A possible influence of the spindle fibre on crossing-over in Drosophila

The distribution of genes in the second and third chromosomes of Drosophila melanogaster suggests that crossing-over may be influenced by the spindle fibre at least in neighboring regions. The disparity between the genetic and cytological maps of these chromosomes especially in the spindle fibre regions (Dobzhanzky 2,3) is consistent with such a view

GenomeFingerprinter and universal genome fingerprint analysis for systematic comparative genomics

How to compare whole genome sequences at large scale has not been achieved via conventional methods based on pair-wisely base-to-base comparison; nevertheless, no attention was paid to handle in-one-sitting a number of genomes crossing genetic category (chromosome, plasmid, and phage) with farther divergences (much less or no homologous) over large size ranges (from Kbp to Mbp). We created a new method, GenomeFingerprinter, to unambiguously produce three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections to illustrate whole genome fingerprints. We further developed a set of concepts and tools and thereby established a new method, universal genome fingerprint analysis. We demonstrated their applications through case studies on over a hundred of genome sequences. Particularly, we defined the total genetic component configuration (TGCC) (i.e., chromosome, plasmid, and phage) for describing a strain as a system, and the universal genome fingerprint map (UGFM) of TGCC for differentiating a strain as a universal system, as well as the systematic comparative genomics (SCG) for comparing in-one-sitting a number of genomes crossing genetic category in diverse strains. By using UGFM, UGFM-TGCC, and UGFM-TGCC-SCG, we compared a number of genome sequences with farther divergences (chromosome, plasmid, and phage; bacterium, archaeal bacterium, and virus) over large size ranges (6Kbp~5Mbp), giving new insights into critical problematic issues in microbial genomics in the post-genomic era. This paper provided a new method for rapidly computing, geometrically visualizing, and intuitively comparing genome sequences at fingerprint level, and hence established a new method of universal genome fingerprint analysis for systematic comparative genomics.Comment: 63 pages, 15 figures, 5 table

Widespread Gene Conversion in Centromere Cores

Data from maize show that centromeres strongly suppress crossing over and instead undergo frequent genetic exchange in the form of gene conversion

Variability of cell wall polysaccharides composition and hemicellulose enzymatic profile in an apple progeny

The genetic variability of apple cell walls polysaccharides chemical composition and structure was assessed in a progeny of 141 individuals harvested over 2 years. The variability of the hemicelluloses oligosaccharides released by glucanase was analyzed by MALDI-TOF MS. The genetic contribution was distinguished from harvest year as well as from parental crossing patterns and scab resistance selection. Results showed that harvest year had a major impact on cell wall polysaccharide composition and structure. Within each harvest, genetic effect impact more significantly cell wall polysaccharide chemistry than does reciprocal crossing or early scab selection. Uronic acids, glucose, galactose and xylose contents as well as some glucomannan and xyloglucan structures have a high heritability. This first cell wall chemotyping of an apple progeny opens the way for future searches of genetic markers for the chemical variability of cell wall polysaccharides

The system of genetic exchange in <i>Trypanosoma brucei</i> and other trypanosomatids

In this chapter, we discuss our current understanding of the systems of genetic exchange in trypanosomatids and the im-pact the recent genome projects have had on this area of research. We focus mainly on the details of Trypanosoma brucei as it is the most extensively studied of the “trityps”, but will also refer to a recently discovered novel mechanism of genetic exchange in T. cruzi and the apparent rarity of genetic ex-change in Leishmania sp.The system of genetic exchange in Trypanosoma brucei has been known to exist since the late eighties when a genetic cross between different strains was carried out by co-transmission through the tsetse fly. We discuss the segregation of nuclear, chromosomal and kDNA markers and outline the two current models for the mechanism of genetic exchange. We also present how the completion of the genome project has allowed the identification of polymorphic micro and minisatel-lite markers distributed throughout the genome, which have been used to prove formally that meiosis, independent assortment and crossing over occur in this para-site, as would be predicted in a conventional Mendelian system. Such data have been used to construct the first genetic map of T. brucei, which opens up the use of genetic analysis, coupled with positional cloning and the genome sequence, as a tool to identify the genes involved in a range of traits relevant to the disease

Inversions in the third chromosome of wild races of Drosophila pseudoobscura, and their use in the study of the history of the species

Genetic studies showed several years ago that the third chromosomes of wild strains of Drosophila pseudoobscura often carry suppressors of crossing-over. The salivary gland chromosome technique has made it possible to demonstrate not only that these are inverted sections, but also that there are many different inversions present in wild populations inhabiting different geographical regions. So far we have found at least fourteen different gene-sequences in wild stocks, and have found that in most geographical regions several sequences are present, though no single sequence appears to occur throughout the range of the species. There are a number of problems raised by these facts, most of which need further study; the present account is to be regarded only as a preliminary note

An Ultra-High-Density, Transcript-Based, Genetic Map of Lettuce.

We have generated an ultra-high-density genetic map for lettuce, an economically important member of the Compositae, consisting of 12,842 unigenes (13,943 markers) mapped in 3696 genetic bins distributed over nine chromosomal linkage groups. Genomic DNA was hybridized to a custom Affymetrix oligonucleotide array containing 6.4 million features representing 35,628 unigenes of Lactuca spp. Segregation of single-position polymorphisms was analyzed using 213 F7:8 recombinant inbred lines that had been generated by crossing cultivated Lactuca sativa cv. Salinas and L. serriola acc. US96UC23, the wild progenitor species of L. sativa The high level of replication of each allele in the recombinant inbred lines was exploited to identify single-position polymorphisms that were assigned to parental haplotypes. Marker information has been made available using GBrowse to facilitate access to the map. This map has been anchored to the previously published integrated map of lettuce providing candidate genes for multiple phenotypes. The high density of markers achieved in this ultradense map allowed syntenic studies between lettuce and Vitis vinifera as well as other plant species

Reduced efficacy of selection in regions of the Drosophila genome that lack crossing over

BACKGROUND: The recombinational environment is predicted to influence patterns of protein sequence evolution through the effects of Hill-Robertson interference among linked sites subject to selection. In freely recombining regions of the genome, selection should more effectively incorporate new beneficial mutations, and eliminate deleterious ones, than in regions with low rates of genetic recombination. RESULTS: We examined the effects of recombinational environment on patterns of evolution using a genome-wide comparison of Drosophila melanogaster and D. yakuba. In regions of the genome with no crossing over, we find elevated divergence at nonsynonymous sites and in long introns, a virtual absence of codon usage bias, and an increase in gene length. However, we find little evidence for differences in patterns of evolution between regions with high, intermediate, and low crossover frequencies. In addition, genes on the fourth chromosome exhibit more extreme deviations from regions with crossing over than do other, no crossover genes outside the fourth chromosome. CONCLUSION: All of the patterns observed are consistent with a severe reduction in the efficacy of selection in the absence of crossing over, resulting in the accumulation of deleterious mutations in these regions. Our results also suggest that even a very low frequency of crossing over may be enough to maintain the efficacy of selection

Team-Teaching-Based Course Scheduling Using Genetic Algorithm

Scheduling problems occur in various fields, e.g., education, health institutions, transportation, sports, etc. Main scheduling problems in education is course scheduling which creates schedules for students and lecturers. In this study, course scheduling allocates the lecturers in the form of team teaching and courses into the class and a certain time to even out the workload of lecturers per day and a group of students per day in one week without breaking the constraint. The method used in this research is a genetic algorithm where Universitas Bhayangkara Jakarta Raya as the case study. The genetic algorithm process is done by getting several candidate solutions that undergo a process of selection, mutation, and crossing over to produce chromosomes with the best fitness values. The objective function in this research is minimizing the average variance of the workload of lecturers and students per day in one week. The parameters used in genetic algorithm are determined based on the Design of Experiments mechanism (DOE). The optimal parameter values ​​used to run the program are as: population size = 50, with probability of crossing over = 0.4 and probability of mutation = 0.008. The results of scheduling with genetic algorithms show that the value of the workload variance lecturers and students by considering team teaching is better than actual scheduling. The application of the genetic algorithm method results in a decrease in the standard value deviation of the workload of lecturers and a group of students in one week is 0.114 (3.68%) and 3.11 (55.7%). In addition, course scheduling uses a genetic algorithm with consider team teaching better than genetic algorithm without considering team teaching because there is no class schedule that clashes in real conditions

Bayesian Population Genomic Inference of Crossing Over and Gene Conversion

Meiotic recombination is a fundamental cellular mechanism in sexually reproducing organisms and its different forms, crossing over and gene conversion both play an important role in shaping genetic variation in populations. Here, we describe a coalescent-based full-likelihood Markov chain Monte Carlo (MCMC) method for jointly estimating the crossing-over, gene-conversion, and mean tract length parameters from population genomic data under a Bayesian framework. Although computationally more expensive than methods that use approximate likelihoods, the relative efficiency of our method is expected to be optimal in theory. Furthermore, it is also possible to obtain a posterior sample of genealogies for the data using this method. We first check the performance of the new method on simulated data and verify its correctness. We also extend the method for inference under models with variable gene-conversion and crossing-over rates and demonstrate its ability to identify recombination hotspots. Then, we apply the method to two empirical data sets that were sequenced in the telomeric regions of the X chromosome of Drosophila melanogaster. Our results indicate that gene conversion occurs more frequently than crossing over in the su-w and su-s gene sequences while the local rates of crossing over as inferred by our program are not low. The mean tract lengths for gene-conversion events are estimated to be ∼70 bp and 430 bp, respectively, for these data sets. Finally, we discuss ideas and optimizations for reducing the execution time of our algorithm