running into problem with Xpore after merging multiple .fastq and .bam files from same Nanopore run

[annapopova-scripps]

Dear Xpore developers,

Thank you for the software. It was easy to install and handle , until I decided to merge all 50+ .fastq (and .bam) output files from one nanopore run. Each .fastq roughly has 3-4K reads and on its own produces meaningful Xpore output table, when running in comparative mode. Then, I decided to merge five different .fastq (using cat) and .bam (using samtools merge), and no problem was met at the nanopolish index and nanopolish eventalign steps, consistently I observed that eventalign.txt was five times larger than for a single .fastq. However, Xpore dataprep and Xpore diffmod returned dataprep folder and diffmod.table as if a single .fastq was used.

I am currently limited to adding all individual .fastq files as replicates (below), which is stupid. So, I hope, you may have a good recommendation for the situation.

Anna

data: IVT: REP1: ./IVT_dataprep/IVT_1_dataprep REP2: ./IVT_dataprep/IVT_2_dataprep REP3: ./IVT_dataprep/IVT_3_dataprep REP4: ./IVT_dataprep/IVT_4_dataprep REP5: ./IVT_dataprep/IVT_5_dataprep REP6: ./IVT_dataprep/IVT_6_dataprep REP7: ./IVT_dataprep/IVT_7_dataprep REP8: ./IVT_dataprep/IVT_8_dataprep REP9: ./IVT_dataprep/IVT_9_dataprep REP10: ./IVT_dataprep/IVT_10_dataprep

[yuukiiwa]

Hi @annapopova-scripps,

Do you mind sharing the first 10 lines of the eventalign.txt file, please?

I suspect that some entries in the eventalign.txt file have the transcript/gene version while some do not. xpore strips off the gene/transcript version which may explain why it is the size of a single fastq/bam diffmod.table.

Thanks!

Best wishes, Yuk Kei

[annapopova-scripps]

Dear Yuk Kei,

Here are the top 10 lines of the eventalign.txt for the five .fastq files I merged.

Thank you for looking into the issue,

Anna

anna@supernova:~/ONT_files/ONT_runs/nanopolish/Xpore_1/70S$ head 70S_10to14_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 RRSB-RRNA 9 AGTTT 19 t 1 111.91 8.435 0.00199AGTTT 124.34 7.49 -1.46 65656 65662 RRSB-RRNA 9 AGTTT 19 t 2 128.19 5.800 0.01527AGTTT 124.34 7.49 0.45 65610 65656 RRSB-RRNA 10 GTTTG 19 t 3 79.84 1.955 0.01560GTTTG 78.97 1.97 0.39 65563 65610 RRSB-RRNA 11 TTTGA 19 t 4 89.31 2.765 0.01394TTTGA 89.94 2.65 -0.21 65521 65563 RRSB-RRNA 11 TTTGA 19 t 5 88.84 1.758 0.00764TTTGA 89.94 2.65 -0.37 65498 65521 RRSB-RRNA 11 TTTGA 19 t 6 88.55 1.466 0.00963TTTGA 89.94 2.65 -0.46 65469 65498 RRSB-RRNA 12 TTGAT 19 t 7 105.18 2.992 0.00564TTGAT 108.32 4.49 -0.62 65452 65469 RRSB-RRNA 12 TTGAT 19 t 8 103.04 1.315 0.00365TTGAT 108.32 4.49 -1.04 65441 65452 RRSB-RRNA 12 TTGAT 19 t 9 107.48 2.771 0.00232TTGAT 108.32 4.49 -0.16 65434 65441

[yuukiiwa]

Hi @annapopova-scripps,

Sorry for the delayed reply!

I supposed if your eventalign.txt is 5 times larger, then your dataprep is also 5 times larger. Still, when you are comparing the two conditions, the sites that differ are the same, which gives you a diffmod.table with a similar size to using a single .fastq file.

Thanks!

Best wishes, Yuk Kei

[annapopova-scripps]

Hi Yuk Kei,

My eventalign.txt is 5 times larger (5x), BUT dataprep is only 1x, and clearly in the diffmod.table the coverage never exceeds 1000. In fact, from a single .fastq I should get a coverage of ~1500, and from five .fastq files ~7500 per site.

One thing I noticed is that every data.readcount from a single .fastq is the same: idx,n_reads RRSB-RRNA,1001 RRLB-RRNA,1001 Which makes me suspicious that dataprep has a cut off of 1000 per gene. No change every fastq. has same number of reads :)
Do you think this is true?

Best, Anna

[yuukiiwa]

Hi Anna,

The --readcount_max [READCOUNT_MAX] is set to 1,000 reads by default. You can customize it by passing the --readcount_max [READCOUNT_MAX] when running xpore dataprep.

Thanks!

Best wishes, Yuk Kei

[annapopova-scripps]

Hi Yuk Kei,

Thank you for the feedback, that fixed the issue and things look consistent now :)

I want to bother you for few more moments:

1) I noticed that --n_processes option neither in xpore dataprep nor in xpore diffmod when specified does not engage more than one processor in Linux. Did you guys have a chance to evaluate the parallel version of xpore?

2) I have spent time to play with positions returned in the diffmod.table and got an idea how to use z_score and diff_mod_rate combination to filter out most modification and variant positions in the set of control samples. Still, I am worried that diffmod.table contains 10-30 more entries than var/mod positions, and that positions that are known to be unmodified have distinct mu_umodand mu_mod values, and significant individual mod_rate values (as high as 0.9). To test this, I took an in-vitro transcribed dataset, split it into two datasets and ran xpore in a comparative mode. There were hundreds of entries in the diffmod.table with apparently bimodal distribution of ion current detected by Xpore. This is quite alarming to me, and I am wondering if something can be done to adjust the reference model for that or addressed otherwise to make Xpore more sensitive to real modified positions and quantify stoichiometry.

Sincerely,

Anna

[yuukiiwa]

Hi Anna,

1. Because of io, --n_processes uses at most 2 cpus in xpore dataprep. Still, parallel processing should work well in xpore diffmod from my experience.
2. Thanks for raising this! I will discuss with @ploy-np on this.

Thanks!

Best wishes, Yuk Kei