A Novel Method for SNP Identification

In an advance online publication of Genome Research,

research out of the Scripps Translational Science Institute describes a novel method for SNP identification, “SNIP-Seq.” SNIP-Seq utilizes population sequence data to detect SNPs and assign genotypes to individuals. The team used data from a region on chromosome 9p21 of the human genome (sequenced in 48 individuals, with five sequenced in duplicate) and found that many of the novel SNPs identified by SNIP-Seq were validated by pooled sequencing data; they were also confirmed by Sanger sequencing. “Collectively, these results suggest that analysis of population sequencing data is a powerful approach for the accurate detection of SNPs and the assignment of genotypes to individual samples,” the team writes.

Also published in advance online this week, Wilfried Haerty and G. Brian Golding of McMaster University, Ontario, describe their discovery of genome-wide evidence for selection acting on single amino acid repeats. Haerty and Golding tested the effect of splicing on the structure of homopolymer sequences. The team discerned a “relationship between alternative splicing and homopolymer sequences with alternatively spliced genes being enriched in number and length of homopolymer sequences.” They also found lower codon density and longer homocodons, which they say suggests a balance connected with the pressures imposed by selection.

This week in Genome Research, researchers at Harvard and MIT propose an improved method for identifying gene interactions using high-dimensional single-cell morphological data from genetic screens, applied in a systematic computational model to RhoGAP/GTPase regulation in Drosophila melanogaster. The team writes that while their model appears to create only mediocre predictions, it represents a vast improvement from alternative methods. “This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations,” they write.

In a methods paper, Andrew Young of the National Human Genome Research Institute and his colleagues describe a novel strategy for de novo genomic assemblies using short sequence reads and reduced representation libraries. Young et al. developed a method to partition the genome prior to assembly by using two independent restriction enzymes to create overlapping fragment libraries ― each containing a manageable subset of the genome. “Together, these libraries allow us to reassemble the entire genome without the need of a reference sequence,” the team writes. In a proof-of-concept study, the team applied their method in assembling the Drosophila genome, and when compared with the reference genome, they deemed their version significantly comparable.

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