Detection and characterization of novel sequence insertions using paired-end next-generation sequencing.
Pubmed Direct Link
This paper presents a novel pipeline, called NovelSeq, that aims to find novel insertions using paired-end technology. The motivation is to accurately discover the sequences not present in the human genome reference; there are 10-40Mb of sequence estimated to be missing from hg18 (etc.).
One caveat of this method is that it the novel inserted sequences that are detected cannot be similar (homologous) to other sequences already present in the reference. One of my own thoughts has been that a lot of mapping errors found in whole-genome re-sequencing data can be attributed to an incomplete reference, where similar sequences are missing from the reference. This method would not help solve the potential problem I am thinking about.
The idea behind this method is to use unpaired reads, or as they call them one-end anchored reads (OEA). These paired-end reads have the property that one end maps while the other does not (at least not confidently). Finding a cluster of these unpaired reads may suggest there exists a novel sequence insertion, which can be reconstructed using your favorite de novo assembler (Velvet, Abyss, Euler-SR, etc.). This is something everyone knows should be done, but here is an analysis that actually did it (kudos).
They align with mrFast, cluster unpaired reads with mrCar, locally assembly these clusters into contigs with mrSAAB (a Swede?), and finally merge multiple contigs with mrBIG. What ever happened to the Mrs? Nothing in the method strikes me as particularly worrisome, and seems like a reasonable workflow. It is interesting though that some of the assembled contigs had >99% similarity to the human reference. This would indicate their mapping was not sensitive enough to sequencing error (mrFast is not mrSensitive ?).
They compared to previously published data (fosmid-seq from Kidd et al.), but due to the small insert size of their library (<250bp), they could not anchor all their contigs due to repeat elements. Nevertheless, they found that there are some rare variants (deletions in this case) found in the human genome reference. This is to be expected, but will cause havoc when performing re-sequencing experiments.
One thing that was left out in this paper was simulations. There is not accounting for the false-negative rate or other important factors. The authors tried to use previous data to validate their method, but the imprecision of all these methods leaves me unsatisfied with the true power and accuracy of their method.
All in all, a nice application of a number of methods and a good pipeline for directly discovering novel insertions.
Showing posts with label SV. Show all posts
Showing posts with label SV. Show all posts
Wednesday, May 12, 2010
Saturday, May 8, 2010
Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome
Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome.
Pubmed Direct Link
This paper presents a new method to detect structural variant (SV) breakpoints called HYDRA. Since it is a Genome Research (GR) paper, the paper focuses on answering a question in biology, namely detecting SVs in the mouse genome; I will focus on the method.
SVs are detected by clustering discordant matepairs. Matepairs are pairs of reads from the same DNA fragment with some base distance (insert size) between the ends. Discordant reads are reads for which the insert size is outside the expected distribution. Hydra seeks to use multiple mappings per read, where each read may map to multiple locations, albeit with possibly different quality/probability. The method then seeks to select the correct mapping for each read collectively for all reads parsimoniously. This is to detect SVs in regions with segmental duplications, copy number changes, or other repetitive or difficult to map regions. This is an extremely important feature. A minor point is how to store multiple mappings, as a whole genome human resequencing BAM file may be hundreds of gigabytes when there is only one mapping per read. Nevertheless, HYDRA does not report what type of SV occurs, but only that there exists a SV. Further analysis needs to be performed to determine the type of SV (insert/deletion/CNV/inversion/etc.). The authors suggest to use bedtools to do this (their own software). It is yet to be seen if author software will come out that automate this whole process
Pubmed Direct Link
This paper presents a new method to detect structural variant (SV) breakpoints called HYDRA. Since it is a Genome Research (GR) paper, the paper focuses on answering a question in biology, namely detecting SVs in the mouse genome; I will focus on the method.
SVs are detected by clustering discordant matepairs. Matepairs are pairs of reads from the same DNA fragment with some base distance (insert size) between the ends. Discordant reads are reads for which the insert size is outside the expected distribution. Hydra seeks to use multiple mappings per read, where each read may map to multiple locations, albeit with possibly different quality/probability. The method then seeks to select the correct mapping for each read collectively for all reads parsimoniously. This is to detect SVs in regions with segmental duplications, copy number changes, or other repetitive or difficult to map regions. This is an extremely important feature. A minor point is how to store multiple mappings, as a whole genome human resequencing BAM file may be hundreds of gigabytes when there is only one mapping per read. Nevertheless, HYDRA does not report what type of SV occurs, but only that there exists a SV. Further analysis needs to be performed to determine the type of SV (insert/deletion/CNV/inversion/etc.). The authors suggest to use bedtools to do this (their own software). It is yet to be seen if author software will come out that automate this whole process
Sunday, April 11, 2010
Structural Variation Analysis with Strobe Reads.
A very interesting paper on SV detection with "strobe reads', which are/will-be produced by single molecule sequencers (SMS).
Links:
Pubmed Direct Link
The traditional way to detect structural variants (SVs) in next-generation sequencing data is to either search for discordant mate pairs or to explicitly look for SV breakpoints by performing a split-read analysis. In the former, DNA fragments are sequence from both ends with some gap (insert) in between. If after mapping, the two ends fall outside the expected insert size distribution and/or on opposite strands, they are called discordant. In the latter, a longer-ish read is split into two parts, with each mapped independently and then tested to see if each end maps to a different location (a poor man's discordant read). The benefit of the former is that given sufficient coverage, the clone coverage (the coverage implied by spanning events using the insert) is quite high and gives good power, although the exact break point is difficult to resolve. The benefit of the latter is that if a break point is detected, it is detected by directly sequencing through it, although the complexity of the genome is quite high making this type of analysis quite difficult.
A strobe read is produced by a single molecule sequence (SMS), for example by Pacific Biosciences (Pac Bio) or Life Technologies (Starlight), neither which have been released. The strobe sequencing from Pac Bio in a proof of principle gives 2-4 reads of 50-200 bases over a range of 10Kb from a contiguous fragment. The method discussed here, seeks to minimize the potential number of SVs implied by aligning the 2-4 sub-reads. This is solved by formulating the problem as a graph optimization problem and using integer linear programming (as I dust of CLR to refresh my linear programming).
The beauty of the method is that typically each read is aligned independently, but in this case, the multiple alignments for each sub-read that form a read, as well as all reads, are examined together to minimize the expected number of SVs. As SVs have a small prior, this is good idea. Not going into the specifics of various simplifications that yield performance improvements, previous computer science methods are easily applied to this problem to yield a sufficient method to sensitively find SVs with strobe reads.
Always a sucker for ROC curves, strobe SV detection fares quite well, in fact better than using paired end reads or mixed read libraries. The do acknowledge some lack of power when searching for SVs in cancer data, since then all bets are off. I can't wait for real SMS data!
Links:
Pubmed Direct Link
The traditional way to detect structural variants (SVs) in next-generation sequencing data is to either search for discordant mate pairs or to explicitly look for SV breakpoints by performing a split-read analysis. In the former, DNA fragments are sequence from both ends with some gap (insert) in between. If after mapping, the two ends fall outside the expected insert size distribution and/or on opposite strands, they are called discordant. In the latter, a longer-ish read is split into two parts, with each mapped independently and then tested to see if each end maps to a different location (a poor man's discordant read). The benefit of the former is that given sufficient coverage, the clone coverage (the coverage implied by spanning events using the insert) is quite high and gives good power, although the exact break point is difficult to resolve. The benefit of the latter is that if a break point is detected, it is detected by directly sequencing through it, although the complexity of the genome is quite high making this type of analysis quite difficult.
A strobe read is produced by a single molecule sequence (SMS), for example by Pacific Biosciences (Pac Bio) or Life Technologies (Starlight), neither which have been released. The strobe sequencing from Pac Bio in a proof of principle gives 2-4 reads of 50-200 bases over a range of 10Kb from a contiguous fragment. The method discussed here, seeks to minimize the potential number of SVs implied by aligning the 2-4 sub-reads. This is solved by formulating the problem as a graph optimization problem and using integer linear programming (as I dust of CLR to refresh my linear programming).
The beauty of the method is that typically each read is aligned independently, but in this case, the multiple alignments for each sub-read that form a read, as well as all reads, are examined together to minimize the expected number of SVs. As SVs have a small prior, this is good idea. Not going into the specifics of various simplifications that yield performance improvements, previous computer science methods are easily applied to this problem to yield a sufficient method to sensitively find SVs with strobe reads.
Always a sucker for ROC curves, strobe SV detection fares quite well, in fact better than using paired end reads or mixed read libraries. The do acknowledge some lack of power when searching for SVs in cancer data, since then all bets are off. I can't wait for real SMS data!
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