Editing BioMicroCenter:BioInformaticsStaff (section)

== INFORMATICS ANALYSIS ==

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 ! Gene Expression
 ! Isoform Visualization and Quantitation
 ! Functional Analyses
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 |[[Image:BMC_informatics_GeneExpression.png|thumb|center|300px]]
 |[[Image:BMC_informatics_Isoform.png|thumb|center|350px]]
 |[[Image:BMC_informatics_FunctionalAnalyses.png|thumb|center|350px]]
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 |Gene expression analysis can be done using RNA-Seq and microarrays. (A) The RNA-Seq analysis pipeline has been implemented and the method is gaining popularity as sequencing becomes  more affordable. Benefits include flexibility with level of detail collected and a lack of platform-dependent biases. (B) Microarrays are still a viable alternative to genome-wide mRNA analysis. They are generally less expensive and benefit from routine and well-developed processing and analysis methods.
 |RNA-Seq reads compatible with different transcript isoforms are quantitated using a Bayesian analytical  framework (MISO), generating posterior distributions of isoform abundances. Read densities are visualized using Sashimi plots.
 |Core Network of Genes regulated by Braveheart, which drive Cardiac Differentiation, generated using the Spring-embedded algorithm in Cytoscape. A sequence of cardiac transcription factors and genes involved in myofibril organization were not induced during EB differentiation in Bvht-depleted cells. 
From Klattenhoff  et al. (Boyer, Burge labs), Cell 152:570. / Hierarchical clustering of genes identified from a time series microarray experiment. Clustering was  performed using Cluster and the corresponding heatmap was  generated with TreeView.
(collaboration with Walker Lab)
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 ! SNPs and Mutations
 ! ChIPseq Analysis and Motif Finding
 ! Phylogenetic Analyses
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 |[[Image:BMC_informatics_SNPs.png|thumb|center|400px]]
 |[[Image:BMC_informatics_ChIPseq.png|thumb|center|350px]]
 |[[Image:BMC_informatics_Phylo.png|thumb|center|250px]]
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 |NGS variant detection procedure: (A) Input material can either be whole-genome or transcriptome isolate, or a selected subset such as the exome (exon capture) or specific genome regions. Raw sequences are pre-processed and aligned using various options (BWA or Bowtie). The GeZnome Analysis Took Kit (GATK) and SAMtools are used to call variants and snpeff is used to annotate the consequences of those variations. (B) IGV is used to visualize the alignments in the context of the genome and its associated annotations.
 |Characterizations of the significant Chip-Seq peaks. Figures describing proportions of binding sites located around annotated genic locations.  Sequence logos from binding sites identified through MAST. (Horvitz Lab)
 |Large-scale phylogenetic analyses of sequences can be performed using combinations of multiple sequence alignment tools and data visualization packages.
In this example phylogenetic relationships of the hits were visualized using iTOL. (Boyden lab)

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