├── .DS_Store ├── .Rbuildignore ├── .gitignore ├── DESCRIPTION ├── Data ├── .DS_Store ├── DarmanisEtAl │ └── annotation_file │ │ ├── FromGeoSpreadsheet.tsv │ │ ├── GBM_SRA_plate_relation │ │ ├── GBM_TSNE.csv │ │ ├── GBM_metadata.csv │ │ └── SRR_Patient_all_list_sorted ├── GRCH38_MT.fa ├── GRCH38_MT.fa.fai ├── LeifEtAl │ └── annotation_file │ │ ├── SRR_donors_list │ │ ├── target_cell.csv │ │ └── target_mutation.csv ├── McGinnisEtAl │ └── annotation_file │ │ └── jurkat_293t_demuxlet.best └── mouse_MT.fasta ├── MitoTrace.Rproj ├── R └── MitoTrace.R ├── README.md ├── examples ├── Reproduce results in figures 5H & 5I from Leif et al.html ├── Reproduce results in figures 5H & 5I from Leif et al_files │ └── MathJax.js ├── Reproduce_Cell_Leif_et_al.Rmd ├── Reproduce_Cell_Leif_et_al.html ├── Single Cell SMART-SEQ2 data.Rmd ├── Single-Cell-10X-Genomics-data.Rmd ├── Single-Cell-10X-Genomics-data.html ├── Single-Cell-SMART-SEQ2-data.html └── rsconnect │ └── documents │ └── Reproduce_Cell_Leif_et_al.Rmd │ └── rpubs.com │ └── rpubs │ └── Document.dcf ├── images ├── Fig0_Reproduce_result.png ├── Fig2_Smart-seq2.png ├── Fig4_10x_genomics.png ├── MitoTrace.png ├── R1.png ├── barplot.png ├── gene_bar_cov.png └── somatic.png └── man └── checkSequenceNames.Rd /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/.DS_Store -------------------------------------------------------------------------------- /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | *.bam 6 | *.bai 7 | .DS_Store 8 | .Rbuildignore 9 | .gitignore 10 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: MitoTrace 2 | Type: Package 3 | Title: A computational framework for investigating mitochondrial heteroplasmies in RNA sequencing data 4 | Version: 1.0.0 5 | Date: April-11-2023 6 | Author: Mingqiang WANG, Simon Lukas 7 | Maintainer: Mingqiang WANG , Simon Lukas 8 | Depends: R (>= 3.6.0), seqinr (>= 3.4-5), Matrix (>= 1.2-17), Rsamtools (>= 2.0.0) 9 | Description: A user-friendly R package to enable analysis of mitochondrial heteroplasmies and demonstrate the validity and value of our computational framework in a number of exemplary analyses. 10 | License: GPL (>= 2) 11 | RoxygenNote: 6.1.1 12 | -------------------------------------------------------------------------------- /Data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/Data/.DS_Store -------------------------------------------------------------------------------- /Data/GRCH38_MT.fa: -------------------------------------------------------------------------------- 1 | >MT dna:chromosome chromosome:GRCh38:MT:1:16569:1 REF 2 | GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTT 3 | CGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTC 4 | GCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATT 5 | ACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATA 6 | ACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCA 7 | AACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAA 8 | ACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCAC 9 | TTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAAT 10 | CTCATCAATACAACCCCCGCCCATCCTACCCAGCACACACACACCGCTGCTAACCCCATA 11 | CCCCGAACCAACCAAACCCCAAAGACACCCCCCACAGTTTATGTAGCTTACCTCCTCAAA 12 | GCAATACACTGAAAATGTTTAGACGGGCTCACATCACCCCATAAACAAATAGGTTTGGTC 13 | CTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCCGTTCCAGTGAGT 14 | TCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGCAATGCAGCTC 15 | AAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAA 16 | ACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACCGC 17 | GGTCACACGATTAACCCAAGTCAATAGAAGCCGGCGTAAAGAGTGTTTTAGATCACCCCC 18 | TCCCCAATAAAGCTAAAACTCACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGAC 19 | TACGAAAGTGGCTTTAACATATCTGAACACACAATAGCTAAGACCCAAACTGGGATTAGA 20 | TACCCCACTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCTCGCCAGAA 21 | CACTACGAGCCACAGCTTAAAACTCAAAGGACCTGGCGGTGCTTCATATCCCTCTAGAGG 22 | AGCCTGTTCTGTAATCGATAAACCCCGATCAACCTCACCACCTCTTGCTCAGCCTATATA 23 | CCGCCATCTTCAGCAAACCCTGATGAAGGCTACAAAGTAAGCGCAAGTACCCACGTAAAG 24 | ACGTTAGGTCAAGGTGTAGCCCATGAGGTGGCAAGAAATGGGCTACATTTTCTACCCCAG 25 | AAAACTACGATAGCCCTTATGAAACTTAAGGGTCGAAGGTGGATTTAGCAGTAAACTAAG 26 | AGTAGAGTGCTTAGTTGAACAGGGCCCTGAAGCGCGTACACACCGCCCGTCACCCTCCTC 27 | AAGTATACTTCAAAGGACATTTAACTAAAACCCCTACGCATTTATATAGAGGAGACAAGT 28 | CGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGACGAACCAGAGTGTAGCTTAACACA 29 | AAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGAGCTAAACCTA 30 | GCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAA 31 | AGTATAGGCGATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATG 32 | AAAAATTATAACCAAGCATAATATAGCAAGGACTAACCCCTATACCTTCTGCATAATGAA 33 | TTAACTAGAAATAACTTTGCAAGGAGAGCCAAAGCTAAGACCCCCGAAACCAGACGAGCT 34 | ACCTAAGAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAATAGTGGGAAGATTTATA 35 | GGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCAAGATAGAATCTTAG 36 | TTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTC 37 | CAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGAGTAAAAAATTTA 38 | ACACCCATAGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTTCAAGCTCAACACCCA 39 | CTACCTAAAAAATCCCAAACATATAACTGAACTCCTCACACCCAATTGGACCAATCTATC 40 | ACCCTATAGAAGAACTAATGTTAGTATAAGTAACATGAAAACATTCTCCTCCGCATAAGC 41 | CTGCGTCAGATTAAAACACTGAACTGACAATTAACAGCCCAATATCTACAATCAACCAAC 42 | AAGTCATTATTACCCTCACTGTCAACCCAACACAGGCATGCTCATAAGGAAAGGTTAAAA 43 | AAAGTAAAAGGAACTCGGCAAATCTTACCCCGCCTGTTTACCAAAAACATCACCTCTAGC 44 | ATCACCAGTATTAGAGGCACCGCCTGCCCAGTGACACATGTTTAACGGCCGCGGTACCCT 45 | AACCGTGCAAAGGTAGCATAATCACTTGTTCCTTAAATAGGGACCTGTATGAATGGCTCC 46 | ACGAGGGTTCAGCTGTCTCTTACTTTTAACCAGTGAAATTGACCTGCCCGTGAAGAGGCG 47 | GGCATAACACAGCAAGACGAGAAGACCCTATGGAGCTTTAATTTATTAATGCAAACAGTA 48 | CCTAACAAACCCACAGGTCCTAAACTACCAAACCTGCATTAAAAATTTCGGTTGGGGCGA 49 | CCTCGGAGCAGAACCCAACCTCCGAGCAGTACATGCTAAGACTTCACCAGTCAAAGCGAA 50 | CTACTATACTCAATTGATCCAATAACTTGACCAACGGAACAAGTTACCCTAGGGATAACA 51 | GCGCAATCCTATTCTAGAGTCCATATCAACAATAGGGTTTACGACCTCGATGTTGGATCA 52 | GGACATCCCGATGGTGCAGCCGCTATTAAAGGTTCGTTTGTTCAACGATTAAAGTCCTAC 53 | GTGATCTGAGTTCAGACCGGAGTAATCCAGGTCGGTTTCTATCTACNTTCAAATTCCTCC 54 | CTGTACGAAAGGACAAGAGAAATAAGGCCTACTTCACAAAGCGCCTTCCCCCGTAAATGA 55 | TATCATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGC 56 | AGAGCCCGGTAATCGCATAAAACTTAAAACTTTACAGTCAGAGGTTCAATTCCTCTTCTT 57 | AACAACATACCCATGGCCAACCTCCTACTCCTCATTGTACCCATTCTAATCGCAATGGCA 58 | TTCCTAATGCTTACCGAACGAAAAATTCTAGGCTATATACAACTACGCAAAGGCCCCAAC 59 | GTTGTAGGCCCCTACGGGCTACTACAACCCTTCGCTGACGCCATAAAACTCTTCACCAAA 60 | GAGCCCCTAAAACCCGCCACATCTACCATCACCCTCTACATCACCGCCCCGACCTTAGCT 61 | CTCACCATCGCTCTTCTACTATGAACCCCCCTCCCCATACCCAACCCCCTGGTCAACCTC 62 | AACCTAGGCCTCCTATTTATTCTAGCCACCTCTAGCCTAGCCGTTTACTCAATCCTCTGA 63 | TCAGGGTGAGCATCAAACTCAAACTACGCCCTGATCGGCGCACTGCGAGCAGTAGCCCAA 64 | ACAATCTCATATGAAGTCACCCTAGCCATCATTCTACTATCAACATTACTAATAAGTGGC 65 | TCCTTTAACCTCTCCACCCTTATCACAACACAAGAACACCTCTGATTACTCCTGCCATCA 66 | TGACCCTTGGCCATAATATGATTTATCTCCACACTAGCAGAGACCAACCGAACCCCCTTC 67 | GACCTTGCCGAAGGGGAGTCCGAACTAGTCTCAGGCTTCAACATCGAATACGCCGCAGGC 68 | CCCTTCGCCCTATTCTTCATAGCCGAATACACAAACATTATTATAATAAACACCCTCACC 69 | ACTACAATCTTCCTAGGAACAACATATGACGCACTCTCCCCTGAACTCTACACAACATAT 70 | TTTGTCACCAAGACCCTACTTCTAACCTCCCTGTTCTTATGAATTCGAACAGCATACCCC 71 | CGATTCCGCTACGACCAACTCATACACCTCCTATGAAAAAACTTCCTACCACTCACCCTA 72 | GCATTACTTATATGATATGTCTCCATACCCATTACAATCTCCAGCATTCCCCCTCAAACC 73 | TAAGAAATATGTCTGATAAAAGAGTTACTTTGATAGAGTAAATAATAGGAGCTTAAACCC 74 | CCTTATTTCTAGGACTATGAGAATCGAACCCATCCCTGAGAATCCAAAATTCTCCGTGCC 75 | ACCTATCACACCCCATCCTAAAGTAAGGTCAGCTAAATAAGCTATCGGGCCCATACCCCG 76 | AAAATGTTGGTTATACCCTTCCCGTACTAATTAATCCCCTGGCCCAACCCGTCATCTACT 77 | CTACCATCTTTGCAGGCACACTCATCACAGCGCTAAGCTCGCACTGATTTTTTACCTGAG 78 | TAGGCCTAGAAATAAACATGCTAGCTTTTATTCCAGTTCTAACCAAAAAAATAAACCCTC 79 | GTTCCACAGAAGCTGCCATCAAGTATTTCCTCACGCAAGCAACCGCATCCATAATCCTTC 80 | TAATAGCTATCCTCTTCAACAATATACTCTCCGGACAATGAACCATAACCAATACTACCA 81 | ATCAATACTCATCATTAATAATCATAATAGCTATAGCAATAAAACTAGGAATAGCCCCCT 82 | TTCACTTCTGAGTCCCAGAGGTTACCCAAGGCACCCCTCTGACATCCGGCCTGCTTCTTC 83 | TCACATGACAAAAACTAGCCCCCATCTCAATCATATACCAAATCTCTCCCTCACTAAACG 84 | TAAGCCTTCTCCTCACTCTCTCAATCTTATCCATCATAGCAGGCAGTTGAGGTGGATTAA 85 | ACCAAACCCAGCTACGCAAAATCTTAGCATACTCCTCAATTACCCACATAGGATGAATAA 86 | TAGCAGTTCTACCGTACAACCCTAACATAACCATTCTTAATTTAACTATTTATATTATCC 87 | TAACTACTACCGCATTCCTACTACTCAACTTAAACTCCAGCACCACGACCCTACTACTAT 88 | CTCGCACCTGAAACAAGCTAACATGACTAACACCCTTAATTCCATCCACCCTCCTCTCCC 89 | TAGGAGGCCTGCCCCCGCTAACCGGCTTTTTGCCCAAATGGGCCATTATCGAAGAATTCA 90 | CAAAAAACAATAGCCTCATCATCCCCACCATCATAGCCACCATCACCCTCCTTAACCTCT 91 | ACTTCTACCTACGCCTAATCTACTCCACCTCAATCACACTACTCCCCATATCTAACAACG 92 | TAAAAATAAAATGACAGTTTGAACATACAAAACCCACCCCATTCCTCCCCACACTCATCG 93 | CCCTTACCACGCTACTCCTACCTATCTCCCCTTTTATACTAATAATCTTATAGAAATTTA 94 | GGTTAAATACAGACCAAGAGCCTTCAAAGCCCTCAGTAAGTTGCAATACTTAATTTCTGT 95 | AACAGCTAAGGACTGCAAAACCCCACTCTGCATCAACTGAACGCAAATCAGCCACTTTAA 96 | TTAAGCTAAGCCCTTACTAGACCAATGGGACTTAAACCCACAAACACTTAGTTAACAGCT 97 | AAGCACCCTAATCAACTGGCTTCAATCTACTTCTCCCGCCGCCGGGAAAAAAGGCGGGAG 98 | AAGCCCCGGCAGGTTTGAAGCTGCTTCTTCGAATTTGCAATTCAATATGAAAATCACCTC 99 | GGAGCTGGTAAAAAGAGGCCTAACCCCTGTCTTTAGATTTACAGTCCAATGCTTCACTCA 100 | GCCATTTTACCTCACCCCCACTGATGTTCGCCGACCGTTGACTATTCTCTACAAACCACA 101 | AAGACATTGGAACACTATACCTATTATTCGGCGCATGAGCTGGAGTCCTAGGCACAGCTC 102 | TAAGCCTCCTTATTCGAGCCGAGCTGGGCCAGCCAGGCAACCTTCTAGGTAACGACCACA 103 | TCTACAACGTTATCGTCACAGCCCATGCATTTGTAATAATCTTCTTCATAGTAATACCCA 104 | TCATAATCGGAGGCTTTGGCAACTGACTAGTTCCCCTAATAATCGGTGCCCCCGATATGG 105 | CGTTTCCCCGCATAAACAACATAAGCTTCTGACTCTTACCTCCCTCTCTCCTACTCCTGC 106 | TCGCATCTGCTATAGTGGAGGCCGGAGCAGGAACAGGTTGAACAGTCTACCCTCCCTTAG 107 | CAGGGAACTACTCCCACCCTGGAGCCTCCGTAGACCTAACCATCTTCTCCTTACACCTAG 108 | CAGGTGTCTCCTCTATCTTAGGGGCCATCAATTTCATCACAACAATTATCAATATAAAAC 109 | CCCCTGCCATAACCCAATACCAAACGCCCCTCTTCGTCTGATCCGTCCTAATCACAGCAG 110 | TCCTACTTCTCCTATCTCTCCCAGTCCTAGCTGCTGGCATCACTATACTACTAACAGACC 111 | GCAACCTCAACACCACCTTCTTCGACCCCGCCGGAGGAGGAGACCCCATTCTATACCAAC 112 | ACCTATTCTGATTTTTCGGTCACCCTGAAGTTTATATTCTTATCCTACCAGGCTTCGGAA 113 | TAATCTCCCATATTGTAACTTACTACTCCGGAAAAAAAGAACCATTTGGATACATAGGTA 114 | TGGTCTGAGCTATGATATCAATTGGCTTCCTAGGGTTTATCGTGTGAGCACACCATATAT 115 | TTACAGTAGGAATAGACGTAGACACACGAGCATATTTCACCTCCGCTACCATAATCATCG 116 | CTATCCCCACCGGCGTCAAAGTATTTAGCTGACTCGCCACACTCCACGGAAGCAATATGA 117 | AATGATCTGCTGCAGTGCTCTGAGCCCTAGGATTCATCTTTCTTTTCACCGTAGGTGGCC 118 | TGACTGGCATTGTATTAGCAAACTCATCACTAGACATCGTACTACACGACACGTACTACG 119 | TTGTAGCCCACTTCCACTATGTCCTATCAATAGGAGCTGTATTTGCCATCATAGGAGGCT 120 | TCATTCACTGATTTCCCCTATTCTCAGGCTACACCCTAGACCAAACCTACGCCAAAATCC 121 | ATTTCACTATCATATTCATCGGCGTAAATCTAACTTTCTTCCCACAACACTTTCTCGGCC 122 | TATCCGGAATGCCCCGACGTTACTCGGACTACCCCGATGCATACACCACATGAAACATCC 123 | TATCATCTGTAGGCTCATTCATTTCTCTAACAGCAGTAATATTAATAATTTTCATGATTT 124 | GAGAAGCCTTCGCTTCGAAGCGAAAAGTCCTAATAGTAGAAGAACCCTCCATAAACCTGG 125 | AGTGACTATATGGATGCCCCCCACCCTACCACACATTCGAAGAACCCGTATACATAAAAT 126 | CTAGACAAAAAAGGAAGGAATCGAACCCCCCAAAGCTGGTTTCAAGCCAACCCCATGGCC 127 | TCCATGACTTTTTCAAAAAGGTATTAGAAAAACCATTTCATAACTTTGTCAAAGTTAAAT 128 | TATAGGCTAAATCCTATATATCTTAATGGCACATGCAGCGCAAGTAGGTCTACAAGACGC 129 | TACTTCCCCTATCATAGAAGAGCTTATCACCTTTCATGATCACGCCCTCATAATCATTTT 130 | CCTTATCTGCTTCCTAGTCCTGTATGCCCTTTTCCTAACACTCACAACAAAACTAACTAA 131 | TACTAACATCTCAGACGCTCAGGAAATAGAAACCGTCTGAACTATCCTGCCCGCCATCAT 132 | CCTAGTCCTCATCGCCCTCCCATCCCTACGCATCCTTTACATAACAGACGAGGTCAACGA 133 | TCCCTCCCTTACCATCAAATCAATTGGCCACCAATGGTACTGAACCTACGAGTACACCGA 134 | CTACGGCGGACTAATCTTCAACTCCTACATACTTCCCCCATTATTCCTAGAACCAGGCGA 135 | CCTGCGACTCCTTGACGTTGACAATCGAGTAGTACTCCCGATTGAAGCCCCCATTCGTAT 136 | AATAATTACATCACAAGACGTCTTGCACTCATGAGCTGTCCCCACATTAGGCTTAAAAAC 137 | AGATGCAATTCCCGGACGTCTAAACCAAACCACTTTCACCGCTACACGACCGGGGGTATA 138 | CTACGGTCAATGCTCTGAAATCTGTGGAGCAAACCACAGTTTCATGCCCATCGTCCTAGA 139 | ATTAATTCCCCTAAAAATCTTTGAAATAGGGCCCGTATTTACCCTATAGCACCCCCTCTA 140 | CCCCCTCTAGAGCCCACTGTAAAGCTAACTTAGCATTAACCTTTTAAGTTAAAGATTAAG 141 | AGAACCAACACCTCTTTACAGTGAAATGCCCCAACTAAATACTACCGTATGGCCCACCAT 142 | AATTACCCCCATACTCCTTACACTATTCCTCATCACCCAACTAAAAATATTAAACACAAA 143 | CTACCACCTACCTCCCTCACCAAAGCCCATAAAAATAAAAAATTATAACAAACCCTGAGA 144 | ACCAAAATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAGGCCTACCC 145 | GCCGCAGTACTGATCATTCTATTTCCCCCTCTATTGATCCCCACCTCCAAATATCTCATC 146 | AACAACCGACTAATCACCACCCAACAATGACTAATCAAACTAACCTCAAAACAAATGATA 147 | ACCATACACAACACTAAAGGACGAACCTGATCTCTTATACTAGTATCCTTAATCATTTTT 148 | ATTGCCACAACTAACCTCCTCGGACTCCTGCCTCACTCATTTACACCAACCACCCAACTA 149 | TCTATAAACCTAGCCATGGCCATCCCCTTATGAGCGGGCACAGTGATTATAGGCTTTCGC 150 | TCTAAGATTAAAAATGCCCTAGCCCACTTCTTACCACAAGGCACACCTACACCCCTTATC 151 | CCCATACTAGTTATTATCGAAACCATCAGCCTACTCATTCAACCAATAGCCCTGGCCGTA 152 | CGCCTAACCGCTAACATTACTGCAGGCCACCTACTCATGCACCTAATTGGAAGCGCCACC 153 | CTAGCAATATCAACCATTAACCTTCCCTCTACACTTATCATCTTCACAATTCTAATTCTA 154 | CTGACTATCCTAGAAATCGCTGTCGCCTTAATCCAAGCCTACGTTTTCACACTTCTAGTA 155 | AGCCTCTACCTGCACGACAACACATAATGACCCACCAATCACATGCCTATCATATAGTAA 156 | AACCCAGCCCATGACCCCTAACAGGGGCCCTCTCAGCCCTCCTAATGACCTCCGGCCTAG 157 | CCATGTGATTTCACTTCCACTCCATAACGCTCCTCATACTAGGCCTACTAACCAACACAC 158 | TAACCATATACCAATGATGGCGCGATGTAACACGAGAAAGCACATACCAAGGCCACCACA 159 | CACCACCTGTCCAAAAAGGCCTTCGATACGGGATAATCCTATTTATTACCTCAGAAGTTT 160 | TTTTCTTCGCAGGATTTTTCTGAGCCTTTTACCACTCCAGCCTAGCCCCTACCCCCCAAT 161 | TAGGAGGGCACTGGCCCCCAACAGGCATCACCCCGCTAAATCCCCTAGAAGTCCCACTCC 162 | TAAACACATCCGTATTACTCGCATCAGGAGTATCAATCACCTGAGCTCACCATAGTCTAA 163 | TAGAAAACAACCGAAACCAAATAATTCAAGCACTGCTTATTACAATTTTACTGGGTCTCT 164 | ATTTTACCCTCCTACAAGCCTCAGAGTACTTCGAGTCTCCCTTCACCATTTCCGACGGCA 165 | TCTACGGCTCAACATTTTTTGTAGCCACAGGCTTCCACGGACTTCACGTCATTATTGGCT 166 | CAACTTTCCTCACTATCTGCTTCATCCGCCAACTAATATTTCACTTTACATCCAAACATC 167 | ACTTTGGCTTCGAAGCCGCCGCCTGATACTGGCATTTTGTAGATGTGGTTTGACTATTTC 168 | TGTATGTCTCCATCTATTGATGAGGGTCTTACTCTTTTAGTATAAATAGTACCGTTAACT 169 | TCCAATTAACTAGTTTTGACAACATTCAAAAAAGAGTAATAAACTTCGCCTTAATTTTAA 170 | TAATCAACACCCTCCTAGCCTTACTACTAATAATTATTACATTTTGACTACCACAACTCA 171 | ACGGCTACATAGAAAAATCCACCCCTTACGAGTGCGGCTTCGACCCTATATCCCCCGCCC 172 | GCGTCCCTTTCTCCATAAAATTCTTCTTAGTAGCTATTACCTTCTTATTATTTGATCTAG 173 | AAATTGCCCTCCTTTTACCCCTACCATGAGCCCTACAAACAACTAACCTGCCACTAATAG 174 | TTATGTCATCCCTCTTATTAATCATCATCCTAGCCCTAAGTCTGGCCTATGAGTGACTAC 175 | AAAAAGGATTAGACTGAACCGAATTGGTATATAGTTTAAACAAAACGAATGATTTCGACT 176 | CATTAAATTATGATAATCATATTTACCAAATGCCCCTCATTTACATAAATATTATACTAG 177 | CATTTACCATCTCACTTCTAGGAATACTAGTATATCGCTCACACCTCATATCCTCCCTAC 178 | TATGCCTAGAAGGAATAATACTATCGCTGTTCATTATAGCTACTCTCATAACCCTCAACA 179 | CCCACTCCCTCTTAGCCAATATTGTGCCTATTGCCATACTAGTCTTTGCCGCCTGCGAAG 180 | CAGCGGTGGGCCTAGCCCTACTAGTCTCAATCTCCAACACATATGGCCTAGACTACGTAC 181 | ATAACCTAAACCTACTCCAATGCTAAAACTAATCGTCCCAACAATTATATTACTACCACT 182 | GACATGACTTTCCAAAAAACACATAATTTGAATCAACACAACCACCCACAGCCTAATTAT 183 | TAGCATCATCCCTCTACTATTTTTTAACCAAATCAACAACAACCTATTTAGCTGTTCCCC 184 | AACCTTTTCCTCCGACCCCCTAACAACCCCCCTCCTAATACTAACTACCTGACTCCTACC 185 | CCTCACAATCATGGCAAGCCAACGCCACTTATCCAGTGAACCACTATCACGAAAAAAACT 186 | CTACCTCTCTATACTAATCTCCCTACAAATCTCCTTAATTATAACATTCACAGCCACAGA 187 | ACTAATCATATTTTATATCTTCTTCGAAACCACACTTATCCCCACCTTGGCTATCATCAC 188 | CCGATGAGGCAACCAGCCAGAACGCCTGAACGCAGGCACATACTTCCTATTCTACACCCT 189 | AGTAGGCTCCCTTCCCCTACTCATCGCACTAATTTACACTCACAACACCCTAGGCTCACT 190 | AAACATTCTACTACTCACTCTCACTGCCCAAGAACTATCAAACTCCTGAGCCAACAACTT 191 | AATATGACTAGCTTACACAATAGCTTTTATAGTAAAGATACCTCTTTACGGACTCCACTT 192 | ATGACTCCCTAAAGCCCATGTCGAAGCCCCCATCGCTGGGTCAATAGTACTTGCCGCAGT 193 | ACTCTTAAAACTAGGCGGCTATGGTATAATACGCCTCACACTCATTCTCAACCCCCTGAC 194 | AAAACACATAGCCTACCCCTTCCTTGTACTATCCCTATGAGGCATAATTATAACAAGCTC 195 | CATCTGCCTACGACAAACAGACCTAAAATCGCTCATTGCATACTCTTCAATCAGCCACAT 196 | AGCCCTCGTAGTAACAGCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCAT 197 | TCTCATAATCGCCCACGGGCTTACATCCTCATTACTATTCTGCCTAGCAAACTCAAACTA 198 | CGAACGCACTCACAGTCGCATCATAATCCTCTCTCAAGGACTTCAAACTCTACTCCCACT 199 | AATAGCTTTTTGATGACTTCTAGCAAGCCTCGCTAACCTCGCCTTACCCCCCACTATTAA 200 | CCTACTGGGAGAACTCTCTGTGCTAGTAACCACGTTCTCCTGATCAAATATCACTCTCCT 201 | ACTTACAGGACTCAACATACTAGTCACAGCCCTATACTCCCTCTACATATTTACCACAAC 202 | ACAATGGGGCTCACTCACCCACCACATTAACAACATAAAACCCTCATTCACACGAGAAAA 203 | CACCCTCATGTTCATACACCTATCCCCCATTCTCCTCCTATCCCTCAACCCCGACATCAT 204 | TACCGGGTTTTCCTCTTGTAAATATAGTTTAACCAAAACATCAGATTGTGAATCTGACAA 205 | CAGAGGCTTACGACCCCTTATTTACCGAGAAAGCTCACAAGAACTGCTAACTCATGCCCC 206 | CATGTCTAACAACATGGCTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGGTCTTAG 207 | GCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTAATAACCATGCACACTACTATAACC 208 | ACCCTAACCCTGACTTCCCTAATTCCCCCCATCCTTACCACCCTCGTTAACCCTAACAAA 209 | AAAAACTCATACCCCCATTATGTAAAATCCATTGTCGCATCCACCTTTATTATCAGTCTC 210 | TTCCCCACAACAATATTCATGTGCCTAGACCAAGAAGTTATTATCTCGAACTGACACTGA 211 | GCCACAACCCAAACAACCCAGCTCTCCCTAAGCTTCAAACTAGACTACTTCTCCATAATA 212 | TTCATCCCTGTAGCATTGTTCGTTACATGGTCCATCATAGAATTCTCACTGTGATATATA 213 | AACTCAGACCCAAACATTAATCAGTTCTTCAAATATCTACTCATCTTCCTAATTACCATA 214 | CTAATCTTAGTTACCGCTAACAACCTATTCCAACTGTTCATCGGCTGAGAGGGCGTAGGA 215 | ATTATATCCTTCTTGCTCATCAGTTGATGATACGCCCGAGCAGATGCCAACACAGCAGCC 216 | ATTCAAGCAATCCTATACAACCGTATCGGCGATATCGGTTTCATCCTCGCCTTAGCATGA 217 | TTTATCCTACACTCCAACTCATGAGACCCACAACAAATAGCCCTTCTAAACGCTAATCCA 218 | AGCCTCACCCCACTACTAGGCCTCCTCCTAGCAGCAGCAGGCAAATCAGCCCAATTAGGT 219 | CTCCACCCCTGACTCCCCTCAGCCATAGAAGGCCCCACCCCAGTCTCAGCCCTACTCCAC 220 | TCAAGCACTATAGTTGTAGCAGGAATCTTCTTACTCATCCGCTTCCACCCCCTAGCAGAA 221 | AATAGCCCACTAATCCAAACTCTAACACTATGCTTAGGCGCTATCACCACTCTGTTCGCA 222 | GCAGTCTGCGCCCTTACACAAAATGACATCAAAAAAATCGTAGCCTTCTCCACTTCAAGT 223 | CAACTAGGACTCATAATAGTTACAATCGGCATCAACCAACCACACCTAGCATTCCTGCAC 224 | ATCTGTACCCACGCCTTCTTCAAAGCCATACTATTTATGTGCTCCGGGTCCATCATCCAC 225 | AACCTTAACAATGAACAAGATATTCGAAAAATAGGAGGACTACTCAAAACCATACCTCTC 226 | ACTTCAACCTCCCTCACCATTGGCAGCCTAGCATTAGCAGGAATACCTTTCCTCACAGGT 227 | TTCTACTCCAAAGACCACATCATCGAAACCGCAAACATATCATACACAAACGCCTGAGCC 228 | CTATCTATTACTCTCATCGCTACCTCCCTGACAAGCGCCTATAGCACTCGAATAATTCTT 229 | CTCACCCTAACAGGTCAACCTCGCTTCCCCACCCTTACTAACATTAACGAAAATAACCCC 230 | ACCCTACTAAACCCCATTAAACGCCTGGCAGCCGGAAGCCTATTCGCAGGATTTCTCATT 231 | ACTAACAACATTTCCCCCGCATCCCCCTTCCAAACAACAATCCCCCTCTACCTAAAACTC 232 | ACAGCCCTCGCTGTCACTTTCCTAGGACTTCTAACAGCCCTAGACCTCAACTACCTAACC 233 | AACAAACTTAAAATAAAATCCCCACTATGCACATTTTATTTCTCCAACATACTCGGATTC 234 | TACCCTAGCATCACACACCGCACAATCCCCTATCTAGGCCTTCTTACGAGCCAAAACCTG 235 | CCCCTACTCCTCCTAGACCTAACCTGACTAGAAAAGCTATTACCTAAAACAATTTCACAG 236 | CACCAAATCTCCACCTCCATCATCACCTCAACCCAAAAAGGCATAATTAAACTTTACTTC 237 | CTCTCTTTCTTCTTCCCACTCATCCTAACCCTACTCCTAATCACATAACCTATTCCCCCG 238 | AGCAATCTCAATTACAATATATACACCAACAAACAATGTTCAACCAGTAACTACTACTAA 239 | TCAACGCCCATAATCATACAAAGCCCCCGCACCAATAGGATCCTCCCGAATCAACCCTGA 240 | CCCCTCTCCTTCATAAATTATTCAGCTTCCTACACTATTAAAGTTTACCACAACCACCAC 241 | CCCATCATACTCTTTCACCCACAGCACCAATCCTACCTCCATCGCTAACCCCACTAAAAC 242 | ACTCACCAAGACCTCAACCCCTGACCCCCATGCCTCAGGATACTCCTCAATAGCCATCGC 243 | TGTAGTATATCCAAAGACAACCATCATTCCCCCTAAATAAATTAAAAAAACTATTAAACC 244 | CATATAACCTCCCCCAAAATTCAGAATAATAACACACCCGACCACACCGCTAACAATCAA 245 | TACTAAACCCCCATAAATAGGAGAAGGCTTAGAAGAAAACCCCACAAACCCCATTACTAA 246 | ACCCACACTCAACAGAAACAAAGCATACATCATTATTCTCGCACGGACTACAACCACGAC 247 | CAATGATATGAAAAACCATCGTTGTATTTCAACTACAAGAACACCAATGACCCCAATACG 248 | CAAAACTAACCCCCTAATAAAATTAATTAACCACTCATTCATCGACCTCCCCACCCCATC 249 | CAACATCTCCGCATGATGAAACTTCGGCTCACTCCTTGGCGCCTGCCTGATCCTCCAAAT 250 | CACCACAGGACTATTCCTAGCCATGCACTACTCACCAGACGCCTCAACCGCCTTTTCATC 251 | AATCGCCCACATCACTCGAGACGTAAATTATGGCTGAATCATCCGCTACCTTCACGCCAA 252 | TGGCGCCTCAATATTCTTTATCTGCCTCTTCCTACACATCGGGCGAGGCCTATATTACGG 253 | ATCATTTCTCTACTCAGAAACCTGAAACATCGGCATTATCCTCCTGCTTGCAACTATAGC 254 | AACAGCCTTCATAGGCTATGTCCTCCCGTGAGGCCAAATATCATTCTGAGGGGCCACAGT 255 | AATTACAAACTTACTATCCGCCATCCCATACATTGGGACAGACCTAGTTCAATGAATCTG 256 | AGGAGGCTACTCAGTAGACAGTCCCACCCTCACACGATTCTTTACCTTTCACTTCATCTT 257 | GCCCTTCATTATTGCAGCCCTAGCAACACTCCACCTCCTATTCTTGCACGAAACGGGATC 258 | AAACAACCCCCTAGGAATCACCTCCCATTCCGATAAAATCACCTTCCACCCTTACTACAC 259 | AATCAAAGACGCCCTCGGCTTACTTCTCTTCCTTCTCTCCTTAATGACATTAACACTATT 260 | CTCACCAGACCTCCTAGGCGACCCAGACAATTATACCCTAGCCAACCCCTTAAACACCCC 261 | TCCCCACATCAAGCCCGAATGATATTTCCTATTCGCCTACACAATTCTCCGATCCGTCCC 262 | TAACAAACTAGGAGGCGTCCTTGCCCTATTACTATCCATCCTCATCCTAGCAATAATCCC 263 | CATCCTCCATATATCCAAACAACAAAGCATAATATTTCGCCCACTAAGCCAATCACTTTA 264 | TTGACTCCTAGCCGCAGACCTCCTCATTCTAACCTGAATCGGAGGACAACCAGTAAGCTA 265 | CCCTTTTACCATCATTGGACAAGTAGCATCCGTACTATACTTCACAACAATCCTAATCCT 266 | AATACCAACTATCTCCCTAATTGAAAACAAAATACTCAAATGGGCCTGTCCTTGTAGTAT 267 | AAACTAATACACCAGTCTTGTAAACCGGAGATGAAAACCTTTTTCCAAGGACAAATCAGA 268 | GAAAAAGTCTTTAACTCCACCATTAGCACCCAAAGCTAAGATTCTAATTTAAACTATTCT 269 | CTGTTCTTTCATGGGGAAGCAGATTTGGGTACCACCCAAGTATTGACTCACCCATCAACA 270 | ACCGCTATGTATTTCGTACATTACTGCCAGCCACCATGAATATTGTACGGTACCATAAAT 271 | ACTTGACCACCTGTAGTACATAAAAACCCAATCCACATCAAAACCCCCTCCCCATGCTTA 272 | CAAGCAAGTACAGCAATCAACCCTCAACTATCACACATCAACTGCAACTCCAAAGCCACC 273 | CCTCACCCACTAGGATACCAACAAACCTACCCACCCTTAACAGTACATAGTACATAAAGC 274 | CATTTACCGTACATAGCACATTACAGTCAAATCCCTTCTCGTCCCCATGGATGACCCCCC 275 | TCAGATAGGGGTCCCTTGACCACCATCCTCCGTGAAATCAATATCCCGCACAAGAGTGCT 276 | ACTCTCCTCGCTCCGGGCCCATAACACTTGGGGGTAGCTAAAGTGAACTGTATCCGACAT 277 | CTGGTTCCTACTTCAGGGTCATAAAGCCTAAATAGCCCACACGTTCCCCTTAAATAAGAC 278 | ATCACGATG 279 | -------------------------------------------------------------------------------- /Data/GRCH38_MT.fa.fai: -------------------------------------------------------------------------------- 1 | MT 16569 54 60 61 2 | -------------------------------------------------------------------------------- /Data/LeifEtAl/annotation_file/SRR_donors_list: -------------------------------------------------------------------------------- 1 | SRR7246333 Donor1_C112_S01 2 | SRR7246334 Donor1_C112_S02 3 | SRR7246335 Donor1_C112_S03 4 | SRR7246336 Donor1_C112_S04 5 | SRR7246337 Donor1_C112_S05 6 | SRR7246338 Donor1_C112_S06 7 | SRR7246339 Donor1_C112_S07 8 | SRR7246340 Donor1_C112_S08 9 | SRR7246341 Donor1_C112_S09 10 | SRR7246342 Donor1_C112_S10 11 | SRR7246343 Donor1_C112_S11 12 | SRR7246344 Donor1_C112_S12 13 | SRR7246345 Donor1_C112_S13 14 | SRR7246346 Donor1_C112_S14 15 | SRR7246347 Donor1_C112_S15 16 | SRR7246348 Donor1_C112_S16 17 | SRR7246349 Donor1_C113_S01 18 | SRR7246350 Donor1_C113_S02 19 | SRR7246351 Donor1_C113_S03 20 | SRR7246352 Donor1_C113_S04 21 | SRR7246353 Donor1_C113_S05 22 | SRR7246354 Donor1_C113_S06 23 | SRR7246355 Donor1_C113_S07 24 | SRR7246356 Donor1_C113_S08 25 | SRR7246357 Donor1_C113_S09 26 | SRR7246358 Donor1_C113_S10 27 | SRR7246359 Donor1_C113_S11 28 | SRR7246360 Donor1_C113_S12 29 | SRR7246361 Donor1_C113_S13 30 | SRR7246362 Donor1_C113_S14 31 | SRR7246363 Donor1_C113_S15 32 | SRR7246364 Donor1_C113_S16 33 | SRR7246365 Donor1_C114_S01 34 | SRR7246366 Donor1_C114_S02 35 | SRR7246367 Donor1_C114_S03 36 | SRR7246368 Donor1_C114_S04 37 | SRR7246369 Donor1_C114_S05 38 | SRR7246370 Donor1_C114_S06 39 | SRR7246371 Donor1_C114_S07 40 | SRR7246372 Donor1_C114_S08 41 | SRR7246373 Donor1_C114_S09 42 | SRR7246374 Donor1_C114_S10 43 | SRR7246375 Donor1_C114_S11 44 | SRR7246376 Donor1_C114_S12 45 | SRR7246377 Donor1_C114_S13 46 | SRR7246378 Donor1_C114_S14 47 | SRR7246379 Donor1_C114_S15 48 | SRR7246380 Donor1_C114_S16 49 | SRR7246381 Donor1_C115_S01 50 | SRR7246382 Donor1_C115_S02 51 | SRR7246383 Donor1_C115_S03 52 | SRR7246384 Donor1_C115_S04 53 | SRR7246385 Donor1_C115_S05 54 | SRR7246386 Donor1_C115_S06 55 | SRR7246387 Donor1_C115_S07 56 | SRR7246388 Donor1_C115_S08 57 | SRR7246389 Donor1_C115_S09 58 | SRR7246391 Donor1_C115_S11 59 | SRR7246390 Donor1_C115_S10 60 | SRR7246392 Donor1_C115_S12 61 | SRR7246393 Donor1_C115_S13 62 | SRR7246394 Donor1_C115_S14 63 | SRR7246395 Donor1_C115_S15 64 | SRR7246396 Donor1_C115_S16 65 | SRR7246399 Donor1_C116_S03 66 | SRR7246397 Donor1_C116_S01 67 | SRR7246398 Donor1_C116_S02 68 | SRR7246401 Donor1_C116_S05 69 | SRR7246400 Donor1_C116_S04 70 | SRR7246404 Donor1_C116_S08 71 | SRR7246402 Donor1_C116_S06 72 | SRR7246403 Donor1_C116_S07 73 | SRR7246405 Donor1_C116_S09 74 | SRR7246407 Donor1_C116_S11 75 | SRR7246406 Donor1_C116_S10 76 | SRR7246409 Donor1_C116_S13 77 | SRR7246408 Donor1_C116_S12 78 | SRR7246410 Donor1_C116_S14 79 | SRR7246411 Donor1_C116_S15 80 | SRR7246412 Donor1_C116_S16 81 | SRR7246413 Donor1_C117_S01 82 | SRR7246414 Donor1_C117_S02 83 | SRR7246415 Donor1_C117_S03 84 | SRR7246417 Donor1_C117_S05 85 | SRR7246419 Donor1_C117_S07 86 | SRR7246418 Donor1_C117_S06 87 | SRR7246422 Donor1_C117_S10 88 | SRR7246420 Donor1_C117_S08 89 | SRR7246421 Donor1_C117_S09 90 | SRR7246424 Donor1_C117_S12 91 | SRR7246423 Donor1_C117_S11 92 | SRR7246425 Donor1_C117_S13 93 | SRR7246427 Donor1_C117_S14 94 | SRR7246428 Donor1_C118_S01 95 | SRR7246429 Donor1_C118_S02 96 | SRR7246431 Donor1_C118_S04 97 | SRR7246430 Donor1_C118_S03 98 | SRR7246432 Donor1_C118_S05 99 | SRR7246433 Donor1_C118_S06 100 | SRR7246434 Donor1_C118_S07 101 | SRR7246435 Donor1_C118_S08 102 | SRR7246437 Donor1_C118_S10 103 | SRR7246436 Donor1_C118_S09 104 | SRR7246438 Donor1_C118_S11 105 | SRR7246440 Donor1_C118_S13 106 | SRR7246439 Donor1_C118_S12 107 | SRR7246441 Donor1_C118_S14 108 | SRR7246442 Donor1_C118_S15 109 | SRR7246443 Donor1_C118_S16 110 | SRR7246446 Donor1_C119_S03 111 | SRR7246445 Donor1_C119_S02 112 | SRR7246444 Donor1_C119_S01 113 | SRR7246447 Donor1_C119_S04 114 | SRR7246448 Donor1_C119_S05 115 | SRR7246449 Donor1_C119_S06 116 | SRR7246450 Donor1_C119_S07 117 | SRR7246451 Donor1_C119_S08 118 | SRR7246452 Donor1_C119_S09 119 | SRR7246453 Donor1_C119_S10 120 | SRR7246416 Donor1_C117_S04 121 | SRR7246455 Donor1_C119_S12 122 | SRR7246456 Donor1_C119_S13 123 | SRR7246457 Donor1_C119_S14 124 | SRR7246458 Donor1_C119_S15 125 | SRR7246459 Donor1_C119_S16 126 | SRR7246460 Donor1_C120_S01 127 | SRR7246461 Donor1_C120_S02 128 | SRR7246462 Donor1_C120_S03 129 | SRR7246463 Donor1_C120_S04 130 | SRR7246464 Donor1_C120_S05 131 | SRR7246465 Donor1_C120_S06 132 | SRR7246466 Donor1_C120_S07 133 | SRR7246468 Donor1_C121_S01 134 | SRR7246469 Donor1_C121_S02 135 | SRR7246470 Donor1_C121_S03 136 | SRR7246471 Donor1_C121_S04 137 | SRR7246472 Donor1_C121_S05 138 | SRR7246474 Donor1_C121_S07 139 | SRR7246475 Donor1_C121_S08 140 | SRR7246476 Donor1_C122_S01 141 | SRR7246477 Donor1_C122_S02 142 | SRR7246478 Donor1_C122_S03 143 | SRR7246454 Donor1_C119_S11 144 | SRR7246479 Donor1_C122_S04 145 | SRR7246480 Donor1_C122_S05 146 | SRR7246481 Donor1_C122_S06 147 | SRR7246482 Donor1_C122_S07 148 | SRR7246483 Donor1_C122_S08 149 | SRR7246484 Donor1_C122_S09 150 | SRR7246485 Donor1_C122_S10 151 | SRR7246486 Donor1_C122_S11 152 | SRR7246487 Donor1_C122_S12 153 | SRR7246488 Donor1_C122_S13 154 | SRR7246489 Donor1_C122_S14 155 | SRR7246490 Donor1_C122_S15 156 | SRR7246491 Donor1_C122_S16 157 | SRR7246492 Donor1_C123_S01 158 | SRR7246493 Donor1_C123_S02 159 | SRR7246494 Donor1_C123_S03 160 | SRR7246495 Donor1_C123_S04 161 | SRR7246496 Donor1_C123_S05 162 | SRR7246498 Donor1_C123_S07 163 | SRR7246497 Donor1_C123_S06 164 | SRR7246499 Donor1_C123_S08 165 | SRR7246500 Donor1_C123_S09 166 | SRR7246467 Donor1_C120_S08 167 | SRR7246501 Donor1_C123_S10 168 | SRR7246502 Donor1_C123_S11 169 | SRR7246503 Donor1_C123_S12 170 | SRR7246504 Donor1_C123_S13 171 | SRR7246505 Donor1_C123_S14 172 | SRR7246506 Donor1_C123_S15 173 | SRR7246473 Donor1_C121_S06 174 | SRR7246507 Donor1_C123_S16 175 | SRR7246508 Donor1_C124_S01 176 | SRR7246509 Donor1_C124_S02 177 | SRR7246510 Donor1_C124_S03 178 | SRR7246511 Donor1_C124_S04 179 | SRR7246512 Donor1_C124_S05 180 | SRR7246513 Donor1_C124_S06 181 | SRR7246514 Donor1_C124_S07 182 | SRR7246516 Donor1_C124_S09 183 | SRR7246515 Donor1_C124_S08 184 | SRR7246517 Donor1_C124_S10 185 | SRR7246518 Donor1_C124_S11 186 | SRR7246519 Donor1_C124_S12 187 | SRR7246520 Donor1_C124_S13 188 | SRR7246521 Donor1_C124_S14 189 | SRR7246522 Donor1_C124_S15 190 | SRR7246524 Donor1_C125_S01 191 | SRR7246523 Donor1_C124_S16 192 | SRR7246525 Donor1_C125_S02 193 | SRR7246526 Donor1_C125_S03 194 | SRR7246527 Donor1_C125_S04 195 | SRR7246528 Donor1_C125_S05 196 | SRR7246529 Donor1_C125_S06 197 | SRR7246530 Donor1_C125_S07 198 | SRR7246531 Donor1_C125_S08 199 | SRR7246532 Donor1_C125_S09 200 | SRR7246533 Donor1_C125_S10 201 | SRR7246534 Donor1_C125_S11 202 | SRR7246535 Donor1_C125_S12 203 | SRR7246536 Donor1_C125_S13 204 | SRR7246537 Donor1_C125_S14 205 | SRR7246538 Donor1_C125_S15 206 | SRR7246539 Donor1_C125_S16 207 | SRR7246540 Donor1_C126_S01 208 | SRR7246541 Donor1_C126_S02 209 | SRR7246543 Donor1_C126_S04 210 | SRR7246544 Donor1_C126_S05 211 | SRR7246546 Donor1_C126_S07 212 | SRR7246547 Donor1_C126_S08 213 | SRR7246548 Donor1_C126_S09 214 | SRR7246549 Donor1_C126_S10 215 | SRR7246550 Donor1_C126_S11 216 | SRR7246552 Donor1_C126_S13 217 | SRR7246551 Donor1_C126_S12 218 | SRR7246553 Donor1_C126_S14 219 | SRR7246555 Donor1_C126_S16 220 | SRR7246554 Donor1_C126_S15 221 | SRR7246556 Donor1_C127_S01 222 | SRR7246557 Donor1_C127_S02 223 | SRR7246559 Donor1_C127_S04 224 | SRR7246558 Donor1_C127_S03 225 | SRR7246560 Donor1_C127_S05 226 | SRR7246562 Donor1_C127_S07 227 | SRR7246561 Donor1_C127_S06 228 | SRR7246563 Donor1_C127_S08 229 | SRR7246564 Donor1_C127_S09 230 | SRR7246565 Donor1_C127_S10 231 | SRR7246566 Donor1_C127_S11 232 | SRR7246568 Donor1_C127_S13 233 | SRR7246571 Donor1_C127_S16 234 | SRR7246567 Donor1_C127_S12 235 | SRR7246569 Donor1_C127_S14 236 | SRR7246570 Donor1_C127_S15 237 | SRR7246573 Donor1_C128_S02 238 | SRR7246572 Donor1_C128_S01 239 | SRR7246575 Donor1_C128_S04 240 | SRR7246574 Donor1_C128_S03 241 | SRR7246578 Donor1_C128_S07 242 | SRR7246577 Donor1_C128_S06 243 | SRR7246580 Donor1_C128_S09 244 | SRR7246579 Donor1_C128_S08 245 | SRR7246581 Donor1_C128_S10 246 | SRR7246584 Donor1_C128_S13 247 | SRR7246582 Donor1_C128_S11 248 | SRR7246583 Donor1_C128_S12 249 | SRR7246585 Donor1_C128_S14 250 | SRR7246586 Donor1_C128_S15 251 | SRR7246587 Donor1_C128_S16 252 | SRR7246588 Donor1_C129_S01 253 | SRR7246589 Donor1_C129_S02 254 | SRR7246590 Donor1_C129_S03 255 | SRR7246593 Donor1_C129_S06 256 | SRR7246591 Donor1_C129_S04 257 | SRR7246592 Donor1_C129_S05 258 | SRR7246594 Donor1_C129_S07 259 | SRR7246595 Donor1_C129_S08 260 | SRR7246596 Donor1_C129_S09 261 | SRR7246597 Donor1_C129_S10 262 | SRR7246598 Donor1_C129_S11 263 | SRR7246599 Donor1_C129_S12 264 | SRR7246600 Donor1_C129_S13 265 | SRR7246602 Donor1_C129_S14 266 | SRR7246605 Donor1_C101_S01 267 | SRR7246606 Donor1_C101_S02 268 | SRR7246607 Donor1_C101_S03 269 | SRR7246608 Donor1_C101_S04 270 | SRR7246609 Donor1_C101_S05 271 | SRR7246610 Donor1_C101_S06 272 | SRR7246611 Donor1_C101_S07 273 | SRR7246612 Donor1_C101_S08 274 | SRR7246613 Donor1_C101_S09 275 | SRR7246614 Donor1_C101_S10 276 | SRR7246615 Donor1_C101_S11 277 | SRR7246542 Donor1_C126_S03 278 | SRR7246616 Donor1_C101_S12 279 | SRR7246545 Donor1_C126_S06 280 | SRR7246617 Donor1_C101_S13 281 | SRR7246618 Donor1_C101_S14 282 | SRR7246619 Donor1_C101_S15 283 | SRR7246622 Donor1_C102_S02 284 | SRR7246623 Donor1_C102_S03 285 | SRR7246620 Donor1_C101_S16 286 | SRR7246621 Donor1_C102_S01 287 | SRR7246624 Donor1_C102_S04 288 | SRR7246626 Donor1_C102_S06 289 | SRR7246627 Donor1_C102_S07 290 | SRR7246628 Donor1_C102_S08 291 | SRR7246625 Donor1_C102_S05 292 | SRR7246629 Donor1_C102_S09 293 | SRR7246631 Donor1_C102_S11 294 | SRR7246630 Donor1_C102_S10 295 | SRR7246632 Donor1_C102_S12 296 | SRR7246633 Donor1_C102_S13 297 | SRR7246634 Donor1_C102_S14 298 | SRR7246639 Donor1_C103_S03 299 | SRR7246637 Donor1_C103_S01 300 | SRR7246636 Donor1_C102_S16 301 | SRR7246635 Donor1_C102_S15 302 | SRR7246638 Donor1_C103_S02 303 | SRR7246641 Donor1_C103_S05 304 | SRR7246643 Donor1_C103_S07 305 | SRR7246644 Donor1_C103_S08 306 | SRR7246642 Donor1_C103_S06 307 | SRR7246640 Donor1_C103_S04 308 | SRR7246645 Donor1_C104_S01 309 | SRR7246646 Donor1_C104_S02 310 | SRR7246647 Donor1_C104_S03 311 | SRR7246648 Donor1_C104_S04 312 | SRR7246649 Donor1_C104_S05 313 | SRR7246650 Donor1_C104_S06 314 | SRR7246651 Donor1_C104_S07 315 | SRR7246652 Donor1_C104_S08 316 | SRR7246653 Donor1_C104_S09 317 | SRR7246654 Donor1_C104_S10 318 | SRR7246576 Donor1_C128_S05 319 | SRR7246655 Donor1_C104_S11 320 | SRR7246659 Donor1_C104_S15 321 | SRR7246658 Donor1_C104_S14 322 | SRR7246657 Donor1_C104_S13 323 | SRR7246656 Donor1_C104_S12 324 | SRR7246660 Donor1_C104_S16 325 | SRR7246661 Donor1_C105_S01 326 | SRR7246662 Donor1_C105_S02 327 | SRR7246667 Donor1_C105_S07 328 | SRR7246664 Donor1_C105_S04 329 | SRR7246666 Donor1_C105_S06 330 | SRR7246665 Donor1_C105_S05 331 | SRR7246663 Donor1_C105_S03 332 | SRR7246668 Donor1_C105_S08 333 | SRR7246669 Donor1_C105_S09 334 | SRR7246670 Donor1_C105_S10 335 | SRR7246672 Donor1_C105_S12 336 | SRR7246671 Donor1_C105_S11 337 | SRR7246673 Donor1_C105_S13 338 | SRR7246677 Donor1_C106_S02 339 | SRR7246676 Donor1_C106_S01 340 | SRR7246675 Donor1_C105_S14 341 | SRR7246678 Donor1_C106_S03 342 | SRR7246679 Donor1_C106_S04 343 | SRR7246680 Donor1_C106_S05 344 | SRR7246684 Donor1_C106_S09 345 | SRR7246681 Donor1_C106_S06 346 | SRR7246685 Donor1_C106_S10 347 | SRR7246682 Donor1_C106_S07 348 | SRR7246683 Donor1_C106_S08 349 | SRR7246686 Donor1_C106_S11 350 | SRR7246687 Donor1_C106_S12 351 | SRR7246688 Donor1_C106_S13 352 | SRR7246691 Donor1_C106_S16 353 | SRR7246692 Donor1_C107_S01 354 | SRR7246690 Donor1_C106_S15 355 | SRR7246693 Donor1_C107_S02 356 | SRR7246695 Donor1_C107_S04 357 | SRR7246694 Donor1_C107_S03 358 | SRR7246696 Donor1_C107_S05 359 | SRR7246697 Donor1_C107_S06 360 | SRR7246698 Donor1_C107_S07 361 | SRR7246700 Donor1_C107_S09 362 | SRR7246701 Donor1_C107_S10 363 | SRR7246702 Donor1_C107_S11 364 | SRR7246703 Donor1_C107_S12 365 | SRR7246704 Donor1_C107_S13 366 | SRR7246705 Donor1_C107_S14 367 | SRR7246707 Donor1_C107_S16 368 | SRR7246708 Donor1_C108_S01 369 | SRR7246709 Donor1_C108_S02 370 | SRR7246710 Donor1_C108_S03 371 | SRR7246712 Donor1_C108_S05 372 | SRR7246711 Donor1_C108_S04 373 | SRR7246715 Donor1_C108_S08 374 | SRR7246716 Donor1_C108_S09 375 | SRR7246718 Donor1_C108_S11 376 | SRR7246717 Donor1_C108_S10 377 | SRR7246719 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Donor1_C132_S11 468 | SRR7246814 Donor1_C132_S12 469 | SRR7246815 Donor1_C132_S13 470 | SRR7246816 Donor1_C132_S14 471 | SRR7246755 Donor1_C110_S16 472 | SRR7246817 Donor1_C132_S15 473 | SRR7246818 Donor1_C132_S16 474 | SRR7246819 Donor1_C133_S01 475 | SRR7246820 Donor1_C133_S02 476 | SRR7246821 Donor1_C133_S03 477 | SRR7246822 Donor1_C133_S04 478 | SRR7246713 Donor1_C108_S06 479 | SRR7246823 Donor1_C133_S05 480 | SRR7246824 Donor1_C133_S06 481 | SRR7246825 Donor1_C133_S07 482 | SRR7246826 Donor1_C133_S08 483 | SRR7246827 Donor1_C133_S09 484 | SRR7246828 Donor1_C133_S10 485 | SRR7246830 Donor1_C133_S12 486 | SRR7246829 Donor1_C133_S11 487 | SRR7246777 Donor1_C130_S07 488 | SRR7246776 Donor1_C130_S06 489 | SRR7246831 Donor1_C133_S13 490 | SRR7246833 Donor1_C133_S15 491 | SRR7246834 Donor1_C133_S16 492 | SRR7246832 Donor1_C133_S14 493 | SRR7246835 Donor1_C134_S01 494 | SRR7246836 Donor1_C134_S02 495 | SRR7246837 Donor1_C134_S03 496 | SRR7246838 Donor1_C134_S04 497 | SRR7246839 Donor1_C134_S05 498 | SRR7246785 Donor1_C130_S15 499 | SRR7246840 Donor1_C134_S06 500 | SRR7246841 Donor1_C134_S07 501 | SRR7246842 Donor1_C134_S08 502 | SRR7246844 Donor1_C134_S10 503 | SRR7246845 Donor1_C134_S11 504 | SRR7246843 Donor1_C134_S09 505 | SRR7246846 Donor1_C134_S12 506 | SRR7246847 Donor1_C134_S13 507 | SRR7246849 Donor1_C134_S15 508 | SRR7246848 Donor1_C134_S14 509 | SRR7246850 Donor1_C134_S16 510 | SRR7246852 Donor1_C135_S02 511 | SRR7246851 Donor1_C135_S01 512 | SRR7246853 Donor1_C135_S03 513 | SRR7246855 Donor1_C135_S05 514 | SRR7246854 Donor1_C135_S04 515 | SRR7246856 Donor1_C135_S06 516 | SRR7246794 Donor1_C131_S08 517 | SRR7246859 Donor1_C135_S09 518 | SRR7246857 Donor1_C135_S07 519 | SRR7246858 Donor1_C135_S08 520 | SRR7246860 Donor1_C135_S10 521 | SRR7246861 Donor1_C135_S11 522 | SRR7246862 Donor1_C135_S12 523 | SRR7246863 Donor1_C135_S13 524 | SRR7246865 Donor1_C135_S14 525 | SRR7246800 Donor1_C131_S14 526 | SRR7246810 Donor1_C132_S08 527 | -------------------------------------------------------------------------------- /Data/LeifEtAl/annotation_file/target_cell.csv: -------------------------------------------------------------------------------- 1 | "Donor1_C101_S01" 2 | "Donor1_C101_S02" 3 | "Donor1_C101_S03" 4 | "Donor1_C101_S04" 5 | "Donor1_C101_S05" 6 | "Donor1_C101_S06" 7 | "Donor1_C101_S07" 8 | "Donor1_C101_S08" 9 | "Donor1_C101_S09" 10 | "Donor1_C101_S10" 11 | "Donor1_C101_S11" 12 | "Donor1_C101_S12" 13 | "Donor1_C101_S13" 14 | "Donor1_C101_S14" 15 | "Donor1_C101_S15" 16 | "Donor1_C101_S16" 17 | "Donor1_C102_S01" 18 | "Donor1_C102_S02" 19 | "Donor1_C102_S03" 20 | "Donor1_C102_S04" 21 | "Donor1_C102_S05" 22 | "Donor1_C102_S06" 23 | "Donor1_C102_S07" 24 | "Donor1_C102_S08" 25 | "Donor1_C102_S09" 26 | "Donor1_C102_S10" 27 | "Donor1_C102_S11" 28 | "Donor1_C102_S12" 29 | "Donor1_C102_S13" 30 | "Donor1_C102_S14" 31 | "Donor1_C102_S15" 32 | "Donor1_C102_S16" 33 | "Donor1_C103_S01" 34 | "Donor1_C103_S02" 35 | "Donor1_C103_S03" 36 | "Donor1_C103_S04" 37 | "Donor1_C103_S05" 38 | "Donor1_C103_S06" 39 | "Donor1_C103_S07" 40 | "Donor1_C103_S08" 41 | "Donor1_C104_S01" 42 | "Donor1_C104_S02" 43 | "Donor1_C104_S03" 44 | "Donor1_C104_S04" 45 | "Donor1_C104_S05" 46 | "Donor1_C104_S06" 47 | "Donor1_C104_S07" 48 | "Donor1_C104_S08" 49 | "Donor1_C104_S09" 50 | "Donor1_C104_S10" 51 | "Donor1_C104_S11" 52 | "Donor1_C104_S12" 53 | "Donor1_C104_S13" 54 | "Donor1_C104_S14" 55 | "Donor1_C104_S15" 56 | "Donor1_C104_S16" 57 | "Donor1_C105_S01" 58 | "Donor1_C105_S02" 59 | "Donor1_C105_S03" 60 | "Donor1_C105_S04" 61 | "Donor1_C105_S05" 62 | "Donor1_C105_S06" 63 | "Donor1_C105_S07" 64 | "Donor1_C105_S08" 65 | "Donor1_C105_S09" 66 | "Donor1_C105_S10" 67 | "Donor1_C105_S11" 68 | "Donor1_C105_S12" 69 | "Donor1_C105_S13" 70 | "Donor1_C105_S14" 71 | "Donor1_C106_S01" 72 | "Donor1_C106_S02" 73 | "Donor1_C106_S03" 74 | "Donor1_C106_S04" 75 | "Donor1_C106_S05" 76 | "Donor1_C106_S06" 77 | "Donor1_C106_S07" 78 | "Donor1_C106_S08" 79 | "Donor1_C106_S09" 80 | "Donor1_C106_S10" 81 | "Donor1_C106_S11" 82 | "Donor1_C106_S12" 83 | "Donor1_C106_S13" 84 | "Donor1_C106_S14" 85 | "Donor1_C106_S15" 86 | "Donor1_C106_S16" 87 | "Donor1_C107_S01" 88 | "Donor1_C107_S02" 89 | "Donor1_C107_S03" 90 | "Donor1_C107_S04" 91 | "Donor1_C107_S05" 92 | "Donor1_C107_S06" 93 | "Donor1_C107_S07" 94 | "Donor1_C107_S08" 95 | "Donor1_C107_S09" 96 | "Donor1_C107_S10" 97 | "Donor1_C107_S11" 98 | "Donor1_C107_S12" 99 | "Donor1_C107_S13" 100 | "Donor1_C107_S14" 101 | "Donor1_C107_S15" 102 | "Donor1_C107_S16" 103 | "Donor1_C108_S01" 104 | "Donor1_C108_S02" 105 | "Donor1_C108_S03" 106 | "Donor1_C108_S04" 107 | "Donor1_C108_S05" 108 | "Donor1_C108_S06" 109 | "Donor1_C108_S07" 110 | "Donor1_C108_S08" 111 | "Donor1_C108_S09" 112 | "Donor1_C108_S10" 113 | "Donor1_C108_S11" 114 | "Donor1_C108_S12" 115 | "Donor1_C108_S13" 116 | "Donor1_C108_S14" 117 | "Donor1_C108_S15" 118 | "Donor1_C108_S16" 119 | "Donor1_C109_S01" 120 | "Donor1_C109_S02" 121 | "Donor1_C109_S03" 122 | "Donor1_C109_S04" 123 | "Donor1_C109_S05" 124 | "Donor1_C109_S06" 125 | "Donor1_C109_S07" 126 | "Donor1_C109_S08" 127 | "Donor1_C109_S09" 128 | "Donor1_C109_S10" 129 | "Donor1_C109_S11" 130 | "Donor1_C109_S12" 131 | "Donor1_C109_S13" 132 | "Donor1_C109_S14" 133 | "Donor1_C109_S15" 134 | "Donor1_C109_S16" 135 | "Donor1_C110_S01" 136 | "Donor1_C110_S02" 137 | "Donor1_C110_S03" 138 | "Donor1_C110_S04" 139 | "Donor1_C110_S05" 140 | "Donor1_C110_S06" 141 | "Donor1_C110_S07" 142 | "Donor1_C110_S08" 143 | "Donor1_C110_S09" 144 | "Donor1_C110_S10" 145 | "Donor1_C110_S11" 146 | "Donor1_C110_S12" 147 | "Donor1_C110_S13" 148 | "Donor1_C110_S14" 149 | "Donor1_C110_S15" 150 | "Donor1_C110_S16" 151 | "Donor1_C111_S01" 152 | "Donor1_C111_S02" 153 | "Donor1_C111_S03" 154 | "Donor1_C111_S04" 155 | "Donor1_C111_S05" 156 | "Donor1_C111_S06" 157 | "Donor1_C111_S07" 158 | "Donor1_C111_S08" 159 | "Donor1_C111_S09" 160 | "Donor1_C111_S10" 161 | "Donor1_C111_S11" 162 | "Donor1_C111_S12" 163 | "Donor1_C111_S13" 164 | "Donor1_C111_S14" 165 | "Donor1_C112_S01" 166 | "Donor1_C112_S02" 167 | "Donor1_C112_S03" 168 | "Donor1_C112_S04" 169 | "Donor1_C112_S05" 170 | "Donor1_C112_S06" 171 | "Donor1_C112_S07" 172 | "Donor1_C112_S08" 173 | "Donor1_C112_S09" 174 | "Donor1_C112_S10" 175 | "Donor1_C112_S11" 176 | "Donor1_C112_S12" 177 | "Donor1_C112_S13" 178 | "Donor1_C112_S14" 179 | "Donor1_C112_S15" 180 | "Donor1_C112_S16" 181 | "Donor1_C113_S01" 182 | "Donor1_C113_S02" 183 | "Donor1_C113_S03" 184 | "Donor1_C113_S04" 185 | "Donor1_C113_S05" 186 | "Donor1_C113_S06" 187 | "Donor1_C113_S07" 188 | "Donor1_C113_S08" 189 | "Donor1_C113_S09" 190 | "Donor1_C113_S10" 191 | "Donor1_C113_S11" 192 | "Donor1_C113_S12" 193 | "Donor1_C113_S13" 194 | "Donor1_C113_S14" 195 | "Donor1_C113_S15" 196 | "Donor1_C113_S16" 197 | "Donor1_C114_S01" 198 | "Donor1_C114_S02" 199 | "Donor1_C114_S03" 200 | "Donor1_C114_S04" 201 | "Donor1_C114_S05" 202 | "Donor1_C114_S06" 203 | "Donor1_C114_S07" 204 | "Donor1_C114_S08" 205 | "Donor1_C114_S09" 206 | "Donor1_C114_S10" 207 | "Donor1_C114_S11" 208 | "Donor1_C114_S12" 209 | "Donor1_C114_S13" 210 | "Donor1_C114_S14" 211 | "Donor1_C114_S15" 212 | "Donor1_C114_S16" 213 | "Donor1_C115_S01" 214 | "Donor1_C115_S02" 215 | "Donor1_C115_S03" 216 | "Donor1_C115_S04" 217 | "Donor1_C115_S05" 218 | "Donor1_C115_S06" 219 | "Donor1_C115_S07" 220 | "Donor1_C115_S08" 221 | "Donor1_C115_S09" 222 | "Donor1_C115_S10" 223 | "Donor1_C115_S11" 224 | "Donor1_C115_S12" 225 | "Donor1_C115_S13" 226 | "Donor1_C115_S14" 227 | "Donor1_C115_S15" 228 | "Donor1_C115_S16" 229 | "Donor1_C116_S01" 230 | "Donor1_C116_S02" 231 | "Donor1_C116_S03" 232 | "Donor1_C116_S04" 233 | "Donor1_C116_S05" 234 | "Donor1_C116_S06" 235 | "Donor1_C116_S07" 236 | "Donor1_C116_S08" 237 | "Donor1_C116_S09" 238 | "Donor1_C116_S10" 239 | "Donor1_C116_S11" 240 | "Donor1_C116_S12" 241 | "Donor1_C116_S13" 242 | "Donor1_C116_S14" 243 | "Donor1_C116_S15" 244 | "Donor1_C116_S16" 245 | "Donor1_C117_S01" 246 | "Donor1_C117_S02" 247 | "Donor1_C117_S03" 248 | "Donor1_C117_S04" 249 | "Donor1_C117_S05" 250 | "Donor1_C117_S06" 251 | "Donor1_C117_S07" 252 | "Donor1_C117_S08" 253 | "Donor1_C117_S09" 254 | "Donor1_C117_S10" 255 | "Donor1_C117_S11" 256 | "Donor1_C117_S12" 257 | "Donor1_C117_S13" 258 | "Donor1_C117_S14" 259 | "Donor1_C118_S01" 260 | "Donor1_C118_S02" 261 | "Donor1_C118_S03" 262 | "Donor1_C118_S04" 263 | "Donor1_C118_S05" 264 | "Donor1_C118_S06" 265 | "Donor1_C118_S07" 266 | "Donor1_C118_S08" 267 | "Donor1_C118_S09" 268 | "Donor1_C118_S10" 269 | "Donor1_C118_S11" 270 | "Donor1_C118_S12" 271 | "Donor1_C118_S13" 272 | "Donor1_C118_S14" 273 | "Donor1_C118_S15" 274 | "Donor1_C118_S16" 275 | "Donor1_C119_S01" 276 | "Donor1_C119_S02" 277 | "Donor1_C119_S03" 278 | "Donor1_C119_S04" 279 | "Donor1_C119_S05" 280 | "Donor1_C119_S06" 281 | "Donor1_C119_S07" 282 | "Donor1_C119_S08" 283 | "Donor1_C119_S09" 284 | "Donor1_C119_S10" 285 | "Donor1_C119_S11" 286 | "Donor1_C119_S12" 287 | "Donor1_C119_S13" 288 | "Donor1_C119_S14" 289 | "Donor1_C119_S15" 290 | "Donor1_C119_S16" 291 | "Donor1_C120_S01" 292 | "Donor1_C120_S02" 293 | "Donor1_C120_S03" 294 | "Donor1_C120_S04" 295 | "Donor1_C120_S05" 296 | "Donor1_C120_S06" 297 | "Donor1_C120_S07" 298 | "Donor1_C120_S08" 299 | "Donor1_C121_S01" 300 | "Donor1_C121_S02" 301 | "Donor1_C121_S03" 302 | "Donor1_C121_S04" 303 | "Donor1_C121_S05" 304 | "Donor1_C121_S06" 305 | "Donor1_C121_S07" 306 | "Donor1_C121_S08" 307 | "Donor1_C122_S01" 308 | "Donor1_C122_S02" 309 | "Donor1_C122_S03" 310 | "Donor1_C122_S04" 311 | "Donor1_C122_S05" 312 | "Donor1_C122_S06" 313 | "Donor1_C122_S07" 314 | "Donor1_C122_S08" 315 | "Donor1_C122_S09" 316 | "Donor1_C122_S10" 317 | "Donor1_C122_S11" 318 | "Donor1_C122_S12" 319 | "Donor1_C122_S13" 320 | "Donor1_C122_S14" 321 | "Donor1_C122_S15" 322 | "Donor1_C122_S16" 323 | "Donor1_C123_S01" 324 | "Donor1_C123_S02" 325 | "Donor1_C123_S03" 326 | "Donor1_C123_S04" 327 | "Donor1_C123_S05" 328 | "Donor1_C123_S06" 329 | "Donor1_C123_S07" 330 | "Donor1_C123_S08" 331 | "Donor1_C123_S09" 332 | "Donor1_C123_S10" 333 | "Donor1_C123_S11" 334 | "Donor1_C123_S12" 335 | "Donor1_C123_S13" 336 | "Donor1_C123_S14" 337 | "Donor1_C123_S15" 338 | "Donor1_C123_S16" 339 | "Donor1_C124_S01" 340 | "Donor1_C124_S02" 341 | "Donor1_C124_S03" 342 | "Donor1_C124_S04" 343 | "Donor1_C124_S05" 344 | "Donor1_C124_S06" 345 | "Donor1_C124_S07" 346 | "Donor1_C124_S08" 347 | "Donor1_C124_S09" 348 | "Donor1_C124_S10" 349 | "Donor1_C124_S11" 350 | "Donor1_C124_S12" 351 | "Donor1_C124_S13" 352 | "Donor1_C124_S14" 353 | "Donor1_C124_S15" 354 | "Donor1_C124_S16" 355 | "Donor1_C125_S01" 356 | "Donor1_C125_S02" 357 | "Donor1_C125_S03" 358 | "Donor1_C125_S04" 359 | "Donor1_C125_S05" 360 | "Donor1_C125_S06" 361 | "Donor1_C125_S07" 362 | "Donor1_C125_S08" 363 | "Donor1_C125_S09" 364 | "Donor1_C125_S10" 365 | "Donor1_C125_S11" 366 | "Donor1_C125_S12" 367 | "Donor1_C125_S13" 368 | "Donor1_C125_S14" 369 | "Donor1_C125_S15" 370 | "Donor1_C125_S16" 371 | "Donor1_C126_S01" 372 | "Donor1_C126_S02" 373 | "Donor1_C126_S03" 374 | "Donor1_C126_S04" 375 | "Donor1_C126_S05" 376 | "Donor1_C126_S06" 377 | "Donor1_C126_S07" 378 | "Donor1_C126_S08" 379 | "Donor1_C126_S09" 380 | "Donor1_C126_S10" 381 | "Donor1_C126_S11" 382 | "Donor1_C126_S12" 383 | "Donor1_C126_S13" 384 | "Donor1_C126_S14" 385 | "Donor1_C126_S15" 386 | "Donor1_C126_S16" 387 | "Donor1_C127_S01" 388 | "Donor1_C127_S02" 389 | "Donor1_C127_S03" 390 | "Donor1_C127_S04" 391 | "Donor1_C127_S05" 392 | "Donor1_C127_S06" 393 | "Donor1_C127_S07" 394 | "Donor1_C127_S08" 395 | "Donor1_C127_S09" 396 | "Donor1_C127_S10" 397 | "Donor1_C127_S11" 398 | "Donor1_C127_S12" 399 | "Donor1_C127_S13" 400 | "Donor1_C127_S14" 401 | "Donor1_C127_S15" 402 | "Donor1_C127_S16" 403 | "Donor1_C128_S01" 404 | "Donor1_C128_S02" 405 | "Donor1_C128_S03" 406 | "Donor1_C128_S04" 407 | "Donor1_C128_S05" 408 | "Donor1_C128_S06" 409 | "Donor1_C128_S07" 410 | "Donor1_C128_S08" 411 | "Donor1_C128_S09" 412 | "Donor1_C128_S10" 413 | "Donor1_C128_S11" 414 | "Donor1_C128_S12" 415 | "Donor1_C128_S13" 416 | "Donor1_C128_S14" 417 | "Donor1_C128_S15" 418 | "Donor1_C128_S16" 419 | "Donor1_C129_S01" 420 | "Donor1_C129_S02" 421 | "Donor1_C129_S03" 422 | "Donor1_C129_S04" 423 | "Donor1_C129_S05" 424 | "Donor1_C129_S06" 425 | "Donor1_C129_S07" 426 | "Donor1_C129_S08" 427 | "Donor1_C129_S09" 428 | "Donor1_C129_S10" 429 | "Donor1_C129_S11" 430 | "Donor1_C129_S12" 431 | "Donor1_C129_S13" 432 | "Donor1_C129_S14" 433 | "Donor1_C130_S01" 434 | "Donor1_C130_S02" 435 | "Donor1_C130_S03" 436 | "Donor1_C130_S04" 437 | "Donor1_C130_S05" 438 | "Donor1_C130_S06" 439 | "Donor1_C130_S07" 440 | "Donor1_C130_S08" 441 | "Donor1_C130_S09" 442 | "Donor1_C130_S10" 443 | "Donor1_C130_S11" 444 | "Donor1_C130_S12" 445 | "Donor1_C130_S13" 446 | "Donor1_C130_S14" 447 | "Donor1_C130_S15" 448 | "Donor1_C130_S16" 449 | "Donor1_C131_S01" 450 | "Donor1_C131_S02" 451 | "Donor1_C131_S03" 452 | "Donor1_C131_S04" 453 | "Donor1_C131_S05" 454 | "Donor1_C131_S06" 455 | "Donor1_C131_S07" 456 | "Donor1_C131_S08" 457 | "Donor1_C131_S09" 458 | "Donor1_C131_S10" 459 | "Donor1_C131_S11" 460 | "Donor1_C131_S12" 461 | "Donor1_C131_S13" 462 | "Donor1_C131_S14" 463 | "Donor1_C131_S15" 464 | "Donor1_C131_S16" 465 | "Donor1_C132_S01" 466 | "Donor1_C132_S02" 467 | "Donor1_C132_S03" 468 | "Donor1_C132_S04" 469 | "Donor1_C132_S05" 470 | "Donor1_C132_S06" 471 | "Donor1_C132_S07" 472 | "Donor1_C132_S08" 473 | "Donor1_C132_S09" 474 | "Donor1_C132_S10" 475 | "Donor1_C132_S11" 476 | "Donor1_C132_S12" 477 | "Donor1_C132_S13" 478 | "Donor1_C132_S14" 479 | "Donor1_C132_S15" 480 | "Donor1_C132_S16" 481 | "Donor1_C133_S01" 482 | "Donor1_C133_S02" 483 | "Donor1_C133_S03" 484 | "Donor1_C133_S04" 485 | "Donor1_C133_S05" 486 | "Donor1_C133_S06" 487 | "Donor1_C133_S07" 488 | "Donor1_C133_S08" 489 | "Donor1_C133_S09" 490 | "Donor1_C133_S10" 491 | "Donor1_C133_S11" 492 | "Donor1_C133_S12" 493 | "Donor1_C133_S13" 494 | "Donor1_C133_S14" 495 | "Donor1_C133_S15" 496 | "Donor1_C133_S16" 497 | "Donor1_C134_S01" 498 | "Donor1_C134_S02" 499 | "Donor1_C134_S03" 500 | "Donor1_C134_S04" 501 | "Donor1_C134_S05" 502 | "Donor1_C134_S06" 503 | "Donor1_C134_S07" 504 | "Donor1_C134_S08" 505 | "Donor1_C134_S09" 506 | "Donor1_C134_S10" 507 | "Donor1_C134_S11" 508 | "Donor1_C134_S12" 509 | "Donor1_C134_S13" 510 | "Donor1_C134_S14" 511 | "Donor1_C134_S15" 512 | "Donor1_C134_S16" 513 | "Donor1_C135_S01" 514 | "Donor1_C135_S02" 515 | "Donor1_C135_S03" 516 | "Donor1_C135_S04" 517 | "Donor1_C135_S05" 518 | "Donor1_C135_S06" 519 | "Donor1_C135_S07" 520 | "Donor1_C135_S08" 521 | "Donor1_C135_S09" 522 | "Donor1_C135_S10" 523 | "Donor1_C135_S11" 524 | "Donor1_C135_S12" 525 | "Donor1_C135_S13" 526 | "Donor1_C135_S14" 527 | -------------------------------------------------------------------------------- /Data/LeifEtAl/annotation_file/target_mutation.csv: -------------------------------------------------------------------------------- 1 | "9507T>C" 2 | "779T>C" 3 | "15639T>C" 4 | "2465T>C" 5 | "8978T>C" 6 | "14355T>C" 7 | "10191T>C" 8 | "12736G>A" 9 | "6712A>G" 10 | "11642G>A" 11 | "1082A>G" 12 | "4653A>G" 13 | "2981A>G" 14 | "2623A>G" 15 | "6050T>C" 16 | "13327A>G" 17 | "3776G>A" 18 | "15003G>A" 19 | "12565T>C" 20 | "10586G>A" 21 | "7275T>C" 22 | "8381A>C" 23 | "13093G>A" 24 | "14093T>C" 25 | "3744A>G" 26 | "14305G>A" 27 | "12560A>G" 28 | "7340G>A" 29 | "11456G>A" 30 | "10166T>C" 31 | "7588G>A" 32 | "5914A>G" 33 | "7754G>A" 34 | "7660T>C" 35 | "7054G>A" 36 | "2646G>A" 37 | "11562G>A" 38 | "6574G>A" 39 | "6504G>A" 40 | "10680G>A" 41 | "3241A>C" 42 | "3934G>A" 43 | "4770G>A" 44 | "11622A>C" 45 | "8014A>G" 46 | "6757T>C" 47 | "3995A>G" 48 | "1446A>G" 49 | "9910T>C" 50 | -------------------------------------------------------------------------------- /Data/mouse_MT.fasta: -------------------------------------------------------------------------------- 1 | >MT dna:chromosome chromosome:GRCm38:MT:1:16299:1 REF 2 | GTTAATGTAGCTTAATAACAAAGCAAAGCACTGAAAATGCTTAGATGGATAATTGTATCC 3 | CATAAACACAAAGGTTTGGTCCTGGCCTTATAATTAATTAGAGGTAAAATTACACATGCA 4 | AACCTCCATAGACCGGTGTAAAATCCCTTAAACATTTACTTAAAATTTAAGGAGAGGGTA 5 | TCAAGCACATTAAAATAGCTTAAGACACCTTGCCTAGCCACACCCCCACGGGACTCAGCA 6 | GTGATAAATATTAAGCAATAAACGAAAGTTTGACTAAGTTATACCTCTTAGGGTTGGTAA 7 | ATTTCGTGCCAGCCACCGCGGTCATACGATTAACCCAAACTAATTATCTTCGGCGTAAAA 8 | CGTGTCAACTATAAATAAATAAATAGAATTAAAATCCAACTTATATGTGAAAATTCATTG 9 | TTAGGACCTAAACTCAATAACGAAAGTAATTCTAGTCATTTATAATACACGACAGCTAAG 10 | ACCCAAACTGGGATTAGATACCCCACTATGCTTAGCCATAAACCTAAATAATTAAATTTA 11 | ACAAAACTATTTGCCAGAGAACTACTAGCCATAGCTTAAAACTCAAAGGACTTGGCGGTA 12 | CTTTATATCCATCTAGAGGAGCCTGTTCTATAATCGATAAACCCCGCTCTACCTCACCAT 13 | CTCTTGCTAATTCAGCCTATATACCGCCATCTTCAGCAAACCCTAAAAAGGTATTAAAGT 14 | AAGCAAAAGAATCAAACATAAAAACGTTAGGTCAAGGTGTAGCCAATGAAATGGGAAGAA 15 | ATGGGCTACATTTTCTTATAAAAGAACATTACTATACCCTTTATGAAACTAAAGGACTAA 16 | GGAGGATTTAGTAGTAAATTAAGAATAGAGAGCTTAATTGAATTGAGCAATGAAGTACGC 17 | ACACACCGCCCGTCACCCTCCTCAAATTAAATTAAACTTAACATAATTAATTTCTAGACA 18 | TCCGTTTATGAGAGGAGATAAGTCGTAACAAGGTAAGCATACTGGAAAGTGTGCTTGGAA 19 | TAATCATAGTGTAGCTTAATATTAAAGCATCTGGCCTACACCCAGAAGATTTCATGACCA 20 | ATGAACACTCTGAACTAATCCTAGCCCTAGCCCTACACAAATATAATTATACTATTATAT 21 | AAATCAAAACATTTATCCTACTAAAAGTATTGGAGAAAGAAATTCGTACATCTAGGAGCT 22 | ATAGAACTAGTACCGCAAGGGAAAGATGAAAGACTAATTAAAAGTAAGAACAAGCAAAGA 23 | TTAAACCTTGTACCTTTTGCATAATGAACTAACTAGAAAACTTCTAACTAAAAGAATTAC 24 | AGCTAGAAACCCCGAAACCAAACGAGCTACCTAAAAACAATTTTATGAATCAACTCGTCT 25 | ATGTGGCAAAATAGTGAGAAGATTTTTAGGTAGAGGTGAAAAGCCTAACGAGCTTGGTGA 26 | TAGCTGGTTACCCAAAAAATGAATTTAAGTTCAATTTTAAACTTGCTAAAAAAACAACAA 27 | AATCAAAAAGTAAGTTTAGATTATAGCCAAAAGAGGGACAGCTCTTCTGGAACGGAAAAA 28 | ACCTTTAATAGTGAATAATTAACAAAACAGCTTTTAACCATTGTAGGCCTAAAAGCAGCC 29 | ACCAATAAAGAAAGCGTTCAAGCTCAACATAAAATTTCAATTAATTCCATAATTTACACC 30 | AACTTCCTAAACTTAAAATTGGGTTAATCTATAACTTTATAGATGCAACACTGTTAGTAT 31 | GAGTAACAAGAATTCCAATTCTCCAGGCATACGCGTATAACAACTCGGATAACCATTGTT 32 | AGTTAATCAGACTATAGGCAATAATCACACTATAAATAATCCACCTATAACTTCTCTGTT 33 | AACCCAACACCGGAATGCCTAAAGGAAAGATCCAAAAAGATAAAAGGAACTCGGCAAACA 34 | AGAACCCCGCCTGTTTACCAAAAACATCACCTCTAGCATTACAAGTATTAGAGGCACTGC 35 | CTGCCCAGTGACTAAAGTTTAACGGCCGCGGTATCCTGACCGTGCAAAGGTAGCATAATC 36 | ACTTGTTCCTTAATTAGGGACTAGCATGAACGGCTAAACGAGGGTCCAACTGTCTCTTAT 37 | CTTTAATCAGTGAAATTGACCTTTCAGTGAAGAGGCTGAAATATAATAATAAGACGAGAA 38 | GACCCTATGGAGCTTAAATTATATAACTTATCTATTTAATTTATTAAACCTAATGGCCCA 39 | AAAACTATAGTATAAGTTTGAAATTTCGGTTGGGGTGACCTCGGAGAATAAAAAATCCTC 40 | CGAATGATTATAACCTAGACTTACAAGTCAAAGTAAAATCAACATATCTTATTGACCCAG 41 | ATATATTTTGATCAACGGACCAAGTTACCCTAGGGATAACAGCGCAATCCTATTTAAGAG 42 | TTCATATCGACAATTAGGGTTTACGACCTCGATGTTGGATCAGGACATCCCAATGGTGTA 43 | GAAGCTATTAATGGTTCGTTTGTTCAACGATTAAAGTCCTACGTGATCTGAGTTCAGACC 44 | GGAGCAATCCAGGTCGGTTTCTATCTATTTACGATTTCTCCCAGTACGAAAGGACAAGAG 45 | AAATAGAGCCACCTTACAAATAAGCGCTCTCAACTTAATTTATGAATAAAATCTAAATAA 46 | AATATATACGTACACCCTCTAACCTAGAGAAGGTTATTAGGGTGGCAGAGCCAGGAAATT 47 | GCGTAAGACTTAAAACCTTGTTCCCAGAGGTTCAAATCCTCTCCCTAATAGTGTTCTTTA 48 | TTAATATCCTAACACTCCTCGTCCCCATTCTAATCGCCATAGCCTTCCTAACATTAGTAG 49 | AACGCAAAATCTTAGGGTACATACAACTACGAAAAGGCCCTAACATTGTTGGTCCATACG 50 | GCATTTTACAACCATTTGCAGACGCCATAAAATTATTTATAAAAGAACCAATACGCCCTT 51 | TAACAACCTCTATATCCTTATTTATTATTGCACCTACCCTATCACTCACACTAGCATTAA 52 | GTCTATGAGTTCCCCTACCAATACCACACCCATTAATTAATTTAAACCTAGGGATTTTAT 53 | TTATTTTAGCAACATCTAGCCTATCAGTTTACTCCATTCTATGATCAGGATGAGCCTCAA 54 | ACTCCAAATACTCACTATTCGGAGCTTTACGAGCCGTAGCCCAAACAATTTCATATGAAG 55 | TAACCATAGCTATTATCCTTTTATCAGTTCTATTAATAAATGGATCCTACTCTCTACAAA 56 | CACTTATTACAACCCAAGAACACATATGATTACTTCTGCCAGCCTGACCCATAGCCATAA 57 | TATGATTTATCTCAACCCTAGCAGAAACAAACCGGGCCCCCTTCGACCTGACAGAAGGAG 58 | AATCAGAATTAGTATCAGGGTTTAACGTAGAATACGCAGCCGGCCCATTCGCGTTATTCT 59 | TTATAGCAGAGTACACTAACATTATTCTAATAAACGCCCTAACAACTATTATCTTCCTAG 60 | GACCCCTATACTATATCAATTTACCAGAACTCTACTCAACTAACTTCATAATAGAAGCTC 61 | TACTACTATCATCAACATTCCTATGGATCCGAGCATCTTATCCACGCTTCCGTTACGATC 62 | AACTTATACATCTTCTATGAAAAAACTTTCTACCCCTAACACTAGCATTATGTATGTGAC 63 | ATATTTCTTTACCAATTTTTACAGCGGGAGTACCACCATACATATAGAAATATGTCTGAT 64 | AAAAGAATTACTTTGATAGAGTAAATTATAGAGGTTCAAGCCCTCTTATTTCTAGGACAA 65 | TAGGAATTGAACCTACACTTAAGAATTCAAAATTCTCCGTGCTACCTAAACACCTTATCC 66 | TAATAGTAAGGTCAGCTAATTAAGCTATCGGGCCCATACCCCGAAAACGTTGGTTTAAAT 67 | CCTTCCCGTACTAATAAATCCTATCACCCTTGCCATCATCTACTTCACAATCTTCTTAGG 68 | TCCTGTAATCACAATATCCAGCACCAACCTAATACTAATATGAGTAGGCCTGGAATTCAG 69 | CCTACTAGCAATTATCCCCATACTAATCAACAAAAAAAACCCACGATCAACTGAAGCAGC 70 | AACAAAATACTTCGTCACACAAGCAACAGCCTCAATAATTATCCTCCTGGCCATCGTACT 71 | CAACTATAAACAACTAGGAACATGAATATTTCAACAACAAACAAACGGTCTTATCCTTAA 72 | CATAACATTAATAGCCCTATCCATAAAACTAGGCCTCGCCCCATTCCACTTCTGATTACC 73 | AGAAGTAACTCAAGGGATCCCACTGCACATAGGACTTATTCTTCTTACATGACAAAAAAT 74 | TGCTCCCCTATCAATTTTAATTCAAATTTACCCGCTACTCAACTCTACTATCATTTTAAT 75 | ACTAGCAATTACTTCTATTTTCATAGGGGCATGAGGAGGACTTAACCAAACACAAATACG 76 | AAAAATTATAGCCTATTCATCAATTGCCCACATAGGATGAATATTAGCAATTCTTCCTTA 77 | CAACCCATCCCTCACTCTACTCAACCTCATAATCTATATTATTCTTACAGCCCCTATATT 78 | CATAGCACTTATACTAAATAACTCTATAACCATCAACTCAATCTCACTTCTATGAAATAA 79 | AACTCCAGCAATACTAACTATAATCTCACTGATATTACTATCCCTAGGAGGCCTTCCACC 80 | ACTAACAGGATTCTTACCAAAATGAATTATCATCACAGAACTTATAAAAAACAACTGTCT 81 | AATTATAGCAACACTCATAGCAATAATAGCTCTACTAAACCTATTCTTTTATACTCGCCT 82 | AATTTATTCCACTTCACTAACAATATTTCCAACCAACAATAACTCAAAAATAATAACTCA 83 | CCAAACAAAAACTAAACCCAACCTAATATTTTCCACCCTAGCTATCATAAGCACAATAAC 84 | CCTACCCCTAGCCCCCCAACTAATTACCTAGAAGTTTAGGATATACTAGTCCGCGAGCCT 85 | TCAAAGCCCTAAGAAAACACACAAGTTTAACTTCTGATAAGGACTGTAAGACTTCATCCT 86 | ACATCTATTGAATGCAAATCAATTGCTTTAATTAAGCTAAGACCTCAACTAGATTGGCAG 87 | GAATTAAACCTACGAAAATTTAGTTAACAGCTAAATACCCTATTACTGGCTTCAATCTAC 88 | TTCTACCGCCGAAAAAAAAAAATGGCGGTAGAAGTCTTAGTAGAGATTTCTCTACACCTT 89 | CGAATTTGCAATTCGACATGAATATCACCTTAAGACCTCTGGTAAAAAGAGGATTTAAAC 90 | CTCTGTGTTTAGATTTACAGTCTAATGCTTACTCAGCCATTTTACCTATGTTCATTAATC 91 | GTTGATTATTCTCAACCAATCACAAAGATATCGGAACCCTCTATCTACTATTCGGAGCCT 92 | GAGCGGGAATAGTGGGTACTGCACTAAGTATTTTAATTCGAGCAGAATTAGGTCAACCAG 93 | GTGCACTTTTAGGAGATGACCAAATTTACAATGTTATCGTAACTGCCCATGCTTTTGTTA 94 | TAATTTTCTTCATAGTAATACCAATAATAATTGGAGGCTTTGGAAACTGACTTGTCCCAC 95 | TAATAATCGGAGCCCCAGATATAGCATTCCCACGAATAAATAATATAAGTTTTTGACTCC 96 | TACCACCATCATTTCTCCTTCTCCTAGCATCATCAATAGTAGAAGCAGGAGCAGGAACAG 97 | GATGAACAGTCTACCCACCTCTAGCCGGAAATCTAGCCCATGCAGGAGCATCAGTAGACC 98 | TAACAATTTTCTCCCTTCATTTAGCTGGAGTGTCATCTATTTTAGGTGCAATTAATTTTA 99 | TTACCACTATTATCAACATGAAACCCCCAGCCATAACACAGTATCAAACTCCACTATTTG 100 | TCTGATCCGTACTTATTACAGCCGTACTGCTCCTATTATCACTACCAGTGCTAGCCGCAG 101 | GCATTACTATACTACTAACAGACCGCAACCTAAACACAACTTTCTTTGATCCCGCTGGAG 102 | GAGGGGACCCAATTCTCTACCAGCATCTGTTCTGATTCTTTGGGCACCCAGAAGTTTATA 103 | TTCTTATCCTCCCAGGATTTGGAATTATTTCACATGTAGTTACTTACTACTCCGGAAAAA 104 | AAGAACCTTTCGGCTATATAGGAATAGTATGAGCAATAATGTCTATTGGCTTTCTAGGCT 105 | TTATTGTATGAGCCCACCACATATTCACAGTAGGATTAGATGTAGACACACGAGCTTACT 106 | TTACATCAGCCACTATAATTATCGCAATTCCTACCGGTGTCAAAGTATTTAGCTGACTTG 107 | CAACCCTACACGGAGGTAATATTAAATGATCTCCAGCTATACTATGAGCCTTAGGCTTTA 108 | TTTTCTTATTTACAGTTGGTGGTCTAACCGGAATTGTTTTATCCAACTCATCCCTTGACA 109 | TCGTGCTTCACGATACATACTATGTAGTAGCCCATTTCCACTATGTTCTATCAATGGGAG 110 | CAGTGTTTGCTATCATAGCAGGATTTGTTCACTGATTCCCATTATTTTCAGGCTTCACCC 111 | TAGATGACACATGAGCAAAAGCCCACTTCGCCATCATATTCGTAGGAGTAAACATAACAT 112 | TCTTCCCTCAACATTTCCTGGGCCTTTCAGGAATACCACGACGCTACTCAGACTACCCAG 113 | ATGCTTACACCACATGAAACACTGTCTCTTCTATAGGATCATTTATTTCACTAACAGCTG 114 | TTCTCATCATGATCTTTATAATTTGAGAGGCCTTTGCTTCAAAACGAGAAGTAATATCAG 115 | TATCGTATGCTTCAACAAATTTAGAATGACTTCATGGCTGCCCTCCACCATATCACACAT 116 | TCGAGGAACCAACCTATGTAAAAGTAAAATAAGAAAGGAAGGAATCGAACCCCCTAAAAT 117 | TGGTTTCAAGCCAATCTCATATCCTATATGTCTTTCTCAATAAGATATTAGTAAAATCAA 118 | TTACATAACTTTGTCAAAGTTAAATTATAGATCAATAATCTATATATCTTATATGGCCTA 119 | CCCATTCCAACTTGGTCTACAAGACGCCACATCCCCTATTATAGAAGAGCTAATAAATTT 120 | CCATGATCACACACTAATAATTGTTTTCCTAATTAGCTCCTTAGTCCTCTATATCATCTC 121 | GCTAATATTAACAACAAAACTAACACATACAAGCACAATAGATGCACAAGAAGTTGAAAC 122 | CATTTGAACTATTCTACCAGCTGTAATCCTTATCATAATTGCTCTCCCCTCTCTACGCAT 123 | TCTATATATAATAGACGAAATCAACAACCCCGTATTAACCGTTAAAACCATAGGGCACCA 124 | ATGATACTGAAGCTACGAATATACTGACTATGAAGACCTATGCTTTGATTCATATATAAT 125 | CCCAACAAACGACCTAAAACCTGGTGAACTACGACTGCTAGAAGTTGATAACCGAGTCGT 126 | TCTGCCAATAGAACTTCCAATCCGTATATTAATTTCATCTGAAGACGTCCTCCACTCATG 127 | AGCAGTCCCCTCCCTAGGACTTAAAACTGATGCCATCCCAGGCCGACTAAATCAAGCAAC 128 | AGTAACATCAAACCGACCAGGGTTATTCTATGGCCAATGCTCTGAAATTTGTGGATCTAA 129 | CCATAGCTTTATGCCCATTGTCCTAGAAATGGTTCCACTAAAATATTTCGAAAACTGATC 130 | TGCTTCAATAATTTAATTTCACTATGAAGCTAAGAGCGTTAACCTTTTAAGTTAAAGTTA 131 | GAGACCTTAAAATCTCCATAGTGATATGCCACAACTAGATACATCAACATGATTTATCAC 132 | AATTATCTCATCAATAATTACCCTATTTATCTTATTTCAACTAAAAGTCTCATCACAAAC 133 | ATTCCCACTGGCACCTTCACCAAAATCACTAACAACCATAAAAGTAAAAACCCCTTGAGA 134 | ATTAAAATGAACGAAAATCTATTTGCCTCATTCATTACCCCAACAATAATAGGATTCCCA 135 | ATCGTTGTAGCCATCATTATATTTCCTTCAATCCTATTCCCATCCTCAAAACGCCTAATC 136 | AACAACCGTCTCCATTCTTTCCAACACTGACTAGTTAAACTTATTATCAAACAAATAATG 137 | CTAATCCACACACCAAAAGGACGAACATGAACCCTAATAATTGTTTCCCTAATCATATTT 138 | ATTGGATCAACAAATCTCCTAGGCCTTTTACCACATACATTTACACCTACTACCCAACTA 139 | TCCATAAATCTAAGTATAGCCATTCCACTATGAGCTGGAGCCGTAATTACAGGCTTCCGA 140 | CACAAACTAAAAAGCTCACTTGCCCACTTCCTTCCACAAGGAACTCCAATTTCACTAATT 141 | CCAATACTTATTATTATTGAAACAATTAGCCTATTTATTCAACCAATGGCATTAGCAGTC 142 | CGGCTTACAGCTAACATTACTGCAGGACACTTATTAATACACCTAATCGGAGGAGCTACT 143 | CTAGTATTAATAAATATTAGCCCACCAACAGCTACCATTACATTTATTATTTTACTTCTA 144 | CTCACAATTCTAGAATTTGCAGTAGCATTAATTCAAGCCTACGTATTCACCCTCCTAGTA 145 | AGCCTATATCTACATGATAATACATAATGACCCACCAAACTCATGCATATCACATAGTTA 146 | ATCCAAGTCCATGACCATTAACTGGAGCCTTTTCAGCCCTCCTTCTAACATCAGGTCTAG 147 | TAATATGATTTCACTATAATTCAATTACACTATTAACCCTTGGCCTACTCACCAATATCC 148 | TCACAATATATCAATGATGACGAGACGTAATTCGTGAAGGAACCTACCAAGGCCACCACA 149 | CTCCTATTGTACAAAAAGGACTACGATATGGTATAATTCTATTCATCGTCTCGGAAGTAT 150 | TTTTCTTTGCAGGATTCTTCTGAGCGTTCTATCATTCTAGCCTCGTACCAACACATGATC 151 | TAGGAGGCTGCTGACCTCCAACAGGAATTTCACCACTTAACCCTCTAGAAGTCCCACTAC 152 | TTAATACTTCAGTACTTCTAGCATCAGGTGTTTCAATTACATGAGCTCATCATAGCCTTA 153 | TAGAAGGTAAACGAAACCACATAAATCAAGCCCTACTAATTACCATTATACTAGGACTTT 154 | ACTTCACCATCCTCCAAGCTTCAGAATACTTTGAAACATCATTCTCCATTTCAGATGGTA 155 | TCTATGGTTCTACATTCTTCATGGCTACTGGATTCCATGGACTCCATGTAATTATTGGAT 156 | CAACATTCCTTATTGTTTGCCTACTACGACAACTAAAATTTCACTTCACATCAAAACATC 157 | ACTTCGGATTTGAAGCCGCAGCATGATACTGACATTTTGTAGACGTAGTCTGACTTTTCC 158 | TATACGTCTCCATTTATTGATGAGGATCTTACTCCCTTAGTATAATTAATATAACTGACT 159 | TCCAATTAGTAGATTCTGAATAAACCCAGAAGAGAGTAATTAACCTGTACACTGTTATCT 160 | TCATTAATATTTTATTATCCCTAACGCTAATTCTAGTTGCATTCTGACTCCCCCAAATAA 161 | ATCTGTACTCAGAAAAAGCAAATCCATATGAATGCGGATTCGACCCTACAAGCTCTGCAC 162 | GTCTACCATTCTCAATAAAATTTTTCTTGGTAGCAATTACATTTCTATTATTTGACCTAG 163 | AAATTGCTCTTCTACTTCCACTACCATGAGCAATTCAAACAATTAAAACCTCTACTATAA 164 | TAATTATAGCCTTTATTCTAGTCACAATTCTATCTCTAGGCCTAGCATATGAATGAACAC 165 | AAAAAGGATTAGAATGAACAGAGTAAATGGTAATTAGTTTAAAAAAAATTAATGATTTCG 166 | ACTCATTAGATTATGATGATGTTCATAATTACCAATATGCCATCTACCTTCTTCAACCTC 167 | ACCATAGCCTTCTCACTATCACTTCTAGGGACACTTATATTTCGCTCTCACCTAATATCC 168 | ACATTACTATGCCTGGAAGGCATAGTATTATCCTTATTTATTATAACTTCAGTAACTTCC 169 | CTAAACTCCAACTCCATAAGCTCCATACCAATCCCCATCACCATCTTAGTTTTCGCAGCC 170 | TGCGAAGCAGCTGTAGGACTAGCCCTACTAGTAAAAGTTTCAAACACGTACGGAACAGAT 171 | TACGTCCAAAATCTCAACCTACTACAATGCTAAAAATTATTCTTCCCTCACTAATGCTAC 172 | TACCACTAACCTGACTATCAAGCCCTAAAAAAACCTGAACAAACGTAACCTCATATAGTT 173 | TTCTAATTAGTTTAACCAGCCTAACACTTCTATGACAAACCGACGAAAATTATAAAAACT 174 | TTTCAAATATATTCTCCTCAGACCCCCTATCCACACCATTAATTATTTTAACAGCCTGAT 175 | TACTGCCACTAATATTAATAGCTAGCCAAAACCACCTAAAAAAAGATAATAACGTACTAC 176 | AAAAACTCTACATCTCAATACTAATCAGCTTACAAATTCTCCTAATCATAACCTTTTCAG 177 | CAACTGAACTAATTATATTTTATATTTTATTTGAAGCAACCTTAATCCCAACACTTATTA 178 | TTATTACCCGATGAGGGAACCAAACTGAACGCCTAAACGCAGGGATTTATTTCCTATTTT 179 | ATACCCTAATCGGTTCTATTCCACTGCTAATTGCCCTCATCTTAATCCAAAACCATGTAG 180 | GAACCCTAAACCTCATAATTTTATCATTCACAACACACACCTTAGACGCTTCATGATCTA 181 | ACAACTTACTATGGTTGGCATGCATAATAGCATTTCTTATTAAAATACCATTATATGGAG 182 | TTCACCTATGACTACCAAAAGCCCATGTTGAAGCTCCAATTGCTGGGTCAATAATTCTAG 183 | CAGCTATTCTTCTAAAATTAGGTAGTTACGGAATAATTCGCATCTCCATTATTCTAGACC 184 | CACTAACAAAATATATAGCATACCCCTTCATCCTTCTCTCCCTATGAGGAATAATTATAA 185 | CTAGCTCAATCTGCTTACGCCAAACAGATTTAAAATCACTAATCGCCTACTCCTCAGTTA 186 | GCCACATAGCACTTGTTATTGCATCAATCATAATCCAAACTCCATGAAGCTTCATAGGAG 187 | CAACAATACTAATAATCGCACATGGCCTCACATCATCACTCCTATTCTGCCTAGCAAACT 188 | CCAACTACGAACGGATCCACAGCCGTACTATAATCATGGCCCGAGGACTTCAAATGGTCT 189 | TCCCACTTATAGCCACATGATGACTGATAGCAAGTCTAGCTAATCTAGCTCTACCCCCTT 190 | CAATCAATCTAATAGGAGAATTATTCATTACCATATCATTATTTTCTTGATCAAACTTTA 191 | CCATTATTCTTATAGGAATTAACATTATTATTACAGGTATATACTCAATATACATAATTA 192 | TTACCACCCAACGCGGCAAACTAACCAACCATATAATTAACCTCCAACCCTCACACACAC 193 | GAGAACTAACACTAATAGCCCTTCACATAATTCCACTTATTCTTCTAACTACCAGTCCAA 194 | AACTAATTACAGGCCTGACAATATGTGAATATAGTTTACAAAAAACATTAGACTGTGAAT 195 | CTGACAACAGGAAATAAACCTCCTTATTCACCAAGAAAGATTGCAAGAACTGCTAATTCA 196 | TGCTTCCATGTTTAAAAACATGGCTTTCTTACTTTTATAGGATAATAGTAATCCATTGGT 197 | CTTAGGAACCAAAAACCTTGGTGCAAATCCAAATAAAAGTAATCAATATTTTCACAACCT 198 | CAATCTTATTAATCTTCATTCTTCTACTATCCCCAATCCTAATTTCAATATCAAACCTAA 199 | TTAAACACATCAACTTCCCACTGTACACCACCACATCAATCAAATTCTCCTTCATTATTA 200 | GCCTCTTACCCCTATTAATATTTTTCCACAATAATATAGAATATATAATTACAACCTGGC 201 | ACTGAGTCACCATAAATTCAATAGAACTTAAAATAAGCTTCAAAACTGACTTTTTCTCTA 202 | TCCTGTTTACATCTGTAGCCCTTTTTGTCACATGATCAATTATACAATTCTCTTCATGAT 203 | ATATACACTCAGACCCAAACATCAATCGATTCATTAAATATCTTACACTATTCCTGATTA 204 | CCATGCTTATCCTCACCTCAGCCAACAACATATTTCAACTTTTCATTGGCTGAGAAGGGG 205 | TGGGAATTATATCTTTCCTACTAATTGGATGATGGTACGGACGAACAGACGCAAATACTG 206 | CAGCCCTACAAGCAATCCTCTATAACCGCATCGGAGACATCGGATTCATTTTAGCTATAG 207 | TTTGATTTTCCCTAAACATAAACTCATGAGAACTTCAACAGATTATATTCTCCAACAACA 208 | ACGACAATCTAATTCCACTTATAGGCCTATTAATCGCAGCTACAGGAAAATCAGCACAAT 209 | TTGGCCTCCACCCATGACTACCATCAGCAATAGAAGGCCCTACACCAGTTTCAGCACTAC 210 | TACACTCAAGTACAATAGTAGTTGCAGGAATTTTCCTACTGGTCCGATTCCACCCCCTCA 211 | CGACTAATAATAACTTTATTTTAACAACTATACTTTGCCTCGGAGCCCTAACCACATTAT 212 | TTACAGCTATTTGTGCTCTCACCCAAAACGACATCAAAAAAATCATTGCCTTCTCTACAT 213 | CAAGCCAACTAGGCCTGATAATAGTGACGCTAGGAATAAACCAACCACACCTAGCATTCC 214 | TACACATCTGTACCCACGCATTCTTCAAAGCTATACTCTTTATATGCTCTGGCTCAATCA 215 | TTCATAGCCTGGCAGACGAACAAGACATCCGAAAAATAGGAAACATCACAAAAATCATAC 216 | CATTCACATCATCATGCCTAGTAATCGGAAGCCTCGCCCTCACAGGAATACCATTCCTAA 217 | CAGGGTTCTACTCAAAAGACCTAATTATTGAAGCAATTAATACCTGCAACACCAACGCCT 218 | GAGCCCTACTAATTACACTAATCGCCACTTCTATAACAGCTATGTACAGCATACGAATCA 219 | TTTACTTCGTAACAATAACAAAACCGCGTTTTCCCCCCCTAATCTCCATTAACGAAAATG 220 | ACCCAGACCTCATAAACCCAATCAAACGCCTAGCATTCGGAAGCATCTTTGCAGGATTTG 221 | TCATCTCATATAATATTCCACCAACCAGCATTCCAGTCCTCACAATACCATGATTTTTAA 222 | AAACCACAGCCCTAATTATTTCAGTATTAGGATTCCTAATCGCACTAGAACTAAACAACC 223 | TAACCATAAAACTATCAATAAATAAAGCAAATCCATATTCATCCTTCTCAACTTTACTGG 224 | GGTTTTTCCCATCTATTATTCACCGCATTACACCCATAAAATCTCTCAACCTAAGCCTAA 225 | AAACATCCCTAACTCTCCTAGACTTGATCTGGTTAGAAAAAACCATCCCAAAATCCACCT 226 | CAACTCTTCACACAAACATAACCACTTTAACAACCAACCAAAAAGGCTTAATTAAATTGT 227 | ACTTTATATCATTCCTAATTAACATCATCTTAATTATTATCTTATACTCAATTAATCTCG 228 | AGTAATCTCGATAATAATAAAAATACCCGCAAACAAAGATCACCCAGCTACTACCATCAT 229 | TCAAGTAGCACAACTATATATTGCCGCTACCCCAATCCCTCCTTCCAACATAACTCCAAC 230 | ATCATCAACCTCATACATCAACCAATCTCCCAAACCATCAAGATTAATTACTCCAACTTC 231 | ATCATAATAATTAAGCACACAAATTAAAAAAACCTCTATAATCACCCCCAATACTAAAAA 232 | ACCCAAAATTAATCAGTTAGATCCCCAAGTCTCTGGATATTCCTCAGTAGCTATAGCAGT 233 | CGTATATCCAAACACAACCAACATCCCCCCTAAATAAATTAAAAAAACTATTAAACCTAA 234 | AAACGATCCACCAAACCCTAAAACCATTAAACAACCAACAAACCCACTAACAATTAAACC 235 | TAAACCTCCATAAATAGGTGAAGGCTTTAATGCTAACCCAAGACAACCAACCAAAAATAA 236 | TGAACTTAAAACAAAAATATAATTATTCATTATTTCTACACAGCATTCAACTGCGACCAA 237 | TGACATGAAAAATCATCGTTGTAATTCAACTACAGAAACACCTAATGACAAACATACGAA 238 | AAACACACCCATTATTTAAAATTATTAACCACTCATTCATTGACCTACCTGCCCCATCCA 239 | ACATTTCATCATGATGAAACTTTGGGTCCCTTCTAGGAGTCTGCCTAATAGTCCAAATCA 240 | TTACAGGTCTTTTCTTAGCCATACACTACACATCAGATACAATAACAGCCTTTTCATCAG 241 | TAACACACATTTGTCGAGACGTAAATTACGGGTGACTAATCCGATATATACACGCAAACG 242 | GAGCCTCAATATTTTTTATTTGCTTATTCCTTCATGTCGGACGAGGCTTATATTATGGAT 243 | CATATACATTTATAGAAACCTGAAACATTGGAGTACTTCTACTGTTCGCAGTCATAGCCA 244 | CAGCATTTATAGGCTACGTCCTTCCATGAGGACAAATATCATTCTGAGGTGCCACAGTTA 245 | TTACAAACCTCCTATCAGCCATCCCATATATTGGAACAACCCTAGTCGAATGAATTTGAG 246 | GGGGCTTCTCAGTAGACAAAGCCACCTTGACCCGATTCTTCGCTTTCCACTTCATCTTAC 247 | CATTTATTATCGCGGCCCTAGCAATCGTTCACCTCCTCTTCCTCCACGAAACAGGATCAA 248 | ACAACCCAACAGGATTAAACTCAGATGCAGATAAAATTCCATTTCACCCCTACTATACAA 249 | TCAAAGATATCCTAGGTATCCTAATCATATTCTTAATTCTCATAACCCTAGTATTATTTT 250 | TCCCAGACATACTAGGAGACCCAGACAACTACATACCAGCTAATCCACTAAACACCCCAC 251 | CCCATATTAAACCCGAATGATATTTCCTATTTGCATACGCCATTCTACGCTCAATCCCCA 252 | ATAAACTAGGAGGTGTCCTAGCCTTAATCTTATCTATCCTAATTTTAGCCCTAATACCTT 253 | TCCTTCATACCTCAAAGCAACGAAGCCTAATATTCCGCCCAATCACACAAATTTTGTACT 254 | GAATCCTAGTAGCCAACCTACTTATCTTAACCTGAATTGGGGGCCAACCAGTAGAACACC 255 | CATTTATTATCATTGGCCAACTAGCCTCCATCTCATACTTCTCAATCATCTTAATTCTTA 256 | TACCAATCTCAGGAATTATCGAAGACAAAATACTAAAATTATATCCATGTCTTGATAGTA 257 | TAAACATTACTCTGGTCTTGTAAACCTGAAATGAAGATCTTCTCTTCTCAAGACATCAAG 258 | AAGAAGGAGCTACTCCCCACCACCAGCACCCAAAGCTGGTATTCTAATTAAACTACTTCT 259 | TGAGTACATAAATTTACATAGTACAACAGTACATTTATGTATATCGTACATTAAACTATT 260 | TTCCCCAAGCATATAAGCTAGTACATTAAATCAATGGTTCAGGTCATAAAATAATCATCA 261 | ACATAAATCAATATATATACCATGAATATTATCTTAAACACATTAAACTAATGTTATAAG 262 | GACATATCTGTGTTATCTGACATACACCATACAGTCATAAACTCTTCTCTTCCATATGAC 263 | TATCCCCTTCCCCATTTGGTCTATTAATCTACCATCCTCCGTGAAACCAACAACCCGCCC 264 | ACCAATGCCCCTCTTCTCGCTCCGGGCCCATTAAACTTGGGGGTAGCTAAACTGAAACTT 265 | TATCAGACATCTGGTTCTTACTTCAGGGCCATCAAATGCGTTATCGCCCATACGTTCCCC 266 | TTAAATAAGACATCTCGATGGTATCGGGTCTAATCAGCCCATGACCAACATAACTGTGGT 267 | GTCATGCATTTGGTATCTTTTTATTTTGGCCTACTTTCATCAACATAGCCGTCAAGGCAT 268 | GAAAGGACAGCACACAGTCTAGACGCACCTACGGTGAAGAATCATTAGTCCGCAAAACCC 269 | AATCACCTAAGGCTAATTATTCATGCTTGTTAGACATAAATGCTACTCAATACCAAATTT 270 | TAACTCTCCAAACCCCCCACCCCCTCCTCTTAATGCCAAACCCCAAAAACACTAAGAACT 271 | TGAAAGACATATAATATTAACTATCAAACCCTATGTCCTGATCAATTCTAGTAGTTCCCA 272 | AAATATGACTTATATTTTAGTACTTGTAAAAATTTTACAAAATCATGTTCCGTGAACCAA 273 | AACTCTAATCATACTCTATTACGCAATAAACATTAACAA 274 | -------------------------------------------------------------------------------- /MitoTrace.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Package 19 | PackageUseDevtools: Yes 20 | PackageInstallArgs: --no-multiarch --with-keep.source 21 | PackageRoxygenize: rd,collate,namespace,vignette 22 | -------------------------------------------------------------------------------- /R/MitoTrace.R: -------------------------------------------------------------------------------- 1 | #' @title MitoTrace - an R package for the investigation of mitochondrial heteroplasmies from single-cell RNA sequencing data. 2 | #' 3 | #' @description The MitoTrace function calculates mitochondrial heteroplasmies in (single-cell) RNA sequencing data based on the reads pileups of the mitochondrial genome. 4 | #' 5 | #' @usage MitoTrace(bam_list = bams, ref_fasta = fasta_loc, name = "MT", max_depth = "", 6 | #' min_base_quality = "", min_mapq = "", min_nucleotide_depth = "", min_minor_allele_depth = "") 7 | #' 8 | #' @param bams_list Vector of absolute path(s) pointing to BAM alignment file(s). 9 | #' @param ref_fasta Absolute path to the mitochondrial reference genome in FASTA format. 10 | #' @param name Name of mitochondrial genome as specified in the BAM files. Sequence names can be check with the checkSequenceNames() function. 11 | #' @param tag_name The name of the tag corresponding the cellular barcode. Default = "CB". For droplet scRNA-seq only. 12 | #' @param barcodes The barcode list corresponding to the cells. For 10X genomics scRNA-seq data only. 13 | #' @param min_read The minimum number of read counts to be considered a valid barcode (cell) in the analysis. Default = 1000. For droplet scRNAseq technologies only. 14 | #' @param max_depth The maximum depth of reads considered at any position. 15 | #' @param min_base_quality The minimum read base quality below which the base is ignored when summarizing pileup information. 16 | #' @param min_mapq The minimum mapping quality below which the entire reads is ignored. 17 | #' @param min_nucleotide_depth integer(1); minimum count of each nucleotide at a given position required for said nucleotide to appear in the result. 18 | #' @param min_minor_allele_depth integer(1); minimum count of all nucleotides other than the major allele at agiven position. 19 | #' 20 | #' @details result <- MitoTrace(bam_list = bams, ref_fasta = fasta_loc, chr_name = "MT", max_depth = "1e6", min_base_quality=25, min_mapq=30, min_nucleotide_depth=0, min_minor_allele_depth=0) 21 | #' @details See packageDescription("MitoTrace") for more details. 22 | #' 23 | #' @note This package could not only apply for the analysis of single-cell data but also for bulk seuqencing data. 24 | #' 25 | #' @return Read counts matrix and coverage matrix. 26 | #' 27 | #' @author Mingqiang WANG , Simon Lukas 28 | #' 29 | #' @references The current source code of MitoTrace is from https://github.com/lkmklsmn/MitoTrace. 30 | 31 | 32 | # check BAM chromosome names 33 | checkSequenceNames <- function(bam_list){ 34 | require(Rsamtools) 35 | tmp <- scanBamHeader(bam_list[[1]]) 36 | targets <- names(tmp[[1]][[1]]) 37 | #text <- names(tmp[[1]][[2]]) 38 | targets#[which(text == "@SQ")] 39 | } 40 | 41 | # define the MitoTrace main function 42 | MitoTrace <- function(bam_list = bams, 43 | fasta = fasta_loc, 44 | chr_name = "MT", 45 | tag_name = "CB", 46 | barcodes = NULL, 47 | min_read = 1000, 48 | max_depth= 1e6, 49 | min_base_quality= 25, 50 | min_mapq = 30, 51 | min_nucleotide_depth = 0, 52 | min_minor_allele_depth = 0){ 53 | require(Rsamtools) 54 | require(Matrix) 55 | require(seqinr) 56 | 57 | # if(length(bam_list) == 1) { 58 | # singlefile <- T 59 | # } 60 | # else{ 61 | # singlefile=F 62 | # } 63 | 64 | # if(singlefile) bam_list <- rep(bam_list, 2) 65 | 66 | bases <- c("A", "C", "G", "T") 67 | 68 | # Load in reference FASTA 69 | reffasta <- seqinr::read.fasta(fasta) 70 | mitoChr = attr(reffasta, "name") 71 | maxpos = length(reffasta[[1]]) 72 | reference_genome <- data.frame( 73 | postion = 1:maxpos, 74 | base = toupper(unname(reffasta)[[1]])[1:maxpos] 75 | ) 76 | 77 | which <- GRanges(seqnames = chr_name, ranges = IRanges(1, maxpos)) 78 | 79 | if(length(bam_list) == 1) combinedBam <- TRUE 80 | if(length(bam_list) > 1) combinedBam <- FALSE 81 | # Define list of BAM files 82 | bam_name_list_array <- BamFileList(bam_list) 83 | 84 | if(combinedBam){ 85 | print("Extracting barcodes from single BAM file and running pileup for each barcode separately") 86 | 87 | params <- ScanBamParam(tag = tag_name, which = which) 88 | 89 | if(is.null(barcodes)){ 90 | barcodes_tmp <- scanBam(bam_list, param = params) 91 | good_barcodes <- names(which(table(barcodes_tmp[[1]][[1]][[1]]) > min_read)) 92 | } 93 | if(!is.null(barcodes)){ 94 | good_barcodes <- barcodes[[1]] 95 | } 96 | 97 | # Run pileup command 98 | total_mpileups <- lapply(good_barcodes, function(x){ 99 | 100 | filter <- list(x) 101 | names(filter) <- tag_name 102 | 103 | pileup_bam <- pileup(bam_list, 104 | scanBamParam=ScanBamParam(tagFilter = filter, which=which), 105 | pileupParam=PileupParam(distinguish_strands= FALSE, 106 | max_depth= max_depth, 107 | min_base_quality=min_base_quality, 108 | min_mapq=min_mapq, 109 | min_nucleotide_depth=min_nucleotide_depth, 110 | min_minor_allele_depth=min_minor_allele_depth 111 | )) 112 | bases <- c("A", "T", "C", "G") 113 | base_counts <- lapply(bases, function(base){ 114 | mutation <- subset(pileup_bam, nucleotide == base) 115 | data.frame(mutation$pos, mutation$count) 116 | }) 117 | 118 | names(base_counts) <- bases 119 | base_counts 120 | 121 | }) 122 | 123 | names(total_mpileups) <- good_barcodes 124 | } 125 | else{ 126 | print("Running pileup on each BAM file separately") 127 | 128 | # Define list of BAM files 129 | bam_name_list_array <- BamFileList(bam_list) 130 | 131 | # Run pileup command 132 | total_mpileups <- lapply(bam_name_list_array, function(x){ 133 | 134 | pileup_bam <- pileup(x, 135 | scanBamParam=ScanBamParam(which=which), 136 | pileupParam=PileupParam(distinguish_strands= FALSE, 137 | max_depth= max_depth, 138 | min_base_quality=min_base_quality, 139 | min_mapq=min_mapq, 140 | min_nucleotide_depth=min_nucleotide_depth, 141 | min_minor_allele_depth=min_minor_allele_depth 142 | )) 143 | bases <- c("A", "T", "C", "G") 144 | base_counts <- lapply(bases, function(base){ 145 | mutation <- subset(pileup_bam, nucleotide == base) 146 | data.frame(mutation$pos, mutation$count) 147 | }) 148 | 149 | names(base_counts) <- bases 150 | base_counts 151 | 152 | }) 153 | 154 | nom <- unlist(lapply(bam_list, basename)) 155 | names(total_mpileups) <- nom 156 | } 157 | 158 | # Count number of bases at each position 159 | res_counts <- lapply(bases, function(base){ 160 | 161 | allpos <- 1:maxpos 162 | 163 | counts_base_allcells <- do.call(cbind, lapply(total_mpileups, function(x){ 164 | count_base_cell <- x[[base]] 165 | count_base_cell$mutation.count[match(allpos, count_base_cell$mutation.pos)] 166 | })) 167 | 168 | counts_base_allcells[which(is.na(counts_base_allcells))] <- 0 169 | rownames(counts_base_allcells) <- paste0(allpos, reference_genome$base, ">", base) 170 | colnames(counts_base_allcells) <- names(total_mpileups) 171 | as(counts_base_allcells, "sparseMatrix") 172 | }) 173 | names(res_counts) <- bases 174 | 175 | # Calculate total read coverage at each position 176 | coverage <- matrix(0, nrow(res_counts[[1]]), ncol(res_counts[[1]])) 177 | rownames(coverage) <- as.character(1:maxpos) 178 | colnames(coverage) <- colnames(res_counts[[1]]) 179 | lapply(1:4, function(x) coverage <<- coverage + data.matrix(res_counts[[x]])) 180 | 181 | # Remove reference allele sites 182 | res_counts2 <- lapply(bases, function(base){ 183 | tmp <- res_counts[[base]] 184 | tmp[which(reference_genome$base != base),] 185 | }) 186 | names(res_counts2) <- bases 187 | 188 | # Merge into one table and order 189 | allpos <- unique(unlist(lapply(res_counts2, rownames))) 190 | pos_tmp <- allpos 191 | pos_tmp <- unlist(lapply(pos_tmp, function(x) substr(x, 1, nchar(x) - 3))) 192 | pos_tmp <- as.numeric(pos_tmp) 193 | allpos <- allpos[order(pos_tmp)] 194 | matr <- matrix(0, length(allpos), ncol(res_counts2[["A"]])) 195 | rownames(matr) <- allpos 196 | colnames(matr) <- colnames(res_counts2[["A"]]) 197 | lapply(res_counts2, function(x){ 198 | ok <- intersect(rownames(matr), rownames(x)) 199 | matr[ok,] <<- data.matrix(x[ok,]) 200 | }) 201 | 202 | counts <- matr 203 | 204 | counts <- as(counts, "sparseMatrix") 205 | coverage <- as(coverage, "sparseMatrix") 206 | 207 | # if(singlefile){ 208 | # nom <- colnames(counts)[1] 209 | # counts <- as.data.frame(counts[,1]) 210 | # coverage <- as.data.frame(coverage[,1]) 211 | # colnames(counts) <- colnames(coverage) <- nom 212 | # } 213 | 214 | return(list(read_counts = counts, coverage = coverage)) 215 | } 216 | 217 | # define function to calculate allele frequency based on MitoTrace Rdata object 218 | calc_allele_frequency <- function(object){ 219 | tmp <- rownames(object[[1]]) 220 | pos <- unlist(lapply(tmp, function(x) substr(x, 1, nchar(x) - 3))) 221 | pos <- as.numeric(pos) 222 | (object[[1]])/(object[[2]][pos,] + 0.0001) 223 | } 224 | 225 | # define the MitoTrace plot coverage depth function 226 | MitoDepth <- function(mae = mae_res, species = "human", mt_ann = mt_ann){ 227 | 228 | # set the gene label location 229 | if(species == "human"){ 230 | location_human <- c(288.55, 577, 1124.55, 1602, 2450.05, 3230, 3784.55, 231 | 3900, 4300, 4700, 4990.55, 5150, 5550, 5950, 6350, 6750, 232 | 7150.55, 7300, 7718, 7927.55, 8295, 8469.05, 8867.05, 9598.55, 233 | 9991, 10231.55, 10405, 10618.05, 11448.55, 11750, 12200, 12650, 234 | 13242.55, 14411.05, 14674, 15317.05, 15700, 16200, 16584.55) 235 | } 236 | 237 | # set color 238 | require("RColorBrewer") 239 | col= c(brewer.pal(n = 12, name = "Set3"), brewer.pal(n = 11, name = "Spectral"), brewer.pal(n = 9, name = "Set1"), brewer.pal(n = 8, name = "Accent")) 240 | 241 | # plot the coverave depth # single sample 242 | ymax <- max(log(mae[[2]][,1])) 243 | plot(row.names(mae[[2]]), log(mae[[2]][,1]), 244 | type = "l", 245 | col="brown1", 246 | xlab="MT genome postion", 247 | ylab="log2(Coverage depth)", 248 | main="scRNA-seq coverage depth cross MT genome", 249 | ylim=c(-5,ymax)) 250 | 251 | # plot the MT gene bar 252 | for (i in 1:39) { 253 | if(grepl("TR", mt_ann$gene[i])){ 254 | rect(mt_ann[i,2], -2.5, mt_ann[i,3], -1, col=col[i], border = "white") 255 | text(location[i], -0.5, mt_ann$gene[i], cex = 0.9) 256 | } 257 | else if(grepl("RN", mt_ann$gene[i])){ 258 | rect(mt_ann[i,2], -2.5, mt_ann[i,3], -1, col=col[i], border = "white") 259 | text(location[i], -0.5, mt_ann$gene[i], cex = 0.9) 260 | } 261 | else{ 262 | rect(mt_ann[i,2], -4, mt_ann[i,3], -2.5, col=col[i], border = "white") 263 | text(location[i], -4.5, mt_ann$gene[i], cex = 0.9) 264 | } 265 | } 266 | 267 | } 268 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

4 | 5 | MitoTrace, a user-friendly R package for the analysis of mitochondrial variation in next generation sequencing data. 6 | 7 | ## Prerequisites 8 | 9 | MitoTrace runs on 32-bit or 64-bit GNU/Linux R environment and requires the following dependencies: \* R (\>= 3.6.1) \* seqinr (\>= 3.4-5) \* Matrix (\>= 1.2-17) \* Rsamtools (\>= 2.0.0) 10 | 11 | Please make sure these packages (and correct versions) are installed or install yourself in the following way: 12 | 13 | install.packages("seqinr") 14 | install.packages("Matrix") 15 | if (!requireNamespace("BiocManager", quietly = TRUE)) 16 | install.packages("BiocManager") 17 | BiocManager::install("Rsamtools") 18 | 19 | ## Install 20 | MitoTrace is designed for the R programming language and statistical computing environment. If you want to use the lastest version of MitoTrace, please install it directly from GitHub. First, you need to install and load the devtools package. Then use install_github("lkmklsmn/MitoTrace") as follow line in your R console: 21 | 22 | install.packages("devtools") 23 | library("devtools") 24 | install_github("lkmklsmn/MitoTrace") 25 | 26 | ## Usage 27 | 28 | MitoTrace plots the read coverage across the mitochondria human or mouse genome 29 | 30 | MitoDepth(bam_list = bams, species = "human", mt_ann = mt_ann) 31 | 32 | MitoTrace extracts read coverage and alternative allele counts across all mitochondrial genome positions 33 | 34 | mae_res <- MitoTrace(bam_list = bams, fasta = fasta_loc, chr_name = "MT") 35 | 36 | MitoTrace calculates allele frequency for each mitochondrial position 37 | 38 | af <- calc_allele_frequency(mae_res) 39 | 40 | ## Examples 41 | 42 | Please check the *examples* folder for Markdown files, or use the following link to open. 43 | 44 | 1. [MitoTrace reproduces the heteroplamsy reported in Leif's lineage tracing study analysis[1]](https://htmlpreview.github.io/?https://github.com/lkmklsmn/MitoTrace/blob/master/examples/Reproduce_Cell_Leif_et_al.html) 45 | 46 | 2. [MitoTrace identifies personal variants in SMART-seq2 data data[2]](https://htmlpreview.github.io/?https://github.com/lkmklsmn/MitoTrace/blob/master/examples/Single-Cell-SMART-SEQ2-data.html) 47 | 48 | 3. [MitoTrace distinguishes different cell lines in 10X Genomics data[3]](https://htmlpreview.github.io/?https://github.com/lkmklsmn/MitoTrace/blob/master/examples/Single-Cell-10X-Genomics-data.html) 49 | 50 | ## License 51 | 52 | `MitoTrace` uses GNU General Public License GPL-3. 53 | 54 | ## References 55 | 56 | 1. Ludwig, L.S., et al., Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics. Cell, 2019. 57 | 2. Darmanis, S., et al., Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma. Cell Rep, 2017. 58 | 3. McGinnis, G., et al., DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Systems, 2019. 59 | 60 | ### Note 61 | 62 | Our optimized, efficient and user-friendly R package `MitoTrace` is currently under development. 63 | -------------------------------------------------------------------------------- /examples/Reproduce results in figures 5H & 5I from Leif et al_files/MathJax.js: -------------------------------------------------------------------------------- 1 | /* 2 | * /MathJax.js 3 | * 4 | * Copyright (c) 2009-2017 The MathJax Consortium 5 | * 6 | * Licensed under the Apache License, Version 2.0 (the "License"); 7 | * you may not use this file except in compliance with the License. 8 | * You may obtain a copy of the License at 9 | * 10 | * http://www.apache.org/licenses/LICENSE-2.0 11 | * 12 | * Unless required by applicable law or agreed to in writing, software 13 | * distributed under the License is distributed on an "AS IS" BASIS, 14 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 15 | * See the License for the 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Change the column name 44 | colnames(af) <- unlist(lapply(colnames(af), function(x) strsplit(x, ".", fixed=T)[[1]][1])) 45 | 46 | # Exclude cells that do not have the label 47 | af01 <- af[ ,na.omit(ann$V1[match(colnames(af), ann$V1)])] 48 | 49 | # Change the column name of new dataframe 50 | colnames(af01) <- na.omit(ann$V2[match(colnames(af), ann$V1)]) 51 | donors <- unlist(lapply(colnames(af01), function(x) paste(strsplit(x, "_", fixed = T)[[1]][1:2], collapse = "_"))) 52 | ``` 53 | 54 | Boxplot to show the selected colony-specific mutations, as it's shown in Figure 5I 55 | ```{r} 56 | # plot first row 57 | par(mfrow = c(2,6)) 58 | boxplot(rev(split(af01["779T>C", ], donors == "Donor1_C101")), main="779 T>C",ylab="Variant heteroplasmy", names=c("C101", "Other")) 59 | boxplot(rev(split(af01["8978T>C", ], donors == "Donor1_C103")), main="8979 T>C", names=c("C103","Other")) 60 | boxplot(rev(split(af01["6712A>G", ], donors == "Donor1_C107")), main="6712 A>G", names=c("C107","Other")) 61 | boxplot(rev(split(af01["1082A>G", ], donors == "Donor1_C109")), main="1082 A>G", names=c("C109","Other")) 62 | boxplot(rev(split(af01["3776G>A", ], donors == "Donor1_C112")), main="3776 T>A", names=c("C112","Other")) 63 | boxplot(rev(split(af01["7275T>C", ], donors == "Donor1_C114")), main="7275 T>C", names=c("C114","Other")) 64 | # plot second row 65 | boxplot(rev(split(af01["13093G>A", ], donors == "Donor1_C116")), main="13093 G>A", ylab="Variant heteroplasmy", names=c("C116","Other")) 66 | boxplot(rev(split(af01["7340G>A", ], donors == "Donor1_C118")), main="7340 G>A", names=c("C118","Other")) 67 | boxplot(rev(split(af01["7754G>A", ], donors == "Donor1_C120")), main="7755 G>A", names=c("C120","Other")) 68 | boxplot(rev(split(af01["2646G>A", ], donors == "Donor1_C124")), main="2648 G>A", names=c("C124","Other")) 69 | boxplot(rev(split(af01["11622A>C", ], donors == "Donor1_C132")), main="11623 T>C", names=c("C132","Other")) 70 | boxplot(rev(split(af01["1446A>G", ], donors == "Donor1_C135")), main="1448 A>G", names=c("C135","Other")) 71 | 72 | par(mfrow = c(1,1)) 73 | ``` 74 | 75 | Read the target mutation 76 | ```{r} 77 | target_mutation <- read.csv("../Data/LeifEtAl/annotation_file/target_mutation.csv", header = FALSE) 78 | selected_mutation <- af01[target_mutation$V1, ] 79 | ``` 80 | 81 | Read the the cell order 82 | ```{r} 83 | cell_order <- read.csv("../Data/LeifEtAl/annotation_file/target_cell.csv", header = FALSE) 84 | selected_mutation <- selected_mutation[, cell_order$V1] 85 | ``` 86 | 87 | Visulized the colony-specific mutations by heapmap, as it's shown in Figure 5H 88 | ```{r} 89 | col_fun = colorRamp2(c(0, 0.1, 0.2), c("white", "firebrick", "firebrick4")) 90 | Heatmap(as.matrix(selected_mutation), 91 | col= col_fun, 92 | name = "Allele\nFrequency", 93 | show_column_names = FALSE, 94 | cluster_columns = FALSE, 95 | cluster_rows = FALSE, 96 | column_title="Specific colonies", 97 | row_title = "Mitochondiral mutations", 98 | row_names_gp = gpar(fontsize = 6)) 99 | ``` 100 | 101 | -------------------------------------------------------------------------------- /examples/Single Cell SMART-SEQ2 data.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "MitoTrace works on single cell RNA sequencing Smart-seq2 data" 3 | author: "Mingqiang Wang & Lukas Simon" 4 | date: "April 11, 2023" 5 | output: html_document 6 | --- 7 | 8 | In this analysis, we demonstrate that MitoTrace could identify the germline mutations on single-cell Smart-seq2 data which publicly available from Darmanis et al. (Darmanis et al. 2017). 9 | 10 | Load R libraries 11 | ```{r} 12 | library(MitoTrace) 13 | library(pheatmap) 14 | ``` 15 | 16 | Read the BAM files, these BAM files can be downloaded from the link: (https://drive.google.com/open?id=1oGK9_RE5t2CkzFKlg24dhaKmItmgRmMt). 17 | ```{r} 18 | bams <- list.files("../Data/DarmanisEtAl/bam_file", full.names = T, pattern = ".bam$") 19 | ``` 20 | 21 | Read the human GRCH38 Mitochondrial reference genome 22 | ```{r} 23 | fasta_loc <- "../Data/GRCH38_MT.fa" 24 | ``` 25 | 26 | MitoTrace calculates alternative allele counts and read coverage for each nucleotide position 27 | ```{r} 28 | mae_res <- MitoTrace(bam_list = bams, fasta = fasta_loc) 29 | ``` 30 | 31 | MitoTrace calculate the allele frequencies 32 | ```{r} 33 | af <- calc_allele_frequency(mae_res) 34 | colnames(af) <- unlist(lapply(colnames(af), function(x) { a <- strsplit(x, "\\.") [[1]][1]})) 35 | ``` 36 | 37 | Read the relevant label files originated from the publication (Darmanis et al. 2017). 38 | ```{r} 39 | ssr_list <- read.table("../Data/DarmanisEtAl/annotation_file/SRR_Patient_all_list_sorted", sep = "\t") 40 | ok <- intersect(colnames(af), ssr_list$V1) 41 | ssr_list <- ssr_list[match(colnames(af), as.character(ssr_list$V1)),] 42 | ``` 43 | 44 | Read the plate relation file 45 | ```{r} 46 | plate_relation <- read.table("../Data/DarmanisEtAl/annotation_file/GBM_SRA_plate_relation", sep = "\t", header = T) 47 | plate_relation <- plate_relation[match(colnames(af), as.character(plate_relation$sra)),] 48 | ``` 49 | 50 | Read metadata file 51 | ```{r} 52 | meta <- read.table("../Data/DarmanisEtAl/annotation_file/GBM_metadata.csv") 53 | ``` 54 | 55 | Read the geo file 56 | ```{r} 57 | geo <- read.delim("../Data/DarmanisEtAl/annotation_file/FromGeoSpreadsheet.tsv", row.names = 1) 58 | geo <- geo[as.character(plate_relation$plate.well),] 59 | ``` 60 | 61 | Read the tSNE matrix 62 | ```{r} 63 | tsne <- read.table("../Data/DarmanisEtAl/annotation_file/GBM_TSNE.csv") 64 | ``` 65 | 66 | Split by allele frequency 67 | ```{r} 68 | asplit <- split(as.character(ssr_list$V1), ssr_list$V2) 69 | sample_af_means <- do.call(cbind, lapply(asplit, function(x){ 70 | subm_af <- af[,x] 71 | rowMeans(subm_af, na.rm = T) 72 | })) 73 | cutoff <- 0.75 74 | ok <- which(apply(sample_af_means, 1, function(x) sum(x > cutoff)) > 0) 75 | ``` 76 | 77 | Identify informative germline mutations using AOV 78 | ```{r} 79 | pvals <- apply(af[ok,], 1, function(x) try(summary(aov(x ~ geo$V8))[[1]][1, 5])) 80 | ok <- names(which(pvals < 1e-100)) 81 | ``` 82 | 83 | Generate the heatmap to plot the data 84 | ```{r} 85 | asplit <- split(colnames(af), geo$V8) 86 | samples_ok <- unlist(lapply(asplit, function(x) sample(x, 200))) 87 | indices_ok <- match(samples_ok, colnames(af)) 88 | tmp <- af[ok, indices_ok] 89 | anno_col <- data.frame(patient = geo$V8[indices_ok]) 90 | rownames(anno_col) <- colnames(tmp) 91 | pheatmap(tmp, show_colnames = F, annotation_col = anno_col, cluster_cols = T) 92 | ``` 93 | 94 | Highlight some identified germline mutation sites 95 | ```{r} 96 | par(mfrow = c(1,4)) 97 | boxplot(split(tmp["11864T>C",], anno_col$patient == "BT_S1"), main = "11864T>C", xlab = "BT_S1", ylab = "Alternative allele frequency") 98 | boxplot(split(tmp["4924G>A",], anno_col$patient == "BT_S2"), main = "4924G>A", xlab = "BT_S2", ylab = "Alternative allele frequency") 99 | boxplot(split(tmp["4790A>G",], anno_col$patient == "BT_S4"), main = "4790A>G", xlab = "BT_S4", ylab = "Alternative allele frequency") 100 | boxplot(split(tmp["709G>A",], anno_col$patient == "BT_S6"), main = "709G>A", xlab = "BT_S6", ylab = "Alternative allele frequency") 101 | par(mfrow = c(1,1)) 102 | ``` 103 | 104 | 105 | tSNE visualization of the cell-cell distance matrix by mitochondrial heteroplasmy profiles separated 4 patients. 106 | ```{r} 107 | vars <- apply(af, 1, var) 108 | ok <- names(tail(sort(vars), 500)) 109 | tmp <- data.matrix(af[ok,]) 110 | tmp <- tmp[,which(apply(tmp, 2, var) > 0)] 111 | correl <- cor(tmp) 112 | tData <- Rtsne::Rtsne(1 - abs(correl), perplexity = 20) 113 | plot(tData$Y, col = as.character(meta$Sample.name.color), main = "Patient", pch =20) 114 | ``` 115 | 116 | tSNE visualization of the cell-cell distance matrix by cell type identify 117 | ```{r} 118 | tsne <- tsne[rownames(meta),] 119 | plot(tsne, col = as.character(meta$Sample.name.color), pch = 16, xlab = "tSNE1", ylab = "tSNE2") 120 | ``` 121 | 122 | tSNE visualization of the cell cluster by cell type 123 | ```{r} 124 | plot(tsne, col = as.character(meta$Cluster_2d_color), pch = 16, xlab = "tSNE1", ylab = "tSNE2") 125 | ``` 126 | 127 | 128 | 129 | 130 | 131 | ```{r} 132 | 133 | 134 | ``` 135 | -------------------------------------------------------------------------------- /examples/Single-Cell-10X-Genomics-data.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "MitoTrace distinguishes different cell lines in 10X Genomics data data" 3 | author: "Mingqiang Wang & Lukas Simon" 4 | date: "April 3, 2023" 5 | output: html_document 6 | --- 7 | 8 | In this analysis, we demonstrate that MitoTrace identifies germline mutations in droplet-based single-cell RNA sequencing data (10X Genomics). The underlying BAM file is available from McGinnis et al. (McGinnis et al. 2019). 9 | 10 | Load R libraries 11 | ```{r message=FALSE} 12 | library("data.table") 13 | library("MitoTrace") 14 | library("ggplot2") 15 | ``` 16 | 17 | Read the BAM files, the bam file we used could be downloaded from here (https://ucsf.app.box.com/s/vg1bycvsjgyg63gkqsputprq5rxzjl6k) or (https://drive.google.com/open?id=1y3WnnLnjLf4ZMNkyiqIqPPWzKPpgnZDP). Please use samtools to re-index the bam file 18 | ```{r} 19 | bams <- list.files("../../data/", full.names = T, pattern = ".bam$") 20 | ``` 21 | 22 | Read the annotation files 23 | ```{r} 24 | demuxlet <- fread("../Data/McGinnisEtAl/annotation_file/jurkat_293t_demuxlet.best") 25 | ``` 26 | 27 | Read the human GRCH38 Mitochondrial reference genome 28 | ```{r} 29 | fasta_loc <- "../Data/GRCH38_MT.fa" 30 | ``` 31 | 32 | MitoTrace calculates alternative allele counts and read coverage for each nucleotide position 33 | ```{r warning=FALSE} 34 | mae_res <- MitoTrace(bam_list = bams, fasta = fasta_loc, chr_name = "MT", min_read = 100) 35 | ``` 36 | 37 | MitoTrace calculates the allele frequencies 38 | ```{r} 39 | af <- calc_allele_frequency(mae_res) 40 | ``` 41 | 42 | Perform unsupervised dimension reduction on highly variable alles. 43 | ```{r} 44 | ok <- intersect(colnames(af), demuxlet$BARCODE) 45 | af <- af[,ok] 46 | cell_line <- sapply(demuxlet$BEST,function(x){strsplit(x,"-")[[1]][[2]]}) 47 | cell_line <- cell_line[match(ok, demuxlet$BARCODE)] 48 | 49 | good_variants <- names(which(rowMeans(af) > 0.3)) 50 | good_variants <- names(tail(sort(apply(af, 1, var)), 20)) 51 | af_hv <- data.matrix(af[good_variants, ]) 52 | ydata <- prcomp(t(af_hv)) 53 | 54 | aframe <- data.frame(cell_line, ydata$x) 55 | ggplot(aes(PC1, PC2, color = cell_line), data = aframe) + geom_point() 56 | 57 | anno <- data.frame(cell_line) 58 | rownames(anno) <- colnames(af_hv) 59 | pheatmap::pheatmap(af_hv, show_colnames = F, annotation_col = anno) 60 | ``` 61 | 62 | Can we identify doublets? Build classifier on highly informative alleles. 63 | ```{r} 64 | pvals <- apply(af[which(Matrix::rowMeans(af) > 0), ], 1, function(x){ 65 | splitz <- split(x, cell_line) 66 | wilcox.test(splitz[[1]], splitz[[2]])$p.value 67 | }) 68 | good <- names(which(pvals < 1e-5)) 69 | 70 | af_hv <- data.matrix(af[good, ]) 71 | ydata <- prcomp(t(af_hv)) 72 | 73 | cell_line2 <- demuxlet$BEST[match(ok, demuxlet$BARCODE)] 74 | cell_line2[grep("DBL", cell_line2)] <- "DBL" 75 | aframe <- data.frame(cell_line2, ydata$x) 76 | ggplot(aes(PC1, PC2, color = cell_line2), data = aframe) + geom_point() 77 | 78 | 79 | ``` 80 | -------------------------------------------------------------------------------- /examples/rsconnect/documents/Reproduce_Cell_Leif_et_al.Rmd/rpubs.com/rpubs/Document.dcf: -------------------------------------------------------------------------------- 1 | name: Document 2 | title: 3 | username: 4 | account: rpubs 5 | server: rpubs.com 6 | hostUrl: rpubs.com 7 | appId: https://api.rpubs.com/api/v1/document/1037654/71ece52950f34edab883cdb10ee099c0 8 | bundleId: https://api.rpubs.com/api/v1/document/1037654/71ece52950f34edab883cdb10ee099c0 9 | url: http://rpubs.com/publish/claim/1037654/e9beab56ab7343698fb991a1bef6bb4b 10 | when: 1683180310.1365 11 | lastSyncTime: 1683180310.13651 12 | -------------------------------------------------------------------------------- /images/Fig0_Reproduce_result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/Fig0_Reproduce_result.png -------------------------------------------------------------------------------- /images/Fig2_Smart-seq2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/Fig2_Smart-seq2.png -------------------------------------------------------------------------------- /images/Fig4_10x_genomics.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/Fig4_10x_genomics.png -------------------------------------------------------------------------------- /images/MitoTrace.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/MitoTrace.png -------------------------------------------------------------------------------- /images/R1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/R1.png -------------------------------------------------------------------------------- /images/barplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/barplot.png -------------------------------------------------------------------------------- /images/gene_bar_cov.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/gene_bar_cov.png -------------------------------------------------------------------------------- /images/somatic.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lkmklsmn/MitoTrace/e82b1241af83177d27fff54e5a3f0fef238b855c/images/somatic.png -------------------------------------------------------------------------------- /man/checkSequenceNames.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/MitoTrace.R 3 | \name{checkSequenceNames} 4 | \alias{checkSequenceNames} 5 | \title{MitoTrace - an R package for the investigation of mitochondrial heteroplasmies.} 6 | \usage{ 7 | MitoTrace(bam_list = bams, ref_fasta = fasta_loc, name = "MT", max_depth = "", 8 | min_base_quality = "", min_mapq = "", min_nucleotide_depth = "", min_minor_allele_depth = "") 9 | } 10 | \arguments{ 11 | \item{bams_list}{Vector of absolute path(s) pointing to BAM alignment file(s).} 12 | 13 | \item{ref_fasta}{Absolute path to the mitochondrial reference genome in FASTA format.} 14 | 15 | \item{name}{Name of mitochondrial genome as specified in the BAM files. Sequence names can be check with the checkSequenceNames() function.} 16 | 17 | \item{tag_name}{The name of the tag corresponding the cellular barcode. Default = "CB". For droplet scRNAseq only.} 18 | 19 | \item{min_read}{The minimum number of read counts to be considered a valid barcode (cell) in the analysis. Default = 1000. For droplet scRNAseq technologies only.} 20 | 21 | \item{max_depth}{The maximum depth of reads considered at any position.} 22 | 23 | \item{min_base_quality}{The minimum read base quality below which the base is ignored when summarizing pileup information.} 24 | 25 | \item{min_mapq}{The minimum mapping quality below which the entire reads is ignored.} 26 | 27 | \item{min_nucleotide_depth}{integer(1); minimum count of each nucleotide at a given position required for said nucleotide to appear in the result.} 28 | 29 | \item{min_minor_allele_depth}{integer(1); minimum count of all nucleotides other than the major allele at agiven position.} 30 | } 31 | \value{ 32 | Read counts matrix and coverage matrix. 33 | } 34 | \description{ 35 | The MitoTrace function calculates mitochondrial heteroplasmies in (single-cell) RNA sequencing data based on the reads pileups of the mitochondrial genome. 36 | } 37 | \details{ 38 | result <- MitoTrace(bam_list = bams, ref_fasta = fasta_loc, chr_name = "MT", max_depth = "1e6", min_base_quality=25, min_mapq=30, min_nucleotide_depth=0, min_minor_allele_depth=0) 39 | 40 | See packageDescription("MitoTrace") for more details. 41 | } 42 | \note{ 43 | This package could not only apply for the analysis of single-cell data but also for bulk seuqencing data. 44 | } 45 | \references{ 46 | The current source code of MitoTrace is from https://github.com/lkmklsmn/MitoTrace. 47 | } 48 | \author{ 49 | Mingqiang WANG , Simon Lukas 50 | } 51 | --------------------------------------------------------------------------------