Monthly Archives: April 2015

SGD Help: Reference Sequence
The annotation of the Saccharomyces cerevisiae strain S288C Reference Genome Sequence in SGD is described in different ways on different pages. Access to GenBank and RefSeq files for the 16...

By: Saccharomyces Genome Database

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SGD Help: Reference Sequence - Video

The term "single-nucleotide polymorphism" (SNP) refers to a single base change in DNA sequence between two individuals. SNPs are the most common type of genetic variation in plant and animal genomes and are, thus, an important resource to biologists. The ubiquity of these markers and the fact that these polymorphisms show variation at such a fine scale (i.e., at the individual level) makes them ideal markers for many applications, such as population-level genetic diversity studies and genetic mapping in plants.

The growing popularity of next-generation sequencing has made SNPs a pervasive genetic marker in many areas of plant biology. The ever-increasing throughput of sequencing platforms has resulted in the ability to easily identify and genotype thousands of SNPs across numerous individuals to uncover genetic variation among and within populations. This technique, however, becomes quite challenging when the species of interest has undergone whole genome duplication events (i.e., polyploidy), as is common in many plant lineages.

Researchers at Texas A&M and the Southern Plains Agricultural Research Center have developed a strategy that simplifies the discovery of useful SNPs within the complex genome of cotton. The protocol is freely available in a recent issue of Applications in Plant Sciences.

"Cotton presents a challenge for SNP marker discovery due to the polyploid origin of the two most widely grown species," says Dr. Alan Pepper, an author of the study. "All plants have duplicated sequences, whether due to whole genome duplication, duplication of segments of chromosomes, duplication by retroviruses, or duplication by unequal crossing over. When you are looking for potential SNPs, particularly without a reference genome, you run the risk of identifying sequence differences between duplicated sequences rather than differences between individuals. This problem is particularly acute in recent allopolyploids."

Allopolyploid species are the product of hybridization between two divergent taxa. The genomes of these plants, therefore, contain two very similar copies of their genes--one from each parent.

According to Pepper, "A problem arises when our computational methods accidentally align DNA regions that are duplicated within the genomes of the plants being studied, rather than mapping the orthologous regions between the plants."

Enter the strategy presented by Pepper and colleagues.

Using the Illumina next-generation sequencing platform, over 50 million DNA reads were collected from restriction enzyme-digested DNA from four Gossypium species. The team then filtered these reads to enrich for orthologous DNA fragments.

Pepper explains, "One of the exciting things about this approach is that it employs a widely used, well-supported, off-the-shelf bioinformatics software known as Stacks (written by Julian Catchen at the University of Oregon) as a "filter" to enrich for pairs of fragments that are likely to be alleles of a single, orthologous region, rather than paralogs or homeologs."

The new method allows for the detection of polymorphisms between individuals, which will be useful for downstream applications such as marker-assisted selection, linkage and QTL mapping, and genetic diversity studies.

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Simplifying SNP discovery in the cotton genome

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Newswise Many microbes cannot be cultivated in a laboratory setting, hindering attempts to understand Earths microbial diversity. Since microbes are heavily involved in, and critically important to environmental processes from nutrient recycling, to carbon processing, to the fertility of topsoils, to the health and growth of plants and forests, accurately characterizing them, as a basis for understanding their activities, is a major goal of the Department of Energy (DOE). One approach has been to study collected DNA extracted from the complex microbial community, or the metagenome, in order to describe its DNA-coded parts catalog and understand how microbes respond and adapt to environmental changes. Studying a population rather than an individual raises different obstacles on the path to knowledge. The challenges of assembling genes and genomic fragments into meaningful sequence information for an unknown microbe has been likened to putting together a jigsaw puzzle without knowing what the final picture should look like, or even if you have all the pieces.

For metagenomics, said Jillian Banfield of the University of California, Berkeley and Lawrence Berkeley National Laboratorys Earth Sciences Division, a longtime collaborator of the DOE Joint Genome Institute (DOE JGI), a DOE Office of Science User Facility, it is like reconstructing puzzles from a mixture of pieces from many different puzzlesand not knowing what any of them look like. Part of the problem lies in the fact that the more commonly used sequencing machines generate data in short lengths or fragments, on the order of a few hundred base pairs of DNA. Additionally, short-read assemblers may not be able to distinguish among multiple occurrences of the same or similar sequences and will therefore either fail to place them in the correct context, or eliminate them entirely from the final assembly, in the same way that putting together a jigsaw puzzle with many small pieces that look the same, is difficult. The result of this are gaps that indicate not all of the microbes in a community can be identified through the application of environmental genomics.

In a study published on the cover of the April 2015 edition of Genome Research, a team including DOE JGI and Berkeley Lab researchers compared two ways of using the next generation Illumina sequencing machines, one of which--TruSeq Synthetic Long-Reads--produced significantly longer reads than the other. Metagenome data were generated from the Berkeley Lab-led DOE subsurface biogeochemistry field study site in Rifle, Colorado by a Banfield-led team. They evaluated the accuracy of the genomes reconstructed from the sequences produced by the two Illumina technologies to learn more about the microbes present in lower amounts than others and better determine the species richness of the metagenome samples.

The project is part of the Berkeley Lab Genomes-to-Watershed Scientific Focus Area (SFA), which involves over 50 scientists from Berkeley Lab and other institutions including UC Berkeley, Pacific Northwest National Laboratory, Colorado School of Mines, and Oak Ridge National Laboratory. The Genomes-to-Watershed SFA is led by geophysicist Susan Hubbard, the director of Berkeley Labs Earth Sciences Division. Its goal is to develop an approach for gaining a predictive understanding of complex, biologically based system interactions from the genome to the watershed scale. Jill Banfield is a co-lead of the Metabolic Potential component of this team project, which focuses on characterizing prevalent metabolic pathways in subsurface microbial communities that mediate carbon and electron flux, and using that information to inform genome-enabled watershed reactive transport simulators. Banfield describes the Metabolic Potential component of the SFA effort in this video, and some of her groups other recent groundbreaking subsurface ecogenomic findings associated with this project can be found here.

Revisiting Microbial Communities in Rifle, Colorado

For the study, the team used sediment samples collected from an aquifer adjacent to the Colorado River, which had been used for previous experiments. For one of these earlier efforts the DOE JGI sequenced Rifle Site microbial communities and was able to completely reconstruct a high quality genome of a previously unknown organism from short-read assemblies. Additionally, the findings revealed that many of the bacteria and archaea found in the samples had not been previously recognized or sampled.

For their study, the researchers compared the sequences and assemblies generated from Illuminas short read technology with the data from the newer, longer-read technology that generates read lengths of up around 8,000 base pairs. They found that the longer reads captured more of the communitys diverse species. For instance, using short read technology, they previously identified just over 160 microbial species within a sediment sample. Using the longer-read technology, though, over 400 microbial species from the sample could be phylogenetically classified, though some accounted for just 0.1 percent of the community.

The studys first author, Itai Sharon of UC Berkeley, pointed out that they also identified species that previously failed to assemble due to the presence of closely related species within the sample. These close relatives, accounting for as much as 15 percent of the community, confounded the assembly algorithm. These populations were pretty much missed by the short read assemblies because assemblers tend to fail at the presence of multiple closely related species and strains. Using algorithms that we developed for analyzing the long reads we were able to reconstruct genome architecture for these populations, he said.

Originally posted here:
Longer DNA Fragments Reveal Rare Species Diversity