Animal Genomics

Aim:  The  aims  are  to  study  the  genetic  diversity  and  population  structure  of  Tilapia  fish  species  in Nigeria  using  mtDNA  D-loop  region    and  time  divergence  of  these  various  groups  of  fish  to  give  a baseline  information  for  fish  management  and  conservation  programs.
Methodology:  Blood  samples  from  four  species  of  tilapia  were  collected  with  the  use  of  FTA  cards for  DNA  extraction  and  PCR  amplification.  A  941bp  long  fragment,  including  the  D-loop,  was subsequently  used  for  analysis.  Molecular  diversity  indices  and  Tajima’s  selective  test  were determined.  A  phylogenetic  tree  was  constructed  for  all  the  four  fish  species  using  UPGMA. The divergence  time  among  the  four  fish  species  using  MEGA5  software.
Place  and  Duration  of Study : The study  was  carried  out  in  the  Biotechnology  Centre,  Federal University  of  Agriculture,  Abeokuta  Nigeria  between.  Genotyping  and  Optimization  of  PCR conditions  were  carried  out  at  STAB  VIDA  Lda,  Madan  Parque,  Rua  dos  Inventores,  2825-182 Caparica  Portugal.  Total  duration  of  study  was  between  February  to  August  2015.
Results:  Sarotherodon  melanotheron  has  the  highest  nucleotide  diversity  (π),  mean  number  of pairwise  differences  and  number  of  polymorphic  site,  while  the  least  was  observed  in  Sarotherodon galiIaeus.  The  highest  number  of  monomorphic  site  and  the  highest  sequences  conservation percentage  was observed  in  Sarotherodon  galiIaeus. The  Neighbour  joining  tree  revealed  six  (6)  clusters  with  no  clear  sub-structuring  among  the sampled  fish  populations.  The  smallest  divergence  time  (about  31  mya)  was  observed  between Sarotherodon  galilaeus  and  Sarotherodon  melanotheron.  Sarotherodon  spp  diverged  from Oreochromis  niloticus  about  32.5  mya.
Conclusion:  The  present  study  revealed  that  the  sampled  Tilapia  species  from  Nigeria  represent  a single  stock.  The  existing  low  levels  of  genetic  variability  observed  here  may  compromise  the evolutionary  plasticity  of  the  Tilapia  species  population.  These  findings  indicate  an  urgent  need  for the  careful  monitoring  of  the  harvesting  of  Tilapia  species  in  Nigeria.

Neutrigenomics In perceiving that supplements: 1) change quality articulation, 2) may modify ordinary digestion, and 3) influence wellbeing status.the academic network has revitalized to characterize between connections between eating regimen, wellbeing and illness procedures utilizing atomic methods. This subdiscipline of practical genomics, named nourishing genomics or nutrigenomics, attempts to determine the impact of dietary synthetic concoctions upon the genome and to expand our comprehension of how dietary constituents impact digestion. Nutrigenomics utilizes a wide arrangement of 'omic' strategies in light of the fact that no single method is appropriate for breaking down every extraordinary sort of particle or results. In nourishment inquire about GEP might be utilized for three unmistakable purposes. 1) To aid the ID and portrayal of essential sub-atomic pathways that might be affected, either decidedly or contrarily, by supplements; 2) To give bits of knowledge upon explicit components that trigger such useful or negative impacts and; 3) To recognize explicit qualities changed by supplements that may demonstrate significant as sub-atomic biomarkers or supplement sensors and in quality disclosure. Fundamental foci for GEP in aquaculture will be creature wellbeing and welfare-and nourishment related and for specific rearing. This is on the grounds that on a worldwide premise ailment causes billions of dollars in misfortunes and customers request meat items untainted by synthetic compounds and anti-toxins. Accordingly, advancement of feed-based immunoprotectants, particularly those that are normally happening, speaks to a levelheaded way to deal with fighting infection and a significant decrease of dependence upon synthetic concoctions.

ucks are believed to be have domesticated by Romans in Europe and Malays in Asia few thousands of years ago. As per FAOSTAT, 2014 leading producers of duck meat in the world are China followed by France, Malaysia, Myanmar and Vietnam.Of the total ducks reared in India 90% are indigenous and form the second largest species contributing for egg production. As per, livestock census 2007 in India ducks constituted about 8.52% of total poultry population and counted for about 27.43 million in number.In India, West Bengal, Assam, Kerala, Andhra Pradesh and Tamil Nadu are the leading states in duck population. Ducks are reared for egg, meat and the size of egg is about 4.5% of ducks body weight whereas, that of poultry is 3.3 % of its body weight. Ducks are more prolific compared to chicken and are more adapted to free range system of rearing. Duck rearing in India is concentrated in the hands of small and marginalized sections of society and has not seen the light of industrialization as in case of poultry. The states along the coastal belt of India with inland water resources provide the required natural potential for duck rearing. Considering the hardiness, active foraging ability, adaptability of the ducks to grow in free range system and the less cost input in rearing them, these act as a reliable source of livelihood for many poor rural farmers. ADVANTAGES OF DUCK REARING: 1. They require less care, attention and lay eggs which are heavier that poultry eggs by about 15-20 gram. 2. Comparatively ducks have more economic egg laying life than poultry 3. They are hardy, more resistant to diseases compared to poultry and can grow in marshy and wet lands which are not adaptable for rearing other domestic animals. 4. They lay eggs at early hours of day, do not require extensive housing system and are free from cannibalism and other vices. 5. Ducks feed on insects, bugs, snails and for this reason integrated farming of duck with fish and duck with paddy cultivation is suggested to be economical. D Duck farming-An alternative to poverty alleviation

The objective of this study was to investigate the effects of strain and non-genetic factors on egg qualities and carcass characteristics of indigenous Guinea fowl. The study was carried out at the Poultry Section of the Department of Animal Science Education, University of Education, Winneba, Mampong-Ashanti, Ghana, from 2014 to 2016. Four strains of local Guinea fowls; namely Pearl, Lavender, White and Black were used. Three non-genetic factors were considered; season of hatch, generation and sex of bird. Data were collected on 603 local Guinea fowls for the study. Data were analysed using the General Linear Procedure (GLM) of SAS. Results obtained showed that egg weight was significantly (p<0.05) affected by generation. Parental generation had higher value (37.9±3.98) than the first filial generation (37.64±0.93); however, strain and season had no significant (p>0.05) effect on egg weight and hatch weight. Generation had significant (p<0.05) effect on yolk weight. Parental and first filial generations had 13.7±0.24 and 13.2±0.21 yolk weights respectively. Haugh unit was significantly influenced by strain; white had highest (79.04±2.49) Haugh unit, followed by lavender (77.67±2.03), black (76.51±2.49) and pearl (71.14±1.2). Generation had significant (p<0.05) influence on live and dressing weights. Sex had no significant (p>0.05) effect on head, neck and shank weights. The interaction of sex and Strain had significant (p<0.05) influence on live, heart and intestine weights. Generation had no significant (p<0.05) effect on carcass characteristics. Strain had significantly (p<0.05) influence on moisture and carbohydrate. In conclusion, the strains and non-genetics factors (sex, season and generation) influence the egg and carcass qualities of indigenous Guinea fowl, hence the effects of these factors must be considered in genetic selection of indigenous Guinea fowls.

Mars Petcare introduced the first direct-to-consumer domestic dog genetic test in 2009 and Basepaws introduced the first direct-to-consumer cat genetic test in 2016. Social science research has evaluated numerous aspects of the human direct-to-consumer market, yet no such exploration has evaluated the occurrence of pet owners pursuing pet genetic tests. Using a mixed methods approach, we conducted an exploratory content analysis of directto-consumer pet genetic company webpages and consumer reviews shared on Amazon. Initial data reviews indicated some companies may be key industry players, relative to others. Our results present content frequency for each group (key industry players, all other companies), though the primary themes for each remained the same. Analysis showed genetic companies are primarily sharing product and purchasing information, along with trustworthiness to establish the merit of the company and their products. Companies also used statements directed towards pet owners that are suggestive of both pets and "pet parents" benefiting from the test results. The primary themes identified in consumer reviews involved consumers sharing their perception about the tests (e.g., accuracy), what aspects of the test results they focused on (e.g., breed information), and experiences with using the test (e.g., ease of use). Amazon reviews were primarily positive, though the companies with smaller review numbers had higher percentages of negative and ambiguous sentiments. Of interest, reviews most often indicated tests were being used to determine a pet's breed identity, while companies most frequently promoted the health advantages of using their products. Reviews revealed some consumers respond to tests by sharing their pet's results with someone or by altering their pet's care. Considering these results in addition to the growing popularity of this industry and the advancements of genomic technology, further research is needed to determine the role pet genetic testing may have in society and on human-animal relationships.

Background and Aim: We previously developed the gene-editing by electroporation (EP) of Cas9 protein method, in which the CRISPR/Cas9 system was introduced into porcine in vitro fertilized (IVF) zygotes through EP to disrupt a target gene. This method should be further developed, and a combination of EP and MI methods should be evaluated in pigs. This study aimed to determine that a combination of microinjection (MI) and EP of CRISPR/Cas9 system could increase the rates of biallelic mutation for triple-gene knockout in porcine blastocysts. We targeted the pancreatic and duodenal homeobox1 (PDX1) gene using cytoplasmic MI 1 h before or after EP, which was used to edit alpha-1,3-galactosyltransferase (GGTA1) and cytidine 32 monophosphate-N-acetylneuraminic acid hydroxylase (CMAH) genes in porcine zygotes. Materials and Methods: We introduced guide RNAs targeting PDX1, GGTA1, and CMAH with the Cas9 protein into IVF zygotes (one-cell stage) through EP 10 h after the start of IVF (IVF; E...

Please click here http://singledrug.com/media/Temporal_Analysis_of_Aging_description.pdf to see it.

I plan to add the following:

I found Osh6 at a very high rank of lifespan extending genes.  That is why I want to take all the genes that are part of the same molecular function as Osh6 and run an expression similarity analysis on SPELL (Serial Pattern of Expression Levels Locator) (see spell.yeastgenome.org/). 

I hope to find genes of unknown functions that could be involved in the same molecular function.  If I find this, I'll validate with my time series plots and TFBS similarity analysis.  That way I hope to predict the function of an unknown gene in lipid metabolism.  I will also try the opposite approach, i.e. I'll take genes of unknown function and look for a group of similarly expressed genes, which belong to the same GO term.

I added 19 pages to the second chapter.  I was able to further improve the accuracy of my yeast lifespan predicting machine learning algorithm by adding more features about lipids.  My last draft had an average prediction error of +/-5 replications.  Now it is less than +/-3 replication for the 50th percentile of the replicative lifespan.  To my knowledge nobody has published any research on about using machine learning and feature selection to predict the replicative age.

If you have any more ideas how I could make my dissertation look better, I'd be very thankful for any new feasible ideas and inspirations.  I have mainly been using Python and R for all my analysis.

I found that ribosomal RNA and proteins are most closely follow the cell cycle and that if knocked out the yeast's lifespan can be extended significantly.  I want to use the different cyclical periodically recurring and highly fluctuating gene expression oscillation pattern to discover the functions of genes we have not yet discovered.  I am interested in this because a friend of mine showed me a cancer dataset that looked very similar.  Since cancer is very similar to the yeast in that it keeps constantly dividing and hence its age can be expressed in replication we could use the same guilt-by-association approach to infer the functions or at least the way their expression is controlled by TFs from genes with similar temporal expression patterns of genes with already known functions.

When analyzing the time series plots of the 62 yeast chaperons I discovered that their expression plot trajectories can easily be clustered into 7 expression patterns, which are consisted with the 7 classes of yeast chaperons.  If I had only taken the average centroid of all 62 chaperons I would have been misled because using this approach could not be applied to potentially discover more new currently still unknown chaperons because no unknown chaperon would have time series trajectories, which would highly correlate with the average chaperon expression pattern of all 7 classes since their in-group expression pattern is very homogeneous but between the 7 different chaperon classes the expression pattern is very different.

I intended to elucidate the different ways by which we age by a very similar clustering algorithm, which I have developed based on time series curves over three subsequent yeast cell cycle consisting of 36 mRNA measures each about 25 minutes apart; thus, allowing to measure very steep mRNA expression fluctuation periods by manifold within the only 90 minutes of the cell cycle because even very brief but rapid expression changes that last not much longer than 1/12th of a cell cycle, of which I found surprisingly many.

Knowledge of the extent of linkage disequi-librium (LD) between markers is crucial for determining the number of markers required for quantitative trait loci mapping, genome-wide association studies, and application of marker-assisted selection. In this study, we surveyed genome-wide LD using three genotyping BeadChips (9, 50, and 80 K) in 11 indigenous Ethiopian cattle populations and the Korean (Hanwoo) cattle breed. The overall mean r 2 values were 0.05 ± 0.12, 0.12 ± 0.20, and 0.20 ± 0.24 in the Ethiopian cattle populations for the 9, 50, and 80 K genotyping BeadChips, respectively. In Hanwoo cattle, these values were respectively 0.06 ± 0.13, 0.15 ± 0.23, and 0.15 ± 0.26. The level of LD was significantly affected by breed, genotyping BeadChip, and chromosome (P \ 0.0001). For Ethiopian cattle populations, a moderate level of LD (r 2 = 0.22) extended at marker distances of 20–40 kb for the indicine-derived 80 K BeadChip, whereas it was only 0.14 for the 50 K BeadChip. As a consequence of the moderate r 2 , genome-wide association studies in Ethiopian cattle populations require 75,000–150,000 indi-cine-derived SNPs (with a MAF C 0.05). We suggest that indicine-derived SNPs maybe more suitable for genome-wide association studies and genomic selection in indigenous Ethiopian (or African) cattle populations.

Not everybody can produce enough saliva: Collecting 1-2 mL of Saliva within 30 minutes (as required by most saliva collection kits) can be very challenging for a significant portion of the population, especially children and elderly. It is well documented that as much as 20% - 46%1 of the population suffers from a condition known as dry mouth (xerostomia), which results from minimal or no saliva production.

In this study, the maternal genetic diversity, phylogenetic relationship and effect of natural selection on indigenous chickens from Nigeria were assessed. A total of 397-bp fragment of the mitochondrial DNA (mtDNA) D-loop region of 171 indigenous chickens from four populations of Nigeria and four commercial egg line strains (two Anak titan, one Giriraja and one Yaffa) as out-groups were analysed. Thirty-one haplotypes (28 from Nigerian chickens and three from commercial strains) and 34 polymorphic sites were identified. The mean haplotypic and nucleotide diversity were found to be 0.39 ± 0.05 and 0.02 ± 0.02, respectively. Majority of Nigerian chicken haplotypes observed were grouped into haplogroup D which originated from Indian subcontinent, suggesting a single maternal lineage. Genetic variation within and between populations accounted for 97.30 and 2.70% of the total genetic variation, respectively, which is in agreement with a recent and maternal founding effect. High number (4) of negatively selected sites observed based on single likelihood ancestral counting (SLAC) model indicated that the sampled Nigerian chicken populations were undergoing purifying selection. This study concluded that there was relatively high genetic diversity and differentiation, thus, this information will probably pave way for further evaluation studies, preservation and improvement of Nigerian chickens as genetic resources towards ensuring food security.

RESUMEN Los análisis genómicos y transcriptómicos para selección y mejoramiento genético animal requieren ADN o ARN de alta concentración y pureza, proveniente de diferentes tejidos incluyendo semen. Los métodos usualmente utilizados para extraer ADN de semen son menos efectivos en cantidad y calidad de ADN, debido a los solventes y diluyente utilizados para la conservación, características físico-químicas de los espermatozoides, y fracción no celular del eyaculado. En este estudio, se proponen modificaciones al método de tiocianato de guanidina, incluyendo un segundo lavado de la muestra con solución buffer fosfato, y dos lavados con solventes orgánicos, uno fuerte (fenol:cloroformo:alcohol isoamílico) y uno débil (cloroformo:alcohol isoamílico), para retirar la proteína y diluyente presentes en la muestra. Además, se propone una incubación por separado con ARNasa para reducir contaminación de ácidos nucleicos en la medición y elaboración de diluciones para amplificación por PCR. La precipitación agregó al isopropanol de la metodología original 3 M de acetato de sodio para retirar restos de posibles inhibidores de la PCR. Finalmente, se incluyeron centrifugaciones de alta velocidad y decantaciones para evitar la necesidad de separación mecánica del ADN y la proteína. El ADN extraído con el método propuesto no presentó degradación, y la calidad y cantidad fueron mejores (P<0.0001), encontrándose una media de 1.84±0.09 en el rango 260/280 y 156.99±7.29 ng/l para la variable de concentración. El presente método de extracción es una alternativa de bajo costo, viable para obtener ADN de semen con características necesarias para análisis genómicos en mamíferos. ABSTRACT The genomic and transcriptomic analyses for selection and genetic improvement require DNA or RNA of high concentration and purity, coming from different tissues, including semen. The DNA may be from hair, saliva, cartilage, blood, or semen. The methods usually used to extract DNA from semen have low efficiency in terms of quantity and quality. This is due to the solvents and dilutors used for semen conservation, physical and chemical characteristics of sperms, and non-cellular fraction of ejaculate. In this study, modifications to the guanidinium thiocyanate method are proposed, including a second washing of the sample using a phosphate buffer solution, and two washing with organic solvents, one strong (phenol:chloroform:Isoamyl alcohol), and one weak (chloroform:isoamyl alcohol), to get rid of the protein and dilutors present in the sample. Additionally, it is proposed a separate incubation with RNA-ase to reduce contamination of nucleic acids during measurement and dilution preparation for PCR amplification. The precipitation added to the isopropanol of the original method 3 M sodium acetate to separate the residuals of potential PCR inhibitors. Finally, there were included high-speed centrifugation and decantation to avoid the need for mechanical separation of the DNA and protein. The DNA extracted with the modified method did not present degradation, and the quality and quantity were better (P<0.0001) than the original, with a mean of 1.84±0.09 in the range of 260/ 280 and 156.99±7.29 ng/l for concentration. The present method of extraction is a viable low-cost alternative to obtain DNA from semen with the necessary characteristics for genomic analysis in mammals.

Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.

Background: Scanning of the genome for selection signatures between breeds may play important role in understanding the underlie causes for observable phenotypic variations. The discovery of high density single nucleotide polymorphisms (SNPs) provide a useful starting point to perform genome–wide scan in pig populations in order to identify loci/candidate genes underlie phenotypic variation in pig breeds and facilitate genetic improvement programs. However, prior to this study genomic region under selection in commercially selected Berkshire and Korean native pig breeds has never been detected using high density SNP markers. To this end, we have genotyped 45 animals using Porcine SNP60 chip to detect selection signatures in the genome of the two breeds by using the F ST approach. Results: In the comparison of Berkshire and KNP breeds using the FDIST approach, a total of 1108 outlier loci (3.48%) were significantly different from zero at 99% confidence level with 870 of the outlier SNPs displaying high level of genetic differentiation (F ST ≥0.490). The identified candidate genes were involved in a wide array of biological processes and molecular functions. Results revealed that 19 candidate genes were enriched in phosphate metabolism (GO: 0006796; ADCK1, VRK1). We have identified a set of candidate genes under selection and have known to be involved in growth, size and pork quality (CART, AGL, CF7L2, MAP2K5, DLK1, GLI3, CA3 and MC3R), ear morphology and size (HMGA2 and SOX5) stress response (ATF2, MSRB3, TMTC3 and SCAF8) and immune response (HCST and RYR1). Conclusions: Some of the genes may be used to facilitate genetic improvement programs. Our results also provide insights for better understanding of the process and influence of breed development on the pattern of genetic variations.

In this study, the maternal genetic diversity, phylogenetic relationship and effect of natural selection on indigenous chickens from Nigeria were assessed. A total of 397-bp fragment of the mitochondrial DNA (mtDNA) D-loop region of 171 indigenous chickens from four populations of Nigeria and four commercial egg line strains (two Anak titan, one Giriraja and one Yaffa) as out-groups were analysed. Thirty-one haplotypes (28 from Nigerian chickens and three from commercial strains) and 34 polymorphic sites were identified. The mean haplotypic and nucleotide diversity were found to be 0.39 ± 0.05 and 0.02 ± 0.02, respectively. Majority of Nigerian chicken haplotypes observed were grouped into haplogroup D which originated from Indian subcontinent, suggesting a single maternal lineage. Genetic variation within and between populations accounted for 97.30 and 2.70% of the total genetic variation, respectively, which is in agreement with a recent and maternal founding effect. High number (...

The common bed bug (Cimex lectularius) has been a persistent pest of humans for thousands of years, yet the genetic basis of the bed bug's basic biology and adaptation to dense human environments is largely unknown. Here we report the assembly, annotation and phylogenetic mapping of the 697.9-Mb Cimex lectularius genome, with an N50 of 971 kb, using both long and short read technologies. A RNA-seq time course across all five developmental stages and male and female adults generated 36,985 coding and noncoding gene models. The most pronounced change in gene expression during the life cycle occurs after feeding on human blood and included genes from the Wolbachia endosymbiont, which shows a simultaneous and coordinated host/commensal response to haematophagous activity. These data provide a rich genetic resource for mapping activity and density of C. lectularius across human hosts and cities, which can help track, manage and control bed bug infestations.

How Can Genomic Tools Contribute to the Conservation of Endangered Organisms
Call for Papers

All living organisms are faced with a multitude of challenges in their natural environment, such as climate change, disease predation, competition, and habitat disturbance. In the short term, animals and plants can acclimatize to shifting environmental conditions by developing and expressing particular traits in response to local environmental conditions (phenotypic plasticity). Organisms can also react to the shifting environment by dispersing, even if such an option is not always available, for example, if the landscape is too fragmented. The last type of response is evolution via genic selection leading to adaptation. However, populations and species are going to persist in an altered environment, only if they can invoke a response.

The genome of a species contains signatures of these responses that may be studied with genetic markers. We are interested in filling gaps in the knowledge about past demographic history of organisms and which factors ultimately shape genomic variation in populations using the very latest, innovative techniques in next-generation sequencing. The incorporation of technological developments in molecular biology and the ongoing development of genomic tools, like SNPs and next-generation sequencing, and genomic-based approaches, like full genome scans and gene-expression pattern analysis, make it possible to address questions that until now were hard to tackle. There is an urgent need for empirical studies on nonmodel organisms which can contribute to the emerging disciplines of population genomics and landscape genomics. Such studies are necessary if we want to show how these advancements in molecular techniques and approaches might allow conservation genetics to make a big leap forward.

Potential topics include, but are not limited to:

    Genomic consequences of inbreeding
    Inbreeding by environment interaction
    Genomic and epigenomic consequences of outbreeding
    Genomic and epigenomic mechanisms of phenotypic plasticity
    Transcriptome, metabolomics, and proteomic techniques applied to conservation biology
    The emerging discipline of landscape genomics, detection of signature of selection using genomic techniques

Authors can submit their manuscripts via the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/ijg/hcgt/.
Manuscript Due Friday, 25 December 2015
First Round of Reviews Friday, 18 March 2016
Publication Date Friday, 13 May 2016
Lead Guest Editor

    Cino Pertoldi, Aalborg University, Aalborg, Denmark

Guest Editors

    Ettore Randi, Istituto Superiore per la Protezione e la Ricerca Ambientale ISPRA, Roma, Italy
    Aritz R. González, University of the Basque Country, Leioa, Spain
    Philippine Vergeer, Wageningen University, Wageningen, Netherlands
    Joop Ouborg, Experimentele Planten Ecologie, Radboud Universiteit, Nijmegen, Netherlands