15

There are so many reasons why one might want to prefer Illumina over PacBio (also note that it's a false dichotomy, at least Oxford Nanopore is a competitive sequencing platform): The first (IMHO and the most common reason) is still the cost of both sequencing and the instruments. Illumina can sequence a Gbp of data for \$7 - \$93. PacBio sequencing is ...


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

The read length is irrelevant when calculating the mean coverage statistic. It's simply the total number of bases sequenced divided by the target Xome length. In the example provided in the question, $L*N$ is equivalently expressed as $\sum^{N} l_x$, or the total length of sequenced reads. For equal-length reads, this is easier to calculate as number of ...


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 ...


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

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

Many analyses performed on Illumina machines these days require large numbers of reads. For example, most analyses in ChIP-seq, RNA-seq, ATAC-seq etc, need 10s or even 100s of millions of reads for the statistics to work out properly. But this isn't limited to just sequencing as an assay experiments. High depths are important for things like somatic ...


5

Three reasons for Illumina: * Much better for a large number of samples (easily handle 96 samples). * SNP calling is much better - much greater depth * Hardware costs, an Illumina MiSeq machine is cheap PacBio SNP calling has been improved towards the standard of Illumina by DNA modification to the inputs to be sequenced but it depends on the application....


4

A few possibilities: Falcon Try falcon and falcon-unzip. These are designed exactly for your problem and your data: https://github.com/PacificBiosciences/FALCON Not Falcon If you think you have assembled haplotypes (which seems reasonable to expect given enough coverage), you should be able to see the two haplotypes by just doing all pairwise alignments ...


4

1. The quickest way to know if it's a single or multi fast5 is the file name. If it mentions channels and read numbers (e.g. ..._read_9_ch_471_..., then it's a single read file. Alternatively, the multi-read files have only read IDs at the top level (i.e. read_<32-character-string-with-additional-dashes>). Example: $ h5ls <read_file_name>.fast5 | ...


4

I cannot access the pipeline from nanopore, but looking at the python script you provided: with pysam.AlignmentFile(bam_file, "r") as bam: for tid in range(0, bam.nreferences): ref_name = bam.get_reference_name(tid) if len(list(filter(lambda x: x in ref_name, filter_names))) > 0: continue print(ref_name, ...


4

A few comments: Never use N50 as a metric especially for transcriptomes. It has some semblance of relevance for genome assembly, but all that is void for a transcriptome with inherently dynamic lengths. At the end of your IsoSeq pipeline, you should have (ideally) full-length transcripts. Have you considered that you don't miss much in your IsoSeq data? The ...


4

They are all very different in separate regards, but they all refer to different wet-lab and sequencing protocols/technologies. First, PE (paired end) reads are typically short (50-300) reads, most often Illumina HiSeq, MiSeq or NovaSeq protocols. Both pairs originate from a single fragment which is sequenced from either end: ------> PE1 ------------------...


3

Question 1 This command will get you all of supplementary alignments for the reads. This isn't exactly what you want though. You want all of the reads that have more than one mapping. samtools view -f 2048 -h myfile_sorted.bam > supp_only.bam This bash script returns a bam file that contains all alignments for reads that have multiple mappings. It ...


3

LAST has given the best results for me when I've tried to do this, although I agree with @user172818 that it's not a good idea to map really short reads. This is due to a combination of natural sequence duplication in long DNA sequences (e.g. see here), as well as abundant base calling differences present in single-molecule sequencing. Minimising error is ...


3

That's indeed the number of reads and that's quite low. How was your pore occupancy (number of pores sequencing) and your flow cell QC (number of pores good enough for sequencing)? How was your library concentration? Events is an approximation of nucleotides: it's a guess based on the current signal. As far as I know if you multiply this by 1.8 you get ...


3

You could also have a go at Canu. It's designed for long-read assembly (both PacBio and Nanopore), although not specifically for complex population sequencing. It tries to strip a genome down into its unique components, and generates paths from those components that are well-supported from the reads. With regards to polishing, it seems to be the case that ...


3

Nanopolish is necessary if you want to get high-quality consensus. racon etc don't use signal data. They can't achieve high quality. pilon at times doesn't work well. Even if it worked perfectly, it wouldn't help repetitive regions inaccessible by short reads.


3

As recently tweeted by A. Phillippy, Canu v1.9 now supports HiFi reads. Therefore you should just read their manual and look for the implementation :)


2

I've sorted the visualisation out. Here are three alternative representations of repetitive structures for the same sequence: These were generated using the same R script, callable from the command line: $ repaver.r -style dotplot -prefix dotplot MAOA_chrX_43645715-43756016.fa $ repaver.r -style profile -prefix profile MAOA_chrX_43645715-43756016.fa $ ...


2

It could be an idea to fragment the long reads into small sequences, like simulating Illumina reads of 150 bp, and then map these small sequences against the original long reads and extract regions with a high coverage?


2

There is an evaluation of PB Honey and Sniffles algorithms for low coverage PacBio datasets in this preprint and another evaluation is shown on this poster. Both reports agree that optimal is (surprisingly) combination of PB Honey and Sniffles. Author of Sniffles have benchmarked Sniffles against PB Honey, where he shown that Sniffles performs significantly ...


2

I would keep it, in case you need to do another configuration later on. I have used mine for that purpose. I think storage doesn't really matter, I just put it in a drawer somewhere. Keep it away from liquids and extreme temperatures :)


2

Some replies from ONT is: OS X is supported and you can view the installation instructions on our Downloads page linked below. At the moment High Sierra is not officially supported as we have not yet fully tested it, but other users in the community have reported no issues with it so far. The compatibility test only performs a simplistic check ...


2

Try LR_Gapcloser. I've used L_RNA_Scaffolder for trying to scaffold a genome (which turned out to be more complex than I had expected). It looks like LR_Gapcloser (written by the same people) is similar, but designed specifically for scaffolding using long-reads. That page also suggests PBJelly and GMCloser as competing tools.


2

I'd recommend using Canu for correction rather than Pilon, because it has a component that is specifically designed for read-level correction. The newest version of Canu (v1.7) will track all reads through the correction and assembly process: Ensure that every raw read is either corrected or used as evidence for correcting some other raw read. This serves ...


2

It sounds like you're looking for the modifications.csv file, rather than the modifications.gff file. The .gff file is for viewing in SMRT View (or other viewers). You can see schema for both files at PacBio's Methylome Analysis Technical Note. Specifically, modified_base means the algorithm could not determine the exact base and modification. I also found ...


2

You may want to try the longshot tool (https://github.com/pjedge/longshot) developed for calling variants in diploid genomes from long read data.


2

You could try aligning your reads to the draft reference genome with for example minimap2 and calling variants with freebayes. It appears there is a protocol for long reads: https://github.com/ekg/freebayes#re-genotyping-known-variants-and-calling-long-haplotypes Disclaimer: I have not run freebayes myself, and it may be that this is a terrible idea. I ...


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