29

From the manual of Velvet: it must be an odd number, to avoid palindromes. If you put in an even number, Velvet will just decrement it and proceed. the palindromes in biology are defined as reverse complementary sequences. The problem of palindromes is explained in this review: Palindromes induce paths that fold back on themselves. At least one ...


13

To expand on the answer above, in case it isn't clear, we show: Why palindromic sequences must be even in length Why palindromic sequences induce self-loops in a de Bruijn graph Why self loops in a de Bruijn graph are problematic 1. Palindromic sequence ⇒ sequence is of even length Idea: in an odd-length k-mer, its middle nucleotide is 'flipped' in its ...


8

I am not sure what you mean by "fasta alignment file". If you mean a multi-sequence alignment (MSA) in the fasta format, you can't get that because SAM keeps pairwise alignments only and doesn't align inserted sequences. Even if you don't care about inserted sequences, a MSA in fasta is far to big to be practical. Alternatively, by "fasta alignment file", ...


7

Several papers have made this distinction, and a few indeed use different terms to distinguish between them. For example, Kazaux et al. (2016) acknowledge that: These constraints favour the use of a version of the de Bruijn Graph (dBG) dedicated to genome assembly – a version which differs from the combinatorial structure invented by N.G. de Bruijn. ...


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

Just add --dta-cufflinks to your Hisat2 command, so that the output alignment file provides the attributes needed by Cufflinks (XS flags). This should do the job. From manual: Report alignments tailored specifically for Cufflinks. In addition to what HISAT2 does with the above option (--dta), With this option, HISAT2 looks for novel splice sites ...


5

Have you tried Mauve alignment? Its pretty easy once you become familiar and has a GUI for further ease of use. Additionally there are a few online tutorials on how to re-order contigs/ scaffolds using this software. Heres one I use when in need. Mauve contains a function called Mauve Contig Mover (MCM) which can be used to a) compare an assembly to a ...


5

In long-read assembly, "polish" refers to the step to improve the base accuracy of contig sequences. I believe the terminology was originated from the HGAP paper: The final consensus–calling algorithm Quiver, which takes into account all of the underlying data and the raw quality values inherent to SMRT sequencing, then polishes the assembly for final ...


5

I don't think there is a method that would estimate a genome size using raw long reads. The genome size estimates based on raw reads are done by fitting a model to kmer spectra (for instance Genomescope). The kmer spectra built from long reads are really messy due to the high error rate of long reads. That makes fitting of a model quite difficult. These ...


5

Do you mean cytoband coordinates? For the human genome you can find them for example at UCSC. hgdownload.cse.ucsc.edu/goldenPath/hg38/database/cytoBand.txt.gz


5

This could be organism-specific. We don't have a lot of info so far, so I would check a few more things: Run something like FRC_align. Check if there's a clear signal between regions flagged as suspicious by it and your coverage graph. Is it a eukaryote? Plant? Check where mitchondria and chloroplasts are on the plot. They will have different GC/coverage ...


4

I believe all you need to do is to run dnadiff from MUMmer. That will run a comparison and output a number of useful metrics.


4

In addition to the regular De Bruijn graph as depicted on the wikipedia, some implementations in bioinformatics feature additional processing. I guess the main reason figure 1 in the paper you linked (concerning the Velvet genome assembler) is slightly different is that a node represents a series of overlapping k-mers. In order to visualize this as a more ...


4

Depending on the coverage of your data and the complexity of the genome, you could either reassemble the genome de novo or run a reference guided (or reference assisted) assembly. It sounds like you're leaning more towards the latter. There are a couple of reference-guided assembly tools available: AlignGraph and Ragout. These may or may not be appropriate ...


4

One approach to this is to use whatever data you have to iteratively update the reference genome. You can keep chain files along the way so you can convert coordinates (e.g. in gff files) from the original reference to your new pseudoreference. A simple approach might be: Align new data to existing reference Call variants (e.g. samtools mpileup, GATK, or ...


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

The more reads you add the more errors you add into the assembly. This is because additional duplicate reads don't add nodes/edges to the de Bruijn graph, but those with errors do. By preselecting those reads aligning to your desired contigs, you may be further exacerbating this effect and further hindering SOAPdenovo2's ability to do k-mer correction (after ...


4

Disclaimer, this answer is based on feeling I got from talks and papers, but I do not have any hard reference supporting it. I believe that genome polishing is a technique that was introduced for correction of individual bases of long noisy read assemblies - i.e. fixing SNPs and short indels. Guessing a correct base from long reads during assembly seems to ...


4

Short answer: no, there isn't a "standard" method, and there won't be. Long answer: like TAB-delimited and XML, JSON alone is not a specific format. You have to define a schema to give meanings to data. However, unlike TAB-delimited "format" and XML, there is not an official way to define JSON schema. There have been various attempts but all of them are ...


4

You would expect to have high coverage, given the plasmids are short, so de novo assembly would be likely very easy. Given that each plasmid is present in different multiples, you would expect different coverage on each plasmid, so it might be best to approach it as a metagenome-type or transcriptome assembly, rather than a classic genome assembly. ...


4

Since you are already using the Broad Tools sets you can use Picard FastqToSam to make the conversion As far a clipDb I am unfamiliar with that and a quick google search and look at the trimmomatic manual were unhelpful


4

Update 2: I looked into this a little more, with the various data sources. This is related in part to the answer submitted by OP juanjo75es, in addition to discussion on chat. I don't entirely understand the logic, but the general thrust seems to be that SPAdes makes weird assemblies somehow. Some notes that I made: REFERENCE ASSEMBLIES FIV sequence U11820....


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

It's really a misnomer to call StringTie's non-reference based mode 'de-novo.' It's still using the reference genome sequence to guide the transcript assembly, it's just not using the reference annotation. Trinity is truly de-novo in that it it assembles the transcript from the overlap of the reads without mapping them to a reference genome sequence. If ...


3

Your intuition is correct. stringTie is just looking at clumps of alignments and how they might relate to each other (either due to spliced alignments or proximity). Trinity is doing the more computationally difficult task of finding parts of reads that overlap with each other and trying to link them together into longer sequences. Whether it makes sense to ...


3

This should be very easy to do. Here are some options: Use a tool like Exonerate or GeneWise that can align protein sequences to genomic DNA while attempting to model splice sites etc. As you said, blast. There's nothing special in what you describe, really, this is what tBLASTn is for. Just use your protein as the query sequence and tBLASTn as the blast ...


3

Let's first assume DNA only has one strand. An assembly de Bruijn graph is a subgraph of a complete de Bruijn graph. It contains a vertex u if u is a k-mer in reads; it contains an edge u->v, if u and v are adjacent k-mers on a read. Alternatively, we note that an edge u->v is represented by a (k+1)-mer. An assembly de Bruijn graph can be considered a ...


3

If it is a short-read draft assembly and you have long-reads (ONT or Pacbio) run links to scaffold the genome and then run Pilon iteratively to try to polish and fill gaps using the short-reads.


3

You can use nanopolish using the illumina reads. Also have a look at pilon.


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