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Tokinolide B


  • Brand : BIOFRON

  • Catalogue Number : BD-P0053

  • Specification : 98.0%(HPLC)

  • CAS number : 112966-16-2

  • Formula : C24H28O4

  • Molecular Weight : 380.48

  • PUBCHEM ID : 11090206

  • Volume : 10mg

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Catalogue Number


Analysis Method






Molecular Weight




Botanical Source

Structure Type











1.2±0.1 g/cm3


Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.

Flash Point

338.5±29.9 °C

Boiling Point

644.2±55.0 °C at 760 mmHg

Melting Point



InChl Key


WGK Germany


HS Code Reference


Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:112966-16-2) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

No Technical Documents Available For This Product.




Based on the next generation genome sequencing technologies, a variety of biological applications are developed, while alignment is the first step once the sequencing reads are obtained. In recent years, many software tools have been developed to efficiently and accurately align short reads to the reference genome. However, there are still many reads that can’t be mapped to the reference genome, due to the exceeding of allowable mismatches. Moreover, besides the unmapped reads, the reads with low mapping qualities are also excluded from the downstream analysis, such as variance calling. If we can take advantages of the confident segments of these reads, not only can the alignment rates be improved, but also more information will be provided for the downstream analysis.

This paper proposes a method, called RAUR (Re-align the Unmapped Reads), to re-align the reads that can not be mapped by alignment tools. Firstly, it takes advantages of the base quality scores (reported by the sequencer) to figure out the most confident and informative segments of the unmapped reads by controlling the number of possible mismatches in the alignment. Then, combined with an alignment tool, RAUR re-align these segments of the reads. We run RAUR on both simulated data and real data with different read lengths. The results show that many reads which fail to be aligned by the most popular alignment tools (BWA and Bowtie2) can be correctly re-aligned by RAUR, with a similar Precision. Even compared with the BWA-MEM and the local mode of Bowtie2, which perform local alignment for long reads to improve the alignment rate, RAUR also shows advantages on the Alignment rate and Precision in some cases. Therefore, the trimming strategy used in RAUR is useful to improve the Alignment rate of alignment tools for the next-generation genome sequencing.

All source code are available at


re-alignment, unmapped reads, base quality score


Re-alignment of the unmapped reads with base quality score


Xiaoqing Peng,1 Jianxin Wang,corresponding author1 Zhen Zhang,1 Qianghua Xiao,1 Min Li,1 and Yi Pan1,2

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False discovery rate (FDR) methods play an important role in analyzing high-dimensional data. There are two types of FDR, tail area-based FDR and local FDR, as well as numerous statistical algorithms for estimating or controlling FDR. These differ in terms of underlying test statistics and procedures employed for statistical learning.

A unifying algorithm for simultaneous estimation of both local FDR and tail area-based FDR is presented that can be applied to a diverse range of test statistics, including p-values, correlations, z- and t-scores. This approach is semipararametric and is based on a modified Grenander density estimator. For test statistics other than p-values it allows for empirical null modeling, so that dependencies among tests can be taken into account. The inference of the underlying model employs truncated maximum-likelihood estimation, with the cut-off point chosen according to the false non-discovery rate.

The proposed procedure generalizes a number of more specialized algorithms and thus offers a common framework for FDR estimation consistent across test statistics and types of FDR. In comparative study the unified approach performs on par with the best competing yet more specialized alternatives. The algorithm is implemented in R in the “fdrtool” package, available under the GNU GPL from and from the R package


A unified approach to false discovery rate estimation


Korbinian Strimmercorresponding author1

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Equid herpesvirus 8 (EHV-8), formerly known as asinine herpesvirus 3, is an alphaherpesvirus that is closely related to equid herpesviruses 1 and 9 (EHV-1 and EHV-9). The pathogenesis of EHV-8 is relatively little studied and to date has only been associated with respiratory disease in donkeys in Australia and horses in China. A single EHV-8 genome sequence has been generated for strain Wh in China, but is apparently incomplete and contains frameshifts in two genes. In this study, the complete genome sequences of four EHV-8 strains isolated in Ireland between 2003 and 2015 were determined by Illumina sequencing. Two of these strains were isolated from cases of abortion in horses, and were misdiagnosed initially as EHV-1, and two were isolated from donkeys, one with neurological disease. The four genome sequences are very similar to each other, exhibiting greater than 98.4% nucleotide identity, and their phylogenetic clustering together demonstrated that genomic diversity is not dependent on the host. Comparative genomic analysis revealed 24 of the 76 predicted protein sequences are completely conserved among the Irish EHV-8 strains. Evolutionary comparisons indicate that EHV-8 is phylogenetically closer to EHV-9 than it is to EHV-1. In summary, the first complete genome sequences of EHV-8 isolates from two host species over a twelve year period are reported. The current study suggests that EHV-8 can cause abortion in horses. The potential threat of EHV-8 to the horse industry and the possibility that donkeys may act as reservoirs of infection warrant further investigation.


Equid herpesvirus 8: Complete genome sequence and association with abortion in mares


Marie Garvey, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - original draft,1 Nicolas M. Suarez, Data curation, Formal analysis, Investigation, Methodology,2 Karen Kerr, Data curation, Formal analysis, Investigation, Methodology,2 Ralph Hector, Data curation, Formal analysis, Investigation, Methodology,2,¤ Laura Moloney-Quinn, Investigation,1 Sean Arkins, Funding acquisition, Project administration, Supervision,3 Andrew J. Davison, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing - review & editing,2,* and Ann Cullinane, Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing - review & editing1,*

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