The first set of EST resource for gene discovery and marker development in pigeonpea (Cajanus cajanL.)

Background Pigeonpea (Cajanus cajan (L.) Millsp) is one of the major grain legume crops of the tropics and subtropics, but biotic stresses [Fusarium wilt (FW), sterility mosaic disease (SMD), etc.] are serious challenges for sustainable crop production. Modern genomic tools such as molecular markers and candidate genes associated with resistance to these stresses offer the possibility of facilitating pigeonpea breeding for improving biotic stress resistance. Availability of limited genomic resources, however, is a serious bottleneck to undertake molecular breeding in pigeonpea to develop superior genotypes with enhanced resistance to above mentioned biotic stresses. With an objective of enhancing genomic resources in pigeonpea, this study reports generation and analysis of comprehensive resource of FW- and SMD- responsive expressed sequence tags (ESTs). Results A total of 16 cDNA libraries were constructed from four pigeonpea genotypes that are resistant and susceptible to FW ('ICPL 20102' and 'ICP 2376') and SMD ('ICP 7035' and 'TTB 7') and a total of 9,888 (9,468 high quality) ESTs were generated and deposited in dbEST of GenBank under accession numbers GR463974 to GR473857 and GR958228 to GR958231. Clustering and assembly analyses of these ESTs resulted into 4,557 unique sequences (unigenes) including 697 contigs and 3,860 singletons. BLASTN analysis of 4,557 unigenes showed a significant identity with ESTs of different legumes (23.2-60.3%), rice (28.3%), Arabidopsis (33.7%) and poplar (35.4%). As expected, pigeonpea ESTs are more closely related to soybean (60.3%) and cowpea ESTs (43.6%) than other plant ESTs. Similarly, BLASTX similarity results showed that only 1,603 (35.1%) out of 4,557 total unigenes correspond to known proteins in the UniProt database (≤ 1E-08). Functional categorization of the annotated unigenes sequences showed that 153 (3.3%) genes were assigned to cellular component category, 132 (2.8%) to biological process, and 132 (2.8%) in molecular function. Further, 19 genes were identified differentially expressed between FW- responsive genotypes and 20 between SMD- responsive genotypes. Generated ESTs were compiled together with 908 ESTs available in public domain, at the time of analysis, and a set of 5,085 unigenes were defined that were used for identification of molecular markers in pigeonpea. For instance, 3,583 simple sequence repeat (SSR) motifs were identified in 1,365 unigenes and 383 primer pairs were designed. Assessment of a set of 84 primer pairs on 40 elite pigeonpea lines showed polymorphism with 15 (28.8%) markers with an average of four alleles per marker and an average polymorphic information content (PIC) value of 0.40. Similarly, in silico mining of 133 contigs with ≥ 5 sequences detected 102 single nucleotide polymorphisms (SNPs) in 37 contigs. As an example, a set of 10 contigs were used for confirming in silico predicted SNPs in a set of four genotypes using wet lab experiments. Occurrence of SNPs were confirmed for all the 6 contigs for which scorable and sequenceable amplicons were generated. PCR amplicons were not obtained in case of 4 contigs. Recognition sites for restriction enzymes were identified for 102 SNPs in 37 contigs that indicates possibility of assaying SNPs in 37 genes using cleaved amplified polymorphic sequences (CAPS) assay. Conclusion The pigeonpea EST dataset generated here provides a transcriptomic resource for gene discovery and development of functional markers associated with biotic stress resistance. Sequence analyses of this dataset have showed conservation of a considerable number of pigeonpea transcripts across legume and model plant species analysed as well as some putative pigeonpea specific genes. Validation of identified biotic stress responsive genes should provide candidate genes for allele mining as well as candidate markers for molecular breeding

DNA hybridization to mismatched templates: a chip study

High-density oligonucleotide arrays are among the most rapidly expanding technologies in biology today. In the {\sl GeneChip} system, the reconstruction of the target concentration depends upon the differential signal generated from hybridizing the target RNA to two nearly identical templates: a perfect match (PM) and a single mismatch (MM) probe. It has been observed that a large fraction of MM probes repeatably bind targets better than the PMs, against the usual expectation from sequence-specific hybridization; this is difficult to interpret in terms of the underlying physics. We examine this problem via a statistical analysis of a large set of microarray experiments. We classify the probes according to their signal to noise ($S/N$) ratio, defined as the eccentricity of a (PM, MM) pair's `trajectory' across many experiments. Of those probes having large $S/N$ ($>3$) only a fraction behave consistently with the commonly assumed hybridization model. Our results imply that the physics of DNA hybridization in microarrays is more complex than expected, and they suggest new ways of constructing estimators for the target RNA concentration.Comment: 3 figures 1 tabl

Multiple superconducting gap and anisotropic spin fluctuations in iron arsenides: Comparison with nickel analog

We present extensive 75As NMR and NQR data on the superconducting arsenides PrFeAs0.89F0.11 (Tc=45 K), LaFeAsO0.92F0.08 (Tc=27 K), LiFeAs (Tc = 17 K) and Ba0.72K0.28Fe2As2 (Tc = 31.5 K) single crystal, and compare with the nickel analog LaNiAsO0.9F0.1 (Tc=4.0 K) . In contrast to LaNiAsO0.9F0.1 where the superconducting gap is shown to be isotropic, the spin lattice relaxation rate 1/T1 in the Fe-arsenides decreases below Tc with no coherence peak and shows a step-wise variation at low temperatures. The Knight shift decreases below Tc and shows a step-wise T variation as well. These results indicate spinsinglet superconductivity with multiple gaps in the Fe-arsenides. The Fe antiferromagnetic spin fluctuations are anisotropic and weaker compared to underdoped copper-oxides or cobalt-oxide superconductors, while there is no significant electron correlations in LaNiAsO0.9F0.1. We will discuss the implications of these results and highlight the importance of the Fermi surface topology.Comment: 6 pages, 11 figure

Electronic Structure of the BaFe2_2As2_2 Family of Iron Pnictides

We use high resolution angle-resolved photoemission spectroscopy to study the band structure and Fermi surface topology of the BaFe$_2$As$_2$ iron pnictides. We observe two electron bands and two hole bands near the X-point, $(\pi,\pi)$ of the Brillouin zone, in the paramagnetic state for different doping levels, including electron-doped Ba(Co$_{0.06}$Fe$_{0.94}$)$_2$As$_2$, undoped BaFe$_2$As$_2$, and hole-doped Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$. Among these four bands, only the electron bands cross the Fermi level, forming two electron pockets around X, while the hole bands approach but never reach the Fermi level. We show that the band structure of the BaFe$_2$As$_2$ family matches reasonably well with the prediction of LDA calculations after a momentum-dependent shift and renormalization. Our finding resolves a number of inconsistencies regarding the electronic structure of pnictides.Comment: 5 pages, 4 figure

Solving the riddle of the bright mismatches: hybridization in oligonucleotide arrays

HDONA technology is predicated on two ideas. First, the differential between high-affinity (perfect match, PM) and lower-affinity (mismatch, MM) probes is used to minimize cross-hybridization. Second, several short probes along the transcript are combined, introducing redundancy. Both ideas have shown problems in practice: MMs are often brighter than PMs, and it is hard to combine the pairs because their brightness often spans decades. Previous analysis suggested these problems were sequence-related; publication of the probe sequences has permitted us an in-depth study of this issue. Our results suggest that fluorescently labeling the nucleotides interferes with mRNA binding, causing a catch-22 since, to be detected, the target mRNA must both glow and stick to its probe: without labels it cannot be seen even if bound, while with too many it won't bind. We show that this conflict causes much of the complexity of HDONA raw data, suggesting that an accurate physical understanding of hybridization by incorporating sequence information is necessary to perfect microarray analysis.Comment: 4 figure

Measurement of (n,) reaction cross section of W-186-isotope at neutron energy of 20.02±0.58 MeV

The cross-section of 186W(n,)187W reaction has been measured at an average neutron energy of 20.02±0.58 MeV by using activation technique. The 27Al(n,)24Na and 115In(n,n´)115mIn reactions have been used for absolute neutron flux measurement. Theoretically the reaction cross-sections have been calculated by using the TALYS-1.9 code. The results from the present work and the EXFOR based literature data have been compared with the evaluated data and calculated data from TALYS-1.9 code

Spallation reactions. A successful interplay between modeling and applications

The spallation reactions are a type of nuclear reaction which occur in space by interaction of the cosmic rays with interstellar bodies. The first spallation reactions induced with an accelerator took place in 1947 at the Berkeley cyclotron (University of California) with 200 MeV deuterons and 400 MeV alpha beams. They highlighted the multiple emission of neutrons and charged particles and the production of a large number of residual nuclei far different from the target nuclei. The same year R. Serber describes the reaction in two steps: a first and fast one with high-energy particle emission leading to an excited remnant nucleus, and a second one, much slower, the de-excitation of the remnant. In 2010 IAEA organized a worskhop to present the results of the most widely used spallation codes within a benchmark of spallation models. If one of the goals was to understand the deficiencies, if any, in each code, one remarkable outcome points out the overall high-quality level of some models and so the great improvements achieved since Serber. Particle transport codes can then rely on such spallation models to treat the reactions between a light particle and an atomic nucleus with energies spanning from few tens of MeV up to some GeV. An overview of the spallation reactions modeling is presented in order to point out the incomparable contribution of models based on basic physics to numerous applications where such reactions occur. Validations or benchmarks, which are necessary steps in the improvement process, are also addressed, as well as the potential future domains of development. Spallation reactions modeling is a representative case of continuous studies aiming at understanding a reaction mechanism and which end up in a powerful tool.Comment: 59 pages, 54 figures, Revie

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus

Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% for TBLH-BMD, and 39% for TB-LM, with a shared genetic component of 43%. We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci: _WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5_. Variants in the _TOM1L2/SREBF1_ locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that _SREBF1_ is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass