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9,10-Anthracenedione, 3-[[6-O-acetyl-2-O-(6-deoxy-?-L-mannopyranosyl)-?-D-glucopyranosyl]oxy]-1,6-dihydroxy-2-methyl-


  • Brand : BIOFRON

  • Catalogue Number : AV-P11599

  • Specification : 98%

  • CAS number : 87686-87-1

  • Formula : C29H32O15

  • Molecular Weight : 620.56

  • PUBCHEM ID : 14539982

  • Volume : 10mg

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Catalogue Number


Analysis Method






Molecular Weight




Botanical Source

Structure Type






[(2~{R},3~{S},4~{S},5~{R},6~{S})-6-(4,7-dihydroxy-3-methyl-9,10-dioxoanthracen-2-yl)oxy-3,4-dihydroxy-5-[(2~{S},3~{R},4~{R},5~{R},6~{S})-3,4,5-trihydroxy-6-methyloxan-2-yl]oxyoxan-2-yl]methyl acetate


[6-(4,7-dihydroxy-3-methyl-9,10-dioxoanthracen-2-yl)oxy-3,4-dihydroxy-5-(3,4,5-trihydroxy-6-methyloxan-2-yl)oxyoxan-2-yl]methyl acetate



1.7±0.1 g/cm3


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

Flash Point

294.2±27.8 °C

Boiling Point

899.5±65.0 °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#:87686-87-1) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

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The soil worm Enchytraeus crypticus (Oligochaeta) is an ecotoxicology model species that, until now, was without genome or transcriptome sequence information. The present research aims at studying the transcriptome of Enchytraeus crypticus, sampled from multiple test conditions, and the construction of a high-density microarray for functional genomic studies.

Over 1.5 million cDNA sequence reads were obtained representing 645 million nucleotides. After assembly, 27,296 contigs and 87,686 singletons were obtained, from which 44% and 25% are annotated as protein-coding genes, respectively, sharing homology with other animal proteomes. Concerning assembly quality, 84% of the contig sequences contain an open reading frame with a start codon while E. crypticus homologs were identified for 92% of the core eukaryotic genes. Moreover, 65% and 77% of the singletons and contigs without known homologs, respectively, were shown to be transcribed in an independent microarray experiment. An Agilent 180 K microarray platform was designed and validated by hybridizing cDNA from 4 day zinc- exposed E. crypticus to the concentration corresponding to 50% reduction in reproduction after three weeks (EC50). Overall, 70% of all probes signaled expression above background levels (mean signal + 1x standard deviation). More specifically, the probes derived from contigs showed a wider range of average intensities when compared to probes derived from singletons. In total, 522 significantly differentially regulated transcripts were identified upon zinc exposure. Several significantly regulated genes exerted predicted functions (e.g. zinc efflux, zinc transport) associated with zinc stress. Unexpectedly, the microarray data suggest that zinc exposure alters retro transposon activity in the E. crypticus genome.

An initial investigation of the E. crypticus transcriptome including an associated microarray platform for future studies proves to be a valuable resource to investigate functional genomics mechanisms of toxicity in soil environments and to annotate a potentially large number of lineage specific genes that are responsive to environmental stress conditions.


Ecotoxicogenomics, Next-generation pyrosequencing, Invertebrate, Zinc, Annelid, 454 sequencing


Transcriptome assembly and microarray construction for Enchytraeus crypticus, a model oligochaete to assess stress response mechanisms derived from soil conditions


Marta P Castro-Ferreira,1,2 Tjalf E de Boer,1 John K Colbourne,3,4 Riet Vooijs,1 Cornelis AM van Gestel,1 Nico M van Straalen,1 Amadeu MVM Soares,2 Monica JB Amorim,2 and Dick Roelofscorresponding author1

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Maternal smoking during pregnancy (MSDP) and secondhand smoke (SHS) exposure are associated with a myriad of negative health effects for both mother and child. However, less is known regarding social determinants for SHS exposure, which may differ from those of maternal smoking during pregnancy (MSDP). To identify social determinants for SHS exposure only, MSDP only, and MSDP and SHS exposure, data were obtained from all pregnant women (18-54?years; N?=?726) in waves 1 and 2 of the Population Assessment of Tobacco and Health Study (2014-2015). Multiple logistic regressions were conducted using SAS 9.4. Smoke exposure during pregnancy was common; 23.0% reported SHS exposure only, 6.1% reported MSDP only, and 11.8% reported both SHS exposure and MSDP. Results demonstrate that relationships between smoke exposure during pregnancy and social determinants vary by type of exposure. Women at risk for any smoke exposure during pregnancy include those who are unmarried and allow the use of combustible tobacco products within the home. Those who are at higher risk for SHS exposure include those who are younger in age, and those who are earlier in their pregnancy. Those who are at higher risk for maternal smoking include those with fair/poor mental health status and those who believe that others’ view tobacco use more positively. These results suggest the need for implementing more comprehensive policies that promote smoke-free environments. Implementing these strategies have the potential to improve maternal and fetal health outcomes associated with tobacco smoke exposure.


Pregnancy, Maternal smoking, Secondhand smoke, Social determinants


Social determinants of smoke exposure during pregnancy: Findings from waves 1 & 2 of the Population Assessment of Tobacco and Health (PATH) Study


Elizabeth K. Do,a,? Tiffany L. Green,a Elizabeth C. Prom-Wormley,b and Bernard F. Fuemmelera

Publish date

2018 Dec;




The challenges when developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize a de Bruijn graph, with which complexity grows linearly with data size while suffering from errors and repeats. Although one can correct the errors by inspecting the topological structure of the graph, this is not an easy task when there are too many branches. Two research directions are to improve either the graph reliability or the path search precision, and in this study, we focused on the former.

We present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. In the proposed approach, we built contigs by direct read alignment within a distance margin and performed a junction search to construct splicing graphs. While doing so, a contig of length l was represented by a 4 × l matrix (called a consensus matrix), in which each element was the base count of the aligned reads so far. A representative sequence was obtained by taking the majority in each column of the consensus matrix to be used for further read alignment. Once the splicing graphs had been obtained, we used IsoLasso to find paths with a noticeable read depth. The experiments using real and simulated reads show that the method provided considerable improvement in sensitivity and moderately better performance when comparing sensitivity and precision. This was achieved by the error-aware graph construction using the consensus matrix, with which the reads having errors were made usable for the graph construction (otherwise, they might have been eventually discarded). This improved the quality of the coverage depth information used in the subsequent path search step and finally the reliability of the graph.

De novo assembly is mainly used to explore undiscovered isoforms and must be able to represent as many reads as possible in an efficient way. In this sense, TraRECo provides us with a potential alternative for improving graph reliability even though the computational burden is much higher than the single k-mer in the de Bruijn graph approach.


RNA-Seq, de novo transcriptome assembly, greedy approach, consensus matrix, read error correction


TraRECo: a greedy approach based de novo transcriptome assembler with read error correction using consensus matrix


Seokhyun Yoon,1 Daeseung Kim,2 Keunsoo Kang,corresponding author2 and Woong June Park3

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