20

If you want something quick and dirty you could rapidly index the FASTA with samtools faidx and then put the lengths column through R (other languages are available) on the command line. samtools faidx $fasta cut -f2 $fasta.fai | Rscript -e 'data <- as.numeric (readLines ("stdin")); summary(data); hist(data)' This outputs a statistical summary, and ...


12

Statistics for nanopore reads are tricky because of the huge range of read lengths that can be present in a single run. I have found that the best way to display lengths is by using a log scale on both the x axis (length) and the y axis (sequenced bases, or counts, depending on preference). I have written my own scripts for doing this: one for generating ...


9

awk '{if(NR%4==1) print $1, $5}' file.fastq | sed -e "s/ start_time=/, /" -e "s/^@//" The awk command gets the first of every 4 lines, printing the first and fifth "word". sed is then used to strip the initial @ and replace start_time= with ,. The output on your example file is: 93a12f52-95e5-40c7-8c3e-70bf94ed0720, 2017-07-04T06:42:43Z ff37e422-a25f-404c-...


9

Simulators designed specifically for Oxford Nanopore: NanoSim NanoSim-H SiLiCO ReadSim DeepSimulator General long read simulators: Loresim Loresim 2 FASTQsim LongISLND For an exhaustive list of existing read simulators, see page 15 of my thesis, Novel computational techniques for mapping and classifying Next-Generation Sequencing data.


8

By chance, just today I've heard of a nanopore read simulator, NanoSim. It is released under a GPL license. I have never used it, though...


8

The quick way to get the number of alignments on each reference is samtools idxstats my_bam.bam Number of reads on each reference is column 3. Although, as has been pointed out, this will give you the total number of alignments per reference, not the total number of reads (each read might give rise to more than one alignment). That said I do tend to us ...


7

The parameter is used to determine how much sequence STAR indexes on each side of a splice junction to improve its alignment accuracy. For very long reads, this may not be ideal. I am not sure if STAR is capable of including multiple splice junctions since a long read is more than likely to span more than one. It may be worthwhile to consider aligning ...


7

Using Biopython and matplotlib would seem like the way to go, indeed. It really just boils down to three lines of code to get that graph: import Bio, pandas lengths = map(len, Bio.SeqIO.parse('/path/to/the/seqs.fasta', 'fasta')) pandas.Series(lengths).hist(color='gray', bins=1000) Of course you might want to make a longer script that's callable from the ...


7

There are also third party free and open source basecallers that haven't been developed by Oxford Nanopore. Of particular note is Chiron, which gave the best uncorrected assembly identity among the base callers that Ryan Wick tested. It's slower than Albacore, but appears to be more customisable, and could theoretically be used to model any DNA feature that ...


7

Insertions and deletions are the dominant error mode of long read sequencing, including nanopore sequencing. What you see is not unexpected. Things may have improved by now if you would download the raw fast5 data and repeat the basecalling. There is no need to gunzip the fastq.gz prior to alignment. Your commands for alignment look alright, except that (if ...


6

In addition to the already mentioned NanoSim, there is also SiLiCO and ReadSim (although it hasn't been updated in over 2 years, so I am not sure how relevant it is at this point considering how fast the technology is progressing).


6

Qualimap will do this for you. Go to qualimap.bioinfo.cipf.es Run qualimap (default params are fine) on each BAM file Open up the HTML output, and you can read off the %identity (they measure the opposite, i.e. mismatch rate, but 100% - mismatch rate is %identity of course), indel rate, etc. One thing to watch out for (you don't mention it in your ...


6

Since the string start_time will only appear on the header line, or else you don't have a valid fastq file, you can simply do: $ perl -ne '/^@(\S+).*start_time=(.*)/ && print "$1, $2\n"' file.fastq 93a12f52-95e5-40c7-8c3e-70bf94ed0720,2017-07-04T06:42:43Z ff37e422-a25f-404c-8314-ef1733f9c30c,2017-07-04T06:56:41Z Alternatively, since you mentioned ...


6

You can't resolve 20kb near identical repeats/segdups with 10kb reads. All you can do is to bet your luck on a few excessively long reads spanning some units by chance. For divergent copies, it is worth looking at this paper. It uses Illumina reads to identify k-mers in unique regions and ignores non-unique k-mers at the overlapping stage. The paper said ...


6

You might want to look into Unicycler (manuscript with more information can be found here); even though it is supposed to be used with bacterial genomes only, it might work well with a small genome such as a mitochondrion's. Beware that if you happen to have very long reads, you might end up with an assembly with multiple copies of the circular genome: you ...


6

NOTICE: I have altered my answer slightly from the original as I have turned the original script into a pip installable program (with tests) and have updated the links and code snippets accordingly. The essence of the answer is still exactly the same. This is something I have been meaning to get around to for a while, so thanks for the prompt. I have ...


6

Yes, you can click "stop acquisition" and your run won't be negatively affected. All of the reads are saved as they are generated. I am not sure how this will impact live basecalling though if that is something that you do.


6

Yes, your understanding is largely correct. This originates from the situation that for each detector on a nanopore array there are 4 pores. I'll explain mux scans and groups, but this is outdated information as now another system is used. So a MinION FC has 2048 pores with 512 sensors. Not all of these pores will be equally suitable for sequencing. At the ...


5

I also wrote a package to create various plots from Oxford Nanopore sequencing data and alignments: NanoPlot. It can be installed through pip (see also the README on Github). In addition to multiple plots also a limited NanoStats output is created (see also NanoStat). Data can be presented using: A fastq file (optionally compressed) A bam file The ...


5

There was a Structural Variant breakout session at the London Calling conference this year. Unfortunately I didn't attend that session, but MinION community members have access to Constance Donnell's summary of that: https://community.nanoporetech.com/posts/breakout-structural-varia Here are my attempts at grabbing non-creative chunks from those notes: ...


5

DNASTAR's software is for purchase, but high quality. GenVision Pro does genomic visualization, including Sashimi plots. Edit: not sure why this answer is being downvoted, unless it's because the software isn't free. OP has tried IGV and SeqMonk, I mentioned an alternative he might not have heard of. Here is a video demonstrating the use of Sashimi plots ...


5

There are several potential approaches. For example: histogram of sequence lengths in the Biopython tutorial plot_distribution from the Ruby-based biopieces framework various solutions to get sequence length including bioawk and EMBOSS infoseq As to which of these are "quick and efficient" using a 10 GB file...it's hard to say in advance. You may have to ...


5

You specifically asked about FASTA files, but it's important to always consider read length and quality jointly when assessing high-error long-read data. FASTA files do not provide the quality. This information will help you determine how successful the run was, how many reads were 'high quality', etc. I originally posted a full answer here, suggesting ...


5

I've had great results using minimap2, particularly when combined with a pre-treatment of Canu for error correction (using minimap2 for the read-to-read mapping): # correct reads ~/install/canu/canu-1.6/Linux-amd64/bin/canu overlapper=minimap \ genomeSize=100M minReadLength=100 minOverlapLength=30 -correct \ -p 4T1_BC06 -d 4T1_BC06 \ -nanopore-raw ...


5

First of all - yes, you can generate FAST5 files and basecall later. Basecalling during the sequencing run is useful if you want results more quickly. You can also recall your FAST5 files with multiple basecallers, if desired. There are several ways to basecall currently: MinKNOW Albacore Guppy MinKNOW uses an embedded version of Albacore to perform its ...


5

It's worth noting two things as of Dec 2018: Albacore is being deprecated (but is still available from the Nanopore developer portal). Guppy is under active development, so Ryan Wick's comparisons may not reflect the current state of things. (The claim is that the current, just-released version of Guppy uses a new "flip-flop" algorithm that improves ...


5

It is important to train an error model as NanoSim does, as we do not fully understand the error processes involved in both the nanopore sequencing process and the basecalling process. Any sort of read simulator that does not use a species-specific model is just not going to produce realistic simulated reads. As far as datasets go, I would recommend looking ...


5

I believe you have multiread fast5 files, as generated by recent versions of MinKNOW. A conversion script is available to convert these to the older, one-read one-file format. AFAIK Albacore is being deprecated and no version will be made supporting the multiread fast5 files, as Albacore is being replaced by Guppy. The command line to convert from multi ...


4

bioawk could be reasonably efficient for this kind of task. $ bioawk -c fastx '{histo[length($seq)]++} END {for (l in histo) print l,histo[l]}' \ | sort -n 0 33270 1 1542 2 1132 3 3397 4 8776 5 11884 6 12474 7 14341 8 13165 9 15467 10 21089 11 30469 12 45204 13 62311 14 88744 15 115767 16 140770 17 191810 18 313088 19 ...


4

It's important to always consider read length and quality jointly with high-error read data, and current long-read technologies (e.g., MinION and PacBio) have high error rates. Considering read length and quality jointly will help you determine how successful the run was, how many reads were 'high quality', whether the longer reads are 'real' (or just pore ...


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