Similar Datasets
Project description:Despite its demonstrated biological significance, time of day is a broadly overlooked biological variable in preclinical and clinical studies. How time of day affects the influence of peripheral tumors on central (brain) function remains unspecified. Thus, we tested the hypothesis that peripheral mammary cancer tumors alter the transcriptome of immune responses in the brain and that these responses vary based on time of day; we predicted that time of day sampling bias would alter the interpretation of the results. Brain tissues collected at mid dark and mid light from mammary tumor-bearing and vehicle injected mice were analyzed using the Nanostring nCounter Mouse Neuroinflammation Panel V1. Peripheral mammary tumors significantly affected expression within the brain of over 100 unique genes of the 770 represented in the panel, and fewer than 25% of these genes were affected similarly across the day. Indeed, between 65-75% of GO biological processes represented by the differentially expressed genes were dependent upon time of day of sampling. The implications of time-of-day sampling bias in interpretation of research studies cannot be understated. We encourage considering time of day as a significant biological variable in studies and to appropriately control for it and clearly report time of day in findings.
2024-01-09 | GSE249022 | GEO
Project description:The objective was to study the transcriptomic changes in adipose tissue in the early stages of lactation, specifically in Bos Taurus, Holstein dairy cattle as a function of milk production and genetic merit. Chip quality backgrounds averaged below 50 units, and 3'/5' bias on control genes < 2.0. Correlations among replicates were > 0.85. The design was a simple paired sampling, with time (30 d prepartum and 14 d postpartum as the sampling times. There was no dietary manipulation. Animals were all first calving Holstein heifers, all raised on the same farm on the same diet
2010-05-27 | E-GEOD-21535 | biostudies-arrayexpress
Project description:Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and nucleic acid structure. These assays are converging on single-nucleotide resolution measurements, but the sequence preferences of molecular biology enzymes can interfere with the accurate interpretation of the data. Enzymatic sequence preferences manifest more prominently as the resolution of these assays increase. We developed seqOutBias to seek out enzymatic sequence bias from experimental data and scale individual sequence reads to correct the bias. We show that this software efficiently and successfully corrects the sequence bias resulting from DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data.
2016-12-21 | GSE92674 | GEO
Project description:Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. Using common sets of reference samples, we evaluated the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. As part of this larger study, we assessed sequencing bias and reproducibility using an equimolar pool of 1,152 small RNA sequences ranging from 15-90 nt, and primarily comprised of annotated human microRNAs. We observed extensive protocol-specific and sequence-specific bias that was largely mitigated in protocols employing sequencing adapters with randomized end-nucleotides. We find that sequencing bias is highly reproducible across labs using the same library preparation technologies, and use the data to calculate inter-protocol bias correction factors. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols.
2018-07-09 | GSE94584 | GEO
Project description:Background. Chronic fatiguing illness remains a poorly understood syndrome of unknown pathogenesis. We attempted to identify biomarkers for chronic fatiguing illness using microarrays to query the transcriptome in peripheral blood leukocytes. Methods. Cases were 44 individuals who were clinically evaluated and found to meet standard international criteria for chronic fatigue syndrome or idiopathic chronic fatigue, and controls were their monozygotic co-twins who were clinically evaluated and never had even one month of impairing fatigue. Biological sampling conditions were standardized and RNA stabilizing media were used. These methodological features provide rigorous control for bias resulting from case-control mismatched ancestry and experimental error. Individual gene expression profiles were assessed using Affymetrix Human Genome U133 Plus 2.0 arrays. Findings. There were no significant differences in gene expression for any transcript. Conclusions. Contrary to our expectations, we were unable to identify a biomarker for chronic fatiguing illness in the transcriptome of peripheral blood leukocytes suggesting that positive findings in prior studies may have resulted from experimental bias.
2009-05-12 | GSE16059 | GEO