Myopia, or nearsightedness, is highly prevalent in Asian countries and is considered a serious public health issue globally. High-grade myopia can predispose individuals to myopic maculopathy, premature cataracts, retinal detachment, and glaucoma. A recent study implicated zinc finger protein 644 isoform 1 (ZNF644) variants with non-syndromic high-grade myopia in a Chinese-Asian population. Herein we focused on investigating the role for ZNF644 variants in high-grade myopia in a United States (US) cohort.
DNA from a case cohort of 131 subject participants diagnosed with high-grade myopia was screened for ZNF644 variants. Spherical refractive error of -≤-6.00 diopters (D) in at least one eye was defined as affected. All coding, intron/exon boundaries were screened using Sanger sequencing. Single nucleotide allele frequencies were determined by screening 672 ethnically matched controls.
Sequencing analysis did not detect previously reported mutations. However, our analysis identified 2 novel single nucleotide variants (c.725C>T, c.821A>T) in 2 high-grade myopia individuals- one Caucasian and one African American, respectively. These variants were not found in normal controls. A rare variant - dbsSNP132 (rs12117237→c.2119A>G) - with a minor allele frequency of 0.2% was present in 6 additional cases, but was also present in 5 controls.
Our study has identified two novel variants in ZNF644 associated with high-grade myopia in a US cohort. Our results suggest that ZNF644 may play a role in myopia development.
Faculty of Computer Science, Dalhousie University, 6050 University Avenue, PO BOX 15000, Halifax, NS B3H 4R2, Canada.
Determining the taxonomic lineage of DNA sequences is an important step in metagenomic analysis. Short DNA fragments from next-generation sequencing projects and microbes that lack close relatives in reference sequenced genome databases pose significant problems to taxonomic attribution methods. Our new classification algorithm, RITA (Rapid Identification of Taxonomic Assignments), uses the agreement between composition and homology to accurately classify sequences as short as 50 nt in length by assigning them to different classification groups with varying degrees of confidence. RITA is much faster than the hybrid PhymmBL approach when comparable homology search algorithms are used, and achieves slightly better accuracy than PhymmBL on an artificial metagenome. RITA can also incorporate prior knowledge about taxonomic distributions to increase the accuracy of assignments in data sets with varying degrees of taxonomic novelty, and classified sequences with higher precision than the current best rank-flexible classifier. The accuracy on short reads can be increased by exploiting paired-end information, if available, which we demonstrate on a recently published bovine rumen data set. Finally, we develop a variant of RITA that incorporates accelerated homology search techniques, and generate predictions on a set of human gut metagenomes that were previously assigned to different 'enterotypes'. RITA is freely available in Web server and standalone versions.
 | Table 5 Approximate lab time needed to complete each sequencing objective. |
 | Table 6 Costs of sequencing 3200 loci. |
#!/usr/bin/perl use warnings; use strict; while (<>) { unless (/^\@/) { warn "$_ should have had an \@ at the start and it didn't\n"; next; } my $id1 = $_; my $seq = <>; my $id2 = <>; my $qual = <>; if ($seq =~/^[@+]/) { warn "Sequence '$seq' looked like an id"; next; } if ($qual =~/^[@+]/) { warn "Quality '$qual' looked like an id"; next; } if ($id2 !~ /^\+/) { warn "Midline '$id2' didn't start with a +"; next; } if ($qual =~ /[GATCN]{20,}/) { warn "Quality '$qual' looked like sequence"; next; } if (length($seq) != length($qual)) { warn "Seq $seq and Qual $qual weren't the same length"; next; } print $id1,$seq,$id2,$qual; }Hey guys,
I am constantly trying to improve my R code. I ran into an issue today where I had to draw random samples from several groups, with equal size from each group, from an extremely large dataset. I tried several functions I found online and I received errors about R memory issues. Thus, I had to write my own function. I got it to work, but I'd appreciate any feedback on how to improve my code. I attached it via the link below.
Sequencing Uncovers a 9,000 Mile Walkabout
A lock of hair and the HiSeq 2000 system identify a human migration wave that took more than 3,000 generations and 10,000 years to complete.
Introduction
Archaeological evidence dates the Aboriginal presence in Australia to ~50,000 years before the present (BP), making them one of the earliest known populations of modern humans outside of Africa. Recognized as Australia's founding population, scientists theorized that the ancestors of today's Aborigines arrived on the continent from a single-wave migration out of Africa into Europe and Asia. However, recent whole-genome studies date the Europe/Asia split to have occurred between 17,000 and 43,000 BP, more than 10,000 years after the earliest Aborigine archeological evidence. Could the founding Australian Aborigine population be the result of an earlier migration wave? The answer was foundi by sequencing a century- old lock of hair with the HiSeq 2000 system1.
Long Forgotten Sample Proves Valuable
Morten Rasmussen, Ph.D., postdoctoral fellow in Dr. Eske Willerslev's lab at the University of Copenhagen, stumbled upon the ancient hair sample during a visit to the University of Cambridge. The team had recently experienced success in sequencing DNA from the hair of a Saqqaq individual found in the Greenland ice, uncovering an unknown migration of Old World humans to the New World Arctic2. During a discussion of that research, the Cambridge scientists mentioned they had additional ancient hair samples in their archives, including several 100-year old Aborigine hair segments. "We were intrigued, since the samples were just old enough that we could assume they were likely from Aborigines of unmixed origin," said Dr. Rasmussen.
