Dataset Information

PAIR: polymorphic Alu insertion recognition.

ABSTRACT:

Background

Alu polymorphisms are some of the most common polymorphisms in the genome, yet few methods have been developed for their detection.

Methods

We present algorithms to discover Alu polymorphisms using paired-end high throughput sequencing data from multiple individuals. We consider the problem of identifying sites containing polymorphic Alu insertions.

Results

We give efficient and practical algorithms that detect polymorphic Alus, both those that are inserted with respect to the reference genome and those that are deleted. The algorithms have a linear time complexity and can be run on a standard desktop machine in a very short amount of time on top of the output of tools standard for sequencing analysis.

Conclusions

In our simulated dataset we are able to locate 98.1% of Alus inserted with respect to the reference and 97.7% of Alus deleted, our simulations also show an excellent correlations between the deletions detected in parents and children. We further run our algorithms on publicly available data from the 1000 genomes project and find several thousand Alu polymorphisms in each individual.

SUBMITTER: Sveinbjornsson JI

PROVIDER: S-EPMC3358660 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Publications

PAIR: polymorphic Alu insertion recognition.

Sveinbjörnsson Jón Ingi JI Halldórsson Bjarni V BV

BMC bioinformatics 20120419

<h4>Background</h4>Alu polymorphisms are some of the most common polymorphisms in the genome, yet few methods have been developed for their detection.<h4>Methods</h4>We present algorithms to discover Alu polymorphisms using paired-end high throughput sequencing data from multiple individuals. We consider the problem of identifying sites containing polymorphic Alu insertions.<h4>Results</h4>We give efficient and practical algorithms that detect polymorphic Alus, both those that are inserted with ...[more]

PMID: 22537046

Similar Datasets

Project description:Seven human-specific Alu markers were studied in 574 unrelated individuals from 10 endogamous groups and 2 hill tribes of Tamil Nadu and Kerala states. DNA was isolated, amplified by PCR-SSP, and subjected to agarose gel electrophoresis, and genotypes were assigned for various Alu loci. Average heterozygosity among caste populations was in the range of 0.292-0.468. Among tribes, the average heterozygosity was higher for Paliyan (0.3759) than for Kani (0.2915). Frequency differences were prominent in all loci studied except Alu CD4. For Alu CD4, the frequency was 0.0363 in Yadavas, a traditional pastoral and herd maintaining population, and 0.2439 in Narikuravars, a nomadic gypsy population. The overall genetic difference (Gst) of 12 populations (castes and tribes) studied was 3.6%, which corresponds to the Gst values of 3.6% recorded earlier for Western Asian populations. Thus, our study confirms the genetic similarities between West Asian populations and South Indian castes and tribes and supported the large scale coastal migrations from Africa into India through West Asia. However, the average genetic difference (Gst) of Kani and Paliyan tribes with other South Indian tribes studied earlier was 8.3%. The average Gst of combined South and North Indian Tribes (CSNIT) was 9.5%. Neighbor joining tree constructed showed close proximity of Kani and Paliyan tribal groups to the other two South Indian tribes, Toda and Irula of Nilgiri hills studied earlier. Further, the analysis revealed the affinities among populations and confirmed the presence of North and South India specific lineages. Our findings have documented the highly diverse (micro differentiated) nature of South Indian tribes, predominantly due to isolation, than the endogamous population groups of South India. Thus, our study firmly established the genetic relationship of South Indian castes and tribes and supported the proposed large scale ancestral migrations from Africa, particularly into South India through West Asian corridor.

Dataset Information

PAIR: polymorphic Alu insertion recognition.

Background

Methods

Results

Conclusions

Publications

PAIR: polymorphic Alu insertion recognition.

Similar Datasets

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