Provided by: pbgenomicconsensus_2.0.0+20151210-1_all 
NAME
quiver-faq - frequently asked questions regarding the quiver genomic consensus caller
QUESTIONS
What are EviCons? GenomicConsensus? Quiver? Plurality?
GenomicConsensus is the current PacBio consensus and variant calling suite. It contains a
main program, variantCaller.py, which provides two consensus / variant calling algorithms:
Plurality and Quiver. These algorithms can be run by calling variantCaller.py
--algorithm=[quiver|plurality] or by going through the convenience wrapper scripes quiver
and plurality.
EviCons was the previous generation PacBio variant caller (removed in software release
v1.3.1).
A separate package called ConsensusCore is a C++ library where all the computation behind
Quiver is done (and is transparent to the user after installation).
What is Plurality?
Plurality is a very simple variant calling algorithm: it stacks up the aligned reads
(alignment as produced by BLASR, or alternate mapping tool), and for each column under a
reference base, calls the most abundant (i.e., the plurality) read base (or bases, or
deletion) as the consensus at that reference position.
Why is Plurality a weak algorithm?
Plurality does not perform any local realignment. This means it is heavily biased by the
alignment produced by the mapper (BLASR, typically). It also means that it is insensitive
at detecting indels. Consider this example:
Reference AAAA
----
Aligned A-AA
reads AA-A
-AAA
----
Plurality AAAA
consensus
Note here that every read has a deletion and the correct consensus call would be "AAA",
but due to the mapper's freedom in gap-placement at the single-read level, the plurality
sequence is "AAAA"---so the deletion is missed. Local realignment, which plurality does
not do, but which could be considered as implicit in the Quiver algorithm, essentially
pushes the gaps here to the same column, thus identifying the deletion. While plurality
could be adjusted to use a simple "gap normalizing" realignment, in practice noncognate
extras (spurious non-homopolymer base calls) in the midst of homopolymer runs pose
challenges.
What is Quiver?
Quiver is a more sophisticated algorithm that finds the maximum likelihood template
sequence given PacBio reads of the template. PacBio reads are modeled using a conditional
random field approach that prescribes a probability to a read given a template sequence.
In addition to the base sequence of each read, Quiver uses several additional QV
covariates that the basecaller provides. Using these covariates provides additional
information about each read, allowing more accurate consensus calls.
Quiver does not use the alignment provided by the mapper (BLASR, typically), except for
determining how to group reads together at a macro level. It implicitly performs its own
realignment, so it is highly sensitive to all variant types, including indels---for
example, it resolves the example above with ease.
The name Quiver reflects a consensus-calling algorithm that is QV-aware.
What does Quiver put in its output files?
There are three output files from Quiver:
• A consensus FASTA file containing the consensus sequence
• A consensus FASTQ file containing the consensus sequence with quality annotations
• A variants GFF file containing a filtered, annotated list of variants identified
It is important to note that the variants included in the output variants GFF file are
filtered by coverage and quality, so not all variants that are apparent in comparing the
reference to the consensus FASTA output will correspond to variants in the output variants
GFF file.
To enable all output files, the following can be run (for example):
% quiver -j16 aligned_reads.cmp.h5 -r ref.fa
-o consensus.fa -o consensus.fq
-o variants.gff
The extension is used to determine the output file format.
What does it mean that Quiver's consensus is de novo?
Quiver's consensus is de novo in the sense that the reference and the reference alignment
are not used to inform the consensus output. Only the reads factor into the determination
of the consensus.
The only time the reference sequence is used to make consensus calls - when the
--noEvidenceConsensusCall flag is set to reference or lowercasereference (the default)- is
when there is no effective coverage in a genomic window, so Quiver has no evidence for
computing consensus. One can set --noEvidenceConsensusCall=nocall to avoid using the
reference even in zero coverage regions.
What is Quiver's accuracy?
Quiver's expected accuracy is a function of coverage and chemistry. The C2 chemistry (no
longer available), P6-C4 and P4-C2 chemistries provide the most accuracy. Nominal
consensus accuracy levels are as follows:
┌─────────────────┬──────────────────────────┬───────┐
│Coverage │ Expected consensus │ │
│ │ accuracy │ │
├─────────────────┼──────────────────────────┼───────┤
│C2, P4-C2, P6-C4 │ P5-C3 │ │
├─────────────────┼──────────────────────────┼───────┤
│10x │ > Q30 │ > Q30 │
├─────────────────┼──────────────────────────┼───────┤
│20x │ > Q40 │ > Q40 │
├─────────────────┼──────────────────────────┼───────┤
│40x │ > Q50 │ > Q45 │
├─────────────────┼──────────────────────────┼───────┤
│60-80x │ ~ Q60 │ > Q55 │
└─────────────────┴──────────────────────────┴───────┘
The "Q" values referred to are Phred-scaled quality values:
q = -10 \log_{10} p_{error}
for instance, Q50 corresponds to a p_error of 0.00001---an accuracy of 99.999%. These
accuracy expectations are based on routine validations performed on multiple bacterial
genomes before each chemistry release.
What are the residual errors after applying Quiver?
If there are errors remaining applying Quiver, they will almost invariably be homopolymer
run-length errors (insertions or deletions).
Does Quiver need to know what sequencing chemistry was used?
At present, the Quiver model is trained per-chemistry, so it is very important that Quiver
knows the sequencing chemistries used.
If SMRT Analysis software was used to build the cmp.h5 file, the cmp.h5 will be loaded
with information about the sequencing chemistry used for each SMRT Cell, and Quiver will
automatically identify the right parameters to use.
If custom software was used to build the cmp.h5, or an override of Quiver's autodetection
is desired, then the chemistry or model must be explicity entered. For example:
% quiver -p P4-C2 ...
% quiver -p P4-C2.AllQVsMergingByChannelModel ...
Can a mix of chemistries be used in a cmp.h5 file for Quiver?
Yes! Quiver automatically sees the chemistry per-SMRT Cell, so it can figure out the
right parameters for each read and model them appropriately.
Chemistry mixtures of P6-C4, P4-C2, P5-C3, and C2 are supported. If other chemistries are
mixed in a cmp.h5, Quiver will give undefined results. However, Quiver can still be used
on any cmp.h5 file containing sequencing reads from a single chemistry.
What are the QVs that Quiver uses?
Quiver uses additional QV tracks provided by the basecaller. These QVs may be looked at
as little breadcrumbs that are left behind by the basecaller to help identify positions
where it was likely that errors of a given type occurred. Formally, the QVs for a given
read are vectors of the same length as the number of bases called; the QVs used are as
follows:
• DeletionQV
• InsertionQV
• MergeQV
• SubstitutionQV
• DeletionTag
To find out if your cmp.h5 file is loaded with these QV tracks, run the command
% h5ls -rv aligned_reads.cmp.h5
and look for the QV track names in the output. If your cmp.h5 file is lacking some of
these tracks, Quiver will still run, though it will issue a warning that its performance
will be suboptimal.
Why is Quiver making errors in some region?
The most likely cause for true errors made by Quiver is that the coverage in the region
was low. If there is 5x coverage over a 1000-base region, then 10 errors in that region
can be expected.
It is important to understand that the effective coverage available to Quiver is not the
full coverage apparent in plots---Quiver and Plurality both filter out ambiguously mapped
reads by default. The remaining coverage after filtering is called the /effective
coverage/. See the next section for discussion of MapQV.
If you have verified that there is high effective coverage in the region in question, it
is highly possible---given the high accuracy Quiver can achieve---that the apparent errors
actually reflect true sequence variants. Inspect the FASTQ output file to ensure that the
region was called at high confidence; if an erroneous sequence variant is being called at
high confidence, please report a bug to us.
What does Quiver do for genomic regions with no effective coverage?
For regions with no effective coverage, no variants are outputted, and the FASTQ
confidence is 0.
The output in the FASTA and FASTQ consensus sequence tracks is dependent on the setting of
the --noEvidenceConsensusCall flag. Assuming the reference in the window is "ACGT", the
options are:
┌─────────────────────────────────┬──────────────────┐
│--noEvidenceConsensusCall=... │ Consensus output │
├─────────────────────────────────┼──────────────────┤
│nocall (default in 1.4) │ NNNN │
├─────────────────────────────────┼──────────────────┤
│reference │ ACGT │
├─────────────────────────────────┼──────────────────┤
│lowercasereference (new post │ acgt │
│1.4, and the default) │ │
└─────────────────────────────────┴──────────────────┘
What is MapQV and why is it important?
MapQV is a single scalar Phred-scaled QV per aligned read that reflects the mapper's
degree of certainty that the read aligned to this part of the reference and not some
other. Unambigously mapped reads will have a high MapQV (typically 255), while a read
that was equally likely to have come from two parts of the reference would have a MapQV of
3.
MapQV is pretty important when you want highly accurate variant calls. Quiver and
Plurality both filter out aligned reads with a MapQV below 20 (by default), so as not to
call a variant using data of uncertain genomic origin.
This can be problematic if using Quiver to get a consensus sequence. If the genome of
interest contains long (relative to the library insert size) highly-similar repeats, the
effective coverage (after MapQV filtering) may be reduced in the repeat regions---this is
termed these MapQV dropouts. If the coverage is sufficiently reduced in these regions,
Quiver will not call consensus in these regions---see What does Quiver do for genomic
regions with no effective coverage?.
If you want to use ambiguously mapped reads in computing a consensus for a denovo
assembly, the MapQV filter can be turned off entirely. In this case, the consensus for
each instance of a genomic repeat will be calculated using reads that may actually be from
other instances of the repeat, so the exact trustworthiness of the consensus in that
region may be suspect. The next section describes how to disable the MapQV filter.
How can the MapQV filter be turned off and when should it be?
The MapQV filter can be disabled using the flag --mapQvThreshold=0 (shorthand: -m=0). If
running a Quiver job via SMRT Portal, this can be done by unchecking the "Use only
unambiguously mapped reads" option. Consider this in de novo assembly projects, but it is
not recommended for variant calling applications.
How can variant calls made by Quiver be inspected or validated?
When in doubt, it is easiest to inspect the region in a tool like SMRT View, which enables
you to view the reads aligned to the region. Deletions and substitutions should be fairly
easy to spot; to view insertions, right-click on the reference base and select "View
Insertions Before...".
What are the filtering parameters that Quiver uses?
Quiver limits read coverage, filters reads by MapQV, and filters variants by quality and
coverage.
• The overall read coverage used to call consensus in every window is 100x by default, but
can be changed using -X=value.
• The MapQV filter, by default, removes reads with MapQV < 20. This is configured using
--mapQvThreshold=value / -m=value
• Variants are only called if the read coverage of the site exceeds 5x, by default---this
is configurable using -x=value. Further, they will not be called if the confidence
(Phred-scaled) does not exceed 40---configurable using -q=value.
What happens when the sample is a mixture, or diploid?
At present, Quiver assumes a haploid sample, and the behavior of Quiver on sample mixtures
or diploid/polyploid samples is undefined. The program will not crash, but the output
results are not guaranteed to accord with any one of the haplotypes in the sample, as
opposed to a potential patchwork.
Why would I want to iterate the mapping+Quiver process?
Some customers using Quiver for polishing highly repetitive genomes have found that if
they take the consensus FASTA output of Quiver, use it as a new reference, and then
perform mapping and Quiver again to get a new consensus, they get improved results from
the second round of Quiver.
This can be explained by noting that the output of the first round of Quiver is more
accurate than the initial draft consensus output by the assembler, so the second round's
mapping to the Quiver consensus can be more sensitive in mapping reads from repetitive
regions. This can then result in improved consensus in those repetitive regions, because
the reads have been assigned more correctly to their true genomic loci. However there is
also a possibility that the potential shifting of reads around from one rounds' mapping to
the next might alter borderline (low confidence) consensus calls even away from repetitive
regions.
We recommend the (mapping+Quiver) iteration for customers polishing repetitive genomes,
and it could also prove useful for resequencing applications. However we caution that
this is very much an exploratory procedure and we make no guarantees about its
performance. In particular, borderline consensus calls can change when the procedure is
iterated, and the procedure is not guaranteed to be convergent.
Is iterating the (mapping+Quiver) process a convergent procedure?
We have seen many examples where (mapping+Quiver), repeated many times, is evidently not a
convergent procedure. For example, a variant call may be present in iteration n, absent
in n+1, and then present again in n+2. It is possible for subtle changes in mapping to
change the set of reads examined upon inspecting a genomic window, and therefore result in
a different consensus sequence there. We expect this to be the case primarily for
"borderline" (low confidence) base calls.
SEE ALSO
quiver(1) plurality(1) variantCaller(1) pbgff(5)