rep2]))            leaf_Z71_mean = np.mean(np.array([new_leaf_Z71_rep1, new_leaf_Z71_rep2]))            leaf_Z71_std = np.std(np.array([new_leaf_Z71_rep1, new_leaf_Z71_rep2]))            spike_Z32_mean = np.mean(np.array([new_spike_Z32_rep1, new_spike_Z32_rep2]))            spike_Z32_std = np.std(np.array([new_spike_Z32_rep1, new_spike_Z32_rep2]))            spike_Z39_mean = np.mean(np.array([new_spike_Z39_rep1, new_spike_Z39_rep2]))            spike_Z39_std = np.std(np.array([new_spike_Z39_rep1, new_spike_Z39_rep2]))            spike_Z65_mean = np.mean(np.array([new_spike_Z65_rep1, new_spike_Z65_rep2]))            spike_Z65_std = np.std(np.array([new_spike_Z65_rep1, new_spike_Z65_rep2]))            latet_lepto_mean = np.mean(np.array([new_latet_lepto_rep1, new_latet_lepto_rep2]))            latet_lepto_std = np.std(np.array([new_latet_lepto_rep1, new_latet_lepto_rep2]))            diplo_dia_mean = np.mean(np.array([new_diplo_dia_rep1, new_diplo_dia_rep2]))            diplo_dia_std = np.std(np.array([new_diplo_dia_rep1, new_diplo_dia_rep2]))            zygo_pachy_mean = np.mean(np.array([new_zygo_pachy_rep1, new_zygo_pachy_rep2]))            zygo_pachy_std = np.std(np.array([new_zygo_pachy_rep1, new_zygo_pachy_rep2]))            metaphaseI_mean = np.mean(np.array([new_metaphaseI_rep1, new_metaphaseI_rep2]))            metaphaseI_std = np.std(np.array([new_metaphaseI_rep1, new_metaphaseI_rep2]))            grain_Z71_mean = np.mean(np.array([new_grain_Z71_rep1, new_grain_Z71_rep2]))            grain_Z71_std = np.std(np.array([new_grain_Z71_rep1, new_grain_Z71_rep2]))            grain_Z75_mean = np.mean(np.array([new_grain_Z75_rep1, new_grain_Z75_rep2]))            grain_Z75_std = np.std(np.array([new_grain_Z75_rep1, new_grain_Z75_rep2]))            grain_Z85_mean = np.mean(np.array([new_grain_Z85_rep1, new_grain_Z85_rep2]))            grain_Z85_std = np.std(np.array([new_grain_Z85_rep1, new_grain_Z85_rep2]))            root_max = np.max(np.array([new_root_Z10_rep1, new_root_Z10_rep2, new_root_Z13_rep1, new_root_Z13_rep2, new_root_Z39_rep1, new_root_Z39_rep2]))            stem_max = np.max(np.array([new_stem_Z30_rep1, new_stem_Z30_rep2, new_stem_Z32_rep1, new_stem_Z32_rep2, new_stem_Z65_rep1, new_stem_Z65_rep2]))            leaf_max = np.max(np.array([new_leaf_Z10_rep1, new_leaf_Z10_rep2, new_leaf_Z23_rep1, new_leaf_Z23_rep2, new_leaf_Z71_rep1, new_leaf_Z71_rep2]))            spike_max = np.max(np.array([new_spike_Z32_rep1, new_spike_Z32_rep2, new_spike_Z39_rep1, new_spike_Z39_rep2, new_spike_Z65_rep1, new_spike_Z65_rep2]))            grain_max = np.max(np.array([new_grain_Z71_rep1, new_grain_Z71_rep2, Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep,                                           new_grain_Z85_rep1, new_grain_Z85_rep2]))            stamen_max = np.mean(np.array([new_stamen, new_latet_lepto_rep1, new_latet_lepto_rep2, new_diplo_dia_rep1, new_diplo_dia_rep2,                                            new_zygo_pachy_rep1, new_zygo_pachy_rep2, new_metaphaseI_rep1, new_metaphaseI_rep2]))            organs.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (Geneid, Chr, root_max, stem_max, leaf_max, spike_max, grain_max                                                                   , stamen_max, new_carpel))            Wheat_10DPA_mean = np.mean(np.array([Wheat_Room1_10DPA, Wheat_Room1_10DPA_Rep,Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep]))            Wheat_10DPA_std = np.std(np.array([Wheat_Room1_10DPA, Wheat_Room1_10DPA_Rep,Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep]))            Wheat_AL_20DPA_mean = np.mean(np.array([Wheat_Room1_AL_20DPA, Wheat_Room1_AL_20DPA_Rep,Wheat_Room2_AL_20DPA, Wheat_Room2_AL_20DPA_Rep, Wheat_Room1_AL_20DPA_Extra1, Wheat_Room1_AL_20DPA_Extra2]))            Wheat_AL_20DPA_std = np.std(np.array([Wheat_Room1_AL_20DPA, Wheat_Room1_AL_20DPA_Rep,Wheat_Room2_AL_20DPA, Wheat_Room2_AL_20DPA_Rep, Wheat_Room1_AL_20DPA_Extra1, Wheat_Room1_AL_20DPA_Extra2]))            Wheat_SE_20DPA_mean = np.mean(np.array([Wheat_Room1_SE_20DPA, Wheat_Room1_SE_20DPA_Rep, Wheat_Room2_SE_20DPA, Wheat_Room2_SE_20DPA_Rep]))            Wheat_SE_20DPA_std = np.std(np.array([Wheat_Room1_SE_20DPA, Wheat_Room1_SE_20DPA_Rep, Wheat_Room2_SE_20DPA, Wheat_Room2_SE_20DPA_Rep]))            Wheat_TC_20DPA_mean = np.mean(np.array([Wheat_Room1_TC_20DPA, Wheat_Room1_TC_20DPA_Rep, Wheat_Room2_TC_20DPA, Wheat_Room2_TC_20DPA_Rep]))            Wheat_TC_20DPA_std = np.std(np.array([Wheat_Room1_TC_20DPA, Wheat_Room1_TC_20DPA_Rep, Wheat_Room2_TC_20DPA, Wheat_Room2_TC_20DPA_Rep]))            Wheat_REF_20DPA_mean = np.mean(np.array([Wheat_Room1_REF_20DPA, Wheat_Room1_REF_20DPA_Rep, Wheat_Room2_REF_20DPA, Wheat_Room2_REF_20DPA_Rep]))            Wheat_REF_20DPA_std = np.std(np.array([Wheat_Room1_REF_20DPA, Wheat_Room1_REF_20DPA_Rep, Wheat_Room2_REF_20DPA, Wheat_Room2_REF_20DPA_Rep]))            Wheat_SE_30DPA_mean = np.mean(np.array([Wheat_Room1_SE_30DPA, Wheat_Room1_SE_30DPA_Rep, Wheat_Room2_SE_30DPA, Wheat_Room2_SE_30DPA_Rep]))            Wheat_SE_30DPA_std = np.std(np.array([Wheat_Room1_SE_30DPA, Wheat_Room1_SE_30DPA_Rep, Wheat_Room2_SE_30DPA, Wheat_Room2_SE_30DPA_Rep]))            Wheat_AL_SE_30DPA_mean = np.mean(np.array([Wheat_Room1_AL_SE_30DPA, Wheat_Room1_AL_SE_30DPA_Rep, Wheat_Room2_AL_SE_30DPA, Wheat_Room2_AL_SE_30DPA_Rep]))            Wheat_AL_SE_30DPA_std = np.std(np.array([Wheat_Room1_AL_SE_30DPA, Wheat_Room1_AL_SE_30DPA_Rep, Wheat_Room2_AL_SE_30DPA, Wheat_Room2_AL_SE_30DPA_Rep]))            wheat_23_mean = np.mean(np.array([wheat_23_1, wheat_23_2, wheat_23_3]))            wheat_23_std = np.std(np.array([wheat_23_1, wheat_23_2, wheat_23_3]))            wheat_4_mean = np.mean(np.array([wheat_4_1, wheat_4_2, wheat_4_3]))            wheat_4_std = np.std(np.array([wheat_4_1, wheat_4_2, wheat_4_3]))            print "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \                  "\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \                  "\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \                  "\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % \                  (Geneid, Chr, Start, End, Strand, Length, root_Z10_mean, root_Z13_mean,root_Z39_mean, stem_Z30_mean,                   stem_Z32_mean, stem_Z65_mean, leaf_Z10_mean, leaf_Z23_mean, leaf_Z71_mean, spike_Z32_mean,                   spike_Z39_mean, spike_Z65_mean, new_carpel, new_carpel_like_structure, new_stamen, latet_lepto_mean,                   diplo_dia_mean, zygo_pachy_mean, metaphaseI_mean, grain_Z71_mean, grain_Z75_mean, grain_Z85_mean,                   Wheat_10DPA_mean, Wheat_AL_20DPA_mean, Wheat_SE_20DPA_mean, Wheat_TC_20DPA_mean, Wheat_REF_20DPA_mean,                   Wheat_SE_30DPA_mean, Wheat_AL_SE_30DPA_mean, wheat_23_mean, wheat_4_mean,                   root_Z10_std, root_Z13_std, root_Z39_std, stem_Z30_std, stem_Z32_std, stem_Z65_std, leaf_Z10_std,                   leaf_Z23_std, leaf_Z71_std, spike_Z32_std, spike_Z39_std, spike_Z65_std, 'null', 'null', 'null',                   latet_lepto_std, diplo_dia_std, zygo_pachy_std, metaphaseI_std, grain_Z71_std, grain_Z75_std,                   grain_Z85_std, Wheat_10DPA_std, Wheat_AL_20DPA_std, Wheat_SE_20DPA_std, Wheat_TC_20DPA_std,                   Wheat_REF_20DPA_std, Wheat_SE_30DPA_std, Wheat_AL_SE_30DPA_std, wheat_23_std, wheat_4_std)organs.close()