Table of results for comparative analysis between glycerol and cholesterol.

<p>Breakdown of the number of differentially essential genes identified by the resampling method, in each condition (glycerol and cholesterol). Differentially essential genes are those with an adjusted p-value <i>q</i> < 0.05.</p><p>Table of results for comparative analysis between glycerol and cholesterol.</p

Hidden Markov Model Diagram.

<p>The HMM is fully connected, allowing transitions between each of the states. Transition probabilities and parameters are estimated in such a way that the HMM will remain in the state which best represents the read-counts observed. (a) Essential regions (“ES”) are mostly devoid of insertions, (c) while non-essential regions (“NE”) contain read-counts around the global mean. (b) Growth-defect regions (“GD”), and (d) growth-advantage regions (“GA”) represent those areas with significantly suppressed or inflated read-counts.</p

Table of results obtained from resampling, comparing replicates grown in glycerol versus cholesterol.

<p>Table of results obtained from resampling, comparing replicates grown in glycerol versus cholesterol.</p

Table of Bayesian/Gumbel Results for H37Rv grown in glycerol.

<p>Breakdown of essentiality calls for the glycerol datasets obtained by the Bayesian/Gumbel method. Essential and Non-Essential genes are those genes whose posterior probability of essentiality exceeds the dynamic thresholds of essentiality. Uncertain genes are those who do not exceed these thresholds, and “Too Small” represents those genes who are too small for reliable analysis.</p><p>Table of Bayesian/Gumbel Results for H37Rv grown in glycerol.</p

Picture of the main TRANSIT interface.

<p>Picture of the main TRANSIT interface.</p

Table of HMM Results for H37Rv grown in glycerol.

<p>Distribution of state calls for the glycerol datasets obtained by the HMM method. Essential states represent those regions which are mostly devoid of insertions. Non-Essential regions contain read-counts that are close to the mean read-count in the dataset. Growth-Defect regions and Growth-Advantage regions represent those regions which have significantly suppressed or increased read-counts.</p><p>Table of HMM Results for H37Rv grown in glycerol.</p

Resampling histogram for gene Rv0017c.

<p>Rv0017c has 23 TA sites, and the sum of the observed counts at the TA sites in this genes <i>in vitro</i> was 1,318 and <i>in vivo</i> was 399, therefore the observed difference in counts is -918. To determine the significance of this difference, 10,000 permutations of the counts at the TA sites among the datasets was generated and the observed differences plotted as a histogram showing that a difference as extreme as -918 almost never occurs by chance. The p-value is determined by the tail of this distribution to be 0.003 (30 out of 10,000).</p

TPP flowchart.

<p>Reads in .fasta, .fastq or fastq.gz format are taken in as input, and mapped to the genome to get read-counts at individual TA sites. A .wig formatted file is returned as output, containing the coordinates and the read-counts at all TA sites in the genome.</p

Exploring Key Orientations at Protein–Protein Interfaces with Small Molecule Probes

Small molecule probes that selectively perturb protein–protein interactions (PPIs) are pivotal to biomedical science, but their discovery is challenging. We hypothesized that conformational resemblance of semirigid scaffolds expressing amino acid side-chains to PPI-interface regions could guide this process. Consequently, a data mining algorithm was developed to sample huge numbers of PPIs to find ones that match preferred conformers of a selected semirigid scaffold. Conformations of one such chemotype (<b>1aaa</b>; all methyl side-chains) matched several biomedically significant PPIs, including the dimerization interface of HIV-1 protease. On the basis of these observations, four molecules <b>1</b> with side-chains corresponding to the matching HIV-1 dimerization interface regions were prepared; all four inhibited HIV-1 protease via perturbation of dimerization. These data indicate this approach may inspire design of small molecule interface probes to perturb PPIs

Domain discovery.

<p>A. Genes categorized by domain-level resolution of regional requirement. B. Genes categorized as containing only required regions (blue), containing both required and non-required regions (navy) and containing no required regions (yellow) were assessed for requirement along the entire length of the gene, creating a single p-value describing the statistical underrepresentation of insertion reads within the whole gene. For each category, the number of genes across the range of p-values are plotted. C. For genes with both required and non-required regions, the likelihood that the relative position within the gene resides in a required region.</p