Functional analysis of duplicate pair CIK1–VIK1 (A) Genetic interaction profile similarity. Functional analysis of duplicate pair CIK1–VIK1 (A) Genetic.

Slides:

Advertisements

Similar presentations

GFP‐Sed5p localization defect in sgt2Δ.

Advertisements

Tuning the response of the DPI

Principles of using neural networks for predicting molecular traits from DNA sequence Principles of using neural networks for predicting molecular traits.

Statistical analysis of the influence of network regulators on topology clusters in the oleate network. Statistical analysis of the influence of network.

Network topology reflects target gene expression profile and function.

Inferred dynamic model of transcriptional regulatory network.

Hnf4g importance in human colon cancer organoids and regulation of the Hnf4a locus Hnf4g importance in human colon cancer organoids and regulation of the.

(A) Graph depicting interaction between the five regulatory motifs measured by synergy and cooccurrence. (A) Graph depicting interaction between the five.

Sensitivity of RNA‐seq.

Analysis of in silico flux changes along the exponential growth phase: (A) In silico flux changes from 24 to 36 h, from 36 to 48 h, and from 48 to 60 h.

Motif detectability corresponds to the phylogenetic profile of the cognate transcription factor. Motif detectability corresponds to the phylogenetic profile.

Tight regulation of gene expression variation is coupled to promoter architecture. Tight regulation of gene expression variation is coupled to promoter.

Heatmaps of the gene mutation distributions in oncogene‐signaling blocks for six representative cancer types. Heatmaps of the gene mutation distributions.

Exo‐metabolomics profiling of major metabolites elucidated the metabolic capabilities of monospecies Exo‐metabolomics profiling of major metabolites elucidated.

Two competing models for Rab domain identity generation and endosome conversion. Two competing models for Rab domain identity generation and endosome conversion.

Proteins with hotspot mutations are often in the interaction networks with known cancer driver proteins Proteins with hotspot mutations are often in the.

Validation of the CCE associations by mass spectrometry.

(C–F) Complete interaction networks (representing both baits and preys) for selected groups of baits. (C–F) Complete interaction networks (representing.

Mononucleotide models describe sequence‐to‐shape relationships well

Topological overlap matrix (TOM) plots of weighted, gene coexpression networks constructed from one mouse studies (A–F) and four human studies including.

Clustering of CD34+ single cells

Comparative analysis of RNA and protein profiles.

Highly correlating interaction profiles can predict functional similarity Highly correlating interaction profiles can predict functional similarity AROC.

Large‐scale image‐based CRISPR‐Cas9 gene perturbation profiling

Buffering of non‐functional mRNA co‐regulation likely is a passive process Buffering of non‐functional mRNA co‐regulation likely is a passive process Percentage.

Model training of generalized Lotka–Volterra (gLV) to time‐resolved measurements of monospecies and pairwise assemblages Model training of generalized.

Correlating protein to mRNA and proteins per mRNA ratios

Coupling immunophenotypes to Drop‐seq data

Network derived from large‐scale fractionation predicts 48 protein complexes and communities Network derived from large‐scale fractionation predicts 48.

Hierarchical structure in the yeast transcription regulatory network.

Anastasia Baryshnikova Cell Systems

Heat maps showing global relative growth phenotype and comparison between measured and predicted values. Heat maps showing global relative growth phenotype.

Networks of ERK substrates.

Alignment time for Clustal Omega (red), MAFFT (blue), MUSCLE (green) and Kalign (purple) against the number of sequences of HomFam test sets. Alignment.

The transcript profiles in the three human cell lines based on RNA sequencing (RNA‐seq). The transcript profiles in the three human cell lines based on.

The limiting, degrading entity hypothesis

Feature‐based COG enrichment analysis

Examples for intrachromosomal mRNA co‐regulation patches

Volume 49, Issue 2, Pages (January 2013)

Multi‐Omics Factor Analysis: model overview and downstream analyses

Phenotypic profiling by between‐well comparison of feature profiles

Effect of the loss of Kar4 on the induction of various promoters

Neurogenins induce a network of transcription factors that mediate iNGN neurogenesisA network of transcription factors involved in iNGN neurogenesis was.

Antisense transcription, measurement, and correction

Evaluation of assay performance with positive and negative binary reference sets Evaluation of assay performance with positive and negative binary reference.

Allele‐specific expression at single‐cell resolution

Two‐way unsupervised uncentered unnormalized hierarchical clustering using a Pearson's correlation of the phenotypic profiles of 4281 nonessential genes.

Transcriptomic and epigenetic changes associated with Factor 1 in the scMT data Transcriptomic and epigenetic changes associated with Factor 1 in the scMT.

48 clinically relevant GPCR receptors mapped using MYTH

Comparison of proteomics and RNA‐Seq data.

Characteristics of tissue‐specific co‐expression networks (CNs)‏

Submodules of the light yellow module define distinct interneuron classes with common developmental origins. Submodules of the light yellow module define.

Simulation showing that the cell length variability of the entire population can mask abnormal cell length variability at a specific cell cycle period.

Modeling patterns of p53 activation dynamics from natural language

An integrated NHR network.

Design and optimization of the computational model.

Large‐scale image‐based CRISPR‐Cas9 gene perturbation profiling

Subnetworks enriched for the hallmarks of cancer.

Community diversity and metagenome depth interact to influence assembly quality. Community diversity and metagenome depth interact to influence assembly.

Melting behavior of protein complexes

In vivo imaging of the wound bed at later time points (related to Fig 7)‏ In vivo imaging of the wound bed at later time points (related to Fig 7) A–CRepresentative.

Nedd4 proteins. Nedd4 proteins. (A) Schematic representation of rNedd4‐1, hNedd4‐1 and hNedd4‐2 (not to scale), with % amino‐acid identity between them.

Antisense expression associates with larger gene expression variability. Antisense expression associates with larger gene expression variability. (A–D)

Drug sensitivity described by LD50 is similar in cells cultured in 2D or as organoids Drug sensitivity described by LD50 is similar in cells cultured in.

Global and focused views of human interaction map.

Dynamic regulatory map and static network for yeast response to AA starvation. Dynamic regulatory map and static network for yeast response to AA starvation.

Integration of proteomic data into the model

Many of the diseases associated with high coevolution scores share genetic components. Many of the diseases associated with high coevolution scores share.

Intestinal differentiation is coupled with global rewiring of gene expression Intestinal differentiation is coupled with global rewiring of gene expression.

P-network of 2,616 prokaryote genomes based on chromosomal sequences with rRNA genes removed. P-network of 2,616 prokaryote genomes based on chromosomal.

Presentation transcript:

Functional analysis of duplicate pair CIK1–VIK1 (A) Genetic interaction profile similarity. Functional analysis of duplicate pair CIK1–VIK1 (A) Genetic interaction profile similarity. Similarity scores were taken from Costanzo et al (2010) and represent a combination of array side and query side correlations (Materials and methods). Nodes shown include all first neighbors of the three primary genes of interest (CIK1, VIK1 and KAR3). A threshold of 0.2 was used as in Costanzo et al (2010) and edges between first neighbors of genes of interest have been removed for clarity. (B) Genetic interactions. SGA genetic interaction scores from Costanzo et al (2010) highlight differences between CIK1 and VIK1. Green lines represent positive interactions, whereas red lines represent negative interactions. The opacity of the line is proportional to the strength of the interaction. Benjamin VanderSluis et al. Mol Syst Biol 2010;6:429 © as stated in the article, figure or figure legend