Problems and Solutions for Estimating Indel Rates and Length Distributions

Cartwright, Reed A.

doi:10.1093/molbev/msn275

Cited by 67 publications

(89 citation statements)

References 42 publications

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“…First, compared with SNPs, indels occur at approximately eightfold lower rates (Lunter 2007;Cartwright 2009), which makes them more difficult to detect. Second, reads arising from indel sequence are generally more difficult to map to the correct location in the genome (Li et al 2008).…”

mentioning

confidence: 99%

Dindel: Accurate indel calls from short-read data

Albers

Lunter²,

MacArthur³

et al. 2010

Genome Res.

399

344

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Small insertions and deletions (indels) are a common and functionally important type of sequence polymorphism. Most of the focus of studies of sequence variation is on single nucleotide variants (SNVs) and large structural variants. In principle, high-throughput sequencing studies should allow identification of indels just as SNVs. However, inference of indels from next-generation sequence data is challenging, and so far methods for identifying indels lag behind methods for calling SNVs in terms of sensitivity and specificity. We propose a Bayesian method to call indels from short-read sequence data in individuals and populations by realigning reads to candidate haplotypes that represent alternative sequence to the reference. The candidate haplotypes are formed by combining candidate indels and SNVs identified by the read mapper, while allowing for known sequence variants or candidates from other methods to be included. In our probabilistic realignment model we account for base-calling errors, mapping errors, and also, importantly, for increased sequencing error indel rates in long homopolymer runs. We show that our method is sensitive and achieves low false discovery rates on simulated and real data sets, although challenges remain. The algorithm is implemented in the program Dindel, which has been used in the 1000 Genomes Project call sets.

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mentioning

confidence: 99%

Dindel: Accurate indel calls from short-read data

Albers

Lunter²,

MacArthur³

et al. 2010

Genome Res.

399

344

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show abstract

“…Although indel events occur approximately eightfold less often than substitution mutations (Lunter 2007;Cartwright 2009), their impact upon functional sequence may well be more profound than that exerted by single-nucleotide substitutions. Indels may induce, for example, frame shifts in coding regions and secondary structure changes in RNAs, suggesting that stronger purifying selection may often act upon them.…”

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confidence: 99%

Massive turnover of functional sequence in human and other mammalian genomes

Meader

Ponting²,

Lunter³

2010

Genome Res.

103

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Despite the availability of dozens of animal genome sequences, two key questions remain unanswered: First, what fraction of any species' genome confers biological function, and second, are apparent differences in organismal complexity reflected in an objective measure of genomic complexity? Here, we address both questions by applying, across the mammalian phylogeny, an evolutionary model that estimates the amount of functional DNA that is shared between two species' genomes. Our main findings are, first, that as the divergence between mammalian species increases, the predicted amount of pairwise shared functional sequence drops off dramatically. We show by simulations that this is not an artifact of the method, but rather indicates that functional (and mostly noncoding) sequence is turning over at a very high rate. We estimate that between 200 and 300 Mb (;6.5%-10%) of the human genome is under functional constraint, which includes five to eight times as many constrained noncoding bases than bases that code for protein. In contrast, in D. melanogaster we estimate only 56-66 Mb to be constrained, implying a ratio of noncoding to coding constrained bases of about 2. This suggests that, rather than genome size or protein-coding gene complement, it is the number of functional bases that might best mirror our naïve preconceptions of organismal complexity.

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“…Indel detection is still challenging, largely owing to both their lower frequencies compared with those of SNVs 32,33 and to mapping difficulties 34 . Although existing alignment tools are adequate for mapping reads containing SNVs, they lack the necessary accuracy and sensitivity for reads that overlap with indels or structural variants.…”

Section: Dissecting Genomic Changes In Cancermentioning

confidence: 99%

Expanding the computational toolbox for mining cancer genomes

et al. 2014

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High-throughput DNA sequencing has revolutionized cancer genomics with numerous discoveries relevant to cancer diagnosis and treatment. The latest sequencing and analysis methods have successfully identified somatic alterations including single nucleotide variants (SNVs), insertions and deletions (indels), structural aberrations, and gene fusions. Additional computational techniques have proved useful to define those mutations, genes, and molecular networks that drive diverse cancer phenotypes as well as determine clonal architectures in tumour samples. Collectively, these tools have advanced the study of genomic, transcriptomic, epigenomic alterations and their association to clinical properties. Here, we review cancer genomics software and the insights that have been gained from their application.

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Problems and Solutions for Estimating Indel Rates and Length Distributions

Cited by 67 publications

References 42 publications

Dindel: Accurate indel calls from short-read data

Dindel: Accurate indel calls from short-read data

Massive turnover of functional sequence in human and other mammalian genomes

Expanding the computational toolbox for mining cancer genomes

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