tested on Debian11, gpu114, also with SLURM20 last modified 8 July 2022
cd /nfs/scistore16/itgrp/bioinf/projects/DA0010/original-paper-data-analysis-jean-01Apr22
module load samtools/1.14
module load bcftools/1.14
mkdir -p snakemake2samtools fastq -@24 -0 snakemake2/SRR11868220-orig-R1.fastq \
--i1 snakemake2/SRR11868220-orig-I1.fastq \
--index-format i11 --quality-tag QT \
--barcode-tag BC \
CBYRBANXX_1_20180406B_20180408.bam &samtools fastq -@24 -0 /dev/null \
--i1 snakemake2/SRR11868220-orig-I2.fastq \
--index-format i9 \
--quality-tag Q2 \
--barcode-tag B2 CBYRBANXX_1_20180406B_20180408.bam &The first letters such as ACTG1 are the gene name for the guide RNA The next letters are the level of evidence (compare fig2 B in https://science.sciencemag.org/content/370/6519/935) and is set to "nC" for neutral controls and "dC" for cell proliferation controls (mnemonic for "depletion/death controls") and "pC" for positive controls (which were not further analyzed). The last number such as for 1 for ACTG1_AJ_1 is guide RNA number for that gene. The captial letters such as GGAAGAAGAGATCGCCGCGC are the guide RNA sequence.
gRNA.txt Click to expand!
ACTG1_AJ_1 GGAAGAAGAGATCGCCGCGC
ACTG1_AJ_2 CCGTGTTTCCTTCCATCGTC
ACTG1_AJ_3 TGACGATGCCATGCTCAATG
ACTG1_AJ_4 AGAGTCCATGACAATGCCAG
AKT3_LOE3_1 TTACCATTGTGAAAGAAGGT
AKT3_LOE3_2 ACACCCGCTCTCTCGACAAA
AKT3_LOE3_3 TTGGTTCGAGAGAAGGCAAG
AKT3_LOE3_4 ATTTCATGTAGATACTCCAG
ALG12_LOE3_1 GCGAAAGCACGTAAACCGCG
ALG12_LOE3_2 GCGTGCAGTAGAACATCAGG
ALG12_LOE3_3 AGTCTTGGCTGTACAAAGCG
ALG12_LOE3_4 CAGCGAAAGCACGTAAACCG
ALG1_LOE2_1 GCATGGCCAACGACAGCGCG
ALG1_LOE2_2 GCGCGTGGTACTGCATACGG
ALG1_LOE2_3 ACTCCGTACTGGAAAACTCG
ALG1_LOE2_4 CCACTTGGCCAGCAGAACGA
ALG3_LOE3_1 GCACATGAAGAAAAAGACGA
ALG3_LOE3_2 CCGAGGCACTGACATCCGCA
ALG3_LOE3_3 CTATGACTATACCCAACTGC
ALG3_LOE3_4 ACTGAGGAAGAGCAGCACCA
ANKRD11_AJ_1 AGGCACTTACACTTCCAGCG
ANKRD11_AJ_2 GGTGCGGAGGTGAACACCAA
ANKRD11_AJ_3 GTGAACCTCCTGTTAGGCAA
ANKRD11_AJ_4 ACAGGAGGTTCACCATCGTG
ARFGEF2_LOE2_1 ACCTTCTAGGAATGTCCGCA
ARFGEF2_LOE2_2 AGTAAGGGTAGCCTTGGCAG
ARFGEF2_LOE2_3 AGATGTTCTTCCAACCTGAG
ARFGEF2_LOE2_4 AGGGCCCTCAGACTGATGAA
ARNT2_LOE3_1 GGATGTGCATGGGCTCGCGG
ARNT2_LOE3_2 GATTCCATCGGAGTTATGCC
ARNT2_LOE3_3 GATGGCTTTGATCCTCAGGG
ARNT2_LOE3_4 TCATCCATGAGGATGTGCAT
ASNS_LOE3_1 AGGATCAGATGAACTTACGC
ASNS_LOE3_2 ACAACAGTTCGTGCTTCAGT
ASNS_LOE3_3 TCCTTCTCCTGAAAAAGCCG
ASNS_LOE3_4 GGTCGCCAGAGAATCTCTTT
ASPM_LOE1_1 GCGTCAAATGGCACATAGCA
ASPM_LOE1_2 GCTCGGAAAAGAAAGAGCGA
ASPM_LOE1_3 TCTGCAGTTAGAGACCTTGG
ASPM_LOE1_4 GTATTTAATAGGCTATGTGG
ASXL3_LOE3_1 TCTTTGCATATGCAGCACAA
ASXL3_LOE3_2 ATTCAGACAGGCTAATGGAG
ASXL3_LOE3_3 TATCCGCAACTGCATTTCTG
ASXL3_LOE3_4 TCTTGTCAACACTAACTTGA
ATM_LOE1_1 TGATAGAGCTACAGAACGAA
ATM_LOE1_2 AAACAATTAAACATCTAGAT
ATM_LOE1_3 TCCAGAATTTTCAAGCCAGA
ATM_LOE1_4 GCCTGCAACCTGCTAAGTGT
ATP6V0C_dC_1 GCCTATGGCACAGCCAAGAG
ATP6V0C_dC_2 TTGCGGCCATGTCTGTCATG
ATR_LOE2_1 TGAGGCTTAGTAAAGCCCTC
ATR_LOE2_2 ACAATGCTCTCCTTAATGCA
ATR_LOE2_3 AGTCAATTAGATGAACACAT
ATR_LOE2_4 CTAGTAGCATAGCTCGACCA
ATRIP_AJ_1 TGAAGAACTGTACAAGGGGG
ATRIP_AJ_2 GCGAGACTCACTACATCAGA
ATRIP_AJ_3 GCTTGTATCCTGACTCCGTC
ATRIP_AJ_4 GCCAGCAGCACGCTAGGCAG
ATRX_AJ_1 TTACCTGTAAAGTCTTACCA
ATRX_AJ_2 TGAATTCTATACGATCAAGG
ATRX_AJ_3 GAGTTCAGTTGATCATCAAG
ATRX_AJ_4 CATCACTCTGACATCTACCA
BCOR_LOE2_1 GCTGCACCTTAGAGAACGAT
BCOR_LOE2_2 TGATCCCTCAGGAGTCCAGG
BCOR_LOE2_3 TCAGTGATGTTAGTCCCCTG
BCOR_LOE2_4 ATTGCAGCGAAATATGCGGG
BLM_AJ_1 TACGGCCACAGCTAATCCCA
BLM_AJ_2 GTAGTCAGTAAACAGTCCCC
BLM_AJ_3 TGATAATTGCTGTAAAACAA
BLM_AJ_4 TCACTTGATGGCCCTATGGA
BRF1_LOE2_1 CCTCTACCTGGTCTGCCGTA
BRF1_LOE2_2 AATTCAGGCCAGTACCGCCA
BRF1_LOE2_3 CTACCTGGTCTGCCGTACGG
BRF1_LOE2_4 GGATGTACTGGGCGAGCACA
CASC5_LOE3_1 TCCTGTACTGATAATTTGGA
CASC5_LOE3_2 GCAATAGCCAAAAAAGAACG
CASC5_LOE3_3 TTAGTGATACGACACAAGAT
CASC5_LOE3_4 CATAAAGTCTATTTGAGCAA
CASK_LOE1_1 CATCGTCGTACAACACTGAA
CASK_LOE1_2 GTAAGGATGATCATAATTGG
CASK_LOE1_3 GAAAGCCTGTAGACGTCTGG
CASK_LOE1_4 TTACCTTCTGTACTTAACCC
CCDC88A_LOE3_1 ATAGCACCCCAGAAATCCCT
CCDC88A_LOE3_2 GGTTGCCGCACATATTCAAG
CCDC88A_LOE3_3 AATGGCACAGAAACAAAGTA
CCDC88A_LOE3_4 GATTTTCTATATCTAAACCC
CDC16_dC_1 GACATTTCCCAGTCGCTGGA
CDC16_dC_2 GGTAAACAACTTGCATGGAA
CDC45_AJ_1 AGCCAGCTCAAACATCACCA
CDC45_AJ_2 GAACCAGCGTATATTGCACG
CDC45_AJ_3 TAGTGGAGCAAACCATGCGG
CDC45_AJ_4 CATACTGCTCGTAGTCAAAG
CDC6_LOE3_1 TCAGGAGATTTGTCAGGAAG
CDC6_LOE3_2 TCAACTGGACAGCAAAGGCC
CDC6_LOE3_3 TTTGCGACAGACTTTACTGT
CDC6_LOE3_4 TCACAGATAGAATTCTACCT
CDK5RAP2_LOE1_1 GCTCCGCATCTATTTCCTTG
CDK5RAP2_LOE1_2 CTGCTCCAGTTGCTTCCGCA
CDK5RAP2_LOE1_3 CCTTGAGGAAAGAATGCAAC
CDK5RAP2_LOE1_4 ATAATCAGAGAGCGCATGAG
CDK6_LOE2_1 GCCGCTCTCCACCATCCGCG
CDK6_LOE2_2 CCAGCAGTACGAATGCGTGG
CDK6_LOE2_3 CAACACTCCAGAGATCCACG
CDK6_LOE2_4 TTAGATCGCGATGCACTACT
CDKL5_AJ_1 ATGTCCACGGACTTTCCATA
CDKL5_AJ_2 CAGAGTACGTTGCCACCAGA
CDKL5_AJ_3 TTATTTTTCAGCGCTCCCTA
CDKL5_AJ_4 TACCTTCACCTACAACCCCA
CDT1_LOE3_1 GGCCTCCTACCGCTTCCGCC
CDT1_LOE3_2 GCATGTCAAGGAGCACCACA
CDT1_LOE3_3 ACCTTTGCCAAGGTCCAGCG
CDT1_LOE3_4 AGCGGCTGCAGCGCTTAGAA
CENPE_LOE2_1 TGTATGGCAGAATCGATGAT
CENPE_LOE2_2 GCAGGTACTATATTTGCCTA
CENPE_LOE2_3 ACATCTTCTCGAATAATTAA
CENPE_LOE2_4 TCAGGTGTGCGGCTCAAGGA
CENPF_LOE3_1 GGTTGAAAATGAAAAAACCG
CENPF_LOE3_2 CCTGATGCCGGGCAATGTCG
CENPF_LOE3_3 ACACCAAGTCAATATTATAG
CENPF_LOE3_4 TATGGGCTAGTCTTTCCTGT
CENPJ_LOE2_1 ACTCACACGACATACCCGGA
CENPJ_LOE2_2 CGTGTTATACTGTTTCCCAA
CENPJ_LOE2_3 GACTAACCAGAGCACTTCCG
CENPJ_LOE2_4 GCTGGGGTCATATTAAATCG
CEP135_LOE2_1 GAAGGAGGTCTGCAATGTAA
CEP135_LOE2_2 TGTTATCAGCCACTTGAAGG
CEP135_LOE2_3 GAGATGAGTCACCAGCCACA
CEP135_LOE2_4 TCTCAACTGAAAGATACGAA
CEP152_LOE2_1 GTATCTGAAAGATAACCGGA
CEP152_LOE2_2 GATTTCCAGTTGGATCCGCT
CEP152_LOE2_3 ACAGGAAGTGACACATTCGA
CEP152_LOE2_4 CCTACCGGACTGCAACAAGT
CEP63_LOE3_1 GTTGGATGTGACACATAAGG
CEP63_LOE3_2 GCAACAGGTATCTTCACTGG
CEP63_LOE3_3 CCAAAAATCACAGGGAAGAT
CEP63_LOE3_4 TTAAGTGTTGAATTTCTGAT
CIT_LOE3_1 TGAACCAGAGAAGAATTCGT
CIT_LOE3_2 GTTCACAGCGTTCATCTGAT
CIT_LOE3_3 TATCTCGAAGCACAAGCCCG
CIT_LOE3_4 ACTGGAGACAAGATTGCGGG
COG7_LOE3_1 CCAAGAGGTGAACCACGCCG
COG7_LOE3_2 TGTTGAAGCCCTAAAACAGG
COG7_LOE3_3 AGCCAGTCCACAGATTGTAG
COG7_LOE3_4 TCGAGCAGCAGCTAGCCAAC
CREBBP_LOE3_1 GTACGAGTCTGCCAACAGCA
CREBBP_LOE3_2 ATTGCCCCCCTCCAAACACG
CREBBP_LOE3_3 GCGTCCACAGCAATATCCAA
CREBBP_LOE3_4 GGTAGCCTATGCTAAGAAAG
CSNK2A1_LOE3_1 GATTGATCATGAGCACAGAA
CSNK2A1_LOE3_2 GTTTCAATGATATCTTGGGC
CSNK2A1_LOE3_3 TGGACATGACAATTATGATC
CSNK2A1_LOE3_4 CTAGTTGGTGAGGATAGCCA
CTCF_LOE3_1 GTTTGTGCAGTTATGCCAGC
CTCF_LOE3_2 GTGTGATTACGCTTGTAGAC
CTCF_LOE3_3 TACACTGGCGTAATCGCACA
CTCF_LOE3_4 CGATCCAAATTTGAACGCCG
CTNNB1_LOE3_1 TTACCCAAGCATTTTCACCA
CTNNB1_LOE3_2 TTAGTCCTCTGATAACAATT
CTNNB1_LOE3_3 TGTCTGCTCTAGTAATAAGC
CTNNB1_LOE3_4 ACATCCAAAGAGTAGCTGCA
DDB2_LOE1_1 GGGGCGTAATACAATCTCGG
DDB2_LOE1_2 ACAACAATGAGGTTGTAGCG
DDB2_LOE1_3 GGAGGGCTACATCCTTGGCG
DDB2_LOE1_4 TGACTCAGACTGCCTCTGGG
DHCR7_LOE3_1 TGTCCTTCGCAGCGAAGCAG
DHCR7_LOE3_2 TCTGCCATGACCACTTCGGG
DHCR7_LOE3_3 GGGCTACTACATCTTCCGGG
DHCR7_LOE3_4 GCGGCTTTCCTCGTTATAGG
DKC1_LOE3_1 GATTCGACGGATACTTCGGG
DKC1_LOE3_2 TAGGCCCTAGAAACTCTGAC
DKC1_LOE3_3 GTGGCTGTATGATAACCACA
DKC1_LOE3_4 TCAGATGCAGGAGCTTCGGA
DNA2_AJ_1 GCCACACGCCAAATGCTAAT
DNA2_AJ_2 GCAATAATACCAATATCAGA
DNA2_AJ_3 CATCTTGACTTAGATTTAAG
DNA2_AJ_4 TGCTTCACGGTTTAGCACCA
DNM1L_LOE3_1 ATAGTGTTAAGGCCACCAAG
DNM1L_LOE3_2 AAATAGCTACGGTGAACCCG
DNM1L_LOE3_3 GCTAATGAACAATAATATAG
DNM1L_LOE3_4 GGCACAAATAAAGCAGGACG
DONSON_AJ_1 GCTCCAATACGAGGGAACAG
DONSON_AJ_2 GGGCTTCCGAAAGCCGCCCG
DONSON_AJ_3 GGCTCTGCTGGAAGGTACAA
DONSON_AJ_4 GTTTTAATACTCCAGTCCAC
DPAGT1_LOE3_1 GATGCAGGCCAAGTATCGGG
DPAGT1_LOE3_2 AGCAGGAATTAACGGCCTAG
DPAGT1_LOE3_3 ACAGGCCTACTTACCAGTTG
DPAGT1_LOE3_4 GTGAAATAGACCATGAGGAG
DPM1_LOE3_1 ACCAGCAGCCACACGATGAG
DPM1_LOE3_2 ACTACGACTGACTTCCAAGG
DPM1_LOE3_3 ACTCGCTACAAAGGAAATGG
DPM1_LOE3_4 GTTGAATTATACTATTGGCG
DPP6_AJ_1 TGGTGTCAGAAGTATGACCG
DPP6_AJ_2 TGATGCCGCCTGATGATCCA
DPP6_AJ_3 GTACCTCTCGGAAAACGCAG
DPP6_AJ_4 ACTTTACCACCACCGCGCCG
DYM_LOE3_1 CTTACCTGCTAGTTGGTGCA
DYM_LOE3_2 TAGTGAGTTGAAACTCTTGG
DYM_LOE3_3 AGTAGAAGCTATATCAACAA
DYM_LOE3_4 GAATGGTTCTTATTACCACC
DYNC1H1_LOE3_1 GGTATTCGCGAATGGCCCCA
DYNC1H1_LOE3_2 GTGTCATTGTCAAATAGCAG
DYNC1H1_LOE3_3 CGAGGCTGTACTGAAACCCA
DYNC1H1_LOE3_4 ACCCACCTAGTCATTTCACG
DYRK1A_LOE1_1 GAGAAACACCAATTTCCGAG
DYRK1A_LOE1_2 GATGGTTTCGAAACATAAAG
DYRK1A_LOE1_3 TTACAGGAGTACAAACCACC
DYRK1A_LOE1_4 TTTCAACCAAAATACACCCG
EEF1A2_LOE3_1 GGTGGTCTCGAACTTCCAGA
EEF1A2_LOE3_2 TGGACTTTCCGGAGTCCACG
EEF1A2_LOE3_3 TCATCATCCTGAACCACCCG
EEF1A2_LOE3_4 TGTTCTTGATGAAGTCGCGG
EFTUD2_LOE3_1 GAAGTACTACCCAACAGCCG
EFTUD2_LOE3_2 AGAGCGGCTGATCAAGCATG
EFTUD2_LOE3_3 GCTGGCTTGCGCATCTCAGA
EFTUD2_LOE3_4 GCATCGTTCAAGGTTTCCAG
EIF2AK3_LOE3_1 ACTGCAATTATGCTATCAAG
EIF2AK3_LOE3_2 ACTCAGCTCTCCTAGCCCAA
EIF2AK3_LOE3_3 TGAAATATCTAACAATGCCC
EIF2AK3_LOE3_4 CATCTTCCTGCAGATCGCAG
EIF6_dC_1 GCTCTCCGATACCATCCCCG
EIF6_dC_2 GTGCACCACGGGGATGGTAT
EOMES_LOE3_1 AATCTCCTGTCTCATCCAGT
EOMES_LOE3_2 AGAACCGTGCCACAGACCAA
EOMES_LOE3_3 CTACAATGTGTTCGTAGAGG
EOMES_LOE3_4 CTCCTAATACTGGTTCCCAC
EP300_LOE3_1 TCATGCTTCTGACAAAACCG
EP300_LOE3_2 GTATTGTTAGTTACACAACA
EP300_LOE3_3 CATATGCTCGGAAAGTTGAA
EP300_LOE3_4 GTTCTGGTAACTGTAATAAG
ERCC1_LOE1_1 ATTACGTCGCCAAATTCCCA
ERCC1_LOE1_2 GGTACCGCCCAGCTTCCTCG
ERCC1_LOE1_3 GGTGCAGGTTGTGGTAGCGG
ERCC1_LOE1_4 ATTGCGCACGAACTTCAGTA
ERCC2_LOE1_1 ACGTCCTTCCCAAAGCGCAG
ERCC2_LOE1_2 GCACCGGTCAAGGGTCCGGC
ERCC2_LOE1_3 TTTGGCGTAGGTGCTGACAA
ERCC2_LOE1_4 GATCCTGCATGCCAATGTGG
ERCC3_LOE1_1 TAGTTTGTACTCATGCACAT
ERCC3_LOE1_2 CTCAGCCAAGATGTTCCGAA
ERCC3_LOE1_3 ATGGCAACGGAGCAGCCGAT
ERCC3_LOE1_4 AAATGGCTTACTCAACCAAG
ERCC4_LOE1_1 CGACCCCTATGCTCTGACAA
ERCC4_LOE1_2 TGAAGTGGAGCCAAGATACG
ERCC4_LOE1_3 TTTCATTTGTTACACGGCGA
ERCC4_LOE1_4 ATACTTCTCTGACTCGGGAA
ERCC5_LOE1_1 TTCGGGGCTGACCTGCCGCC
ERCC5_LOE1_2 TCAGCCCCGAAGCGCTGGAA
ERCC5_LOE1_3 AGCTGCTGGAGTGCTCCGGG
ERCC5_LOE1_4 GTCAGCCCCGAAGCGCTGGA
ERCC6_LOE2_1 AGAATTGCCACTCTGAACGG
ERCC6_LOE2_2 AGACAGAATGATCCGATGAG
ERCC6_LOE2_3 CCATAAATCCATACCTACTG
ERCC6_LOE2_4 GGTACCATCCATCTTGAGAT
ERCC8_LOE1_1 GCCAAGATATAGTCATAACG
ERCC8_LOE1_2 TGATGTTCACAGATACAGTG
ERCC8_LOE1_3 TCCTGTTCTCACATTCTACA
ERCC8_LOE1_4 GTTTTTGTCCGCACGCCAAA
ESCO2_AJ_1 TTTTGGGTGCGTCCCAGTGA
ESCO2_AJ_2 GTTGCAAATAACTTGCCATC
ESCO2_AJ_3 ATTTTTTGGCTCAGAACCCG
ESCO2_AJ_4 CGTGTCCTGTCTGAACCAAT
FGFR1_LOE2_1 GTTGCCCGCCAACAAAACAG
FGFR1_LOE2_2 GCATGGTTGACCGTTCTGGA
FGFR1_LOE2_3 TCTTACCCACGACATCCAGC
FGFR1_LOE2_4 GTTCCTCCACAGGCACACCG
FOXG1_LOE2_1 AGCGCGTTGTAGCTGAACGG
FOXG1_LOE2_2 TCGAGCGACGACGTGTTCAT
FOXG1_LOE2_3 CCCGGGTCGTCGTAGTGGCG
FOXG1_LOE2_4 AGCGACGACGTGTTCATCGG
GAPDH_dC_1 TGTAGTTGAGGTCAATGAAG
GAPDH_dC_2 TGCTGGCGCTGAGTACGTCG
GMNN_AJ_1 GTATATGGCAGAGCTAATAG
GMNN_AJ_2 TCTGCATCTGGATCTCTTGT
GMNN_AJ_3 AGTGATTCAAAATTATCCAG
GMNN_AJ_4 CTCTCAGTATTGGAAGGAAG
HCCS_LOE2_1 TCATGCATCGGGCATCCTGA
HCCS_LOE2_2 GGAACGCGCCTATGAGTACG
HCCS_LOE2_3 ACATACTTACGCAGCATGAA
HCCS_LOE2_4 TCACAATCAGAATAACGAGC
HDAC8_AJ_1 ATGCTGGACATACTTGACCG
HDAC8_AJ_2 ATGCACTGCATAAGCAGATG
HDAC8_AJ_3 CTATGCAGCAGCTATAGGAG
HDAC8_AJ_4 TCCTGGGGAGAATTTGTGCA
HMGA2_AJ_1 GCGCCTCAGAAGAGAGGACG
HMGA2_AJ_2 ACTGGAGAAAAACGGCCAAG
HMGA2_AJ_3 GCCCTCTCCTAAGAGACCCA
HMGA2_AJ_4 TTATTACTCACCCATTTCCT
h_non-targeting_nC_10 GGGCCCGCATAGGATATCGC
h_non-targeting_nC_1 CGTATTCGACTCTCAACGCG
h_non-targeting_nC_2 GATGGCGCTTCAGTCGTCGG
h_non-targeting_nC_3 ACGGAGGCTAAGCGTCGCAA
h_non-targeting_nC_4 CGCTTCCGCGGCCCGTTCAA
h_non-targeting_nC_5 ATCGTTTCCGCTTAACGGCG
h_non-targeting_nC_6 GTAGGCGCGCCGCTCTCTAC
h_non-targeting_nC_7 CCATATCGGGGCGAGACATG
h_non-targeting_nC_8 TACTAACGCCGCTCCTACAG
h_non-targeting_nC_9 TGAGGATCATGTCGAGCGCC
hrrm1_dC_1 TATTGAAAGATCTTACCGAG
hrrm1_dC_2 GCGATGCATGTGATCAAGCG
hrrm1_dC_3 CCTTGTACCCCAATTCCAAT
hrrm1_dC_4 TGAGAACCTTCCAGTCCCAT
IER3IP1_LOE3_1 TCTGTAAGAACCGTGATGAG
IER3IP1_LOE3_2 GGAACAGACCAGGGAATTGG
IER3IP1_LOE3_3 CGCCATCGCAGTGCTGCACG
IER3IP1_LOE3_4 GATGGCGTTGACGCAGAGCA
IGF1_AJ_1 CGCCAGGTAGAAGAGATGCG
IGF1_AJ_2 TCCGGAGCTGTGATCTAAGG
IGF1_AJ_3 TCAGTTCGTGTGTGGAGACA
IGF1_AJ_4 GTATGGCTCCAGCAGTCGGA
IGF1R_AJ_1 TCGGGCAAGGACCTTCACAA
IGF1R_AJ_2 TGTTTCCGAAATTTACCGCA
IGF1R_AJ_3 CCGAGAGCAGCGACTCCGAG
IGF1R_AJ_4 GATTTCACAGTCAAAATCGG
IGF2_AJ_1 ACTTACTGAAGTAGAAGCCG
IGF2_AJ_2 AGCACTCCTCAACGATGCCA
IGF2_AJ_3 TGGGTGGGTAGAGCAATCAG
IGF2_AJ_4 GTAAGCAGCAATGCAGCACG
IQSEC2_LOE3_1 TTCCTGTCAGACACACCGGT
IQSEC2_LOE3_2 GCATCTACCAGCGCATCCAG
IQSEC2_LOE3_3 GGAGGCTCAATGGTAAGCAG
IQSEC2_LOE3_4 GGAATTCCCCTATCATCTGC
KAT6A_LOE2_1 TTTGGCATACGGGTGAGTGG
KAT6A_LOE2_2 TCGAGATCAAGGCAAAAATG
KAT6A_LOE2_3 CCACTCACCCGTATGCCAAA
KAT6A_LOE2_4 TAGAAAAGTAGCCAACAAGG
KAT6B_AJ_1 AACGCGCTGTGAATAATGGG
KAT6B_AJ_2 CACATGATCGACAAGAGAGA
KAT6B_AJ_3 ATGCGGTCAGTACTTCCCAT
KAT6B_AJ_4 TTTGGCATTCTGGAAAGTGG
KATNB1_LOE2_1 GCTGAACCGGAGACACCGCA
KATNB1_LOE2_2 TGATGGACCACAGGTTGACG
KATNB1_LOE2_3 CTACAAACTCGCCGTACGGG
KATNB1_LOE2_4 GAGGCTACAAACTCGCCGTA
KCNJ6_LOE3_1 GTTCATCCCGTTGAACCAGA
KCNJ6_LOE3_2 ATAGAGACAGAAACCACCAT
KCNJ6_LOE3_3 TGGTTGATCGCATACATACG
KCNJ6_LOE3_4 TCAGGACGTCGAAAGCCCAG
KDM5C_LOE3_1 CCAAGGCTACAACTTTGCCG
KDM5C_LOE3_2 GGGCTACCCGAGCCCACCGA
KDM5C_LOE3_3 GGGCCTCACGAGCCTCAGCC
KDM5C_LOE3_4 TCACCAGTGGAGGTAGTTAA
KIF11_LOE2_1 GGATTGTTCATCAATTGGCG
KIF11_LOE2_2 GACTGAATTACCTTGTTACG
KIF11_LOE2_3 TCTTATCAACAGCTCCAGAA
KIF11_LOE2_4 ACAGGAAACTCTGAGTACAT
KIF14_LOE3_1 CTTCTTCTAGCTGAGAGTGA
KIF14_LOE3_2 TAGCAATGTTGACTATTAAA
KIF14_LOE3_3 TTAGAGTAATGGTAGTAACA
KIF14_LOE3_4 TCAACATATGGTCCATAAAC
KIF5C_LOE3_1 CGAAGCGGAGATCCTCCGCG
KIF5C_LOE3_2 AGGCAAAGCAAACCGACACG
KIF5C_LOE3_3 AGATATCATGGGCAATTCGT
KIF5C_LOE3_4 CCTCTGTCCGAACATCAGTG
KMT2A_AJ_1 CTTGTGTGAACTGTACTGAG
KMT2A_AJ_2 ATATTGCGACCACCAAACTT
KMT2A_AJ_3 GGATGTGAGACAGCAACCCA
KMT2A_AJ_4 GGATCATTAAGACCCCTCGG
LARP7_AJ_1 TGAAAAAATTGACTACTGAT
LARP7_AJ_2 GTGATCACGAACAAAGGTAT
LARP7_AJ_3 AAGTCTACTGGAGATCCAAA
LARP7_AJ_4 TCTCCAGTAGACTTATAATG
LIG4_LOE1_1 GCATCTCCATGAGTTCCAGT
LIG4_LOE1_2 GACGACCTAGAACAACTTAG
LIG4_LOE1_3 TCTGCAATAGCAGCTAGCAT
LIG4_LOE1_4 TATTGGGGTAAAGGATCACG
MCM4_AJ_1 GATGTTCTAAGATTGAGTCA
MCM4_AJ_2 CTGGCTTCCTCACTTAGCCG
MCM4_AJ_3 GCTCTTGTCCTGAGTGACAA
MCM4_AJ_4 CCAAGTCGTTGCATGTATAG
MCPH1_LOE1_1 CCAGGAGATCTATCATGCCG
MCPH1_LOE1_2 GGATGACCACACTTCAACAT
MCPH1_LOE1_3 GCTCGTTTCGGTGCTCTGGG
MCPH1_LOE1_4 GAGAGGCGTAAAGCTCGTTT
MECP2_LOE1_1 GGACACGGAAGCTTAAGCAA
MECP2_LOE1_2 AGAAAAGTACAAACACCGAG
MECP2_LOE1_3 GCCAGCTAAGACTCAGCCCG
MECP2_LOE1_4 GTTGATTGCGTACTTCGAAA
MED12_AJ_1 TCCAACCTGCCCATGCCAGA
MED12_AJ_2 CTGGTCGTAAAGAACCCCAA
MED12_AJ_3 GCATTGGTAAGCGCACAGGA
MED12_AJ_4 TCAGCTGCAAGCGTTCTGGT
MED17_LOE3_1 TGAAGAGGTTAACTGTGCCG
MED17_LOE3_2 GGTTTTTCACCACAATGTGA
MED17_LOE3_3 CTAGGAATTTGGACATCAAG
MED17_LOE3_4 GCAGCACAAATCTTATTGAA
MED25_LOE3_1 AGCCTCCACTGCATTCTGCG
MED25_LOE3_2 TATCCATACCTGGTACTGCG
MED25_LOE3_3 TGATTCACGTAGACCTGGCA
MED25_LOE3_4 GGATTCAGATGAGATCCATG
MRE11A_AJ_1 GCAATCATGACGATCCCACA
MRE11A_AJ_2 TTTCAGGTGGACTATAGTGG
MRE11A_AJ_3 CAATCATGACGATCCCACAG
MRE11A_AJ_4 GTACTTTCAAACCGCAGAGA
MYCN_LOE2_1 GCACAGACTGTAGCCATCCG
MYCN_LOE2_2 GATCCGGAGGCGATTCTGCG
MYCN_LOE2_3 GGACTGGGCGGTGGAACCGG
MYCN_LOE2_4 CCCCGTTCGTTTTAATACCG
MYO18B_LOE3_1 GCTTTTGGAGAAGAGCCGCG
MYO18B_LOE3_2 TGAGATGCCGAGCATCTCCA
MYO18B_LOE3_3 GCACCAGGGCAAGGACCGGG
MYO18B_LOE3_4 CTTCCGGCTGCCGTGCCACG
NAA10_LOE3_1 AGGCTGTGAAGCGTTCCCAC
NAA10_LOE3_2 AGCCAGACCGAGGCGCCGGT
NAA10_LOE3_3 ACGAGAATGGGAAGATTGTG
NAA10_LOE3_4 CCATGGACATATCACCTCAT
NBN_LOE1_1 ATATACCTTTCAATTTGTGG
NBN_LOE1_2 GCTAAGATCAAATAAAAAAA
NBN_LOE1_3 TGTGAAAGTTCAAAAACAGG
NBN_LOE1_4 CAAGCTATATTGCAACTTGG
NDE1_LOE2_1 AGGCCTGGACGACTCCACCG
NDE1_LOE2_2 ACTGACCCCTTATCACCCAA
NDE1_LOE2_3 ATCGTACACGAGGTTCCGGC
NDE1_LOE2_4 GTAGGTCTCCCACAATGTTG
NGLY1_LOE3_1 ACTAGACTCTTGCCTGTCAG
NGLY1_LOE3_2 GAGCTTCAAACCCTACAGCT
NGLY1_LOE3_3 GGCTGTGTTTCCAATCCGGA
NGLY1_LOE3_4 CCTGTAGACCCATTTCACCT
NHEJ1_LOE3_1 GGCATTACAGTGCCAAGTGA
NHEJ1_LOE3_2 GTTGGTTTCAGATCTTCAAC
NHEJ1_LOE3_3 GGAGATTATCCAAATGACAG
NHEJ1_LOE3_4 GCAAGCTGTAGCCACGCCCA
NIPBL_LOE2_1 TTACCCTTGCTAGAATACTG
NIPBL_LOE2_2 CTTTAGCTTGATATGCAACG
NIPBL_LOE2_3 GCTCGTTCTGATTTTAACCG
NIPBL_LOE2_4 TTACTGCTGTGTCATCAAAG
NSDHL_LOE3_1 GCAAGGGTTTGATAATCCCC
NSDHL_LOE3_2 TCATGGCATTTTCGGCCCAA
NSDHL_LOE3_3 GAGGGCGTCGATATCAAGAA
NSDHL_LOE3_4 CCCACCATCCAGTAACAACA
NSUN2_AJ_1 GGATGCCTGGAATCACACAG
NSUN2_AJ_2 GCTGCGGATTGCAACACGCG
NSUN2_AJ_3 TGCTCGTCCATCAAGCCAAG
NSUN2_AJ_4 ATGTGTCTAATGAACTGCCA
OCLN_LOE3_1 GGTAACAAAGATCACCAACG
OCLN_LOE3_2 TGTTGATCTGAAGTGATAGG
OCLN_LOE3_3 ACCCACTCCTCGACATTGGG
OCLN_LOE3_4 GTAAGCTCTTGTATTCCTGT
ORC1_LOE3_1 ACAAATGCCGTTTACAGGCA
ORC1_LOE3_2 GCGTATCACTTCATGAACAG
ORC1_LOE3_3 GGAGATCACCTGTTGAAACG
ORC1_LOE3_4 ACAGTTTGTGTTGATTGAAG
ORC4_LOE3_1 GCTTGCTTCCATTAGATCTA
ORC4_LOE3_2 ATAGCATGTGTAATGACCGC
ORC4_LOE3_3 AATTCATTAAGTGTATCTGC
ORC4_LOE3_4 TCGCCCTAAAGGAAATCACA
ORC6_LOE3_1 CGGCCTCTCCGCACGCACCA
ORC6_LOE3_2 ACCTACTTACCCTGTCCAAG
ORC6_LOE3_3 GCTGGTCTCCGTGGTGCGTG
ORC6_LOE3_4 TAAACTTTCTGGTTTGAACA
PAFAH1B1_LOE1_1 CTAAAAAAAGTGGTAAACCT
PAFAH1B1_LOE1_2 ACACAGAGAATGGGTACGTA
PAFAH1B1_LOE1_3 TTTCTGTAGACTAAAAAAAG
PAFAH1B1_LOE1_4 GAATGCATCAGAACCATGCA
PCNT_LOE1_1 GATCACCGAGAAATTCAGTG
PCNT_LOE1_2 GTGATGCCAATACAACCCCA
PCNT_LOE1_3 GGATGGTCTGAAATTCAGAG
PCNT_LOE1_4 GCGCAATGTAGCCCTCAGGG
PGAP3_LOE3_1 ATACTTACAGTCGTCCCGAC
PGAP3_LOE3_2 AGTGTATGTGGGTCACCGTT
PGAP3_LOE3_3 GGTTGTAGCCATAGTCGAAG
PGAP3_LOE3_4 GTCCACAGTTTTCCACACCA
PHC1_AJ_1 ATAGGGTAAACCCCAGACAT
PHC1_AJ_2 ACACCTCCTCTACACTCCAA
PHC1_AJ_3 CATCCTGATGCCTAATGGGG
PHC1_AJ_4 TATCTTTCAGGTTCAGAACT
PIGA_LOE3_1 TCAGGGATCGATTTGCTTAG
PIGA_LOE3_2 GGATATTTCTGACAGAGTTC
PIGA_LOE3_3 GGTATATGACCGGGTATCAG
PIGA_LOE3_4 GTTTGTATCACAAAGAGACA
PLK4_LOE2_1 TCATGCACCAGATCATCACA
PLK4_LOE2_2 TTGACTGTGTCAGTGTCGAA
PLK4_LOE2_3 GTTTAATGATGGGTCCCAGT
PLK4_LOE2_4 TCATATTACGAGTCAGTAGG
PNKP_LOE2_1 GGACATATCCGGCCGACACG
PNKP_LOE2_2 ACATGACCATGTCTGACACG
PNKP_LOE2_3 CTTCAGGGCTGTCTCACACG
PNKP_LOE2_4 GCAGGCGCGCCACAACAACC
POC1A_AJ_1 CGAAGGAGAGAAGTTCACAC
POC1A_AJ_2 TCAGGTTCTCCCCCGACGGG
POC1A_AJ_3 ACTGGGGTGGAAGTCCACAT
POC1A_AJ_4 CTGTTGCCTTTTCAAGAACG
POLH_LOE1_1 GGTGAGGTTAGCTTTCCCAC
POLH_LOE1_2 AATGAGAGCAGCCATAGAGA
POLH_LOE1_3 AGGCAACTACCCAAAACCAT
POLH_LOE1_4 GTGAGCATCATAGCGGGTAA
POLR2D_dC_1 GCAGCAGAATGAGAGTGCAG
POLR2D_dC_2 GCGGGCTGGCGACGTAGAGG
PQBP1_LOE2_1 AACACCTTGTACCAGCTTGG
PQBP1_LOE2_2 ACAGCGGGCTCCCTTACTAC
PQBP1_LOE2_3 GTGTCTGCATTCCAGTAGTA
PQBP1_LOE2_4 GTCTGCAGCGCAACGGGCAG
PRKDC_AJ_1 GTATGAATAGCTCTGAACTC
PRKDC_AJ_2 GTTGAAGTCCAGGTTTACAA
PRKDC_AJ_3 TATTGACAAAGCTATGCACG
PRKDC_AJ_4 GCTTGTTATTAATACAGAAG
PRPF19_dC_1 GCGCTCCGTGGTTAGCACAG
PRPF19_dC_2 GCTCATCGAGAAGTACATTG
PTEN_pC_2 ATATCACCACACACAGGTAA
PTEN_pC_3 ACTTTGATATCACCACACAC
PTEN_pC_4 CTCCCGCTCCTGGAGCGGGG
PYCR2_LOE3_1 CTATTATCTTGTGAGCCGAC
PYCR2_LOE3_2 GACACGCAGCAACAAGGAGA
PYCR2_LOE3_3 TCACCGTGTTCGGATACAGG
PYCR2_LOE3_4 GCGCCTGGCAATCCAACTCG
QARS_LOE3_1 GTGACTCCGCACATACTCAA
QARS_LOE3_2 GAGGCTAGTGAAGAGCGACA
QARS_LOE3_3 ACTTGACTCGATAGGCTACA
QARS_LOE3_4 AGGCTGCTATTAACAGGCAC
RAB18_LOE3_1 AGGAGTCAAACTGTCACACA
RAB18_LOE3_2 TTAACTCCCAGCTATTATAG
RAB18_LOE3_3 TTTGCTTACGTATTGCCACA
RAB18_LOE3_4 GGCACATTGTACACCATCAC
RAB3GAP1_LOE3_1 GGAGATGGCAAATTTAAGGC
RAB3GAP1_LOE3_2 AATTAGGTACATCGGCAGAG
RAB3GAP1_LOE3_3 TTTAGCTAAATTAGGTACAT
RAB3GAP1_LOE3_4 TCTTGAAATGCGTTTCCGAT
RAB3GAP2_LOE3_1 TTATGTACGCTTCTACACTG
RAB3GAP2_LOE3_2 AACATGCGCAGACAACAGAG
RAB3GAP2_LOE3_3 TGTCTCAGTATATCACTGTA
RAB3GAP2_LOE3_4 GCTGGGAGTACGTTGTTCAG
RAD21_AJ_1 TAAATTACACTCGAACACAT
RAD21_AJ_2 TTGACCTGCCTGAGGAAAAT
RAD21_AJ_3 CTTCATTACAGTCTGCAAGA
RAD21_AJ_4 TCTGTTCAGACTCTAATAGG
RAD50_LOE3_1 GTCCAACCAAAATTGTAAGG
RAD50_LOE3_2 GCGGAATTATAACTACCGAG
RAD50_LOE3_3 TATTTCCTTTGGTTCCAGGA
RAD50_LOE3_4 CGGCAGGTACGTCAGACACA
RAN_dC_1 AGTCAAATGACGTTTCACGA
RAN_dC_2 ATGAACCTCAACACCCAAGG
RARS2_LOE3_1 TCGAATTTCTAGAAAAGGAA
RARS2_LOE3_2 ATTCCCTGTACTACTCCAAA
RARS2_LOE3_3 GTCATCTTTTAGGTTCGACG
RARS2_LOE3_4 GTGCCCTTTGGAGTAGTACA
RBBP8_LOE3_1 GATTCGTTCCTGTTTTAGCT
RBBP8_LOE3_2 CGTTGCCCAAAGATTCCCCA
RBBP8_LOE3_3 TCAGTCTCCAAACTTTCACG
RBBP8_LOE3_4 TCACCAAAAATCAACAGCTG
RBM10_AJ_1 AAGCAGACCCAACTGAACCG
RBM10_AJ_2 GCAGGACGCTACACGATGGA
RBM10_AJ_3 GTCTTGCCGTCGATAGTGAG
RBM10_AJ_4 CACCATGGATTCCATCCTGG
RGS6_LOE3_1 CAGGTACTGACATTGTGCAG
RGS6_LOE3_2 AAGCTCTTGACTGTTCTGAT
RGS6_LOE3_3 GGGAGCCTTATCGCTGCCCA
RGS6_LOE3_4 ACAGTTGAAGCAATACACTT
RIMS3_LOE3_1 CGAGCTGGACCTCAGCGCCG
RIMS3_LOE3_2 CAGGCTCTGCTCTTTGACGA
RIMS3_LOE3_3 TGAAGCACTTGTGGTCCATG
RIMS3_LOE3_4 GGTTCATCTTCCCCACTACC
RPL32_dC_1 CGTAACTGGCGGAAACCCAG
RPL32_dC_2 AGGGTTCGTAGAAGATTCAA
RPL3_dC_1 ATGGAGATCCAGGTGAACGG
RPL3_dC_2 GAAGGTGAAGAGCTTCCCTA
RPS19_dC_1 GGAAGCTGAAAGTCCCCGAA
RPS19_dC_2 GCTGAAAGTCCCCGAATGGG
RPS6KA3_LOE2_1 CCAGAAGTAGTTAATCGTCG
RPS6KA3_LOE2_2 ATTCAGCTTTTCAAACTGAA
RPS6KA3_LOE2_3 ATGCACATCAGCTTTTTCGG
RPS6KA3_LOE2_4 TTGTGCGTGAAGATATTCAA
RTTN_LOE3_1 ACGCTGATCTCATTCAGGAG
RTTN_LOE3_2 GCATCGCCTGGCATTACAGT
RTTN_LOE3_3 GCTCCAGTGTCTGAAAGTGA
RTTN_LOE3_4 GTGACTCTGTCAGAACCAAG
SHH_LOE1_1 GAGGAGTCTCTGCACTACGA
SHH_LOE1_2 AGAGGAGTCTCTGCACTACG
SHH_LOE1_3 GCGCCAGCGGAAGGTATGAA
SHH_LOE1_4 GCTGCGGTCGCGGTCAGACG
SIN3A_LOE3_1 TCTGCAATAGGAATCTACGG
SIN3A_LOE3_2 ATCCCATTATCAGATCGGGG
SIN3A_LOE3_3 GGAGTCCGTCCTGTACACTT
SIN3A_LOE3_4 TTAGCGCTGGTCAGACTACG
SLC1A4_LOE3_1 GAGTTGCGCAATGAACACCG
SLC1A4_LOE3_2 CACCATTGCCATTATCCTGG
SLC1A4_LOE3_3 AACAACGTAGAGCTCAACGC
SLC1A4_LOE3_4 GTCATGAGTAGGCAGCCCAA
SLC25A19_LOE2_1 GGGTGCGCAGAACATCCACG
SLC25A19_LOE2_2 GCTCACCCTGAGCTGCAAAG
SLC25A19_LOE2_3 CCCCAGGTACGGAGATACAA
SLC25A19_LOE2_4 GACCCCAGCGCAAAGTACCA
SLC25A22_LOE3_1 GTACATGCCGAAGTAGCCCT
SLC25A22_LOE3_2 ACTTCTCCTCGGACGCCGGG
SLC25A22_LOE3_3 CGGCTCCAGTCACTTCAGCG
SLC25A22_LOE3_4 GCAGGTGACACCGATCAGCC
SLC2A1_AJ_1 GGAGTTCTACAACCAGACAT
SLC2A1_AJ_2 GGATGCTCTCCCCATAGCGG
SLC2A1_AJ_3 GATGTCCTATCTGAGCATCG
SLC2A1_AJ_4 TGGCCCGGTTCTCCTCGTTG
SLC9A6_AJ_1 AGTTCTATGCTGAGACTCCG
SLC9A6_AJ_2 GAGATACTGCCACTTATGCA
SLC9A6_AJ_3 AATTAAGTAAGAGGGACAAG
SLC9A6_AJ_4 GTCACATTATTTACATCACT
SMARCAL1_AJ_1 AGTATTTGCACACCATAAGG
SMARCAL1_AJ_2 TTGTCTTTTAGGTTGCCAAG
SMARCAL1_AJ_3 GAAGTACTCACCTTTAGGAC
SMARCAL1_AJ_4 CATCGCAGCCTTTTACCGGA
SMC1A_LOE2_1 GCAGGAGTTTGAAGAACGGA
SMC1A_LOE2_2 GCTGGCATAGGTCAATGAGG
SMC1A_LOE2_3 CCATCGCAAGAAAAATATTG
SMC1A_LOE2_4 GTACAAGATCAACAACAAAG
SMC3_LOE2_1 GTCATCTTCGTCCAGAACAG
SMC3_LOE2_2 GCTGGTTTATTCCTTTTCGA
SMC3_LOE2_3 AAAATTTTAGATCAACCAGA
SMC3_LOE2_4 TTTTTCTTAGATCGATAAAG
SMU1_dC_1 AGTAGTCTCCTCCTGCAAGG
SMU1_dC_2 TGTGCTCGATTTTCTCCAGA
SNAP29_LOE2_1 GTACGAGTCCGAGAAGGTTG
SNAP29_LOE2_2 CCTCATGTACGAGTCCGAGA
SNAP29_LOE2_3 TGACCGGCTGACAACCAAAG
SNAP29_LOE2_4 GCATTAAGAGCGTGTTTGGG
SNRPF_dC_1 GCTTTCCTGTTAGTCCATTG
SNRPF_dC_2 GGCTATCTGGTATCTGTAGA
SPOCK1_LOE3_1 TAGCCCTCCTCATTACACCG
SPOCK1_LOE3_2 TGACTCTGTTCGCATCCTCG
SPOCK1_LOE3_3 GAGTCACACGAGTTGAAAAG
SPOCK1_LOE3_4 TGGCCACTCCTACACATCCA
SRCAP_AJ_1 GATAGCGCCAGTTCTTGCGA
SRCAP_AJ_2 CATGGTGATCCGGCACCACG
SRCAP_AJ_3 TTTACTGCGCCGAGTTAAGG
SRCAP_AJ_4 TCTGGGCTCCATAGTAAGTG
SSR4_LOE3_1 GGAGATCTCCACAATGAAGA
SSR4_LOE3_2 GCCTCTGTTTACAGTCAGCG
SSR4_LOE3_3 ATGGCTCTCTATGCTGACGT
SSR4_LOE3_4 AGCACCTCAGTGGACACCCA
STAMBP_LOE2_1 TGGAGTTGAGATTATCCGAA
STAMBP_LOE2_2 GTCGACACTGGAGAGAAACG
STAMBP_LOE2_3 AATGGCATCCATTTACTCTG
STAMBP_LOE2_4 CTAGCGGGCCACCTAGGCCA
STIL_LOE1_1 ATGACGATAAGCAAGTCGGA
STIL_LOE1_2 CATGGAATCAAAAAGTCCCC
STIL_LOE1_3 TCTGTTTAGATATCTTACCA
STIL_LOE1_4 GCGCTCAGTTCACAACGGAT
TAF2_LOE3_1 GTACCGCCATTGGATTAAAG
TAF2_LOE3_2 TTTGTGGTACCCAGTGTAGA
TAF2_LOE3_3 GATGTGGTATGTTTCAGCAA
TAF2_LOE3_4 GTTGATGCAGACGACCTGAT
TagBFP_nC_1 AGTCACCACATACGAAGACG
TagBFP_nC_2 AAGTGCACATCCGAGGGCGA
TagBFP_nC_3 CACGCCCCCGTCTTCGTATG
TagBFP_nC_4 GAGACGCTGTACCCCGCTGA
TBC1D20_AJ_1 ACGTCCGGCGGTCATTGCGG
TBC1D20_AJ_2 GGATTCACCTGGTCAATGAT
TBC1D20_AJ_3 CTACTGGTACCTGAGGTGGT
TBC1D20_AJ_4 AAGTGTTGCTGGACGTCCGG
TBCD_LOE3_1 ACAGTTGGATCTGTTACCGA
TBCD_LOE3_2 CCTTTGTGACTGCAATCTCG
TBCD_LOE3_3 TGATCTTCATGGTAACCAGG
TBCD_LOE3_4 ACAGCTGACTATTTTGCCGT
TCF4_AJ_1 GCAAGACACGAAATCTTCGG
TCF4_AJ_2 TGGCGAGTCCCTATTGTAGT
TCF4_AJ_3 ATGTCCACTTTCCATCGTAG
TCF4_AJ_4 TGCGTGTCTGAAAAGAAGGG
tomato_nC_1 GCGGCCCCCTGCCCTTCGCC
tomato_nC_2 TCACCTTCAGCTTGGCGGTC
tomato_nC_3 GAAGAAGACCATGGGCTGGG
tomato_nC_4 GCATTACGGGGCCGTCGGGG
TRAIP_AJ_1 GGTTTGAGACAGCACCAAGT
TRAIP_AJ_2 CTTCGATCACTCCCGCGACG
TRAIP_AJ_3 GCTCCAAACTCACTGCTCCA
TRAIP_AJ_4 TCGACCGCCTGGTTTTAGAG
TRAPPC9_LOE2_1 CGACATGTGGTAATGCACCA
TRAPPC9_LOE2_2 CCAATTATCGCACACAACCG
TRAPPC9_LOE2_3 GTACCCAGTGACGCTCGTCG
TRAPPC9_LOE2_4 AATTGGTCTACGCCTCCCGA
TRIO_LOE3_1 TTACATCCATACATTCCCGG
TRIO_LOE3_2 TCTCGGCTAGACGTACCTGT
TRIO_LOE3_3 GATCTGCCTGATCTGCTGCG
TRIO_LOE3_4 CCCACAGTGCAAATTTGCAA
TRMT10A_LOE3_1 ATGTTACGCAGAAAACCGAC
TRMT10A_LOE3_2 TTTTACTTGACAAGCCACGG
TRMT10A_LOE3_3 TACTAATCCTCCAATCACAT
TRMT10A_LOE3_4 ACTATCTTGCCCCAACGGAA
TUBB2B_LOE3_1 TGCCTGGCTCCAGATCCACG
TUBB2B_LOE3_2 CTTTCTGTCTCAGTTTTGGG
TUBB2B_LOE3_3 CTAACCGAATCCATCGTGCC
TUBB2B_LOE3_4 GGATCTGGAGCCAGGCACGA
TUBB_LOE3_1 ACTGCCATCTTGAGGCCACG
TUBB_LOE3_2 ACTGCATTCCAGGTCAGTCT
TUBB_LOE3_3 CCACGGCCGATACCTCACCG
TUBB_LOE3_4 GTCTGGGGCAGGTAACAACT
TUBG1_LOE3_1 AGATGGTAGTGACAGTCTAG
TUBG1_LOE3_2 CGGCTACACCCCTCTCACTA
TUBG1_LOE3_3 TGTCATTCAGCCGTTCTAAG
TUBG1_LOE3_4 GTGCCTGAGGAGCGATGCCG
TUBGCP6_LOE2_1 TGTTTTATAGCGACGCCAGG
TUBGCP6_LOE2_2 CACCTGGCAGAGCGAAAACG
TUBGCP6_LOE2_3 ACAACTCACGGTGGGAAACG
TUBGCP6_LOE2_4 TGTCGGATGTGGTTTCCACC
UBE3A_LOE1_1 CTAGCTAGAGATGATCGCTA
UBE3A_LOE1_2 CTACTACCACCAGTTAACTG
UBE3A_LOE1_3 GCTTACCTTGAGAACTCGAA
UBE3A_LOE1_4 CTATGCAAATGTAGTGGGAG
VPS13B_LOE2_1 AGTGAAAGCTGTAGATCCGA
VPS13B_LOE2_2 GTATTGATGGTAATTACCAC
VPS13B_LOE2_3 CAAAATCATCAATCAAACCG
VPS13B_LOE2_4 GCTCTCAGCAGTACTACGGT
VRK1_LOE3_1 GTAGGATTACCCATTGGCCA
VRK1_LOE3_2 ACGAGCATCGATGCACACAA
VRK1_LOE3_3 TTTGTAGCCCCATCAAGACG
VRK1_LOE3_4 GCTCTGGTTTTGCAGCTCGT
WDR4_LOE3_1 TGTGGTCTACATCTTCCAGC
WDR4_LOE3_2 GCTGAAGCTGGCGTCTGCGC
WDR4_LOE3_3 ACAGGCCTGTGGGCGACCAG
WDR4_LOE3_4 GAGTTGGCACCACGGAGATA
WDR62_LOE1_1 GTCCAAGTTCCAGAAGCGAA
WDR62_LOE1_2 GCCCAAGCCACACTACCTTG
WDR62_LOE1_3 CTATGCGCGGAACGATGCAG
WDR62_LOE1_4 GCTGTGGTCCTTATACACGC
WDR73_LOE2_1 CCAGGGCTGCGATTCTCCAA
WDR73_LOE2_2 AGAGATTTCAAAGTGCGCCA
WDR73_LOE2_3 GAGTGAAGAGAGGTTCTACT
WDR73_LOE2_4 CACACCTGCAGATAACAACC
WWOX_LOE3_1 CCAAGATCACATGTGCACCA
WWOX_LOE3_2 GTTTTTTAACAGTCACACCG
WWOX_LOE3_3 GTTTCTTGTTCCCATCCGTA
WWOX_LOE3_4 CCATCTTTGGTGAGACTCCA
XPA_LOE1_1 AATCCACATCATTCACAATG
XPA_LOE1_2 AGAAGCAAAGGAAGTCCGAC
XPA_LOE1_3 TACCTGCAGTTATCACAAGT
XPA_LOE1_4 CTCGATACTCGCCCGCACCG
XPC_LOE1_1 CTTCTCCACGACAATACCCA
XPC_LOE1_2 TGATGGATACATCGTCTGCG
XPC_LOE1_3 GTTTGGCTACTGGCAGACAG
XPC_LOE1_4 ACATTCCCAAACTCGTTCCG
XRCC4_AJ_1 AGAAGCTGATGACATGGCAA
XRCC4_AJ_2 TCAGTACTCTCATCATAGAC
XRCC4_AJ_3 TGTAGAAAATGAGTTATACT
XRCC4_AJ_4 TGTGAGTGCTAAGGAAGCTT
ZBTB18_LOE3_1 GCTGGTCCTTGTAAAAGAGG
ZBTB18_LOE3_2 TGTGACTGCACTGTTCTGGT
ZBTB18_LOE3_3 CAAGCAGGAGAGCGAAAGCG
ZBTB18_LOE3_4 GCACATGAAGATCTGGCCCG
ZEB2_LOE3_1 GCAAACGTGTAGCTACAGAG
ZEB2_LOE3_2 GTCTGTTCAGACATACTAAG
ZEB2_LOE3_3 GTATTCCTACTGCAAGCGGG
ZEB2_LOE3_4 TGTTTGCGCCTCTTGCACCGcut -f 2 gRNAs.txt |perl -pe "s/\n//g" |fold -w 60|sed '1i >gRNAs' > gRNAs.fastaecho "##description: gRNAs from Simon Haendeler" > gRNAs.gtf
echo "##provider: Jean Elbers" >> gRNAs.gtf
echo "##format: gtf" >> gRNAs.gtf
echo "##date: 2021-04-01" >> gRNAs.gtf
i=1
while read a b
do
let k=`echo $b|wc -L`
let j=$i+$k-1
echo -e "gRNAs\tMAKER\tgene\t${i}\t${j}\t.\t+\t.\tgene_id \"${a}\"; gene_name \"${a}\";" >> gRNAs.gtf
echo -e "gRNAs\tMAKER\ttranscript\t${i}\t${j}\t.\t+\t.\tgene_id \"${a}\"; transcript_id \"${a}.T\"; gene_name \"${a}\";" >> gRNAs.gtf
echo -e "gRNAs\tMAKER\texon\t${i}\t${j}\t.\t+\t.\tgene_id \"${a}\"; transcript_id \"${a}.T\"; gene_name \"${a}\";" >> gRNAs.gtf
let i=$i+$k
done < gRNAs.txtversion
/nfs/scistore16/itgrp/jelbers/git/STAR/source/STAR
Usage: STAR [options]... --genomeDir /path/to/genome/index/ --readFilesIn R1.fq R2.fq
Spliced Transcripts Alignment to a Reference (c) Alexander Dobin, 2009-2022
STAR version=2.7.10a_alpha_220601
STAR compilation time,server,dir=2022-06-23T13:48:46+02:00 :/nfs/scistore16/itgrp/jelbers/git/STAR/source
For more details see:
<https://github.com/alexdobin/STAR>
<https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf>
To list all parameters, run STAR --helpmake index
/nfs/scistore16/itgrp/jelbers/git/STAR/source/STAR \
--genomeChrBinNbits 9 --genomeSAindexNbases 7 \
--runMode genomeGenerate \
--genomeDir snakemake2/genome-star-idx \
--genomeFastaFiles gRNAs.fasta \
--sjdbGTFfile gRNAs.gtf \
--sjdbOverhang 19cd snakemake2
bgzip -@24 SRR11868220-orig-I1.fastq && \
bgzip -@24 SRR11868220-orig-I2.fastq && \
bgzip -@24 SRR11868220-orig-R1.fastq &
paste -d "" \
<(bgzip -cd -@4 SRR11868220-orig-I1.fastq.gz|paste - - - - |cut -f 1,2|awk '{print $1,"\n",$2,"\n+","\n","???????????";}' OFS='') \
<(bgzip -cd -@4 SRR11868220-orig-I2.fastq.gz|paste - - - - |cut -f 2|awk '{print "\n",$1,"\n","\n","?????????";}' OFS='') \
<(bgzip -cd -@4 SRR11868220-orig-R1.fastq.gz|paste - - - - |cut -f 2,4|awk '{print "\n",$1,"\n\n",$2;}' OFS='') | \
bgzip -c -@ 24 > SRR11868220-orig-I1-I2-R1.fastq.gz &see https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file
so you can recreate the conda environment, using crispr-licht-04May2022.yml below
mamba create -n crispr-licht-04May2022 -c bioconda -c conda-forge bioconductor-deseq2=1.32.0 \
r-data.table r-dplyr r-optparse r-reshape2 r-MASS r-doparallel r-foreach
conda activate crispr-licht-04May2022
conda env export |sed '$ d' > crispr-licht-04May2022.ymlcrispr-licht-04May2022.yml Click to expand!
name: crispr-licht-04May2022
channels:
- bioconda
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- _r-mutex=1.0.1=anacondar_1
- binutils_impl_linux-64=2.36.1=h193b22a_2
- binutils_linux-64=2.36=hf3e587d_9
- bioconductor-annotate=1.70.0=r41hdfd78af_0
- bioconductor-annotationdbi=1.54.0=r41hdfd78af_0
- bioconductor-biobase=2.52.0=r41hd029910_0
- bioconductor-biocgenerics=0.38.0=r41hdfd78af_0
- bioconductor-biocparallel=1.26.0=r41h399db7b_0
- bioconductor-biostrings=2.60.0=r41hd029910_0
- bioconductor-delayedarray=0.18.0=r41hd029910_0
- bioconductor-deseq2=1.32.0=r41h399db7b_0
- bioconductor-genefilter=1.74.0=r41hba52eb8_0
- bioconductor-geneplotter=1.70.0=r41hdfd78af_0
- bioconductor-genomeinfodb=1.28.0=r41hdfd78af_0
- bioconductor-genomeinfodbdata=1.2.7=r41hdfd78af_1
- bioconductor-genomicranges=1.44.0=r41hd029910_0
- bioconductor-iranges=2.26.0=r41hd029910_0
- bioconductor-keggrest=1.32.0=r41hdfd78af_0
- bioconductor-matrixgenerics=1.4.0=r41hdfd78af_0
- bioconductor-s4vectors=0.30.0=r41hd029910_0
- bioconductor-summarizedexperiment=1.22.0=r41hdfd78af_0
- bioconductor-xvector=0.32.0=r41hd029910_0
- bioconductor-zlibbioc=1.38.0=r41hd029910_0
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- fontconfig=2.14.0=h8e229c2_0
- fonts-conda-ecosystem=1=0
- fonts-conda-forge=1=0
- freetype=2.10.4=h0708190_1
- fribidi=1.0.10=h36c2ea0_0
- gcc_impl_linux-64=10.3.0=hf2f2afa_16
- gcc_linux-64=10.3.0=hc39de41_9
- gettext=0.19.8.1=h73d1719_1008
- gfortran_impl_linux-64=10.3.0=h73f4979_16
- gfortran_linux-64=10.3.0=hb09a455_9
- graphite2=1.3.13=h58526e2_1001
- gsl=2.7=he838d99_0
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- gxx_linux-64=10.3.0=h2593f52_9
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- libcblas=3.9.0=14_linux64_openblas
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- libev=4.33=h516909a_1
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- libiconv=1.16=h516909a_0
- liblapack=3.9.0=14_linux64_openblas
- libnghttp2=1.47.0=he49606f_0
- libopenblas=0.3.20=pthreads_h78a6416_0
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- libssh2=1.10.0=ha35d2d1_2
- libstdcxx-devel_linux-64=10.3.0=he6cfe16_16
- libstdcxx-ng=11.2.0=he4da1e4_16
- libtiff=4.3.0=h542a066_3
- libuuid=2.32.1=h7f98852_1000
- libwebp-base=1.2.2=h7f98852_1
- libxcb=1.13=h7f98852_1004
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- lz4-c=1.9.3=h9c3ff4c_1
- make=4.3=hd18ef5c_1
- ncurses=6.3=h27087fc_1
- openssl=3.0.3=h166bdaf_0
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- r-bh=1.78.0_0=r41hc72bb7e_0
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- r-fastmap=1.1.0=r41h03ef668_0
- r-foreach=1.5.2=r41hc72bb7e_0
- r-formatr=1.12=r41hc72bb7e_0
- r-futile.logger=1.4.3=r41hc72bb7e_1003
- r-futile.options=1.0.1=r41hc72bb7e_1002
- r-generics=0.1.2=r41hc72bb7e_0
- r-getopt=1.20.3=r41ha770c72_2
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- r-glue=1.6.2=r41h06615bd_0
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- r-httr=1.4.3=r41hc72bb7e_0
- r-isoband=0.2.5=r41h03ef668_0
- r-iterators=1.0.14=r41hc72bb7e_0
- r-jsonlite=1.8.0=r41h06615bd_0
- r-labeling=0.4.2=r41hc72bb7e_1
- r-lambda.r=1.2.4=r41hc72bb7e_1
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- r-matrixstats=0.62.0=r41h06615bd_0
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- r-nlme=3.1_157=r41h8da6f51_0
- r-openssl=2.0.0=r41h1f3e0c5_0
- r-optparse=1.7.1=r41hc72bb7e_0
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- r-ps=1.7.0=r41h06615bd_0
- r-purrr=0.3.4=r41hcfec24a_1
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- r-rcurl=1.98_1.6=r41hcfec24a_0
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- r-reshape2=1.4.4=r41h03ef668_1
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- r-stringi=1.7.6=r41h30a9eb7_2
- r-stringr=1.4.0=r41hc72bb7e_2
- r-survival=3.3_1=r41h06615bd_0
- r-sys=3.4=r41hcfec24a_0
- r-testthat=3.1.4=r41h7525677_0
- r-tibble=3.1.7=r41h06615bd_0
- r-tidyselect=1.1.2=r41hc72bb7e_0
- r-utf8=1.2.2=r41hcfec24a_0
- r-vctrs=0.4.1=r41h7525677_0
- r-viridislite=0.4.0=r41hc72bb7e_0
- r-waldo=0.4.0=r41hc72bb7e_0
- r-withr=2.5.0=r41hc72bb7e_0
- r-xml=3.99_0.9=r41h06615bd_0
- r-xtable=1.8_4=r41hc72bb7e_3
- readline=8.1=h46c0cb4_0
- sed=4.8=he412f7d_0
- sysroot_linux-64=2.12=he073ed8_15
- tk=8.6.12=h27826a3_0
- tktable=2.10=hb7b940f_3
- xorg-kbproto=1.0.7=h7f98852_1002
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- xorg-libsm=1.2.3=hd9c2040_1000
- xorg-libx11=1.7.2=h7f98852_0
- xorg-libxau=1.0.9=h7f98852_0
- xorg-libxdmcp=1.1.3=h7f98852_0
- xorg-libxext=1.3.4=h7f98852_1
- xorg-libxrender=0.9.10=h7f98852_1003
- xorg-libxt=1.2.1=h7f98852_2
- xorg-renderproto=0.11.1=h7f98852_1002
- xorg-xextproto=7.3.0=h7f98852_1002
- xorg-xproto=7.0.31=h7f98852_1007
- xz=5.2.5=h516909a_1
- zlib=1.2.11=h166bdaf_1014
- zstd=1.5.2=ha95c52a_0this script calculates p values like the original
paper but uses a negative binomial model
with size factors and dispersion estimated
with DESeq2 can include things such as batch
CRISPR-LICHT-pvalue-8July2022.R Click to expand!
library(DESeq2)
library(data.table)
library(dplyr)
library(optparse)
library(reshape2)
library(MASS)
library(doParallel)
library(foreach)
#we first register a backend specifying the number
# of cores that we wish to use
registerDoParallel(cores = 32)
# print session info
sessionInfo()
# define parameters
option_list = list(
make_option(c("-f", "--file"), type="character", default="countMatrix.csv",
help="path to input file holding the simulated cell lineage counts [default= %default]", metavar="character"),
make_option(c("-o", "--out"), type="character", default="results_list.csv",
help="path to output file holding the results of the diff. caling [default= %default]", metavar="character"),
make_option(c("-b", "--bootstrap"), type="integer", default="10000",
help="number of re-sampling during bootstrapping [default= %default]", metavar="character"),
make_option(c("-l", "--minLCC"), type="integer", default="4",
help="lower lineage cell count threshold to remove dead lineages [default= %default]", metavar="character"),
make_option(c("-u", "--maxLCC"), type="integer", default="Inf",
help="upper lineage cell count threshold to remove dead lineages [default= %default]", metavar="character"),
make_option(c("-s", "--nTstring"), type="character", default="cntrl",
help="regex string to match non-tageting controls (gene names) [default= %default]", metavar="character"),
make_option(c("-w", "--winzor"), type="double", default="0.1",
help="quantile remove prior to get trimmed means [default= %default]", metavar="character")
);
opt_parser = OptionParser(option_list=option_list, add_help_option=TRUE);
opt = parse_args(opt_parser);
if(is.na(opt$maxLCC)){
opt$maxLCC <- Inf
}
print(opt)
R = opt$bootstrap # number of repeated bootstrap sampling steps
minLCC = opt$minLCC # minimal number of cells per lineage to be considererd (e.g.: 4)
maxLCC = opt$maxLCC # maximal number of cells per lineage to be considererd (e.g.: 17000)
nTstring = opt$nTstring # string specifying non-targeting gRNA data in count matrix
winzorizer = opt$winzor # remove top and bottom percent to take means (makes it more robust against outlier)
# define functions
fishers_method <- function(x) return(pchisq(-2*sum(log(x)),df = 2*length(x),lower.tail=FALSE))
# read in count matrix
test <- read.table(opt$file,header=T, row.names="Gene") #read in the empirical data
test3 <- test[,colSums(test) > minLCC & colSums(test) < maxLCC ]
colnames(test3) <- as.integer(as.factor(colnames(test3)))
cts <- as.matrix(test3) # convert data to matrix
coldata <- data.frame("condition"=rep(1,length(test3[1,]))) # setup coldata
row.names(coldata) <- names(test3[1,]) # setup row names for column data
coldata$condition <- factor(coldata$condition) # make condition a factor
dds <- DESeqDataSetFromMatrix(countData = cts, # make a DESeq2 data set, a single condition
colData = coldata,
design = ~ 1)
dds <- estimateSizeFactors(dds, type="poscounts") # estimate size factors with DESeq2
dds <- estimateDispersions(dds) # estimate final dispersion values for all genes
dispersion <- as.numeric(paste(dds@dispersionFunction(1)))
row.data <- as.data.frame(rowData(dds)) # extract the DESeq2 dispersion and mean values
test <- read.table(opt$file,header=T)
test2 <- melt(test,id.vars="Gene") # convert from wide to long format
dta1 <- data.frame("gene.id"=gsub("_\\d+$","",test2[,1],perl=T), # convert ACTG1_AJ_1 to ACTG1_AJ (assumes GeneName_effect_gRNAnumber) # in real(our) data, effect would not be known or included
"grna.id"=test2[,1], # adds ACTG1_AJ_1 for example
"lid.id"=as.integer(test2[,2]), # converts the lineage*sample barcode (20-bp) to an integer such as 1,2,3,etc
"cell.count"=test2[,3], # add the cell count data
"batch"=rep(1,length(test2[,1])))
# remove lineages with less/more, min/max cellcount-threshold
dta <- subset(dta1, cell.count > minLCC & cell.count < maxLCC)
# get list of genes from count matrix (all but the non-targeting gRNA)
#genes <- grep(nTstring, unique(dta$gene.id), invert = TRUE, value = TRUE)
genes <- unique(dta$gene.id)
# get a list of non-targeting gRNA
genes2 <- grep(nTstring, unique(dta$gene.id), invert = FALSE, value = TRUE)
# make dummy data.frame
results <- data.frame(gene = character(), logFC = double(), pval = double(), pvals = character(), stringsAsFactors=FALSE)
# loop over all genes
results <- foreach (gene.idx=1:length(genes), .combine=rbind) %dopar% {
# get gene name
gene <- genes[gene.idx]
# subset data to current gene and controls
gene.data <- dta[grepl(paste0(nTstring,"|",gene), dta$gene.id),]
# get gRNAs names for current gene
if( length(grep(nTstring, gene)) > 0){
gRNAs <- grep(gene, unique(gene.data$grna.id), invert = FALSE, value = TRUE)
}else {
gRNAs <- grep(nTstring, unique(gene.data$grna.id), invert = TRUE, value = TRUE)
}
# make dummy data.frames
lowerTail.pvals <- rep(NA, length(gRNAs))
logFCs <- rep(NA, length(gRNAs))
# loop over all gRNA of that gene
for (grna.idx in 1:length(gRNAs)){
# get gRNA name
gRNA <- gRNAs[grna.idx]
# subset data to current gRNA and controls
gRNA.data <- gene.data[grepl(paste0(nTstring,"|",gRNA), gene.data$grna.id),]
# get number of observed lineages of this gRNA)
lineage.count <- length(gRNA.data[gRNA.data$grna.id == gRNA,]$lid.id)
# lineages
mean <- mean(gRNA.data[gRNA.data$grna.id == gRNA,]$cell.count, na.rm = FALSE, trim = winzorizer)
# sample R-1 time lineage.count lineages from the non-targeting background lineages and determine their means
means <- rep(NA, R-1)
if (length(gRNA.data[grepl(nTstring, gRNA.data$grna.id),]$cell.count) < lineage.count){
print("lineage.count > nTstring cell count, setting lineage.count = nTstring cell count")
lineage.count = length(gRNA.data[grepl(nTstring, gRNA.data$grna.id),]$cell.count)
}
c = 0
repeat{
c = c + 1
means[c] <- mean(sample(gRNA.data[grepl(nTstring, gRNA.data$grna.id),]$cell.count, lineage.count), na.rm = FALSE, trim = winzorizer)
if (c == (R - 1)){
break
}
}
# determine empirical p-value by using negative binomial generalized linear model
# make a new data.frame to add column saying if just the controls are Control:yes
gRNAcontrol.data <- gRNA.data[grepl(nTstring, gRNA.data$grna.id),]
gRNAcontrol.data$Control <- rep("yes",length(gRNAcontrol.data$grna.id))
# make a new data.frame to add a column saying if just the gRNA is a Control:no
gRNAobserved.data <- gRNA.data[grepl(gRNA, gRNA.data$grna.id),]
gRNAobserved.data$Control <- rep("no",length(gRNAobserved.data$grna.id))
# combine the two data.frames from the previous steps
gRNA2.data <- rbind(gRNAobserved.data, gRNAcontrol.data)
# make these things as factors
gRNA2.data$lid.id <- as.factor(gRNA2.data$lid.id)
gRNA2.data$Control <- as.factor(gRNA2.data$Control)
gRNA2.data$batch <- as.factor(gRNA2.data$batch)
# add on size factors from DESeq2
size.factors <- rep('NA',length(gRNA2.data$lid.id))
for (size.factor in 1:length(gRNA2.data$lid.id)){
size.factors[size.factor] <- sizeFactors(dds)[gRNA2.data$lid.id[size.factor]]
}
gRNA2.data$size.factors <- as.numeric(size.factors)
# now do a generalized linear model with a negative binomial function
# and return the pvalue of the Control coefficient
# if more than one batch then add batch to the model
lowerTail.pvals[grna.idx] <- tryCatch(as.numeric(paste(coef(summary(glm(cell.count ~ Control + log(size.factors), family = negative.binomial(dispersion), data=gRNA2.data)))[,4][2])), error=function(err) 1)
# calculate log2 Fold Change from bootstrapped control means and gRNA mean
if(mean != 0){
if(mean(means, na.rm = FALSE) != 0){
logFCs[grna.idx] <- log2(mean/mean(means, na.rm = FALSE))
} else{
logFCs[grna.idx] <- log2(mean/1)
}
} else{
if(mean(means, na.rm = FALSE) != 0){
logFCs[grna.idx] <- log2(1/mean(means, na.rm = FALSE))
} else{
logFCs[grna.idx] <- log2(1/1)
}
}
print(paste(gRNA, "pvals:", signif(lowerTail.pvals[grna.idx], digits=2), "log2FC:", signif(logFCs[grna.idx], digits=2)));
}
# merge pvalues accross all gRNA of a gene using fisher's method
lowerTail.pval <- fishers_method(lowerTail.pvals)
# average logFC from different gRNA weighted by 1-pvalue
lowerTail.logFC <- mean(logFCs, na.rm = FALSE)
lowerTail.logFC <- ifelse(is.na(lowerTail.logFC), 0, lowerTail.logFC)
# dump results into df
dummyLineLowerTail <- data.frame(gene = gene, logFC = lowerTail.logFC, pval = lowerTail.pval, pvals = paste(signif(lowerTail.pvals, digits = 2), collapse="|"), stringsAsFactors=FALSE)
# this part is needed for the parallel foreach loop
rbind(dummyLineLowerTail)
}
# correct p-values for multiple testing
results$padj <- p.adjust(results$pval, method = "BH")
# sort by gene name
results <- results[order(results$gene),]
# write table to file
fwrite(results, opt$out, row.names = FALSE, col.names = TRUE, sep = "\t")```Snakefile-fasta3 Click to expand!
#
shell.executable("/bin/bash")
# how many parts should the FASTQ be split into
SPLIT=16
scattergather:
split=SPLIT
THREADS=24
MEM=64
# set input file name here
IDS, = glob_wildcards("{id}-orig-I1-I2-R1.fastq.gz")
rule all:
input:
raw = expand("{id}-raw.csv", id = IDS),
filtered = expand("{id}-filtered.csv", id = IDS)
# white list
rule whitelist:
input: "{id}-orig-I1-I2-R1.fastq.gz"
output:"{id}-whitelist.txt"
log:
stdout = "{id}-whitelist.out",
stderr = "{id}-whitelist.err"
params: "{id}-whitelist-I1"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load pyston/2.3.4
module load seqtk
module load bcftools/1.14
bgzip -cd -@4 {input} | \
umi_tools whitelist --extract-method=regex \
--bc-pattern="(?P<cell_1>.{{10}})(?P<cell_2>.{{10}})(?P<umi_1>.{{12}})" \
--error-correct-threshold 3 \
--subset-reads=1000000000 \
--plot-prefix {params} \
--random-seed=1 \
--log={log.stdout} \
--error={log.stderr} \
--stdout={output}
'''
# split input FASTQ into 4 parts for faster processing
rule split_fastq:
input: "{id}-orig-I1-I2-R1.fastq.gz"
output: scatter.split("{{id}}-orig-I1-I2-R1-{scatteritem}.fastq.gz")
params: "{id}-orig-I1-I2-R1-%-of-" + str(SPLIT) + ".fastq.gz"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load bbtools/38.82
module load bcftools/1.14
partition.sh in={input} ways={SPLIT} out={params}
for i in `seq 1 {SPLIT}|sort -gr`
do
j=$(($i-1))
mv {wildcards.id}-orig-I1-I2-R1-$j-of-{SPLIT}.fastq.gz {wildcards.id}-orig-I1-I2-R1-$i-of-{SPLIT}.fastq.gz
done
'''
# extract/ error-correct barcodes, gRNAs
rule extract:
input:
reads = "{id}-orig-I1-I2-R1-{scatteritem}.fastq.gz",
whitelist = "{id}-whitelist.txt"
output: "{id}-extract-{scatteritem}.fasta.gz"
log:
stdout = "{id}-extract-{scatteritem}.out",
stderr = "{id}-extract-{scatteritem}.err"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load pyston/2.3.4
module load seqtk
module load bcftools/1.14
module load openblas/0.3.20
bgzip -cd -@4 {input.reads} | \
umi_tools extract --extract-method=regex \
--bc-pattern="(?P<cell_1>.{{10}})(?P<cell_2>.{{10}})(?P<umi_1>.{{12}})" \
--error-correct-cell \
--whitelist {input.whitelist} \
--log={log.stdout} \
--error={log.stderr} |seqtk seq -A |bgzip -c -@ 6 > {output}
'''
## gather splits
rule gather_fasta:
input:
gather.split("{{id}}-extract-{scatteritem}.fasta.gz")
output:
"{id}-extract.fasta.gz"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load bcftools/1.14
cat {input} > {output}
'''
## make input for STAR
### step1
rule STAR_input1:
input: "{id}-extract.fasta.gz"
output: "{id}-corr1-I1-I2-R1.names.fastq.gz"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load bcftools/1.14
module load seqtk
module load bbtools/38.82
bgzip -cd -@4 {input} | \
paste - - | \
fgrep "AGGACGAAACACCG" | \
perl -pe "s/>(\\S+)_(\\w+)_(\\w+)\\t(\\w+)/>\\1\\n\\4\\2\\3/" | \
bbduk.sh int=f literal=AGGACGAAACACCG k=14 ktrim=l in=STDIN.fa out=STDOUT.fa threads=4 | \
perl -pe "s/^([A-Z]+)([A-Z]{{20,20}})([A-Z]{{12,12}})$/\\3\\2\\1/" | \
cut -c-52 | \
seqtk seq -F ? - | \
bgzip -c -@ 4 > {output}
'''
## step2
rule STAR_input2:
input: "{id}-corr1-I1-I2-R1.names.fastq.gz"
output: "{id}-barcodes.txt"
shell:
"""
hostname
unset SLURM_EXPORT_ENV
module purge
module load bcftools/1.14
module load seqtk
bgzip -cd -@4 {input} | \
seqtk trimfq -b 12 - | paste - - - - | cut -f 2 | cut -c-20 | \
sort --parallel={THREADS} -T ./ -S {MEM}G | uniq -c | \
perl -pe 's/( )+/\t/g'|cut -f 2-3 |awk '$1 >= 100' |cut -f 2 > {output}
"""
# STAR1
rule STAR1:
input:
reads = "{id}-corr1-I1-I2-R1.names.fastq.gz",
barcodes = "{id}-barcodes.txt"
output:
map = directory("{id}-STAR/"),
tmp = temp(directory("{id}-STAR-temp/")),
bam = "{id}-STAR/Aligned.out.bam"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
/nfs/scistore16/itgrp/jelbers/git/STAR/source/STAR
echo
/nfs/scistore16/itgrp/jelbers/git/STAR/source/STAR \
--outFileNamePrefix {output.map}/ \
--outTmpDir {output.tmp}/ \
--readFilesCommand zcat \
--genomeDir genome-star-idx \
--outSAMattributes All \
--outSAMtype BAM Unsorted \
--runThreadN {THREADS} \
--readFilesIn {input.reads} \
--soloStrand Unstranded \
--soloType CB_UMI_Simple \
--alignIntronMax 1 \
--soloFeatures Gene \
--soloUMIdedup 1MM_All \
--soloCBstart 13 \
--soloCBlen 20 \
--soloUMIstart 1 \
--soloUMIlen 12 \
--soloBarcodeMate 1 \
--clip5pNbases 32 \
--soloCBwhitelist {input.barcodes} \
--soloCBmatchWLtype Exact
'''
# filterBAM
rule filterBAM:
input: "{id}-STAR/Aligned.out.bam"
output: "{id}-STAR/Aligned.out2.bam"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load samtools/1.14
LC_ALL=C
samtools view -h -@{THREADS} -q 255 {input} | \
fgrep -f <(echo -e "@\\nMD:Z:20\\nMD:Z:19") - | \
samtools view -@{THREADS} -Sb > {output}
'''
# bam2fastq
rule BAM2FASTQ:
input: "{id}-STAR/Aligned.out2.bam"
output: "{id}-corr2-I1-I2-R1.names.fastq.gz"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load samtools/1.14
module load compression-tools
samtools fastq -@{THREADS} {input} | \
pigz -p {THREADS} > {output}
'''
# STAR2
rule STAR2:
input:
reads = "{id}-corr2-I1-I2-R1.names.fastq.gz",
barcodes = "{id}-barcodes.txt"
output:
map = directory("{id}-STAR2/"),
tmp = temp(directory("{id}-STAR2-temp/")),
matrix = "{id}-STAR2/Solo.out/Gene/raw/matrix.mtx",
features = "{id}-STAR2/Solo.out/Gene/raw/features.tsv",
barcodes = "{id}-STAR2/Solo.out/Gene/raw/barcodes.tsv",
matrix2 = "{id}-STAR2/Solo.out/Gene/filtered/matrix.mtx",
features2 = "{id}-STAR2/Solo.out/Gene/filtered/features.tsv",
barcodes2 = "{id}-STAR2/Solo.out/Gene/filtered/barcodes.tsv"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
/nfs/scistore16/itgrp/jelbers/git/STAR/source/STAR
echo
/nfs/scistore16/itgrp/jelbers/git/STAR/source/STAR \
--outFileNamePrefix {output.map}/ \
--outTmpDir {output.tmp}/ \
--readFilesCommand zcat \
--genomeDir genome-star-idx \
--outSAMattributes All \
--outSAMtype BAM Unsorted \
--runThreadN {THREADS} \
--readFilesIn {input.reads} \
--soloStrand Unstranded \
--soloType CB_UMI_Simple \
--alignIntronMax 1 \
--soloFeatures Gene \
--soloUMIdedup 1MM_All \
--soloCBstart 13 \
--soloCBlen 20 \
--soloUMIstart 1 \
--soloUMIlen 12 \
--soloBarcodeMate 1 \
--clip5pNbases 32 \
--soloCBwhitelist {input.barcodes} \
--soloCBmatchWLtype Exact
'''
# matrix to csv
rule mat2csv:
input:
matrix = "{id}-STAR2/Solo.out/Gene/raw/matrix.mtx",
features = "{id}-STAR2/Solo.out/Gene/raw/features.tsv",
barcodes = "{id}-STAR2/Solo.out/Gene/raw/barcodes.tsv"
output:
csv = "{id}-raw.csv",
matrix = "{id}-STAR2/Solo.out/Gene/raw/matrix.mtx.gz",
features = "{id}-STAR2/Solo.out/Gene/raw/features.tsv.gz",
barcodes = "{id}-STAR2/Solo.out/Gene/raw/barcodes.tsv.gz"
params: "{id}-STAR2"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load cellranger
module load compression-tools
pigz -p {THREADS} -fk {input.matrix}
pigz -p {THREADS} -fk {input.features}
pigz -p {THREADS} -fk {input.barcodes}
cellranger mat2csv {params}/Solo.out/Gene/raw/ {output.csv}
'''
# matrix to csv
rule mat2csv2:
input:
matrix = "{id}-STAR2/Solo.out/Gene/filtered/matrix.mtx",
features = "{id}-STAR2/Solo.out/Gene/filtered/features.tsv",
barcodes = "{id}-STAR2/Solo.out/Gene/filtered/barcodes.tsv"
output:
csv = "{id}-filtered.csv",
matrix = "{id}-STAR2/Solo.out/Gene/filtered/matrix.mtx.gz",
features = "{id}-STAR2/Solo.out/Gene/filtered/features.tsv.gz",
barcodes = "{id}-STAR2/Solo.out/Gene/filtered/barcodes.tsv.gz"
params: "{id}-STAR2"
shell:
'''
hostname
unset SLURM_EXPORT_ENV
module purge
module load cellranger
module load compression-tools
pigz -p {THREADS} -fk {input.matrix}
pigz -p {THREADS} -fk {input.features}
pigz -p {THREADS} -fk {input.barcodes}
cellranger mat2csv {params}/Solo.out/Gene/filtered/ {output.csv}
'''conda activate snakemake-7.8.2
snakemake --snakefile Snakefile-fasta3 --dryrun --dag all \
> CRISPR-LICHT.dagCRISPR-LICHT.dag3 Click to expand!
digraph snakemake_dag {
graph[bgcolor=white, margin=0];
node[shape=box, style=rounded, fontname=sans, fontsize=10, penwidth=2];
edge[penwidth=2, color=grey];
0[label = "ready to estimate log2FC and\np values from count table\nusing a negative\nbinomial generlized linear\nmodel with a common dispersion\nestimate and ability to add\n covariates such as batch\njust like DESeq2", color = "0.36 0.6 0.85", style="rounded,dashed"];
2[label = "use STAR on the filtered reads\n to make count table", color = "0.10 0.6 0.85", style="rounded,dashed"];
3[label = "convert filtered BAM to filtered reads", color = "0.62 0.6 0.85", style="rounded,dashed"];
4[label = "filter BAM output from STAR to \nget rid of mismatches", color = "0.46 0.6 0.85", style="rounded,dashed"];
5[label = "use STAR to estimate mismatches\nbetween reads and gRNAs\nno introns allowed", color = "0.00 0.6 0.85", style="rounded,dashed"];
6[label = "convert to format for STAR", color = "0.31 0.6 0.85", style="rounded,dashed"];
7[label = "combine 16 outputs from \numi_tools extract", color = "0.21 0.6 0.85", style="rounded,dashed"];
8[label = "use umi_tools extract on each \nof the 16 input files\n and the whitelist\nto correct barcodes", color = "0.41 0.6 0.85", style="rounded,dashed"];
9[label = "split the original files\ninto 16 pieces for\n16x faster processing", color = "0.26 0.6 0.85", style="rounded,dashed"];
10[label = "use umi_tools whitelist\non original reads\nwith up to 3 errors\nin combined 20-bp\nsample*lineage barcodes\nto make a whitelist", color = "0.51 0.6 0.85", style="rounded,dashed"];
11[label = "make barcode list for STAR", color = "0.05 0.6 0.85", style="rounded,dashed"];
12[label = "convert count table from matrix\nformat to csv format", color = "0.15 0.6 0.85", style="rounded,dashed"];
13[label = "get original reads\nIndex1: 10-bp lineage barcode\nIndex2: 10-bp sample barcode\nR1 (51-bp total):\n12-bp UMI,\nsome random seq,\na constant seq,\nimmediately followed by\nthe 19-20-bp gRNA", color = "0.15 0.6 0.85", style="rounded,dashed"];
14[label = "make a sequence reference for \nSTAR using the gRNAs", color = "0.15 0.6 0.85", style="rounded,dashed"];
12 -> 0
3 -> 2
11 -> 2
4 -> 3
5 -> 4
6 -> 5
11 -> 5
7 -> 6
8 -> 7
9 -> 8
10 -> 8
6 -> 11
2 -> 12
13 -> 9
13 -> 10
14 -> 2
14 -> 5
}
cat CRISPR-LICHT.dag3 | \
dot -Tsvg
snakemake -j 30 --snakefile Snakefile-fasta3 \
--printshellcmds --latency-wait 60 --local-cores 4 --cores all \
--cluster "sbatch --export=NONE --no-requeue --job-name {rule} --mem=64g \
--time=2:00:00 --cpus-per-task=12 --constraint=avx2 --partition=gpu" all > Snakefile.log 2>&1 &CRISPR-LICHT-8July2022raw.slurm
#!/bin/bash
#SBATCH --job-name=raw
#SBATCH --mem=32000
#SBATCH -c 32
#SBATCH --time=8:00:00
#SBATCH --ntasks=1
unset SLURM_EXPORT_ENV
module purge
. "/nfs/scistore16/itgrp/jelbers/miniconda3/etc/profile.d/conda.sh"
conda activate crispr-licht-04May2022
cat SRR11868220-raw.csv |tr ',' '\t' |perl -pe "s/^\t/Gene\t/g" |fgrep -v "tomato" > SRR11868220-raw.notomato.tsv
Rscript CRISPR-LICHT-8July2022.R --out CRISPR-LICHT-8July2022nC.SRR11868220-raw.notomato.tsv.pvalues.tsv \
--nTstring nC --file SRR11868220-raw.notomato.tsv \
> CRISPR-LICHT-8July2022nC.SRR11868220-raw.notomato.tsv.pvalues.log 2>&1sbatch CRISPR-LICHT-8July2022raw.slurmgrep -Po "\S+\spvals:\s\S+\slog2FC:\s+\S+" \
CRISPR-LICHT-8July2022nC.SRR11868220-raw.notomato.tsv.pvalues.log| \
perl -pe "s/\"(\S+)\spvals:\s(\S+)\slog2FC:\s(\S+)\"/\1\t\2\t\3/g"|sort |sed '1i gRNA\tpvals\tlog2FC' > all-non-targeting-gRNAs.tsvall-non-targeting-gRNAs.tsv Click to expand!
gRNA pvals log2FC
ACTG1_AJ_1 0.36 -1.8
ACTG1_AJ_3 0.71 -0.4
ACTG1_AJ_4 0.32 -4.8
AKT3_LOE3_2 0.041 -3.3
AKT3_LOE3_3 0.31 -0.041
ALG12_LOE3_2 0.041 -4.6
ALG12_LOE3_3 0.51 -1.9
ALG12_LOE3_4 0.14 -5
ALG1_LOE2_1 0.5 0.71
ALG1_LOE2_4 0.41 -2.6
ALG3_LOE3_1 0.41 -5
ALG3_LOE3_2 0.16 -4.4
ALG3_LOE3_4 0.41 -5.1
ANKRD11_AJ_1 0.18 -5.1
ANKRD11_AJ_2 0.58 -3
ANKRD11_AJ_3 0.15 -3.2
ANKRD11_AJ_4 0.41 -5.2
ARFGEF2_LOE2_1 0.14 -5.7
ARFGEF2_LOE2_3 0.42 -5.9
ARFGEF2_LOE2_4 0.37 -2.8
ARNT2_LOE3_1 0.1 -2.9
ASNS_LOE3_1 0.4 1.8
ASNS_LOE3_4 0.44 -5.9
ASPM_LOE1_4 0.35 -3.4
ASXL3_LOE3_1 0.45 -5.9
ASXL3_LOE3_2 0.5 -2.3
ASXL3_LOE3_4 0.66 -3.7
ATM_LOE1_3 0.073 -3.8
ATM_LOE1_4 0.72 -0.49
ATRIP_AJ_1 0.31 -4.6
ATRIP_AJ_2 0.2 -3.9
ATRIP_AJ_3 0.37 -3.8
ATRIP_AJ_4 0.22 -4.5
ATR_LOE2_1 0.45 -5.9
ATR_LOE2_2 0.54 -2.5
ATR_LOE2_4 0.45 1.3
ATRX_AJ_2 0.41 -5.6
ATRX_AJ_3 0.45 -5.8
ATRX_AJ_4 0.19 -5
BCOR_LOE2_2 0.41 -5.4
BCOR_LOE2_3 0.28 -3.1
BCOR_LOE2_4 0.68 -0.27
BLM_AJ_1 0.074 -5.5
BLM_AJ_4 0.37 -4.7
BRF1_LOE2_1 0.62 -5.8
CASC5_LOE3_2 0.62 -5.9
CASK_LOE1_1 0.75 -3.2
CASK_LOE1_4 0.58 -0.93
CCDC88A_LOE3_1 0.62 -5.4
CCDC88A_LOE3_2 0.28 -5
CDC45_AJ_2 0.67 -4.6
CDC45_AJ_3 0.22 -3.6
CDC45_AJ_4 0.43 -5.6
CDC6_LOE3_1 0.08 4.4
CDC6_LOE3_2 0.44 -5.8
CDC6_LOE3_3 0.11 -4
CDK5RAP2_LOE1_1 0.25 -3.3
CDK5RAP2_LOE1_2 0.62 -5.1
CDK5RAP2_LOE1_4 0.39 -5.2
CDK6_LOE2_1 0.092 -4.6
CDKL5_AJ_1 0.6 -5.9
CDKL5_AJ_2 0.42 -2.7
CDKL5_AJ_3 0.12 2.8
CDKL5_AJ_4 0.035 -4.2
CDT1_LOE3_1 0.14 -4.3
CDT1_LOE3_4 0.29 -5.4
CENPE_LOE2_2 0.64 -2.1
CENPE_LOE2_3 0.3 -5.1
CENPE_LOE2_4 0.36 -5.8
CENPF_LOE3_3 0.44 0.64
CENPF_LOE3_4 0.45 -5.8
CENPJ_LOE2_2 0.38 -5.4
CENPJ_LOE2_3 0.41 1.2
CENPJ_LOE2_4 0.41 -2.4
CEP135_LOE2_2 0.9 -0.63
CEP135_LOE2_3 0.45 -5.8
CEP152_LOE2_3 0.38 -2.9
CEP63_LOE3_1 0.45 -5.9
CEP63_LOE3_2 0.72 -3.3
CEP63_LOE3_3 0.38 -5
CEP63_LOE3_4 0.41 -1.1
CIT_LOE3_2 0.39 -3.3
CIT_LOE3_3 0.94 -3.5
CIT_LOE3_4 0.41 -5.6
COG7_LOE3_1 0.44 -5.8
COG7_LOE3_2 0.26 -5.7
COG7_LOE3_3 0.71 -0.32
CREBBP_LOE3_1 0.39 -3.4
CREBBP_LOE3_2 0.11 -2.4
CREBBP_LOE3_3 0.39 -3.7
CREBBP_LOE3_4 0.39 -3.9
CSNK2A1_LOE3_2 0.38 -4.9
CTCF_LOE3_1 0.6 -4.5
CTNNB1_LOE3_1 0.26 -4.3
CTNNB1_LOE3_3 0.43 -5.6
CTNNB1_LOE3_4 0.14 -3.6
DDB2_LOE1_1 0.44 -5.9
DDB2_LOE1_2 0.33 -4.3
DDB2_LOE1_3 0.42 -5.8
DDB2_LOE1_4 0.16 -3.1
DHCR7_LOE3_1 0.6 -5.1
DHCR7_LOE3_3 0.42 -5.6
DHCR7_LOE3_4 0.39 -4.4
DKC1_LOE3_1 0.3 -2.2
DNA2_AJ_2 0.2 -4.6
DNM1L_LOE3_1 0.46 -5.7
DNM1L_LOE3_2 0.63 -1.2
DNM1L_LOE3_4 0.43 -2.7
DONSON_AJ_1 0.41 -3.2
DONSON_AJ_2 1 0.44
DONSON_AJ_4 0.31 -5.1
DPAGT1_LOE3_1 0.42 -5.8
DPAGT1_LOE3_2 0.11 -4.9
DPAGT1_LOE3_4 0.32 -5.5
DPM1_LOE3_1 0.19 -3.5
DPM1_LOE3_2 0.14 -3.4
DPM1_LOE3_3 0.45 -5.9
DPM1_LOE3_4 0.31 -2.1
DPP6_AJ_2 0.16 -3.2
DPP6_AJ_4 0.39 0.92
DYM_LOE3_1 0.12 -4.3
DYM_LOE3_3 0.81 0.96
DYNC1H1_LOE3_4 0.43 -5.6
DYRK1A_LOE1_3 0.31 -5.3
EEF1A2_LOE3_3 0.61 -1.2
EEF1A2_LOE3_4 0.41 -5.6
EFTUD2_LOE3_1 0.16 -3.3
EFTUD2_LOE3_2 0.42 -3.2
EIF2AK3_LOE3_2 0.43 -3
EIF2AK3_LOE3_3 0.75 -0.72
EIF2AK3_LOE3_4 0.41 0.15
EIF6_dC_1 0.18 -4.3
EIF6_dC_2 0.092 -4.9
EOMES_LOE3_1 0.8 -0.15
EOMES_LOE3_2 0.55 1.5
EOMES_LOE3_4 0.45 -1.8
EP300_LOE3_2 0.4 -5.3
EP300_LOE3_3 0.38 -4.5
EP300_LOE3_4 0.41 -5.6
ERCC1_LOE1_1 0.26 -5.7
ERCC1_LOE1_2 0.4 -4.7
ERCC1_LOE1_3 0.11 -4.4
ERCC1_LOE1_4 0.73 1.1
ERCC2_LOE1_1 0.31 -2.3
ERCC2_LOE1_2 0.4 -5
ERCC3_LOE1_1 0.43 -5.9
ERCC4_LOE1_1 0.38 -4.4
ERCC4_LOE1_2 0.36 1.3
ERCC5_LOE1_1 0.99 1.3
ERCC5_LOE1_3 0.64 -5.8
ERCC6_LOE2_1 0.82 -2.6
ERCC6_LOE2_3 0.35 -4.3
ERCC6_LOE2_4 0.38 -5
ERCC8_LOE1_1 0.43 -5.6
ERCC8_LOE1_3 0.41 -5
ESCO2_AJ_3 0.39 -4.9
ESCO2_AJ_4 0.64 -5.8
FGFR1_LOE2_1 0.32 -4.5
FGFR1_LOE2_2 0.27 2.3
FGFR1_LOE2_4 0.61 0.25
FOXG1_LOE2_3 0.024 2.3
GAPDH_dC_2 0.21 -4.4
GMNN_AJ_2 0.33 1.3
GMNN_AJ_4 0.6 -5
HCCS_LOE2_2 0.13 -3
HCCS_LOE2_3 0.16 -4
HDAC8_AJ_2 0.6 -5.9
HDAC8_AJ_4 0.31 -4.6
HMGA2_AJ_2 0.29 -4.8
HMGA2_AJ_3 0.52 -3.2
HMGA2_AJ_4 0.043 -3
h_non-targeting_nC_10 0.5 1.4
h_non-targeting_nC_2 0.56 -1.2
h_non-targeting_nC_3 0.33 -5.4
h_non-targeting_nC_4 0.46 -2
h_non-targeting_nC_6 0.4 -1.8
h_non-targeting_nC_9 0.61 -5.3
hrrm1_dC_1 0.64 -1.2
hrrm1_dC_2 0.13 -5.3
hrrm1_dC_4 0.42 1.6
IER3IP1_LOE3_2 0.45 -5.9
IER3IP1_LOE3_3 0.38 -3.3
IER3IP1_LOE3_4 1.5e-19 1.2
IGF1_AJ_1 0.43 -5.6
IGF1_AJ_2 0.91 -2.1
IGF1R_AJ_2 0.53 -5.7
IGF2_AJ_1 0.23 -4.4
IGF2_AJ_2 0.44 2.7
IGF2_AJ_4 0.18 4.4
IQSEC2_LOE3_1 0.12 -3.3
IQSEC2_LOE3_2 0.17 -2
IQSEC2_LOE3_3 0.45 -5.9
KAT6A_LOE2_2 0.68 -4.4
KAT6A_LOE2_4 0.44 -5.9
KAT6B_AJ_1 0.2 -4.4
KAT6B_AJ_2 0.077 -3.4
KAT6B_AJ_4 0.84 -0.42
KATNB1_LOE2_1 0.096 3
KATNB1_LOE2_2 0.43 1.2
KCNJ6_LOE3_2 0.54 -1.9
KCNJ6_LOE3_3 0.068 -3.5
KDM5C_LOE3_1 0.29 2
KDM5C_LOE3_2 0.73 0.71
KDM5C_LOE3_3 0.71 -3.6
KIF11_LOE2_1 0.21 -3.2
KIF11_LOE2_2 0.17 -5.3
KIF11_LOE2_3 0.55 0.98
KIF14_LOE3_1 0.43 -5.6
KIF14_LOE3_3 0.44 -5.8
KIF5C_LOE3_1 0.75 0.2
KIF5C_LOE3_2 0.45 -5.8
KIF5C_LOE3_4 0.21 -5.6
KMT2A_AJ_2 0.21 -4.2
KMT2A_AJ_3 0.66 -0.16
LARP7_AJ_1 0.97 -1.8
LARP7_AJ_3 0.58 -5.6
LARP7_AJ_4 0.39 -4.2
LIG4_LOE1_1 0.45 -5.9
LIG4_LOE1_2 0.28 -5.2
MCM4_AJ_1 0.32 -5
MCPH1_LOE1_1 0.16 -3.2
MCPH1_LOE1_2 0.58 -3.4
MCPH1_LOE1_3 0.43 -5.6
MCPH1_LOE1_4 0.34 -4.6
MECP2_LOE1_1 0.35 -2
MECP2_LOE1_2 0.64 -1.3
MECP2_LOE1_3 0.31 -5.3
MECP2_LOE1_4 0.4 -4.8
MED12_AJ_1 0.16 -2.8
MED12_AJ_3 0.92 -1.3
MED12_AJ_4 0.57 -1.7
MED17_LOE3_1 0.51 1.2
MED17_LOE3_2 0.27 -4.2
MED17_LOE3_3 0.31 -4.6
MED17_LOE3_4 0.43 -5.8
MED25_LOE3_1 0.027 -4.3
MED25_LOE3_2 0.43 -5.8
MED25_LOE3_3 0.42 -5.9
MRE11A_AJ_2 0.38 -5
MYCN_LOE2_2 0.99 -0.97
MYCN_LOE2_3 0.72 -0.82
MYCN_LOE2_4 0.61 -4.7
MYO18B_LOE3_1 0.39 -5.1
MYO18B_LOE3_2 0.35 -4.5
MYO18B_LOE3_3 0.21 -5.2
MYO18B_LOE3_4 0.2 -3.1
NAA10_LOE3_2 0.4 -4.3
NAA10_LOE3_3 0.58 0.37
NBN_LOE1_2 0.37 -5.2
NBN_LOE1_4 0.32 -5.9
NDE1_LOE2_2 0.0099 -3.6
NDE1_LOE2_3 0.42 -5.2
NDE1_LOE2_4 0.2 -4.7
NGLY1_LOE3_1 0.39 -4.2
NGLY1_LOE3_3 0.5 -4.6
NGLY1_LOE3_4 0.33 2.3
NHEJ1_LOE3_1 0.28 -3.4
NHEJ1_LOE3_2 0.6 -5.8
NIPBL_LOE2_3 0.72 1.3
NIPBL_LOE2_4 0.19 -5.2
NSDHL_LOE3_2 0.62 -1.3
NSDHL_LOE3_4 0.094 -3.4
NSUN2_AJ_1 0.6 -5.8
NSUN2_AJ_4 0.86 -2.3
OCLN_LOE3_3 0.43 -5.8
OCLN_LOE3_4 0.31 -5.5
ORC1_LOE3_3 0.58 -5
ORC1_LOE3_4 0.3 -4.8
ORC4_LOE3_2 0.92 -1.7
ORC4_LOE3_4 0.2 -5
ORC6_LOE3_1 0.22 -5.7
PAFAH1B1_LOE1_3 0.91 -0.48
PCNT_LOE1_1 0.44 -5.9
PCNT_LOE1_3 0.39 -5.4
PCNT_LOE1_4 0.73 -3.2
PGAP3_LOE3_3 0.2 -4.9
PGAP3_LOE3_4 0.029 -5.4
PHC1_AJ_2 0.89 -0.66
PHC1_AJ_3 0.34 -5.6
PIGA_LOE3_2 0.27 -4
PIGA_LOE3_4 0.55 -1.9
PLK4_LOE2_1 0.23 -5.3
PLK4_LOE2_2 0.42 -5.9
PLK4_LOE2_3 0.51 1.6
PLK4_LOE2_4 0.9 -0.088
PNKP_LOE2_1 0.22 1.8
PNKP_LOE2_3 0.44 -5.8
PNKP_LOE2_4 0.54 0.95
POC1A_AJ_1 0.23 -2.3
POC1A_AJ_2 0.38 -4.1
POLH_LOE1_2 0.27 -5.7
POLH_LOE1_3 0.096 -3.4
POLR2D_dC_1 0.22 -5.5
POLR2D_dC_2 0.21 -3
PQBP1_LOE2_1 0.45 1.2
PQBP1_LOE2_2 0.61 -4.6
PQBP1_LOE2_3 0.3 -5.4
PQBP1_LOE2_4 0.28 -3.4
PRKDC_AJ_1 0.43 -5.6
PRKDC_AJ_2 0.46 -5.7
PRKDC_AJ_4 0.28 -5.1
PRPF19_dC_2 0.12 -4.5
PTEN_pC_3 0.046 -3.5
PYCR2_LOE3_1 0.64 -5.8
PYCR2_LOE3_2 0.27 1.3
PYCR2_LOE3_4 0.08 -4.9
QARS_LOE3_1 0.93 0.57
QARS_LOE3_2 0.4 -4.5
QARS_LOE3_3 0.13 -3.7
RAB18_LOE3_3 0.38 1.5
RAB18_LOE3_4 0.14 -3.6
RAB3GAP1_LOE3_1 0.75 -0.6
RAB3GAP1_LOE3_2 0.39 -4.2
RAB3GAP1_LOE3_4 0.47 -2.5
RAB3GAP2_LOE3_1 0.3 -5.7
RAB3GAP2_LOE3_2 0.84 -1.6
RAB3GAP2_LOE3_3 0.76 -0.62
RAB3GAP2_LOE3_4 0.53 -2.7
RAD21_AJ_1 0.75 -3
RAD21_AJ_4 0.39 -4.6
RAN_dC_2 0.56 -1.6
RARS2_LOE3_2 0.38 -4.8
RARS2_LOE3_3 0.65 -1.1
RARS2_LOE3_4 0.61 -4.6
RBBP8_LOE3_1 0.43 -5.6
RBBP8_LOE3_2 0.05 -4.3
RBBP8_LOE3_3 0.38 -4.1
RBBP8_LOE3_4 0.3 -5.7
RBM10_AJ_1 0.55 0.49
RBM10_AJ_2 0.42 -5.3
RBM10_AJ_3 0.39 -3.6
RBM10_AJ_4 0.89 0.59
RGS6_LOE3_1 0.11 -5.3
RGS6_LOE3_2 0.16 -3.8
RGS6_LOE3_4 0.19 -4.4
RIMS3_LOE3_1 0.026 -4.3
RIMS3_LOE3_4 0.38 -1.9
RPL32_dC_1 0.39 -3.9
RPS6KA3_LOE2_1 0.04 -3.3
RPS6KA3_LOE2_2 0.18 -5.1
RPS6KA3_LOE2_3 0.42 -3
RTTN_LOE3_1 0.18 -5.6
RTTN_LOE3_2 0.4 -4.9
SHH_LOE1_3 0.41 -5.1
SHH_LOE1_4 0.81 -2.6
SIN3A_LOE3_3 0.6 -5.6
SIN3A_LOE3_4 0.33 -2.8
SLC1A4_LOE3_1 0.35 2.3
SLC1A4_LOE3_4 0.61 -4.8
SLC25A19_LOE2_1 0.3 -5.1
SLC25A19_LOE2_4 0.11 -3.3
SLC25A22_LOE3_2 0.063 -3.9
SLC25A22_LOE3_3 0.52 -2
SLC25A22_LOE3_4 0.41 -5.7
SLC2A1_AJ_1 0.43 -5.6
SLC2A1_AJ_3 0.4 -4.9
SLC2A1_AJ_4 0.65 -5.1
SLC9A6_AJ_4 0.6 -5.2
SMARCAL1_AJ_1 0.17 2.6
SMARCAL1_AJ_3 0.7 0.28
SMARCAL1_AJ_4 0.23 -3.2
SMC1A_LOE2_1 0.67 -4
SMC1A_LOE2_2 0.5 0.92
SMC1A_LOE2_4 0.51 -2.5
SNAP29_LOE2_3 0.4 -4.8
SNAP29_LOE2_4 0.089 -5.1
SNRPF_dC_1 0.1 -5
SPOCK1_LOE3_1 0.46 -1.6
SPOCK1_LOE3_2 0.41 -3
SPOCK1_LOE3_3 0.38 -3.7
SPOCK1_LOE3_4 0.27 0.77
SRCAP_AJ_1 0.61 -4
SRCAP_AJ_4 0.34 -2.2
SSR4_LOE3_2 0.21 -4.6
SSR4_LOE3_3 0.41 -5.6
STAMBP_LOE2_2 0.73 0.24
STAMBP_LOE2_3 0.76 -3.1
STIL_LOE1_2 0.6 -3.9
STIL_LOE1_3 0.87 -0.31
STIL_LOE1_4 0.24 -0.45
TAF2_LOE3_1 1 0.44
TAF2_LOE3_2 0.27 -2.8
TagBFP_nC_1 0.42 -5.2
TagBFP_nC_2 0.5 2
TBC1D20_AJ_1 0.95 0.08
TBC1D20_AJ_2 0.4 -3.2
TBC1D20_AJ_3 0.091 -5.2
TBC1D20_AJ_4 0.65 -4.4
TBCD_LOE3_1 0.42 -5.8
TBCD_LOE3_2 0.52 0.87
TCF4_AJ_1 0.45 -5.8
TCF4_AJ_2 0.13 -4.5
TCF4_AJ_3 0.84 0.67
TRAIP_AJ_1 0.22 -5.4
TRAIP_AJ_2 0.1 -5.3
TRAIP_AJ_3 0.032 -4.6
TRAPPC9_LOE2_2 0.17 2.1
TRAPPC9_LOE2_3 0.4 0.24
TRAPPC9_LOE2_4 0.69 -1
TRIO_LOE3_1 0.71 -1.3
TRIO_LOE3_2 0.83 1.1
TRIO_LOE3_3 0.67 -3.9
TRIO_LOE3_4 0.26 -5.8
TRMT10A_LOE3_1 0.39 -5.2
TRMT10A_LOE3_4 0.6 -5
TUBB2B_LOE3_2 0.39 -4.6
TUBB2B_LOE3_3 0.62 -5.6
TUBB_LOE3_2 0.37 -4.6
TUBB_LOE3_3 0.42 -5.9
TUBG1_LOE3_1 0.5 -4.5
TUBG1_LOE3_2 0.53 1.4
TUBG1_LOE3_3 0.41 -5.6
TUBGCP6_LOE2_1 0.23 -5.7
TUBGCP6_LOE2_3 0.57 -4.6
UBE3A_LOE1_1 0.58 -5.2
UBE3A_LOE1_2 0.19 -5.2
UBE3A_LOE1_3 0.24 -5.4
VPS13B_LOE2_3 0.11 -3.8
VRK1_LOE3_1 0.25 -5.5
VRK1_LOE3_2 0.52 1.4
VRK1_LOE3_3 0.4 -5
VRK1_LOE3_4 0.59 1.7
WDR4_LOE3_1 0.32 -5.6
WDR4_LOE3_2 0.53 2.2
WDR4_LOE3_3 0.37 -3.8
WDR4_LOE3_4 0.39 -3.3
WDR62_LOE1_1 0.4 -4
WDR62_LOE1_2 0.8 -2.8
WDR73_LOE2_2 0.14 -4.9
WDR73_LOE2_4 0.38 -4
WWOX_LOE3_2 0.37 -3.9
WWOX_LOE3_3 0.097 -4.4
XPA_LOE1_2 0.26 -0.1
XPA_LOE1_4 0.07 -4
XPC_LOE1_1 0.35 -3.7
XPC_LOE1_3 0.62 -5.8
XPC_LOE1_4 0.21 3.2
XRCC4_AJ_1 0.37 1.7
XRCC4_AJ_2 0.78 -0.65
XRCC4_AJ_3 0.68 -0.84
ZBTB18_LOE3_1 0.28 -4.4
ZBTB18_LOE3_2 0.06 2.7
ZBTB18_LOE3_3 0.43 -5.8
ZBTB18_LOE3_4 0.41 -5.6
ZEB2_LOE3_3 0.075 2.8
ZEB2_LOE3_4 0.16 -5.2in an R shell
test <- read.table('all-non-targeting-gRNAs.tsv', header=T)
svg('1.svg')
test$colors = grepl("nC",test$gRNA)
test$colors[test$colors == 'TRUE'] = "red"
test$colors[test$colors == 'FALSE'] = "black"
plot(x=test$log2FC,y=-1*log10(test$pvals), col=test$colors, main="non-targeting controls(should have Log2FC=0)=red\nIER3IP1_LOE3_4 is highest point")
dev.off()
svg('2.svg')
test$colors = grepl("pC",test$gRNA)
test$colors[test$colors == 'TRUE'] = "red"
test$colors[test$colors == 'FALSE'] = "black"
plot(x=test$log2FC,y=-1*log10(test$pvals), col=test$colors, main="positive controls (should have +Log2FC)=red\nIER3IP1_LOE3_4 is highest point")
dev.off()
svg('3.svg')
test$colors = grepl("dC",test$gRNA)
test$colors[test$colors == 'TRUE'] = "red"
test$colors[test$colors == 'FALSE'] = "black"
plot(x=test$log2FC,y=-1*log10(test$pvals), col=test$colors, main="negative controls (should have -Log2FC)=red\nIER3IP1_LOE3_4 is highest point")
dev.off()
svg('4.svg')
test <- read.table('all-non-targeting-gRNAs.tsv', header=T)
test <- test[test$pvals > 0.000001,]
test$colors = grepl("nC",test$gRNA)
test$colors[test$colors == 'TRUE'] = "red"
test$colors[test$colors == 'FALSE'] = "black"
plot(x=test$log2FC,y=-1*log10(test$pvals), col=test$colors, main="non-targeting controls (should have Log2FC=0)=red\nwithout IER3IP1_LOE3_4, found\nsignificant in original paper")
dev.off()
svg('5.svg')
test$colors = grepl("pC",test$gRNA)
test$colors[test$colors == 'TRUE'] = "red"
test$colors[test$colors == 'FALSE'] = "black"
plot(x=test$log2FC,y=-1*log10(test$pvals), col=test$colors, main="positive controls (should have +Log2FC)=red\nwithout IER3IP1_LOE3_4, found\nsignificant in original paper")
dev.off()
svg('6.svg')
test$colors = grepl("dC",test$gRNA)
test$colors[test$colors == 'TRUE'] = "red"
test$colors[test$colors == 'FALSE'] = "black"
plot(x=test$log2FC,y=-1*log10(test$pvals), col=test$colors, main="negative controls (should have -Log2FC)=red\nwithout IER3IP1_LOE3_4, found\nsignificant in original paper")
dev.off()
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