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Project description:We provide raw gene sequences of 174 flowering time regulatory genes and gene othologs across a large barley population (895 barley lines) selected from a collection of landrace, cultivated barley, and research varieties of diverse origin. This set represents the whole variety of cultivated barley lifeforms, namely two- and six-row genotypes with winter, spring, and facultative growth habits. We applied a target capture method based on in-solution hybridization using the myBaits® technology (Arbor Biosciences, Ann Arbour, MI, USA) which is based on in-solution biotinylated RNA probes. Baits were designed for flowering time regulatory genes and gene othologs, and used for production of 80mer capture oligonucleotides for hybridization. Genomic DNA was extracted from leaves of a single two-week old barley plant per variety using the cetyl-trimethyl-ammonium bromide (CTAB) method. Physical shearing of genomic DNA was performed with an average size of 275 bp. Library preparation was conducted with KAPA Hyper Prep Kit (KAPA Biosystems, Wilmington, MA). Hybridization of customised RNA baits with capture pools was performed at 65°C for 24 hours. Each pooled sequence capture library was sequenced on an Illumina HiSeq3000 instrument using three lanes to generate paired-end reads per sample. Genome sequencing was conducted at AgriBio, (Centre for AgriBioscience, Bundoora, VIC, Australia).
2019-02-15 | E-MTAB-7362 | biostudies-arrayexpress
Project description:8 neuroblastoma (NB) cell lines (CLB-GA, IMR-32, SH-SY5Y, N206, CHP-902R, LAN-2, SK-N-AS, SJNB-1) their methylome is determined by sequencing after MBD2-capture using MethylCollector (ActiveMotif) 8 NB cell lines were included (CLB-GA, IMR-32, SH-SY5Y, N206, CHP-902R, LAN-2, SK-N-AS, SJNB-1) in this study. After shearing (fragments of about 200 bp), DNA was captured using MBD2-capture (MethylCollector - ActiveMotif) followed by library preparation and multiplexing. Captured sequence tags were sequenced paired-end (2 x 45 bp) on Illumina GAIIx.
2012-09-13 | E-GEOD-31353 | biostudies-arrayexpress
Project description:Current next-generation RNA sequencing methods do not provide accurate quantification of small RNAs within a sample due to sequence-dependent biases in capture, ligation, and amplification during library preparation. We present a method – AQRNA-seq – that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for small RNAs in a sample. The library preparation and data processing steps were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying lengths, and northern blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800 detectable miRNAs that varied as a function of cancer progression, while application to bacterial tRNA pools, a traditionally hard-to-sequence class of RNAs, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced tRNA-driven codon-biased translation. AQRNA-seq thus provides a means to quantitatively map the small RNA landscape in cells.
2021-05-31 | GSE159434 | GEO
Project description:Bait-capture based Single Molecule Footprinting (SMF) data from Sonmezer et al., 2020. SMF data is obtained by treating isolated nuclei with methyltransferases, where binding of proteins on DNA, e.g. nucleosomes and TFs, leave behind unmethylated cytosines as footprints. Data in this experiment comprises SMF data obtained from ES, DNMT-TKO, and neural progenitor (NP) cells. These data were generated by employing Agilent Sure-Select Mouse Methyl-Seq kit, enriching the sample for regulatory regions of mouse genome prior to library preparation. Thus, these data contain high coverage accessibility information at regulatory loci in different cell types.
2021-05-04 | E-MTAB-9123 | biostudies-arrayexpress
Project description:We compare the performance of two library preparation protocols (poly(A) and exome capture) in in vitro degraded RNA samples
2015-08-03 | GSE64113 | GEO
Project description:Total RNAs from exosomes was used for miRNA library preparation and sequencing.The preparation and sequencing of exosome miRNA were performed by Ribobio (Guangzhou, China). First, total RNA samples were fractionated by using 15% Tris-borate-EDTA (TBE) polyacrylamide gel (Invitrogen), then small RNAs ranging from 18 to 30 nucleotides were used for library preparation. Amplification of these small RNAs was then accomplished by PCR. Next, the Illumina HiSeq 2500 platform was used to sequence these RNAs.
2019-04-25 | GSE128278 | GEO
Project description:Library preparation for whole genome bisulphite sequencing (WGBS) is challenging due to side effects of the bisulphite treatment, which leads to extensive DNA damage. Recently, a new generation of methods for bisulphite sequencing library preparation have been devised. They are based on initial bisulphite treatment of the DNA, followed by adaptor tagging of single stranded DNA fragments, and enable WGBS using low quantities of input DNA. In this study, we present a novel approach for quick and cost effective WGBS library preparation that is based on splinted adaptor tagging (SPLAT) of bisulphite-converted single-stranded DNA. Moreover, we validate SPLAT against three commercially available WGBS library preparation techniques, two of which are based on bisulphite treatment prior to adaptor tagging and one is a conventional WGBS method.
2016-10-27 | GSE89213 | GEO
Project description:We compare the performance of two library preparation protocols (poly(A) and exome capture) in in vitro degraded RNA samples VcaP cell were grown, and treated with MDV3100 (enzalutamide) or DHT (dihydrotestosterone), intact RNA was isolated and samples were prepared in technical triplicates using two library preparation protocol. Also cells were subject to in vitro degradation through incubation of the whole cell lysate in 37C for increasing amounts of time. Following incbation paired capture and poly(A) libraries were prepared.
2015-08-03 | E-GEOD-64113 | biostudies-arrayexpress
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.. Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.
2023-09-29 | GSE244325 | GEO