[Elsie]

Thank you Brian, that is really helpful, and incredibly prompt. Thank you so much.

[Brian Bushnell]

The only way around is to try to assemble it first. If it's a bacteria, and you have sufficient coverage, you can get a decent assembly in a few minutes with Velvet. BBMap will not work without an assembly, but it doesn't have to be a good assembly - a quick one with short contigs is fine for this purpose, as long as those contigs are several times larger than read length.

[Elsie]

Thanks Brian, unfortunately I do not have a reference for this sequence!, so I'm assuming no way around this?

[Brian Bushnell]

Hi Elsie,

You have to index the reference first. For example:

bbmap.sh ref=genome.fasta

Wait for that to finish, then map.

-Brian

[Elsie]

Hi Brian,

Thanks so much for this. I am trying to repeat your above commands, using interleaved files, and I get this error, can you help?
Thanks.

bbmap.sh maxindel=200 in=trimmed.fq.gz mhist=mhist.txt bhist=bhist.txt qhist=qhist.txt qahist=qahist.txt
java -Djava.library.path=/bbmap/jni/ -ea -Xmx43110m -cp /bbmap/current/ align2.BBMap build=1 overwrite=true fastareadlen=500 maxindel=200 in=trimmed.fq.gz mhist=mhist.txt bhist=bhist.txt qhist=qhist.txt qahist=qahist.txt
Executing align2.BBMap [build=1, overwrite=true, fastareadlen=500, maxindel=200, in=trimmed.fq.gz, mhist=mhist.txt, bhist=bhist.txt, qhist=qhist.txt, qahist=qahist.txt]

BBMap version 34.56
Set match histogram output to mhist.txt
Set base content histogram output to bhist.txt
Set quality histogram output to qhist.txt
Set quality accuracy histogram output to qahist.txt
Retaining first best site only for ambiguous mappings.
No output file.
Exception in thread "main" java.lang.RuntimeException: Can't find file ref/genome/1/summary.txt
at fileIO.ReadWrite.getRawInputStream(ReadWrite.java:815)
at fileIO.ReadWrite.getInputStream(ReadWrite.java:780)
at fileIO.TextFile.open(TextFile.java:277)
at fileIO.TextFile.<init>(TextFile.java:94)
at dna.Data.setGenome2(Data.java:839)
at dna.Data.setGenome(Data.java:785)
at align2.BBMap.loadIndex(BBMap.java:302)
at align2.BBMap.main(BBMap.java:32)

[kentawan]

http://www.illumina.com/systems/next...ncer/kits.html

I find a NextSeq v2 kit here. Is it something new?

I just gave my local distributor a call. He said that this kit will be ready for shipment on February 2015. Pricing will be the same as the v1 kits!

Finally some hope for NextSeq 500 users!

[ymc]

http://www.illumina.com/systems/nextseq-sequencer/kits.html

I find a NextSeq v2 kit here. Is it something new?

[Brian Bushnell]

One possible trivial reason could be whether mismatches between an index read and the index sequence are allowed. HiSeq and MiSeq allow 1 mismatch by default. But we demultiplex off-instrument and allow zero mismatches.

--
Phillip

We are also allowing 0 mismatches in both cases (and typically end up with >20% of reads in the unknown bin, as a result). Right now our 2 leading candidate hypotheses are:

1) NextSeq has much lower cluster density;
2) NextSeq has a different order of {read1, read2, index1, index2, resynthesis} compared to HiSeq/MiSeq.

[pmiguel]

I would certainly not want to use it for low-coverage variant calling!

Incidentally, though, it seems the NextSeq platform may have a silver lining. Though all of the standard data quality metrics are much worse than HiSeq in my testing, it appears to have a drastically lower cross-contamination rate (reads from one library assigned to a different library) for dual-index pooled libraries, to the point that we are considering using NextSeq over HiSeq for projects in which index cross-contamination is more important than error rate. We are still investigating why the rate is lower.

One possible trivial reason could be whether mismatches between an index read and the index sequence are allowed. HiSeq and MiSeq allow 1 mismatch by default. But we demultiplex off-instrument and allow zero mismatches.

--
Phillip

[Brian Bushnell]

The data quality from the NextSeq is substantially worse than that from a Hiseq with substantially more errors to the point where I'm not certain the data is usable for low to medium coverage whole genome sequence variant calling (1-20x).

I would certainly not want to use it for low-coverage variant calling!

Incidentally, though, it seems the NextSeq platform may have a silver lining. Though all of the standard data quality metrics are much worse than HiSeq in my testing, it appears to have a drastically lower cross-contamination rate (reads from one library assigned to a different library) for dual-index pooled libraries, to the point that we are considering using NextSeq over HiSeq for projects in which index cross-contamination is more important than error rate. We are still investigating why the rate is lower.

[aeonsim]

So I've been getting some initial test data back from a Nextseq 500 and I'm really not happy with it compared to the Hiseq.

The data quality from the NextSeq is substantially worse than that from a Hiseq with substantially more errors to the point where I'm not certain the data is usable for low to medium coverage whole genome sequence variant calling (1-20x).

Attached are a number of different PDF's showing the data compared to HiSeq data from the same facility (same experienced technician's doing all the sequencing, library prep and everything). We resequenced our HiSeq libraries (PCR-Free 550bp insert) to compare like to like and you can clearly see the difference.

Two of the files show GATK's BQSR before after and plots for one of our typical Hiseq libraries (recalQC-randomHiseq.pdf) and a Hiseq library sequenced on the NextSeq (BQSR-NextSeq-Before-After.pdf). The difference is substantial and while these are not the same library the Hiseq is representative of what we usually get.

The second two files show the same library with 4 lanes of NextSeq sequence vs the Same library when sequenced on the Hiseq, you'll clearly be able to determine which comes from which machine (Nxt, Nxt, Nxt, Nxt, Hiseq).

Finally here are some alignment stats from Picard tools for the same library sequenced twice on the NextSeq (two different runs) vs the Stats for the same library from the HiSeq showing a 1-2% reduction in reads aligned and ~80% increase in mismatch rate.

Seq PCT_PF_READS_ALIGNED PF_MISMATCH_RATE PF_HQ_ERROR_RATE
NextSeq_R2 0.964464 0.022694 0.021512
NextSeq_R1 0.955108 0.025834 0.024588
HiSeq 0.973545 0.013678 0.013063


Now the data isn't entirely unusable for WGS if you have enough coverage you can still get variant calls out of it. However they're likely to have a higher FP and if you were looking for rare variants I would be very hesitant to use the data (especially for de novo mutations). For other uses this may be fine, but I've only experience with WGS and RNA-seq so I'll leave that for others to decide.

[Innovelty]

You can (get the long reads )

Provided you have access to the right HiSeq 2500. One can now do 2 x 250 PE runs.

One can... provided one has access to the machine, and more than a tiny pilot grant to work with. Sadly, when one works on non-model insects for non-agricultural/biomedical purposes, and one is only a wee third-year, one might only have ~$2500 to spend on the run itself. (Not that one is complaining. One is really super pleased about that.)

Enough of my de-railing, though -- really looking forward to updates from TonyBrooks, because my application is a de novo transcriptome project, primarily interested in the gene expression. Thanks one and all.

[Brian Bushnell]

The question is whether the error is systematic or random. Random error can be somewhat compensated for by a decent sequence depth.

Yep, I plan to plot the error rate across a genome and see if I can see some kind of pattern, but I have not had time to do that yet.

I'll attempt to post the QC from the Illumina PhiX we sequenced during training.

That would be great!

[TonyBrooks]

Data quality is definitely inferior to both the MiSeq and HiSeq.
It's quick though, and perhaps more suited for counting applications, such as RNA-Seq and ChIPSeq than variant calling.
The question is whether the error is systematic or random. Random error can be somewhat compensated for by a decent sequence depth.

I'll attempt to post the QC from the Illumina PhiX we sequenced during training.

[GenoMax]

You can (get the long reads )

Provided you have access to the right HiSeq 2500. One can now do 2 x 250 PE runs.