Project description:Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.
Project description:Comparative genomic hybridization between Escherichia coli strains to determine core and pan genome content of clinical and environmental isolates Two color experiment, Escherichia coli Sakai (reference), clinical and environmental Escherichia coli strains (testers): At least two replicates including a single dye swap for each reference-tester comparison
Project description:The clinical application of anticancer drugs depends on preclinical models closely recapitulating the molecular profile. We report here the transcriptional profiles of HCT116 (colorectal cancer cells) treated with DMSO or Olaparib for 24 hours. For these two different treatments, also changes of molecule expression are available, which enables their stratification into drug-therapeuticly relevant molecule.
Project description:Chromatin structure and transcription factor localization can be assayed genome-wide by sequencing genomic DNA fractionated by protein occupancy or other properties. However, current technologies involve multiple steps that introduce bias and inefficiency. Here we apply a single-molecule approach to directly sequence chromatin immunoprecipitated DNA with minimal sample manipulation. This method is accurate, compatible with just 50 picograms of DNA and should thus facilitate charting chromatin maps from limited cell populations. Application of a single-molecule approach to directly sequence chromatin immunoprecipitated DNA of the CTCF DNA binding protein.
Project description:The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and analysis issues. We demonstrate the consistency of results within a platform across test sites as well as the high level of cross-platform concordance in terms of genes identified as differentially expressed. The MAQC study provides a rich resource that will help build consensus on the use of microarrays in research, clinical and regulatory settings. Manuscripts related to the MAQC project have been published in Nature Biotechnology, 24(9), September, 2006. More information about the MAQC project can be found at http://edkb.fda.gov/MAQC/.<br><br>Expression data from two distinct reference RNA samples (A and B) in four titration pools were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Sample A = Stratagene Universal Human Reference RNA (UHRR, Catalog #740000), Sample B = Ambion Human Brain Reference RNA (HBRR, Catalog #6050), Sample C = Samples A and B mixed at 75%:25% ratio (A:B); and Sample D = Samples A and B mixed at 25%:75% ratio (A:B). In general, each microarray platform was tested at three sites and each sample was tested in five replicates at each test site. Samples (hybridizations) were named according to the following convention: Platform_Testsite_SampleRelicate. For example, AFX_2_B1 represents the hybridization (array) from platform AFX processed by test site 2 for the first replicate of sample B. Assignment of platform code: ABI = Applied Biosystems (microarray); AFX = Affymetrix; AG1 = Agilent one-color; AGL = Agilent two-color; GEH = GE Healthcare; ILM = Illumina; NCI = NCI two-color (Operon oligos); EPP = Eppendorf; TAQ = TaqMan (Applied Biosystems); QGN = QuantiGene (Panomics); GEX = StaRT-PCR (Gene Express); H25K = TeleChem two-color; H25K1 = TeleChem one-color; BIO = CapitalBio two-color (Operon oligos); BIO1 = CapitalBio one-color (Operon oligos); OPN = Operon two-color (Operon oligos); NMC = Norwegian Microarray Consortium two-color (Operon oligos).
Project description:RNA was extracted from mature ovules of two samples (i.e., WT and myb98) and sequenced with an Illumina Hi-seq 2000 sequencer in the Biodynamic Optical Imaging Center (BIOPIC) of Peking University followed by the analysis on the High Performance Computing Platform of the Center for Life Science.
Project description:Microrrays plataforms with thousands of gene sequences have limited capacity to pick up subtle small changes in gene expression. A focused two-color microarray platform was designed for high replication and to use statistical power to detect small changes Keywords: time course, embryo development
Project description:Targeted protein degradation has unlocked new strategies for modulating previously undruggable proteins; however, existing small molecule approaches are challenging to optimize and largely limited to targets with ligandable pockets. To overcome these challenges, we introduce the HYbrid DegRAding Copolymer (HYDRAC), a polymer-based platform that integrates target-binding peptides with peptide or small-molecule degrons for the selective degradation of disease-relevant proteins. HYDRACs are synthesized easily, exhibit structural tunability, and facilitate the attachment of multivalent payloads. The modular payload capability accommodates diverse target-binding motifs and E3 ligase recruiters—such as VHL, KEAP1, and CRBN—broadening the design space. We apply HYDRACs against two historically intractable targets, MYC and KRAS, achieving robust degradation in vitro and sustained tumor inhibition in murine models. Notably, HYDRACs containing consensus RAS-binding motifs effectively degrade KRAS in cells harboring different alleles, suggesting pan-KRAS potential. We envision the HYDRAC platform as a generalizable approach, greatly expanding the armamentarium for TPD.
Project description:We report the application of single-molecule-based sequencing technology for high-throughput profiling of transcription start sites for two enterobacteria: Escherichia coli and Klebsiella pneumoniae.By obtaining over fourteen billion bases of sequence from 5' RACE (rapid amplification of cDNA ends) followed by deep sequencing, we generated genome-wide TSS (transcription start site) maps for those two species. With experimentally derived TSS datasets, we examined usage of multiple TSSs, length of 5' UTR (untranslated region), SD (Shine-Dalgarno) sequence motif, promoter sequence motif, and dinucleotide preference near TSS site. In addition, we used the TSS datasets to identify sRNAs (small RNAs) in E. coli and K. pneumoniae. Based on these analysis, we compared regulatory elements including promoter, 5' UTR and sRNAs between two species, and investigated similarities and differences of upstream regulatory regions. Moreover, sRNAs were also compared and analyzed in terms of their sequences and target sequences, presenting possible working mechanisms of K. pneumoniae sRNAs by transferring prior knowledge from E. coli sRNAs.