A prediction tool for functional UTR variants including UTR variation annotation.
Python 3.9 and standard packages (scipy==1.10.1, scikit-learn==1.2.1, numpy==1.23.5, pandas==1.1.4, pyBigWig==0.3.21).
SAMTools-1.16
ViennaRNA-2.4.9
MEME-5.3.3
Clone this github repository.
The relevant conservation score files are too large to upload, please downloaded from hg38.phastCons100way.bw and hg38.phyloP100way.bw. Then place the two files in the Database/Conservation directory.
The prediction model for UTR3 is too large, please get it from the release and then place in the Model directory
Note: All analysis is based on the hg38 version of the reference genome.
prepare transcript information.
input file format (tab split):
Chrom Pos Ref Alt Gene Region
chr1 1013541 T C ISG15 5_prime_UTR_variant
chr1 1232241 G A B3GALT6 5_prime_UTR_variant
python3 Sub_MutSeqByMANE.py -v inputfile -r Homo_sapiens_assembly38.fasta > test_5utr.addseq.txt
output file format (tab split):
Gene Chrom Pos Ref Alt MutSeq WholeSeq Region Strand Transcript UTR-len
ISG15 chr1 1013541 T C GGCGGCTGAGAGGCAGCGAACTCATCTTTGCCAGTACAGGAGCT(T/C)GTGCCGTGGCCCACAGCCCACAGCCCACAGCC GGCGGCTGAGAGGCAGCGAACTCATCTTTGCCAGTACAGGAGCTTGTGCCGTGGCCCACAGCCCACAGCCCACAGCC 5_prime_UTR_variant + ENST00000649529:NM_005101 77
B3GALT6 chr1 1232241 G A ACTC(G/A)CGAGTCCGGCCTGGGCCGCCGGCCCGGCGCGGGCGCC ACTCGCGAGTCCGGCCTGGGCCGCCGGCCCGGCGCGGGCGCC 5_prime_UTR_variant + ENST00000379198:NM_080605 42
annotate variant using region-specific features.
input file: test_5utr.addseq.txt
python3 FunUV_annotation.py -f test_5utr.addseq.txt -o test_5utr -r 5UTR
output file format (5UTR):
Var Seq PCS PPS MFE IRES uAUG uStop uORF Kozak-score GC-ratiAC-ratio AG-ratio AT-ratio TC-ratio TG-ratio A-ratio T-ratio C-ratioG-ratio AA-count AT-count AC-count AG-count TA-count TT-counTC-count TG-count CA-count CT-count CC-count CG-count GA-counGT-count GC-count GG-count A-homo T-homo C-homo G-homo AA-dimo AT-dimo AC-dimoAG-dimo TA-dimo TT-dimo TC-dimo TG-dimo CA-dimo CT-dimo CC-dimo CG-dimo GA-dimo GT-dimo GC-dimoGG-dimo sequni
ISG15_chr1_1013541_T_C_5UTR_44 ACTCATCTTTGCCAGTACAGGAGCT(T/C)GTGCCGTGGCCCACAGCCCACAGC 0.0 -0.12399999797344208 1.5999999999999996 0 0 0 0.0 0 0.62 0.58 0.44 0.38 0.56 0.42 0.2 0.18 0.38 0.24 0 1 4 10
B3GALT6_chr1_1232241_G_A_5UTR_4 ACTC(G/A)CGAGTCCGGCCTGGGCCGCCGGCC 0.0 0.026000000536441803 1.0999999999999996 0 0 0 0.0 0 0.7931034482758621 0.5517241379310345 0.4482758620689655 0.20689655172413793 0.5517241379310345 0.4482758620689655 0.10344827586206896 0.10344827586206896 0.4482758620689655 0.3448275862068966 0 0 2 1 0 0 2 1 1
output file format (3UTR):
Var Seq PCS PPS MFE miRNA PAS AU-cls AU-9mer AU-13mer GU CU Pumilio AU AU-elements GC-ratio AC-ratio AG-ratio AT-ratiTC-ratio TG-ratio A-ratio T-ratio C-ratio G-ratio AA-count AT-count AC-counAG-count TA-count TT-count TC-count TG-count CA-count CT-counCC-count CG-count GA-count GT-count GC-count GG-count A-homo T-homo C-homo G-homo AA-dimo AT-dimo AC-dimo AG-dimo TA-dimo TT-dimo TC-dimo TG-dimo CA-dimoCT-dimo CC-dimo CG-dimo GA-dimo GT-dimo GC-dimo GG-dimo sequni AGRN_chr1_1055037_T_C_3UTR_55 CACCAGAGCCCCGCGCCCGCTGTAATTATTTTCTATTTTTGTAAACTTGT(T/C)GCTTTTTGATATGATTTTCTTGCCTGAGTGTTGGCCGGAGGGACTGCTG 1.0 2.302999973297119 2.3999999999999986 0 0 0 0 0 0 0 0 0 0 0.47 0.39 0.4 0.53 0.6 0.61 0.16 0.37 0.23 0.24 3 6 3 18 3 11 2 8 8 5 6 5 8 4 3 33 AGRN_chr1_1055153_G_A_3UTR_171 GTCCAGGCAGCCGTGCTGCAGACAGACCTAGTGCCGAGGGATGGACAGGC(G/A)AGGTGGCAGCGTGGAGGGCTCGGCGTGGATGGCAGCCTCAGGACACACA 0.0 -2.4809999465942383 0.7999999999999972 0 0 0 0 0 0 0 0 0 0 0.66 0.48 0.62 0.34 0.38 0.52 0.22 0.12 0.26 0.4 1 2 6 12 1 0 3 8 12 4 5 5 8 6 12 14 2 1 2 3 1 1 3 1 1 0 1 20
predict variant effect using region-specific features.
input file: test_5utr_annotation.txt
python3 Prepare_forPred.py -v test_5utr_annotation.txt -r 5UTR > test_5utr_subanno.txt python3 FunUV_predict.py -pf test_5utr_subanno.txt -r 5UTR -o test_5utr