These documents refer to an obsolete way of installing and running FALCON. They will remain up for historical context and for individuals still using the older version of FALCON/FALCON_unzip.
The current PacBio Assembly suite documentation which includes new bioconda instructions for installing FALCON, FALCON_unzip and their associated dependencies can be found here pb_assembly
Frequently Asked Questions¶
Can I start from corrected reads?¶
Yes. The option input_type can be set to either
preads. In the case of the latter,
fc_run.py will assume the fasta files in input_fofn are all error-corrected reads and it
will ignore any error correction step and go directly into the final assembly overlapping step.
How do I select a length cutoff?¶
The option length_cutoff controls the read length cutoff used during the
error correction process and length_cutoff_pr controls the cutoff used for the
final assembly overlapping steps. In the final assembly, more reads may not lead to a better assembly due to the
fact that some of the reads can be noisy and create false links in the assembly graph. Sometimes you might want
to re-run the final steps of the assembly pipeline in
2-asm-falcon with different values for
run_falcon_asm.sub.sh as this step is quick relative to the overlap detection steps in the earlier stages
of the pipeline.
If you’re not sure, and you are not compute resource limited, one strategy is to choose a smaller length_cutoff and do the computation once. Later, one can use a different length_cutoff_pr to achieve a more contiguous assembly
In general we recommend that you tune the cutoff so that you’re left with roughly 15x to 20x for final genome assembly.
If you set length_cutoff equal to
-1, FALCON will attempt to autocalculate this cutoff
What’s the difference between a Primary and an Associated contig?¶
Primary contigs can be thought of as the longest continuous stretches of contiguously
assembled sequence, while associate contigs can be thought of mostly as structural
variants that occur over the length of the primary contigs. Thus, each alternate primary contig configuration
(associated contig) can be “associated” with it’s primary based on it’s
Conceptually, if a genome is haploid, then all contigs should be primary contigs. However, in general there will usually still be some associated contigs generated. This is likely due to:
- Sequencing errors
- Segmental duplications.
For the first case, Quiver should help by filtering out low quality contigs. Since there is more sequence in the set of primary contigs for blasr to anchor reads and there is no true unique region in the erroneous associated contigs, the raw read coverage on them should be low. We can thus filter low quality associated contig consensus as there won’t be much raw read data to support them.
For the second case, one could potentially partition the reads into different haplotype groups and construct an assembly graph for each haplotype and generate contigs accordingly.
If a genome is a diploid genome, then most of the associated contigs will be locally alternative alleles. Typically, when there are big structural variations between homologous chromosomes, there will be alternative paths in the assembly graph and the alternative paths correspond to the associated contigs. In such case, the primary contigs are “fused contigs” from both haplotypes.
A slide illustrating the method on a synthetic genome.
What are the differences between a_ctg.fasta and a_ctg_base.fasta¶
a_ctg_base.fasta contains the sequences in the primary contigs fasta that correspond to the associated
a_ctg.fasta. Namely, each sequence of a_ctg_base.fasta is a contiguous sub-sequence of a primary
contig. For each sequence inside `a_ctg_base.fasta, there are one or more associated contigs in a_ctg.fasta.
For a given contig in a_ctg.fa, how can I find it’s primary contig map coordinates?¶
The 2nd field and the 3rd field of the sequence header inside
a_ctg.fa indicate the begin node and the end node of
the contig. For example, if we have a header like
>000000F-001-01 000941458:E 000486369:E 15593 47559 5 0.9969 0.8447
It means the associated contig 000000F-001-01 starts from node 000941458:E and ends at 000486369:E. Thsee two nodes should be also in the path of the corresponding primary contig. The path of the primary contig is fully specified in the file p_ctg_tiling_path, you can find exact beginning and ending points where the associated contig are attached to the primary contigs. However, the coordinates are not conserved after the Quiver consensus step, it might be necessary to do some quite alignment to recalibrate the attaching points after quiver consensus. In some case, you can even just do quick sequence alignment to find the homologous region in the primary contig of an associated contigs.
How does FALCON avoid chimeras given homologous repeat regions on different chromosomes?¶
Such repeats are typically called as “segmental duplications”. Yes, Falcon will collapse these regions if the overlapper can not distinguish the repeats. As discussed above in some case, it is just like the case of a diploid genome, we can potentially resolve the two distinct haplotypes. In other cases, the repeat is more complicated, such as if there are more than 2 copies, (e.g. the middle part of contigs 4006 in page 21 of this slide deck. To resolve these regions, we’ll need to do more investigation to separate the reads into more than two groups to resolve them.
Can Falcon handle X-ploid genome data?¶
Falcon, in its current form, is a “diploid or polyploid aware assembler”. I believe there is no fully specific definition what a “diploid or polyploid assembler” should deliver yet at the moment of this writing. From the point of the genome assembly research field, it is still quite new. There were a couple of papers published before for diploid assemblies. However, the general strategy is the phasing adding reads on top on earlier assembly step.
To some degree, the current Falcon assembler provides a better way to build that foundation for a full diploid / polyploid assembler. Please refer to this slide deck https://speakerdeck.com/jchin/string-graph-assembly-for-diploid-genomes-with-long-reads for some detail. Some technical details of the deck are already obsoleted for a little bit, but the general concept is still applied to most recent code in Falcon.
For a tetraploid genome, depending on the genome structure, I would argue one will get better continuity from the primary contigs if you use Falcon for assembling the genome. However, you will need to do good analysis on both primary and associated contigs (or better, the assembly graph directly) after running Falcon to interpret the results correctly. The primary contigs will be “fused” contigs from all haplotypes unless the differences between haplotypes are big such that the assembler’s overlap segregate them apart already.
There are some prototype work to fully segregate the “fused primary contigs” for diploid case. I just presented the ideas in #SFAF2015 conference. For tetraploid case, it will need some hard-code non-trivial mathematics research work to get it work right.
Why don’t I have two perfectly phased haplotypes after FALCON_unzip?¶
It’s useful to first understand that not all genomes are alike. Haploid genomes are the holy grail of genome assembly as there is only one haplotype phase present and assembly is trivial if you have reads long enough to span repeats. Diploid and (allo/auto)polyploid genomes become difficult as there are two or more haplotype phases present. This fact, coupled with widely varying levels of heterozygosity and structural variation lead to complications during the assembly process. To understand your FALCON output, it’s useful to look at this supplemental figure from the FALCON_unzip paper:
Consider the first line as a cartoon illustrating 3 ranges of heterozygosity (low/medium/high). In general, all genomes will have regions that fall into each of these three categories depending on organismal biology. During the first step of the FALCON assembly process, a diploid aware assembly graph is generated. At this point, in medium heterozygosity regions structural variation information is captured as bubbles or alternative pathways in the assembly graph whereas at high levels of heterozygosity the haplotype phases assemble into distinct primary assembly graphs.
FALCON_unzip add-on module to the FALCON pipeline is an attempt to leverage the heterozygous SNP information to
phase the medium level heterozygosity regions of the genome. Low heterozygosity regions have insufficient SNP
density for phasing, while high heterozygosity regions will likely have already been assembled as distinct haplotypes
in the primary contigs.
FALCON_unzip yields two fasta files. One containing primary contigs, and one containing haplotigs. The primary contigs
fasta file is the main output that most people consider first and should consist of the majority of your genome. Primary
contigs are considered
partially-phased. What this means is that even after the unzipping process, certain regions
with insufficient SNP density are unable to be phased and are thus represented as
collapsed haplotypes. The presence
of these regions of low heterozygosity makes it impossible to maintain phase across the entire primary contig. Thus
primary contigs may contain phase-switches between unzipped regions. The haplotigs file will consist of the
phaseable regions of the genome and are considered fully phased. This means there should be no phase switching within
a haplotig and each haplotig should represent only one phase. See this figure for reference:
It’s also important to note that in high heterozygosity situations, we often see the primary contig fasta file approaching 1.5X+ the expected haploid genome size, due to the assembly of both phases of certain chromosomes or chromosomal regions in the primary assembly.
Also, one needs to consider that FALCON_unzip was designed to phase the plant and fungal genomes in the 2016 Nature Methods paper above, but many people have successfully used it to help phase their genome of interest. But as always with free software on the internet, your mileage may vary.
How much haplotype divergence can FALCON-Unzip handle?¶
The magnitude of haplotype divergence determines the structure of the resulting FALCON-Unzip assembly. Genomic regions with low heterozygisty will be assembled as collapsed haplotype on a single primary contig. Haplotypes up to ~5% diverged will be Unzipped, while highly divergent haplotypes will be assembled on different primary contigs. In the latter case, it is up to the user to identify these contigs as homologous using gene annotation or sequence alignment.
For a variety of FALCON-Unzip assemblies, here is the distribution of haplotype divergence for unzipped regions. Each haplotig was aligned to the corresponding primary contig with nucmer, filtered with delta-filter and divergence was estimated with show-choords. (Data credits to John Williams, Tim Smith, Paolo Ajmone-Marsan, David Hume, Erich Jarvis, John Henning, Dave Hendrix, Carlos Machado, and Iago Hale).
Why does FALCON have trouble assembling my amplicon data?¶
FALCON was designed for whole genome shot gun assembly rather than amplicon assembly. In whole genome shotgun assembly we suppress repetitive high copy regions to assemble less repetitive regions first. When you assemble PCR product of a short region in a genome, FALCON sees the whole thing as a high copy repeat and filters alot of the data out.
You can try to down sample your data and make the daligner block size even smaller ( reduce -s50 in pa_DBsplit_option and ovlp_concurrent_jobs ) and increase the overlap filter thresholds (–max_diff 100 –max_cov 100 in overlap_filtering_setting) to try to make it work, however it’s not really within the scope of the FALCON algorithm.
How do I restart a failed workflow?¶
Often times restarting a FALCON job due to unexplained failure is an easy process. It’s typically just a matter
of removing any sentinel files and re-invoking
fc_run.py fc_run.cfg from the FALCON root directory. Read
this section of the wiki for details.
If your job failed due to
quota or other disk full issues
, you may need to wipe the directory and start over again due to corrupt DB’s