Dataset Information

ABSTRACT: FAIRE-seq and RNA-seq raw data of MSG and PSG

PROVIDER: PRJNA900626 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	SRR22286479_1.fastq.gz	Fastqsanger.gz
	SRR22286479_2.fastq.gz	Fastqsanger.gz
	SRR22286480_1.fastq.gz	Fastqsanger.gz
	SRR22286480_2.fastq.gz	Fastqsanger.gz
	SRR22286481.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

Project description:To identify functions that distinguish the posterior and median cells producing fibroin and sericin in the silk gland of Bombyx mori, serial analysis of gene expression (SAGE) profiles from both silk gland regions were analyzed and compared. The construction of a B. mori reference tag collection extracted from a set of 38000 Bombyx EST sequenced from the 3’ side, helped annotating the SAGE libraries. Most of the tags appeared at similar relative concentration in the two libraries except for those corresponding to silk proteins that were found region-specific and highly abundant. Strikingly, besides tags from silk protein mRNAs, 19 tags were found in the class of high abundance in the median cell library, which were absent in the posterior cell tag collection. Except tags from SP1 mRNA, no PSG specific tags were found in the same class of abundance. The analysis of MSG-specific different transcripts led to suggest that middle silk gland cell realizes more diversified functions as those already known, of synthesis and secretion of the silk sericins. EST libraries from 11 silkworm tissues were 3’-sequenced to ensure the identification of the most terminal tag. 37,920 sequences were analyzed on ABI 3700 or 3730XL sequencers. Electrophoregrams were processed with KB Basecaller (3730XL traces) or with PHRED (3700 traces) to obtain the .phd files from which were extracted text sequences and their corresponding quality files in Fasta format (Phd2Fasta, Green and Ewing, 1995; 2002). Vector sequences and bad quality regions were removed with an home made software after identification by Lucy (Chou and Holmes, 2001). Chimera were removed by an home made software and retrotransposon sequences were masked by RepeatMasker (http://repeatmasker.org). The cleaned sequences were clusterized with TGICL package (TIGR) and assembled into contigs with CAP3 (Huang and Madan, 1999). Contigs were identified with Blastn and Blastx on GenBank and SwissProt/TREMBL, respectively. Some clusters, splitted by CAP3 procedure, have been regrouped on the basis of Blastx identity. Identitag (Keime et al., 2004) was used to extract all possible tags (forward and reverse) to create a reference database. Moreover, a quality index is attached to each tag depending to the presense of poly-A, polyadenylation signal and its proximity to 3-prime extremity in the original mRNA sequence. This database was supplemented with the tags extracted from public B. mori sequences (GenBank, Silkbase) with the same software. Tags were extracted from MSG and PSG libraries (from 2304 and 3072 sequenced inserts respectively) with the Sagenhaft software (Beissbarth et al., 2004) then identified and compared with Identitag. The assessment of significant differences among the two libraries was performed by using the Z-test used for comparison of SAGE libraries of different size (Kal et al., 1999). For graphic purpose and to avoid division by zero we used a tag value of 1 for tags that were not detected in MSG or PSG libraries. Since the two SAGE libraries showed up different TAG number, we used the Z-test for comparing the two mRNA populations.

Project description:These tracks display a synthesis of evidence from different assays as part of the four Open Chromatin track sets. This track displays open chromatin regions and/or transcription factor binding sites identified in multiple cell types by one or more complementary methodologies, DNaseI hypersensitivity (HS) (Duke DNaseI HS), Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) (UNC FAIRE), and chromatin immunoprecipitation (ChIP) for select regulatory factors (UTA TFBS). Each methodology was performed on the same cell type using identical growth conditions. (Note: Data for some or all ChIP experiments may not be available for all cell types). Regions that overlap between methodologies identify regulatory elements that are cross-validated indicating high confidence regions. In addition, multiple lines of evidence suggest that regions detected by a single assay (e.g., DNase-only or FAIRE-only) are also biologically relevant (Song et al., submitted). DNaseI HS data: DNaseI is an enzyme that has long been used to map general chromatin accessibility, and DNaseI "hypersensitivity" is a feature of active cis-regulatory sequences.The use of this method has led to the discovery of functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions, and novel elements. DNaseI hypersensitivity signifies chromatin accessibility following binding of trans-acting factors in place of a canonical nucleosome. FAIRE data: FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements) is a method to isolate and identify nucleosome-depleted regions of the genome. FAIRE was initially discovered in yeast and subsequently shown to identify active regulatory elements in human cells (Giresi et al., 2007). Similar to DNaseI HS, FAIRE appears to identify functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions and novel elements. ChIP data: ChIP (Chromatin Immunoprecipitation) is a method to identify the specific location of proteins that are directly or indirectly bound to genomic DNA. By identifying the binding location of sequence-specific transcription factors, general transcription machinery components, and chromatin factors, ChIP can help in the functional annotation of the open chromatin regions identified by DNaseI HS mapping and FAIRE. Input data: As a background control experiment, we sequenced the input genomic DNA sample that was used for ChIP. Crosslinked chromatin is sheared and the crosslinks are reversed without carrying out the immunoprecipitation step. This sample is otherwise processed in a manner identical to the ChIP sample as described below. The input track is useful in revealing potential artifacts arising from the sequence alignment process such as copy number differences between the reference genome and the sequenced samples, as well as regions of poor sequence alignability. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf For each site, the maximum F-Seq Density Signal value has been calculated for each assay that was performed in that cell type. F-Seq employs Parzen kernel density estimation to create base pair scores (Boyle et al., 2008b). Significant regions, or peaks, were determined by fitting the data to a gamma distribution to calculate p-values. Contiguous regions where p-values were below a 0.05 (DNaseI HS, ChIP) or 0.1 (FAIRE) threshold were considered significant. See assay specific description pages ( Duke DNaseI HS, UNC FAIRE and UTA TFBS) for more details. A Fisher's Combined P-value for DNaseI HS and FAIRE was calculated using Fisher's combined probability test. First, a test statistic is calculated using the formula x^2 = -2*sum(ln(pi)) where pi are the p-values calculated for DNaseI HS and FAIRE. X2 follows a chi-squared distribution, thus a combined p-value can be assigned to this test statistic.

Dataset Information

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets