Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Biological insights of transcription factor through analyzing ChIP-Seq data

ChIP-Seq is a technology for detecting in vivo transcription factor binding sites or histone modification sites on a genome wide scale. How to utilize the large scale data and find out biological insights is a challenging question for us. Here, we analyzed three ChIP-Seq data sets for human HeLa cell, including data of a transcription factor called STAT1, data of RNA polymerase II (Pol2), and data of histone monomethylation (Me1). With these data sets, we looked into the spacial relationship between STAT1 binding sites, Po12 binding sites, Me1 flanked regions and the gene transcription start sites; we checked the intersection of locations of STAT1 binding sites, Pol2 binding sites and Me1 flanked regions; we did de novo motif discovery for the sequences around the STAT1 binding sites, and predicted several transcription factors whose binding sites may form cis-regulatory module with STAT1 binding site; we put the STAT1-centered sequences into different categories based on their spacial relationship with Pol2 binding sites and Me1 flanked regions, and found that the de novo discovered motifs’ occurrence rates are different in sequences of different categories; we also analyzed the ChIP-Seq data along with gene expression data, and found that STAT1 binding may be related with genes’ differential expression under IFN-gamma stimulation. We suggest that further ChIP-Seq experiment be carried out for TFs corresponding to the de novo predicted motifs, and that gene expression be characterized for the IFN-gamma stimulated HeLa cell on the whole genome scale.Science, Faculty ofGraduat

BIOINFORMATICS

package for constructing gene regulatory networks from microarray data by using Bayesian networ

Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework

A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them

Granulocyte-macrophage colony-stimulating factor autoantibodies: a marker of aggressive Crohns disease.

BACKGROUND: Neutralizing autoantibodies (Abs) against granulocyte-macrophage colony-stimulating factor (GM-CSF Ab) have been associated with stricturing ileal Crohns disease (CD) in a largely pediatric patient cohort (total 394, adult CD 57). The aim of this study was to examine this association in 2 independent predominantly adult inflammatory bowel disease patient cohorts. METHODS: Serum samples from 742 subjects from the NIDDK IBD Genetics Consortium and 736 subjects from Australia were analyzed for GM-CSF Ab and genetic markers. We conducted multiple regression analysis with backward elimination to assess the contribution of GM-CSF Ab levels and established CD risk alleles and smoking on ileal disease location in the 477 combined CD subjects from both cohorts. We also determined associations of GM-CSF Ab levels with complications requiring surgical intervention in combined CD subjects in both cohorts. RESULTS: Serum samples from patients with CD expressed significantly higher concentrations of GM-CSF Ab when compared with ulcerative colitis or controls in each cohort. Nonsmokers with ileal CD expressed significantly higher GM-CSF Ab concentrations in the Australian cohort (P = 0.002). Elevated GM-CSF Ab, ileal disease location, and disease duration more than 3 years were independently associated with stricturing/penetrating behavior and intestinal resection for CD. CONCLUSIONS: The expression of high GM-CSF Ab is a risk marker for aggressive CD behavior and complications including surgery. Modifying factors include environmental exposure to smoking and genetic risk markers