No abstract

BackgroundIn upland cotton (Gossypium hirsutum L.), genotypes with the same mature fiber length (FL) might possess different genes and exhibit differential expression of genes related to fiber elongation at different fiber developmental stages. However, there is a lack of information on the genetic variation influencing fiber length and its quantitative trait loci (QTLs) during the fiber elongation stage. In this study, a subset of upland cotton accessions was selected based on a previous GWAS conducted in China and grown in multiple environments to determine the dynamic fiber length at 10, 15, 20, and 25 days post-anthesis (DPA) and maturity. The germplasm lines were genotyped with the Cotton 63 K Illumina single-nucleotide polymorphism (SNP) array for GWAS.ResultsA total of 25, 38, 57, 89 and 88 SNPs showed significant correlations with fiber length at 10, 15, 20 and 25 DPA and maturity, respectively. In addition, 60 more promising SNPs were detected in at least two tests and two FL developmental time points, and 20 SNPs were located within the confidence intervals of QTLs identified in previous studies. The fastest fiber-length growth rates were obtained at 10 to 15 DPA in 69 upland cotton lines and at 15 to 20 DPA in 14 upland cotton accessions, and 10 SNPs showed significant correlations with the fiber-length growth rate. A combined transcriptome and qRT-PCR analysis revealed that two genes (D10G1008 and D13G2037) showed differential expression between two long-fiber genotypes and two short-fiber genotypes.ConclusionsThis study provides important new insights into the genetic basis of the time-dependent fiber-length trait and reveals candidate SNPs and genes for improving fiber length in upland cotton.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5309-2) contains supplementary material, which is available to authorized users.

BackgroundSmall auxin-up RNA (SAUR) gene family is the largest family of early auxin response genes in higher plants, which have been implicated in the regulation of multiple biological processes. However, no comprehensive analysis of SAUR genes has been reported in cotton (Gossypium spp.).ResultsIn the study, we identified 145, 97, 214, and 176 SAUR homologous genes in the sequenced genomes of G. raimondii, G. arboreum, G. hirsutum, and G. barbadense, respectively. A phylogenetic analysis revealed that the SAUR genes can be classified into 10 groups. A further analysis of chromosomal locations and gene duplications showed that tandem duplication and segmental duplication events contributed to the expansion of the SAUR gene family in cotton. An exon-intron organization and motif analysis revealed the conservation of SAUR-specific domains, and the auxin responsive elements existed in most of the upstream sequences. The expression levels of 16 GhSAUR genes in response to an exogenous application of IAA were determined by a quantitative RT-PCR analysis. The genome-wide RNA-seq data and qRT-PCR analysis of selected SAUR genes in developing fibers revealed their differential expressions. The physical mapping showed that 20 SAUR genes were co-localized with fiber length quantitative trait locus (QTL) hotspots. Single nucleotide polymorphisms (SNPs) were detected for 12 of these 20 genes between G. hirsutum and G. barbadense, but no SNPs were identified between two backcross inbred lines with differing fiber lengths derived from a cross between the two cultivated tetraploids.ConclusionsThis study provides an important piece of genomic information for the SAUR genes in cotton and lays a solid foundation for elucidating the functions of SAUR genes in auxin signaling pathways to regulate cotton growth.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4224-2) contains supplementary material, which is available to authorized users.

Despite the advances reached along the last 20 years, anomaly detection in networks is still an immature technology, Nevertheless, the benefits which could be obtained from a better understanding of the problem itself as well as the improvement of these methods. Therefore, in this paper we present a survey on anomaly detection in networks. In order to distinguish between the different approaches used for anomaly detection in networks in a structured way, we have classified those methods into four categories: statistical anomaly detection, classifier based anomaly detection, anomaly detection using machine learning and finite state machine anomaly detection. We describe each method in details and give examples for its applications in networks.

No abstract

No abstract

No abstract

In this study, HLA-DRB1 gene was genotyped by using the microarray technique. Oligonucleotide probes were designed based on partial sequences of various genotypes of HLA-DRB1, and were fixed on a silylated slide to form a microarray. The second exon of HLA-DRB1 gene in the extracted genomic DNA samples was amplified and labelled by means of polymerase chain reaction (PCR); then it was hybridized to the microarray. The microarray was scanned, and the result was analysed in order to determine the genotypes of HLA-DRB1 of the tested sample. A total of 1574 of 1592 clinical samples had accordant results of genotypes in either microarray assay or PCR-SSP assay; 8 of 10 samples that had inconsistent results of genotypes were proved to be microarray-assay reliable by confirmation of DNA sequencing. It is concluded that microarray is an alternative reliable method for HLA-DRB1 genotyping.