Closed Derryxu closed 2 years ago
Sorry to bother you again.I have found that my fault stems from the improper use of Minimap2. However, I have another problem as shown in the figure below, which seems to be not an error but a warning. Will this affect the forecast? We look forward to receiving your reply, thank you very much!
hi @Derryxu , sorry for the late reply
This will not affect the forecast, do not worry. I will try to incorporate some updates later that will prevent this warning from being shown
Regards
Christopher Hendra
Thanks for your reply! Sorry to bother you again. I have another problem with the m6anet-dataprep step. I donnot know how to solve it. The issue is as shown in the figure below. Could you give me some suggestions about this issue? Looking forward to your reply, thank you very much!
Hi @Derryxu ,can you provide the first few lines of your eventalign.txt here?
Thanks for your reply very much! The first few lines of my eventalign.txt as follows. head eventalign.txt
contig position reference_kmer read_index strand event_index event_level_mean event_stdv event_length model_kmer model_mean model_stdv standardized_level start_idx end_idx
chr1 14407 TGCTC 43 t 3516 109.45 2.219 0.01428 GAGCA 107.01 3.02 0.69 7758 7801
chr1 14407 TGCTC 43 t 3515 107.23 1.042 0.00232 GAGCA 107.01 3.02 0.06 7801 7808
chr1 14407 TGCTC 43 t 3514 110.29 2.260 0.01959 GAGCA 107.01 3.02 0.92 7808 7867
chr1 14407 TGCTC 43 t 3513 109.21 1.325 0.00365 GAGCA 107.01 3.02 0.62 7867 7878
chr1 14407 TGCTC 43 t 3512 110.34 1.954 0.01328 GAGCA 107.01 3.02 0.94 7878 7918
chr1 14407 TGCTC 43 t 3511 107.30 2.207 0.00730 GAGCA 107.01 3.02 0.08 7918 7940
chr1 14407 TGCTC 43 t 3510 106.30 1.843 0.00232 GAGCA 107.01 3.02 -0.20 7940 7947
chr1 14407 TGCTC 43 t 3509 107.99 1.709 0.00498 GAGCA 107.01 3.02 0.28 7947 7962
chr1 14407 TGCTC 43 t 3508 107.34 2.370 0.02623 GAGCA 107.01 3.02 0.09 7962 8041
hi @Derryxu, sorry for the late reply
I have noticed that your reference_kmer column seems to be different from your model_kmer columns. Is this always the case throughout your file? Because if so, m6anet will only select those reads that have model_kmer = reference_kmer and it is likely that the preprocessing ends up with no rows after the filter.
hi @Derryxu, I encountered the same problem with m6anet-inference step. Here's the command I used and the error that I got. Could you tell me how do you solve this issue? Thank you very much! (The same command I used in another dataset was no problem) minimap2 -ax map-ont -uf -t 3 --secondary=no Genome_assembly.fasta nanopore_guppy_base.fastq nanopolish eventalign --reads nanopore_guppy_base.fastq --bam guppy_minimap.bam --genome Genome_assembly.fasta --signal-index --scale-events --summary summary.txt --threads 24 > eventalign.txt m6anet-dataprep --eventalign eventalign.txt --out_dir ./ --n_processes 24 m6anet-run_inference --input_dir ./ --out_dir ./ --n_processes 24
Hi , Thanks for releasing the new model for m6anet. I have run the nanopolish, data-prep steps successfully but I am encountering error with the m6anet-inference step. Here's the command I used and the error that I got. Could you give me some suggestions about this issue? Thank you very much!
There are the output files of the m6anet data-prep output directory.
data.index data.json data.log data.readcount eventalign.index
A few lines for each file as follows: data.idextranscript_id,transcript_position,start,end cc6m_2244_T7_ecorv,1965,0,130 cc6m_2244_T7_ecorv,1983,130,236 cc6m_2244_T7_ecorv,2030,236,352 cc6m_2459_T7_ecorv,333,352,470 cc6m_2459_T7_ecorv,338,470,578 cc6m_2459_T7_ecorv,419,578,695 cc6m_2459_T7_ecorv,431,695,824 cc6m_2459_T7_ecorv,496,824,942 cc6m_2459_T7_ecorv,565,942,1048 cc6m_2459_T7_ecorv,589,1048,1168 cc6m_2459_T7_ecorv,641,1168,1284 cc6m_2459_T7_ecorv,647,1284,1406
data.json{"cc6m_2244_T7_ecorv":{"1965":{"AGGACTT":[[0.0059211111,3.8680915033,119.6,0.00266,3.98,121.4,0.0066841026,1.6050512821,85.8]]}}} {"cc6m_2244_T7_ecorv":{"1983":{"TGAACCG":[[0.00299,3.341,117.7,0.00465,7.216,90.4,0.00498,4.315,82.8]]}}} {"cc6m_2244_T7_ecorv":{"2030":{"ATAACCA":[[0.003588125,1.84678125,84.1,0.00299,2.173,90.9,0.003386,2.1706,83.0]]}}} {"cc6m_2459_T7_ecorv":{"333":{"GGGACTT":[[0.0078971429,1.9767142857,118.4,0.00531,5.515,119.4,0.00498,2.502,82.8]]}}} {"cc6m_2459_T7_ecorv":{"338":{"TTAACAA":[[0.00664,3.418,89.2,0.00299,1.14,85.6,0.0056425,1.290125,83.1]]}}} {"cc6m_2459_T7_ecorv":{"419":{"AAAACAT":[[0.01494,5.865,102.8,0.01627,4.07,101.1,0.0044143478,1.0262173913,87.8]]}}} {"cc6m_2459_T7_ecorv":{"431":{"CTAACTT":[[0.0080698246,1.8048596491,91.4,0.0136877778,3.0567777778,102.1,0.00232,1.771,91.2]]}}} {"cc6m_2459_T7_ecorv":{"496":{"CGGACCC":[[0.00232,1.516,117.8,0.0059936111,6.8690555556,110.8,0.00332,1.729,71.5]]}}} {"cc6m_2459_T7_ecorv":{"565":{"TTGACAT":[[0.00664,1.587,103.3,0.01461,4.278,108.9,0.00266,2.246,81.8]]}}} {"cc6m_2459_T7_ecorv":{"589":{"ATAACAA":[[0.0029082353,1.64,83.4,0.00232,1.157,93.0,0.0087265347,2.4270792079,86.7]]}}} {"cc6m_2459_T7_ecorv":{"641":{"ATAACTC":[[0.0064280769,1.6935096154,85.6,0.00232,1.491,88.8,0.00299,1.384,85.5]]}}} {"cc6m_2459_T7_ecorv":{"647":{"CAAACTT":[[0.0093738182,2.9810545455,104.1,0.00365,3.917,102.8,0.0049552,0.87816,91.3]]}}}
data.log
cc6m_2244_T7_ecorv: Data preparation ... Done. cc6m_2459_T7_ecorv: Data preparation ... Done.
data.readcount ‘transcript_id,transcript_position,n_reads cc6m_2244_T7_ecorv,1965,1 cc6m_2244_T7_ecorv,1983,1 cc6m_2244_T7_ecorv,2030,1 cc6m_2459_T7_ecorv,333,1 cc6m_2459_T7_ecorv,338,1 cc6m_2459_T7_ecorv,419,1 cc6m_2459_T7_ecorv,431,1 cc6m_2459_T7_ecorv,496,1 cc6m_2459_T7_ecorv,565,1 cc6m_2459_T7_ecorv,589,1 cc6m_2459_T7_ecorv,641,1 cc6m_2459_T7_ecorv,647,1 ’eventalign.txt
transcript_id,read_index,pos_start,pos_end cc6m_2244_T7_ecorv,0,172,84090 cc6m_2459_T7_ecorv,1,84090,190941